From ce6862b6cbad8964a52e8cfd2452946a7c6b76c8 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 1 Nov 2020 19:30:27 +0000 Subject: [PATCH 01/62] WIP --- .../Julia-1.6-what-has-changed-since-1.0.md | 103 ++++++++++++++++++ .../julia1.0-conflict.png | Bin 0 -> 1558932 bytes .../julia1.6-conflict.png | Bin 0 -> 1633723 bytes 3 files changed, 103 insertions(+) create mode 100644 _posts/Julia-1.6-what-has-changed-since-1.0.md create mode 100644 posts_assets/Julia-1.0-1.6-changes/julia1.0-conflict.png create mode 100644 posts_assets/Julia-1.0-1.6-changes/julia1.6-conflict.png diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md new file mode 100644 index 00000000..aceedf83 --- /dev/null +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -0,0 +1,103 @@ +--- +title: Julia 1.6 - what has changed since Julia 1.0? +layout: default +tags: + - julia +--- + +Julia 1.6 is slated to be the next long-term support release of the Julia programming language. +It has been 2 years since the last LTS, and a lot has changed. +This post is kind of a follow-up to my [Julia 1.0 release run-down]({{site.url}}//2018/06/01/Julia-Favourite-New-Things.html). +But it's going to be even longer, as it is covering the last 5 releases since then. +I am writing this not to break down release by release, +but to highlight features that had you only used Julia 1.0, you wouldn't have seen. +Full details can be found in the NEWS.md, and HISTORY.md +TODO: Insert links to the files in the release-1.6 branch. + + +## Threading +Julia has full support for threading now (as of Julia 1.3). +Not just the limited `@threads for` loops, but full GoLang style threads. +They are tightly integrated with the existing Task/Coroutine system. +In effect threading works by unsetting the sticky flag on a task, so that it is allowed to run on any thread. +This is normally done via the `Threads.@spawn` macro, rather than the `@async` macro. + +Interestingly, the `@threads for` macro still remains, and doesn't actually use much of the new machinery. It still uses the old way which is a bit tigher if the loop durations are almost identical. +But the new threading stuff is fast, on the order of microseconds to send work off to another thread. +Even for uses of `@threads for` the improves have some wins. +`IO` is now thread-safe; `ReentrantLock` was added and is the kind of standard lock that you expect to exist, that has notifications on waiting work etc; and a big one: `@threads for` can now be nested without things silently being wrong. + + +## Pkg stdlib and the General Registry +Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the Pkg stdlib; and the general registry is more policy than software. + +TODO: Write bits for these: + - Full transition off METADATA + - Automatic merging + - Pkg Server: faster updating of registry and download of packages + - Artifacts, BinaryBuilder, Yggdasil, almost no one uses `deps/build.jl` any more + - Scratch, and Preferences + - Tiered Resolution: in particular solving test dependencies of different compat. + - `activate --temp` + - test dependencies + - passing arguments to Pkg.test + +### Precompilation + +Precompilation has been enhanced a lot. +That is not the compilation that runs the first time a function is used in a session, but rather the stuff that runs the first time a package is loaded in an environment. + +For a start, the precompilation cache no longer goes stale every time you swap environments. +This was a massive pain in julia 1.0, especially if you worked on more than one thing at a time. +This was fixed in 1.3 to have multiple caches, for different environments. + +More dramatically, in 1.6 is the parallelism of precompilation. +Precompiling a package requires precompiling all its dependencies first. +This is now done in parallel, and is automatically triggered when you complete Pkg operations. +In contrast, to happening in series the first time a package is loaded. +No more waiting 5 minutes the first time you load an package with a lot of dependencies. +Further the spiffy animated output shows you what is precompiling at a given time; and a progress bar, which makes it feel (even) faster. + + + +### Improved conflict messages +This is one of my own small contributions. +Julia 1.0 conflict messages are a terrifying wall of text. + +Julia 1.0 conflict log +Julia 1.6 conflict log + +Colors were added to make it easier to see which version numbers are referring to which package. +Consecutive lists of version numbers were compressed to continuous ranges. +In 1.6 these ranges are split only if there is a version that exist between them that is not compatible. +In contrast in 1.0, they were split if there was a potential version that could exist that is not compatible (even if that version doesn't currently exist); in practice this mean they split every time the right most nonzero was incremented. + + + +## A ton of new and improved standard library functions: + +[`@time`])(https://github.com/JuliaLang/julia/pull/37678) now reports how much time was spent on compilation. +This is going to help prevent people knew the language from including compilation time in their benchmarks. +It is still better to use [BenchmarkTool.jl's](https://github.com/JuliaCI/BenchmarkTools.jl) `@btime`, since that does multiple samples. +But now that too can report time spend compilation. +It's also useful for identifying if certain functions are taking ages to compile. +Which I guess is its theoretical main point, but I think preventing people benchmarking wrong is going to come up way more often. + +The experimental [`Base.@locals`](https://github.com/JuliaLang/julia/issues/29733 +) was added which returns a dictionary of local variables. +That one surprised me, I though being able to access a dict of local variables would get in the way of the optimizer, since it would prevent it from being able to optimize variables that are used for intermediate values away entirely. +But the compiler folk know better than I do. + +[`splitpath`](https://github.com/JuliaLang/julia/issues/28156) as added, its the opposite of `joinpath`. +Kind of stilly we didn't have that, and had been being me at least since 0.6. + +On things that had been bugging me, I had wanted [`eachslice` and it's special cases: `eachrow` & `eachcol`](https://github.com/JuliaLang/julia/issues/29749) since julia 0.3 when I first started using it. +These are super handy when you want to e.g. iterate through vectors of the rows of a matrix. + + + + + + + + diff --git a/posts_assets/Julia-1.0-1.6-changes/julia1.0-conflict.png b/posts_assets/Julia-1.0-1.6-changes/julia1.0-conflict.png new file mode 100644 index 0000000000000000000000000000000000000000..0b168585ebc7e3ad451f4fc583b1a39a73028690 GIT binary patch literal 1558932 zcmZU)1z1#F+cu1Vf+&JYN~3fMC?zl=4Js|oNJ)3+R_T_;qLq}9Ziengx}>{?8enGc z5AOSUpZ7ife{<|vYt~*dd*yYW=S7(6Yk6X#2ShkHIK+wyGU_-ugqb)v_=AKuE>pS_ 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zeM1ZFT*QVSr46psc>io7`q${#U)g&|uM+7UH8t$g8i@^*gZrHH=AvB=opGs!%&TYB zcNCwa!7TW3sa6@pw094t9}-OaK@?itEha-r;}C5u<;~>U(r(i2cJKL1f<9sz-5B*I=mog(zYXsU0@7zTi!nr4t9$@x%Mi~?k&1>l{rbu6(4tTR{&2~DaLe+ z4*FuF;0*1klkFpqFlz&I%yrs^?u#dubObAQGrPThC=L`WTdDIB2|gB~S8+KUPFH_% zivhvX)K2t%VsGu0o=-CoC!$G==>Q0~kq_D8FzL{CuZ(8bby@lGYKfZaaw29K=t*CJ zr#u>t2U?9zBQ-|Vw=s)N;m1*SE*bOwJx~Y@dX=PB^u|`}+L`ibf;xX|ZP(f6YkAtE zbpKX|qYa?n-fZX~+$|$Z;c`|A(~=bUnePrmBikkI=a-3f%HahZttBf5q8Q73bfhF@1X6akn1!ZuYg=%VVgZDa|2}0Uoa#Z?1`+vP>R)FFmdi)j6DZL7g{QV z-m=v%q%;?Z1`pOV=_R}xe)*Fkhy8Xr={?}Uf6i$>&VR!xboxs?Jp3$$BfRxf&VKPM zmI+DB$jqxZ+P_7CQlO)@_-)pJC*}6ECiPOB@dqfUmQj^^(Pcjx=pSq^?po7PRgf5j zaiC90ZlD Date: Sun, 1 Nov 2020 21:24:55 +0000 Subject: [PATCH 02/62] WIP --- .../Julia-1.6-what-has-changed-since-1.0.md | 33 +++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index aceedf83..6327b230 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -72,7 +72,30 @@ Consecutive lists of version numbers were compressed to continuous ranges. In 1.6 these ranges are split only if there is a version that exist between them that is not compatible. In contrast in 1.0, they were split if there was a potential version that could exist that is not compatible (even if that version doesn't currently exist); in practice this mean they split every time the right most nonzero was incremented. +## Standard Library +### You can now print and interpolate `nothing` into strings. + +This is one of mine [#32148](https://github.com/JuliaLang/julia/pull/32148)., and I find it is such a usability enhancement. +So many small frustrations in Julia 1.0 relating to interpolate a variable containing `nothing` into a string. +Often occurring when you are adding a quick `println` to debug something not being the value you expected; or when building a string for some other error message. +Arguably both of those are better done via other means (`@show`, and values stored in fields in the error type); but we don't always do what is best. +Sometimes it is expedient to just interpolate things into strings without worrying about what type they are. + + +### A bunch of curried functions +Julia seems to have ended up with a convention of providing curried methods of functions if that would be useful as the first argument for `filter`. +e.g. `filter(isequal(2), [1,2,3,2,1])`, is the same is `filter(x->isequal(x, 2), [1,2,3,2,1]`). +In particular these are boolean comparison like functions: +with two arguments, where the thing being compared against is the second. +Julia 1.0 had `isequal`, `==` and `in`. +Since then we have added: +`<`,`<=`,`>`, `>=`, `!=`, `startswith`, `endswith`, and `contains`. +(I added the last 3 😁) + + +Aside: `contains` is argument flipped `occursin`, it was a thing in julia 0.6 but was removed in 1.0. +We added it back expressly to have the curried form, and to match `startswith` and `endswith`. ## A ton of new and improved standard library functions: @@ -95,6 +118,16 @@ On things that had been bugging me, I had wanted [`eachslice` and it's special c These are super handy when you want to e.g. iterate through vectors of the rows of a matrix. +`redirect_stderr` and `redirect_stdout` now work with `devnull`. +So one can run some suppressing output easily as follows: +```julia +julia> redirect_stdout(devnull) do + println("You won't see this") + end +``` +This is just handy, doing it without this is seriously annoying. + + From 1b84cb7f48f2d99b30409a19eb746c4e9607c46e Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Mon, 2 Nov 2020 14:05:26 +0000 Subject: [PATCH 03/62] mul! --- _posts/Julia-1.6-what-has-changed-since-1.0.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index 6327b230..6e8e39cd 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -74,6 +74,23 @@ In contrast in 1.0, they were split if there was a potential version that could ## Standard Library +### 5-arg `mul!`, aka in-place generalized multiplication and addition +The 5 arg `mul!(C, A, B, α, β)` performs the operation equivalent to: `C .= A*B*α + C*β`, where `α`, `β` are scalars and `A`, `B` and `C` are compatible mixes of scalars, matrices and vectors. +I am still the opinion that it should have been called `muladd!`, but this is the human friendly version of `BLAS.gemm!` (i.e. GEneralized Matrix Multiplication) and it's ilk. +It promises to alway compute the in-place mul-add in the most efficient, correct, way for any `AbstractArray` subtype. +In contrast, `BLAS.gemm` computes the same thing, but with a bunch of conditions. +It must be a strided array containing only BLAS-scalars, and if one of the inputs is conjugated/transposed you need to input it in non-conjugated/transposed form, and then tell `BLAS.gemm!` via passing in `'C'` or `T` rather than `N`, *and* none of the arrays are allowed to be aliases (so `copy` them if they are before calling). +5-arg `mul!` takes care of all that for you dispatching to `BLAS.gemm!` or other suitable methods once it has that all sorted. +Further, the existing 3-arg `mul!(C, A, B)` is a special case of it: +`mul!(C, A, B) = mul!(C, A, B, false, true)` (`true` and `false` being 1, and strong 0). +So in general on can just implement the 5-arg form and be done with it. + +I personally didn't care about 5-arg `mul!` at all for a long time. +Yet another in-place function in `LinearAlgebra` that I would never use often enough to remember what it did, and thus wouldn't use it. +But I realized that it is a crucial function for my own area: automatic differentiation. +`mul(C, A, B, true, true)` is the in-place accumulation rule for the reverse mode equivalent of the product rule. + + ### You can now print and interpolate `nothing` into strings. This is one of mine [#32148](https://github.com/JuliaLang/julia/pull/32148)., and I find it is such a usability enhancement. From 76fec391a1a595efacf90edfa966c73787e44923 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Tue, 3 Nov 2020 13:33:53 +0000 Subject: [PATCH 04/62] . --- _posts/Julia-1.6-what-has-changed-since-1.0.md | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index 6e8e39cd..e272021d 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -16,7 +16,7 @@ TODO: Insert links to the files in the release-1.6 branch. ## Threading -Julia has full support for threading now (as of Julia 1.3). +Julia has full support for threading now. Not just the limited `@threads for` loops, but full GoLang style threads. They are tightly integrated with the existing Task/Coroutine system. In effect threading works by unsetting the sticky flag on a task, so that it is allowed to run on any thread. @@ -27,6 +27,11 @@ But the new threading stuff is fast, on the order of microseconds to send work o Even for uses of `@threads for` the improves have some wins. `IO` is now thread-safe; `ReentrantLock` was added and is the kind of standard lock that you expect to exist, that has notifications on waiting work etc; and a big one: `@threads for` can now be nested without things silently being wrong. +A lot of this actually landed in julia 1.2, but julia 1.3 was the release we think of as being for threading, as it gave us `Threads.@spawn`. + +Also in Julia 1.6 we now have `julia -t auto` to start julia with 1 thread per (logical) core. +No more having to remember to set the `JULIA_NUM_THREADS` environment variable before starting it. + ## Pkg stdlib and the General Registry Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the Pkg stdlib; and the general registry is more policy than software. @@ -79,7 +84,7 @@ The 5 arg `mul!(C, A, B, α, β)` performs the operation equivalent to: `C .= A* I am still the opinion that it should have been called `muladd!`, but this is the human friendly version of `BLAS.gemm!` (i.e. GEneralized Matrix Multiplication) and it's ilk. It promises to alway compute the in-place mul-add in the most efficient, correct, way for any `AbstractArray` subtype. In contrast, `BLAS.gemm` computes the same thing, but with a bunch of conditions. -It must be a strided array containing only BLAS-scalars, and if one of the inputs is conjugated/transposed you need to input it in non-conjugated/transposed form, and then tell `BLAS.gemm!` via passing in `'C'` or `T` rather than `N`, *and* none of the arrays are allowed to be aliases (so `copy` them if they are before calling). +It must be a strided array containing only BLAS-scalars, and if one of the inputs is conjugated/transposed you need to input it in non-conjugated/transposed form, and then tell `BLAS.gemm!` via passing in `'C'` or `T` rather than `N`. 5-arg `mul!` takes care of all that for you dispatching to `BLAS.gemm!` or other suitable methods once it has that all sorted. Further, the existing 3-arg `mul!(C, A, B)` is a special case of it: `mul!(C, A, B) = mul!(C, A, B, false, true)` (`true` and `false` being 1, and strong 0). @@ -145,7 +150,9 @@ julia> redirect_stdout(devnull) do This is just handy, doing it without this is seriously annoying. - +## More "Why didn't it always work that way" than I can count +Since 1.0's release there have been so many small improvements to functions that I didn't even know happened, because I assumed they always worked that way. +Things like `startswith` supporting regex. From e2765f6ec441a7fe101a01821b92502228e79d65 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Tue, 3 Nov 2020 21:28:03 +0000 Subject: [PATCH 05/62] . --- _posts/Julia-1.6-what-has-changed-since-1.0.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index e272021d..8ac70ed1 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -116,7 +116,7 @@ Since then we have added: (I added the last 3 😁) -Aside: `contains` is argument flipped `occursin`, it was a thing in julia 0.6 but was removed in 1.0. +Aside: `contains` is argument flipped `occursin`, it was a thing in julia 0.6 but was removed in 1.0 and now has been added back. We added it back expressly to have the curried form, and to match `startswith` and `endswith`. ## A ton of new and improved standard library functions: @@ -149,6 +149,12 @@ julia> redirect_stdout(devnull) do ``` This is just handy, doing it without this is seriously annoying. +`readdir` now accepts a `join=true|false` keyword argument so that it returns paths with the parent dir. +This is good, almost every time I use `readdir` I used it as: +`joinpath.(x, readdir(x))`. +It is slightly cleaner (and faster) to be able to do `readdir(x; join=true)`. +Also added was a `sort` argument, which I don't see the point of so much, since `sort(readdir(x))` seems cleaner than `readdir(x; sort=true)`; and because I rarely rely on processing files in order. + ## More "Why didn't it always work that way" than I can count Since 1.0's release there have been so many small improvements to functions that I didn't even know happened, because I assumed they always worked that way. From a1e56d404893ac0a8fb8c28b36b10b42a7c56e58 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Mon, 16 Nov 2020 13:08:34 +0000 Subject: [PATCH 06/62] . --- .../Julia-1.6-what-has-changed-since-1.0.md | 39 ++++++++++++++----- 1 file changed, 30 insertions(+), 9 deletions(-) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index 8ac70ed1..c7c027c8 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -42,11 +42,13 @@ TODO: Write bits for these: - Pkg Server: faster updating of registry and download of packages - Artifacts, BinaryBuilder, Yggdasil, almost no one uses `deps/build.jl` any more - Scratch, and Preferences - - Tiered Resolution: in particular solving test dependencies of different compat. - `activate --temp` - - test dependencies + - test dependencies. https://discourse.julialang.org/t/activating-test-dependencies/48121/7?u=oxinabox - passing arguments to Pkg.test +### Resolver willing to downgrade packages to install new ones (Tiered Resolution) + + ### Precompilation Precompilation has been enhanced a lot. @@ -66,16 +68,35 @@ Further the spiffy animated output shows you what is precompiling at a given tim ### Improved conflict messages -This is one of my own small contributions. +This is one of my own contributions. Julia 1.0 conflict messages are a terrifying wall of text. Julia 1.0 conflict log Julia 1.6 conflict log +The two main changes are the use of colors, and compressing the version number ranges. +No more giant red wall of numbers. + Colors were added to make it easier to see which version numbers are referring to which package. -Consecutive lists of version numbers were compressed to continuous ranges. -In 1.6 these ranges are split only if there is a version that exist between them that is not compatible. -In contrast in 1.0, they were split if there was a potential version that could exist that is not compatible (even if that version doesn't currently exist); in practice this mean they split every time the right most nonzero was incremented. +There is a bit of a problem that it isn't easy to make sure the colors don't get reused, as there are not many in the 16 color list (especially when you skip a few like black, white and error red). +Due to how Pkg constructs its error messages basically every package in the dependency graph gets some message prepared for it, but not displayed, so just assigning them a color in turn gets it to loop around so doesn't reduce change of colors being reused. +There is away to fix that but it is a big change to add structured log messages that are colored only once they are displayed. +We decided after much debate to assign things colors based on the hash of the package name and shortened UUID. +This means things have consistent colors if you fix one and redo it a still have some remaining. +I think this is going to be a subtle improvement on easy of use. +As a cool hack, you can actually change the color list used. +To get the much larger color list I wanted to use originally you can do: +`append!(Pkg.Resolve.CONFLICT_COLORS, [21:51; 55:119; 124:142; 160:184; 196:220])` + + +The other change was consecutive lists of version numbers were compressed to continuous ranges. +In 1.6 these ranges are split only if there is a version that exists between them that is not compatible. +So normally just a single range, since compatibility is typically monotonic. +In contrast in 1.0, they were split if there was a potential version that could exist that is not compatible (even if that version doesn't currently exist). +This mean they split every time the right most nonzero was incremented. +For something with a lot of pre-1.0 versions that is a lot of numbers. +I think the new version is much cleaner and easier to read. + ## Standard Library @@ -112,7 +133,7 @@ In particular these are boolean comparison like functions: with two arguments, where the thing being compared against is the second. Julia 1.0 had `isequal`, `==` and `in`. Since then we have added: -`<`,`<=`,`>`, `>=`, `!=`, `startswith`, `endswith`, and `contains`. +`isapprox`, `<`,`<=`,`>`, `>=`, `!=`, `startswith`, `endswith`, and `contains`. (I added the last 3 😁) @@ -134,7 +155,7 @@ That one surprised me, I though being able to access a dict of local variables w But the compiler folk know better than I do. [`splitpath`](https://github.com/JuliaLang/julia/issues/28156) as added, its the opposite of `joinpath`. -Kind of stilly we didn't have that, and had been being me at least since 0.6. +Kind of silly we didn't have that, and had been being me at least since 0.6. On things that had been bugging me, I had wanted [`eachslice` and it's special cases: `eachrow` & `eachcol`](https://github.com/JuliaLang/julia/issues/29749) since julia 0.3 when I first started using it. These are super handy when you want to e.g. iterate through vectors of the rows of a matrix. @@ -153,6 +174,7 @@ This is just handy, doing it without this is seriously annoying. This is good, almost every time I use `readdir` I used it as: `joinpath.(x, readdir(x))`. It is slightly cleaner (and faster) to be able to do `readdir(x; join=true)`. +I think for Julia 2.0 we should consider making it the default. Also added was a `sort` argument, which I don't see the point of so much, since `sort(readdir(x))` seems cleaner than `readdir(x; sort=true)`; and because I rarely rely on processing files in order. @@ -163,4 +185,3 @@ Things like `startswith` supporting regex. - From ac689a43f385daf598819a5249b47fc55dd3a040 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sat, 28 Nov 2020 22:09:53 +0000 Subject: [PATCH 07/62] add tiered resolved --- .../Julia-1.6-what-has-changed-since-1.0.md | 34 +++++++++++++++++-- 1 file changed, 31 insertions(+), 3 deletions(-) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index c7c027c8..72c54407 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -37,6 +37,7 @@ No more having to remember to set the `JULIA_NUM_THREADS` environment variable b Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the Pkg stdlib; and the general registry is more policy than software. TODO: Write bits for these: + - Full transition off METADATA - Automatic merging - Pkg Server: faster updating of registry and download of packages @@ -48,6 +49,31 @@ TODO: Write bits for these: ### Resolver willing to downgrade packages to install new ones (Tiered Resolution) +Until the [tiered resolver](https://github.com/JuliaLang/Pkg.jl/pull/1330) was added Julia would not change the version of any currently installed package in order to install a new one. +For example consider + + - **Foo.jl** + - v1 + - v2 + - **Bar** + - v1 compatible with Foo v1 + - v2 compatible with Foo v2 + - **Qux.jl** + - v1: compatible only with Foo v1 + +If you did `pkg"add Foo Bar Qux"` you would end up with **Foo** v1, **Bar** v1, and **Qux** v1. +But if you did first: `pkg"add Foo Bar"` (which would install **Foo** v2, and **Bar** v2), +and then did `pkg"add Qux"`, +then on Julia 1.0 you would get an error, as it would refuse to downgrade **Foo** and **Bar** to v1, as is required to allow **Qux** to be installed. +This meant effectly the package manager is stateful, which turns out is really counter intuitive. +The way to resolve this in practice was to delete the `Manifest.toml` and do it again as a single action. +This was a significant problem for [test time dependencies](https://github.com/JuliaLang/Pkg.jl/issues/1352), which if you had a test time dependencies with a indirect dependencies shared with a main dependency of the package, but that was only compatible with an older version of the indirect depenency, you would be unable to run tests as resolving the test time depenency would fail. + +With the new tiered resolved it will try a number of tiers of relaxing existing versions. +It still wants to avoid changing the versions of currently installed packages if possible, but if that is required then it will do so. +It will in turn first attempt to install without any currently installed package being changed, then will try relaximng the constraint to allow changing the version of indirect dependencies, then to allowing changing the versions of direct dependencies. +This ends up far more intuitive: if there is a compatible set of package versions then they will be found. +Regardless of if new packages are added all at once or one at a time. ### Precompilation @@ -57,8 +83,9 @@ That is not the compilation that runs the first time a function is used in a ses For a start, the precompilation cache no longer goes stale every time you swap environments. This was a massive pain in julia 1.0, especially if you worked on more than one thing at a time. This was fixed in 1.3 to have multiple caches, for different environments. +It's easy to forget this one, but it is actually one of the biggest usability enhancements since 1.0. -More dramatically, in 1.6 is the parallelism of precompilation. +More dramatically, and less easy to overlook, is the parallelism of precompilation added in 1.6. Precompiling a package requires precompiling all its dependencies first. This is now done in parallel, and is automatically triggered when you complete Pkg operations. In contrast, to happening in series the first time a package is loaded. @@ -71,8 +98,9 @@ Further the spiffy animated output shows you what is precompiling at a given tim This is one of my own contributions. Julia 1.0 conflict messages are a terrifying wall of text. -Julia 1.0 conflict log -Julia 1.6 conflict log +![Julia 1.0 conflict log]({{site.url}}/Julia-1.0-1.6-changes/julia1.0-conflict.png) +![Julia 1.6 conflict log]({{site.url}}/Julia-1.0-1.6-changes/julia1.6-conflict.png) + The two main changes are the use of colors, and compressing the version number ranges. No more giant red wall of numbers. From f30a5f1c08be1d452d772c986f51150b12413242 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 29 Nov 2020 22:02:55 +0000 Subject: [PATCH 08/62] wip --- .../Julia-1.6-what-has-changed-since-1.0.md | 56 ++++++++++++++++++- 1 file changed, 53 insertions(+), 3 deletions(-) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index 72c54407..e07f9a81 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -18,8 +18,8 @@ TODO: Insert links to the files in the release-1.6 branch. ## Threading Julia has full support for threading now. Not just the limited `@threads for` loops, but full GoLang style threads. -They are tightly integrated with the existing Task/Coroutine system. -In effect threading works by unsetting the sticky flag on a task, so that it is allowed to run on any thread. +They are tightly integrated with the existing Async/Task/Coroutine system. +In effect threading works by unsetting the sticky flag on a Task, so that it is allowed to run on any thread. This is normally done via the `Threads.@spawn` macro, rather than the `@async` macro. Interestingly, the `@threads for` macro still remains, and doesn't actually use much of the new machinery. It still uses the old way which is a bit tigher if the loop durations are almost identical. @@ -32,6 +32,56 @@ A lot of this actually landed in julia 1.2, but julia 1.3 was the release we thi Also in Julia 1.6 we now have `julia -t auto` to start julia with 1 thread per (logical) core. No more having to remember to set the `JULIA_NUM_THREADS` environment variable before starting it. +## NamedTuple/keyword arguments automatic naming +This feature felt weird when I first read about it, but it has quickly grown on me. +How often do you write some code that does some processing and calls some other method passing on some of its keyword arguments? +For example +```julia +# Primary method all others redirect to this +foo(x::Bar; a=1, b=2, c=3) = ... + +# Method for if x is given as components +foo(x1, x2; a=10, b=20, c=30, comb=+) = foo(Bar(comp(x1, x2)); a=a, b=b, c=c) +``` +This new feature allows one to avoid writing `(...; a=a, b=b, c=c)`, and instead write `(...; a, b, c)`. +This does come with the requirement to seperate keyword arguments from positional arguments by `;`, but I have always done this, and the [BlueStyle guide requires it](https://github.com/invenia/BlueStyle#keyword-arguments). +It feels like we are fully leveraging the distinction of keyword from positional arguments by allowing this. +(In contrast, the distinction vs e.g. C# and Python that allow any positional argument to be passed by name, is to make the name of positional arguements not part of the public API, thus avoiding changing it being a breaking change.) + +The same syntax can be used to create `NamedTuple`. +```julia +julia> product = ["fries", "burger", "drink"]; + +julia> price = [2, 4, 1]; + +julia> (;product, price) +(product = ["fries", "burger", "drink"], price = [2, 4, 1]) +``` +This is particularly cool for constructing `NamedTuple`s of `Vector`s, which is a valid [Tables.jl Table](https://tables.juliadata.org/stable/#Tables.columntable). + +## Performance +### References to the Heap from the Stack +This was promised in 2016 or 2017 as a feature for 1.0 (released 2018). +But we actually didn't get it til 1.5 with [#33886](https://github.com/JuliaLang/julia/issues/33886). +Basically, the process of allocating memory from the heap is fairly slow*, where as allocating memory on the stack is basically a non-op. +Indeed Julia benchmarking tools don't count allocations on the stack as allocations at all. +One can find extensive write ups of heap vs stack allocations and how it works in general (though some mix the C specific factors with the CPU details.) +In julia all mutable objects live are allocated on the heap. +Until recently, immutable objects that contained references to heap allocated objects also had to live on the heap. +i.e. immutable objects with immutable fields (with immutable fields with...) could live on the stack. +But with this change now all immutable object can live on the stack, even if some of their fields live on the heap. +An important consequence of this is that wrapper types, such as the `SubArray` returned from `@view x[1:2]`, now have no overhead to create. +I find that in practice this often adds up to a 10-30% speed-up in real world code. + +(* Its actually really fast, but it is the kind of thing that rapidly adds up.) + +### Invalidations + +## Front-end changes +### Soft-scope in the REPL +### Deprecations are not surpressed by default. + + ## Pkg stdlib and the General Registry Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the Pkg stdlib; and the general registry is more policy than software. @@ -208,7 +258,7 @@ Also added was a `sort` argument, which I don't see the point of so much, since ## More "Why didn't it always work that way" than I can count Since 1.0's release there have been so many small improvements to functions that I didn't even know happened, because I assumed they always worked that way. -Things like `startswith` supporting regex. +Things like `startswith` supporting regex, and (`parse`](https://github.com/JuliaLang/julia/pull/36199) working on `UUID`s. From 191cc721ddcc610aa5d61d246fa5e261b068d4ba Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Wed, 23 Dec 2020 17:40:59 +0000 Subject: [PATCH 09/62] wip --- .../Julia-1.6-what-has-changed-since-1.0.md | 39 ++++++++++++++----- 1 file changed, 29 insertions(+), 10 deletions(-) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index e07f9a81..e4a86939 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -61,7 +61,7 @@ This is particularly cool for constructing `NamedTuple`s of `Vector`s, which is ## Performance ### References to the Heap from the Stack -This was promised in 2016 or 2017 as a feature for 1.0 (released 2018). +This was promised in 2016 as a feature for 1.0 (released 2018). But we actually didn't get it til 1.5 with [#33886](https://github.com/JuliaLang/julia/issues/33886). Basically, the process of allocating memory from the heap is fairly slow*, where as allocating memory on the stack is basically a non-op. Indeed Julia benchmarking tools don't count allocations on the stack as allocations at all. @@ -79,8 +79,17 @@ I find that in practice this often adds up to a 10-30% speed-up in real world co ## Front-end changes ### Soft-scope in the REPL -### Deprecations are not surpressed by default. +### Deprecations are muted by default. +This was me in TODOTODOTODOTODOTODOTODOTODO +The core of the reason is because it was actually breaking things. +Irelivant deprecation warning spam from dependencies of dependencies was causing JuMP and LightGraphs to become too slow to be used. +and because they were from dependencies of dependencies the maintainers of those packages couldn’t even fix them. Let alone the end users. + +Hopefully one day we could bring them back. +What we want is that if the deprecation warning is caused by a function call made from the main module of your current active enviroment, then you should get it. +I kind of know what we need to do to the logger to make that possible. +But it is time i don’t have right now to d ## Pkg stdlib and the General Registry @@ -148,7 +157,7 @@ Further the spiffy animated output shows you what is precompiling at a given tim This is one of my own contributions. Julia 1.0 conflict messages are a terrifying wall of text. -![Julia 1.0 conflict log]({{site.url}}/Julia-1.0-1.6-changes/julia1.0-conflict.png) +![Julia 1.0 conflict log]({{site.url}}/Julia-1.0-1.6-changes/julia1.0-conflict.png) ![Julia 1.6 conflict log]({{site.url}}/Julia-1.0-1.6-changes/julia1.6-conflict.png) The two main changes are the use of colors, and compressing the version number ranges. @@ -176,7 +185,7 @@ For something with a lot of pre-1.0 versions that is a lot of numbers. I think the new version is much cleaner and easier to read. -## Standard Library +## Base and Standard Libraries ### 5-arg `mul!`, aka in-place generalized multiplication and addition The 5 arg `mul!(C, A, B, α, β)` performs the operation equivalent to: `C .= A*B*α + C*β`, where `α`, `β` are scalars and `A`, `B` and `C` are compatible mixes of scalars, matrices and vectors. @@ -203,6 +212,20 @@ Often occurring when you are adding a quick `println` to debug something not bei Arguably both of those are better done via other means (`@show`, and values stored in fields in the error type); but we don't always do what is best. Sometimes it is expedient to just interpolate things into strings without worrying about what type they are. +### `Base.download` now using libcurl +For a very long time the `download` function which retrieves things over HTTP, was implemented with an amazing hack. +It conditionally shelled out to different programs. +On windows it run a mildly scary powershell script. +On Unixen it first tried to use `curl` then if that wasn't installed it tried to use `wget` and then if that wasn't installed it tried to use `fetch`. +It's low-key amazing that this worked as well as it did -- very few complaints. +But as of 1.6 [it now uses libcurl](https://github.com/JuliaLang/julia/pull/37340). +Using libcurl everywhere gives consistency with proxy settings, and protocol support (beyond HTTP) across all platforms. + +It also has more extensive API via the new [Downloads.jl](https://github.com/JuliaLang/Downloads.jl) standard library. +It can do things like progress logging, and it can retrieve headers. +I have [tried getting headers via conditional different commandline download functions](https://github.com/oxinabox/DataDeps.jl/pull/22) before, it is low-key nightmare fuel; and I ended up swapping out the HTTP.jl for that. +It wouldn't be too surprising if eventually we see libcurl swapped out for code extracted from HTTP.jl for a pure julia solution. +HTTP.jl works wonderfully to this, but I suspect untangling the client from the server is just a bit annoying right now, particularly as it is still evolving its API. ### A bunch of curried functions Julia seems to have ended up with a convention of providing curried methods of functions if that would be useful as the first argument for `filter`. @@ -218,7 +241,7 @@ Since then we have added: Aside: `contains` is argument flipped `occursin`, it was a thing in julia 0.6 but was removed in 1.0 and now has been added back. We added it back expressly to have the curried form, and to match `startswith` and `endswith`. -## A ton of new and improved standard library functions: +### A ton of other new and improved standard library functions: [`@time`])(https://github.com/JuliaLang/julia/pull/37678) now reports how much time was spent on compilation. This is going to help prevent people knew the language from including compilation time in their benchmarks. @@ -256,10 +279,6 @@ I think for Julia 2.0 we should consider making it the default. Also added was a `sort` argument, which I don't see the point of so much, since `sort(readdir(x))` seems cleaner than `readdir(x; sort=true)`; and because I rarely rely on processing files in order. -## More "Why didn't it always work that way" than I can count +### More "Why didn't it always work that way" than I can count Since 1.0's release there have been so many small improvements to functions that I didn't even know happened, because I assumed they always worked that way. Things like `startswith` supporting regex, and (`parse`](https://github.com/JuliaLang/julia/pull/36199) working on `UUID`s. - - - - From ba7158a95c564495cc3ac29448893a2083283784 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Mon, 28 Dec 2020 23:04:07 +0000 Subject: [PATCH 10/62] wip --- .../Julia-1.6-what-has-changed-since-1.0.md | 74 ++++++++++++++++--- 1 file changed, 65 insertions(+), 9 deletions(-) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index e4a86939..61e8bb5d 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -73,7 +73,7 @@ But with this change now all immutable object can live on the stack, even if som An important consequence of this is that wrapper types, such as the `SubArray` returned from `@view x[1:2]`, now have no overhead to create. I find that in practice this often adds up to a 10-30% speed-up in real world code. -(* Its actually really fast, but it is the kind of thing that rapidly adds up.) +(* Its actually really fast, but it is the kind of thing that rapidly adds up; and its slow vs operations that can happen without touching RAM.) ### Invalidations @@ -81,24 +81,41 @@ I find that in practice this often adds up to a 10-30% speed-up in real world co ### Soft-scope in the REPL ### Deprecations are muted by default. -This was me in TODOTODOTODOTODOTODOTODOTODO +This was me in [this PR](https://github.com/JuliaLang/julia/pull/35362). +It's not something I am super-happy about. +Though I think it makes sense. +Using deprecated methods is actually fine, as long as your dependencies follow SemVer, and you use `^` (i.e. default) bounding in your Project.toml's `[compat]`. +Which everyone does, because its enforced by the auto-merge script in the General registry. + +Solving deprecations is a thing you should chose to do, actively. +Not casually, when trying to do something else. +In particular, when updating to support a new major release of one of your dependencies, you should run your (integration) tests to see if they succeed. +If they don't succeed, then one removed support for the new version and run julia with deprecation warnings turned on (or set to error); to identify and fix them. +Probably also you are checking the release notes, since you are intentionally updating. + The core of the reason is because it was actually breaking things. -Irelivant deprecation warning spam from dependencies of dependencies was causing JuMP and LightGraphs to become too slow to be used. -and because they were from dependencies of dependencies the maintainers of those packages couldn’t even fix them. Let alone the end users. +Irrelevant deprecation warning spam from dependencies of dependencies was causing JuMP and LightGraphs to become too slow to be used. +Since they were from dependencies of dependencies the maintainers of those JuMP and LightGraphs couldn’t even fix them. +Let alone the end users. + +Deprecation warnings are still turned on by default in tests. +Which makes sense since the tests (unlike normal use), is being run by maintainers of the package itself, not its users. +Though it still doesn't make a huge amount of sense to me, since spam from deprecation warnings floods out your actual errors that you want to see during testing. +For this reason Invenia (my employer) has disabled deprecation warnings in the tests for all our closed source packages, and added a new test job that just does deprecation checking (set to error on deprecation and with the job allowed to fail, just so we have notice). -Hopefully one day we could bring them back. -What we want is that if the deprecation warning is caused by a function call made from the main module of your current active enviroment, then you should get it. +Hopefully one day we can improve the tooling around deprecation warnings. +An ideal behavour would be to only show deprecation warning if directly caused by a function call made from within the package module of your current active environment. I kind of know what we need to do to the logger to make that possible. -But it is time i don’t have right now to d +But it is not yet something I have had time to do. +### Colored Stack-traces +TODODODOTOTOTOTODODEOEDO TODO ## Pkg stdlib and the General Registry Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the Pkg stdlib; and the general registry is more policy than software. TODO: Write bits for these: - - Full transition off METADATA - - Automatic merging - Pkg Server: faster updating of registry and download of packages - Artifacts, BinaryBuilder, Yggdasil, almost no one uses `deps/build.jl` any more - Scratch, and Preferences @@ -106,6 +123,45 @@ TODO: Write bits for these: - test dependencies. https://discourse.julialang.org/t/activating-test-dependencies/48121/7?u=oxinabox - passing arguments to Pkg.test +### Full Transition off METADATA.jl and REQUIRE files, and onto the General Registry +Even though Julia 1.0 had Pkg3 and was supposed to use registries, for a long time the old Pkg2 [METADATA.jl](https://github.com/JuliaLang/METADATA.jl) pseudo-registry was used as the the canonical source of truth. +Registering new releases was made against that, and then a script synchronized [General](https://github.com/JuliaRegistries/General) registry to match it. +This was to allow time for packages to change over to supporting julia 1.0, while still also making releases that supported julia 0.6. +It wasn't until about a year later that this state ended and the General registry became the one source of truth. + +This was kind of sucky, because a lot of the power of Pkg3 was blocked until then. +In particular, since the registries compat was generated from the REQUIRE file, and the REQUIRE files had kind of [gross requirement specification](https://docs.julialang.org/en/v0.6/manual/packages/#Requirements-Specification-1), and everyone basically just lower bounded things, or didn't bound compat at all. +Which made sense because with the only 1 global environment that Pkg2 had, you basically needed the whole ecosystem to be compatible, so restricting was bad. +But Pkg3/Project.toml has the much better default [`^` bounded]](https://julialang.github.io/Pkg.jl/v1/compatibility/#Version-specifier-format-1) to accept only non-breaking changes by SemVer. +(And per project environments, so things don't all have to be compatible -- just things used in a particular project (rather than every project you might ever do). + +#### Automatic Merging +Initially after the change over all PRs to make a new release needed manual review, by one of the very small number of General registry maintainers. + +I am a big fan of [releasing after every non-breaking PR](https://www.oxinabox.net/2019/09/28/Continuous-Delivery-For-Julia-Packages.html). +Why would you not want those bug-fixes and new features released? +It is especially rude to contributors who will make a fix or a feature, but then you don't let them use it because you didn't tag a release. +Plus it makes tracking down bugs easier, if it occurs on a precise version you know the PR that caused it. +But with manual merging it feels bad, to tag 5 releases in a day. +Now they we have automatic merging it is fine. +At time of writing [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl/) was up to 67 releases. + +The big advantage of automatic merging is that it comes with automatic enforcement of standards. +In order to be automerge-able some good standards have to be followed. +One of which is that that no unbounded compat specifiers (e..g `>=1.0`) are permitted; and that everything must have a compat specifier (since unspecified is same as `>=0.0.0`) +That one is particularly great, since if one adds specifiers later that can't be met, then it can trigger downgrades back to the incredible old versions that didn't specify compat and that almost certainly are not compatible. + +To deal with that particular case retro-capping was done to retroactively add bounds to all things that didn't have them. +Which was painful when it was done, since it rewrote compat in the registry, which made it disagree with the `Project.toml`, which is always confusing. +Now that it is done, it is good. + +#### Requirement to have a `Project.toml` +Finally the ability to `pkg> dev` packages that had only a REQUIRE and no `Project.toml` was removed in Julia 1.4. +You can still `pkg> add` them, if they were registered during the transition period. +But to edit the projects they must a `Project.toml` file, which is fine since all the tools to make registering releases also require you to have `Project.toml` now + + + ### Resolver willing to downgrade packages to install new ones (Tiered Resolution) Until the [tiered resolver](https://github.com/JuliaLang/Pkg.jl/pull/1330) was added Julia would not change the version of any currently installed package in order to install a new one. From fba13ea38773fb563ad66e28ec57053073d656e0 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Fri, 1 Jan 2021 15:30:28 +0000 Subject: [PATCH 11/62] wip --- .../Julia-1.6-what-has-changed-since-1.0.md | 57 ++++++++++++++++++- 1 file changed, 55 insertions(+), 2 deletions(-) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index 61e8bb5d..988a4666 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -44,9 +44,9 @@ foo(x::Bar; a=1, b=2, c=3) = ... foo(x1, x2; a=10, b=20, c=30, comb=+) = foo(Bar(comp(x1, x2)); a=a, b=b, c=c) ``` This new feature allows one to avoid writing `(...; a=a, b=b, c=c)`, and instead write `(...; a, b, c)`. -This does come with the requirement to seperate keyword arguments from positional arguments by `;`, but I have always done this, and the [BlueStyle guide requires it](https://github.com/invenia/BlueStyle#keyword-arguments). +This does come with the requirement to separate keyword arguments from positional arguments by `;`, but I have always done this, and the [BlueStyle guide requires it](https://github.com/invenia/BlueStyle#keyword-arguments). It feels like we are fully leveraging the distinction of keyword from positional arguments by allowing this. -(In contrast, the distinction vs e.g. C# and Python that allow any positional argument to be passed by name, is to make the name of positional arguements not part of the public API, thus avoiding changing it being a breaking change.) +In contrast, the distinction vs e.g. C# and Python that allow any positional argument to be passed by name, is to make the name of positional arguments not part of the public API, thus avoiding changing it being a breaking change. The same syntax can be used to create `NamedTuple`. ```julia @@ -59,6 +59,10 @@ julia> (;product, price) ``` This is particularly cool for constructing `NamedTuple`s of `Vector`s, which is a valid [Tables.jl Table](https://tables.juliadata.org/stable/#Tables.columntable). +It is interesting to note that for the logging macros introduced in Julia 1.0, this is how they have always worked. +E.g. `@info "message" foo bar` and `@info "message" foo=foo bar=bar` display the same. +Which has always felt natural. + ## Performance ### References to the Heap from the Stack This was promised in 2016 as a feature for 1.0 (released 2018). @@ -75,8 +79,57 @@ I find that in practice this often adds up to a 10-30% speed-up in real world co (* Its actually really fast, but it is the kind of thing that rapidly adds up; and its slow vs operations that can happen without touching RAM.) +## Internals ### Invalidations +Consider a function `foo` with a method `foo(::Number)`. +If some other function calls it, for example `bar(x::Int) = 2*foo(x)`, the JIT will compile in an instruction for exactly the method instance to call (assuming type-inference work) -- a fast static dispatch, possibly even inlined. +If the user then defines a new more specific method `foo(::Int)`, then the compiled code for `Bar`, need to be invalidated so it will call the new one. +It needs to be recompiled -- which means anything that static dispatches to it needs to be recompiled and so forth. +This is an invalidation. +It's an important feature of the language. +It is key to extensibility. +It doesn't normally cause too many problems. +Since generally basically everything is defined before anything is called, and thus before anything is compiled. + +A notable exception to this is Base an the other standard libraries. +These are compiled into the so-called system image. +Further-more, methods in these standard libraries are some of the most overloaded, thus most likely to have it triggered. + +A bunch of work has gone into dealing with invalidations better. +Not just point-fixes to remove calls that were likely to be invalidated, but several changes to the compiler. +One particular change was not triggering cascading invalidations for methods that couldn't actually be called do the being ambiguous. +As a result a lot of user code doesn't trigger invalidations on 1.6, that did on 1.5. +The end result of this is faster compilation after loading packages, since it doesn't have to recompile a ton of invalidated method instances. +i.e. decreased time to first plot. + + +A full discussion on the invalidations work can be found in [this blog-post](https://julialang.org/blog/2020/08/invalidations/). + +### Manually Created Back-edges for Lowered Code Generated Functions +This is a very niche and not really at all user-facing feature. +To understand why this matters, it's worth understanding how Cassette works. +I wrote [blog-post on this a few years ago](https://invenia.github.io/blog/2019/10/30/julialang-features-part-1#making-cassette). + +In quiet the opposite, Julia 1.3 allowed for the creation of more invalidations though allowing back-edges to be manually attached to the `CodeInfo` for `@generated` functions that return lowered code. +Backedges are the connection from methods back to each method instance that calls them. +This is what allows invalidations to work, as when a method is redefined, it needs to know what things to recompile. +This change allowed those back-edges to be manually specified for `@generated` functions that were working at the lowered code level. +Which is useful since this technique is primarily used for generating code based on the (lowered) code of existing methods. +For example in [Zygote](https://github.com/FluxML/Zygote.jl), generating the gradient code, from the code of the primal method. +So you want to be able to the regeneration of this code when that original method changes. + + +Basically, end of the day is this allows code that uses [Cassette.jl](https://github.com/jrevels/Cassette.jl), and [IRTools.jl](https://github.com/MikeInnes/IRTools.jl) to not suffer from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. +A particular case of this is for [Zygote](https://github.com/FluxML/Zygote.jl) where redefining a function called by the code that was being differentiated did not result in an updated gradient (unless `Zygote.refresh()`) was run. +This was annoying for + +Other things that this allows is two very weird packages that [Nathan Daly](https://github.com/NHDaly) and I came up with at the JuliaCon 2018 hackathon: [StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) and [Tricks.jl](https://github.com/oxinabox/Tricks.jl/). +[StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) relaxes the restrictions on normal `@generated` functions so that they are also safe from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. +[Tricks.jl](https://github.com/oxinabox/Tricks.jl/) uses this feature to make `hasmethod` etc resolve at compile-time, and then get updated if and when new methods are defined. +Which can allow for defing traits like _"anything that defines a `iterate` method"_. + + ## Front-end changes ### Soft-scope in the REPL ### Deprecations are muted by default. From 2f5f945bae3b29a399ed34609c186cdb2a683565 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 3 Jan 2021 00:19:13 +0000 Subject: [PATCH 12/62] wip --- .../Julia-1.6-what-has-changed-since-1.0.md | 63 +++++++++++++++++-- 1 file changed, 58 insertions(+), 5 deletions(-) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index 988a4666..c03d4b5b 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -8,7 +8,7 @@ tags: Julia 1.6 is slated to be the next long-term support release of the Julia programming language. It has been 2 years since the last LTS, and a lot has changed. This post is kind of a follow-up to my [Julia 1.0 release run-down]({{site.url}}//2018/06/01/Julia-Favourite-New-Things.html). -But it's going to be even longer, as it is covering the last 5 releases since then. +But it's going to be even longer, as it is covering the last 5 releases since then; and I am not skipping the major new features. I am writing this not to break down release by release, but to highlight features that had you only used Julia 1.0, you wouldn't have seen. Full details can be found in the NEWS.md, and HISTORY.md @@ -132,6 +132,23 @@ Which can allow for defing traits like _"anything that defines a `iterate` metho ## Front-end changes ### Soft-scope in the REPL +Julia 1.0 removed the notion of soft-scope from the language. +I was very blasé about the change to for-loop bindings in my [1.0 release post](https://www.oxinabox.net/2018/06/01/Julia-Favourite-New-Things.html#for-loop-variable-binding-changes). +Infact, I didn't even mention this particular change. +It was [#19324](https://github.com/JuliaLang/julia/pull/19324) for reference + +This was undone in in Julia 1.5 with [#33864](https://github.com/JuliaLang/julia/pull/33864) for in the REPL only. +Now in the REPL, assigning to a global variable within a for-loop actually assigns that variable, rather than shadowing it with a new variable in that scope. +The same behavior outside the REPL, now gives a warning. + +Personally, this change never affected me because I never write for-loops at global scope that assign variables. +Indeed basically all I code I write is inside functions. +But I do see how this causes problems for some interactive work-flows, especially e.g. when demonstrating something. +See the [main github issue](https://github.com/JuliaLang/julia/issues/28789) and the longest [Discourse thread](https://discourse.julialang.org/t/another-possible-solution-to-the-global-scope-debacle/15894), though there were many others. +It took years of thinking to workout the solution, particularly because many of the more obvious solutions would be breaking in significant ways. + + + ### Deprecations are muted by default. This was me in [this PR](https://github.com/JuliaLang/julia/pull/35362). @@ -172,10 +189,44 @@ TODO: Write bits for these: - Pkg Server: faster updating of registry and download of packages - Artifacts, BinaryBuilder, Yggdasil, almost no one uses `deps/build.jl` any more - Scratch, and Preferences - - `activate --temp` - - test dependencies. https://discourse.julialang.org/t/activating-test-dependencies/48121/7?u=oxinabox - passing arguments to Pkg.test +### Temporary Environments +Julia 1.5 added `pkg> activate --temp` which will create and activate a temporary environment. +This environment is deleted when Julia is exited. +This is incredibly handy for: + + - Reproducing an issue + - Answering questions on Stack-Overflow etc + - Checking the behavior of the last release of a package you currently have `dev`ed. + - Quickly trying out an idea + +Remembering that with Pkg3 installing a version of a package you have installed before is incredibly fast. +Pkg doesn't download it again, it basically just points to the existing version on disk. +So using `activate --temp` to quickly try something is indeed quick. + +A more hacky use of it is after loading (via `using`) a package that you have `dev`'ed, you can `activate --temp` and install some of your test-time dependencies to reproduce a test failure without adding them to the main dependencies in the `Project.toml`. +Though there is a better way if you use `test/Project.toml`. + +### Test Dependencies in own Project.toml +In [Julia 1.0 and 1.1, test specific dependencies are listed in the `[extras]` section](https://julialang.github.io/Pkg.jl/v1/creating-packages/#Test-specific-dependencies-in-Julia-1.0-and-1.1), and under `[targets] test=[...]`; with its compatibility listed in the main `[compat]`. +It seemed like this might be in the future be extended for other things, perhaps documentation. +But it was found for documentation in particular that a seperate Project.toml worked well. +As long as you `dev ..` into its `Manifest.toml` so it uses the right version of the package it is documenting. + +The new `tests/Project.toml` extends that idea. +One part of that extension is to remove the need to `dev ..`, and other part is to make available all the main dependencies. +It actually works on a different mechanism to the docs. +It relies on stacked environments, which is a feature julia has had since 1.0 via `LOAD_PATH`, but that is rarely used. + + +One advantage of this is that you can activate that Project.toml, on top of the your existing environment by adding the test directory to your `LOAD_PATH`. +I wrote some more details on exactly how to do that on [Discourse]( +https://discourse.julialang.org/t/activating-test-dependencies/48121/7?u=oxinabox). +It feels kind of hacky, because it is. +At some point there might be a nicer user-interface for stacked environments like this. + + ### Full Transition off METADATA.jl and REQUIRE files, and onto the General Registry Even though Julia 1.0 had Pkg3 and was supposed to use registries, for a long time the old Pkg2 [METADATA.jl](https://github.com/JuliaLang/METADATA.jl) pseudo-registry was used as the the canonical source of truth. Registering new releases was made against that, and then a script synchronized [General](https://github.com/JuliaRegistries/General) registry to match it. @@ -348,7 +399,7 @@ Since then we have added: Aside: `contains` is argument flipped `occursin`, it was a thing in julia 0.6 but was removed in 1.0 and now has been added back. -We added it back expressly to have the curried form, and to match `startswith` and `endswith`. +We added it back primarily so we could have the curried form, and to match `startswith` and `endswith`. ### A ton of other new and improved standard library functions: @@ -390,4 +441,6 @@ Also added was a `sort` argument, which I don't see the point of so much, since ### More "Why didn't it always work that way" than I can count Since 1.0's release there have been so many small improvements to functions that I didn't even know happened, because I assumed they always worked that way. -Things like `startswith` supporting regex, and (`parse`](https://github.com/JuliaLang/julia/pull/36199) working on `UUID`s. +Things like `startswith` supporting regex, (`parse`](https://github.com/JuliaLang/julia/pull/36199) working on `UUID`s, +`accumulate`, `cumsum`, and `cumprod` supporting arbitrary iterators (https://github.com/JuliaLang/julia/pull/34656). +Julia 1.6 is one hell of a more polished language. \ No newline at end of file From 672ee06204e5cb0c642b43f6927f27b8197f33dc Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 3 Jan 2021 13:54:55 +0000 Subject: [PATCH 13/62] wip --- _posts/Julia-1.6-what-has-changed-since-1.0.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index c03d4b5b..5e4cdb73 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -191,6 +191,15 @@ TODO: Write bits for these: - Scratch, and Preferences - passing arguments to Pkg.test +### BinaryBuilder, Artifacts, Yggdasil and jll packages. + +This story was beginning to be told around julia 1.0 time, +but it wasn't really complete nor built into Pkg until Julia 1.3. + +You can read the documentation on [Artifacts](https://julialang.github.io/Pkg.jl/v1/artifacts/), [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl), and [Yggdasil](https://github.com/JuliaPackaging/Yggdrasil) + for full details +TODODODODODODOD + ### Temporary Environments Julia 1.5 added `pkg> activate --temp` which will create and activate a temporary environment. This environment is deleted when Julia is exited. From 64e51eccbff5b4f42f0653bcc53a8145c4f2ff9e Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sat, 9 Jan 2021 23:22:36 +0000 Subject: [PATCH 14/62] WIP --- .../Julia-1.6-what-has-changed-since-1.0.md | 72 ++++++++++++++++-- .../julia-1.0-code-llvm.png | Bin 0 -> 462466 bytes .../julia-1.0-stacktrace.png | Bin 0 -> 862665 bytes .../julia-1.6-code-llvm.png | Bin 0 -> 399465 bytes .../julia-1.6-stacktrace.png | Bin 0 -> 830267 bytes 5 files changed, 65 insertions(+), 7 deletions(-) create mode 100644 posts_assets/Julia-1.0-1.6-changes/julia-1.0-code-llvm.png create mode 100644 posts_assets/Julia-1.0-1.6-changes/julia-1.0-stacktrace.png create mode 100644 posts_assets/Julia-1.0-1.6-changes/julia-1.6-code-llvm.png create mode 100644 posts_assets/Julia-1.0-1.6-changes/julia-1.6-stacktrace.png diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index 5e4cdb73..f5e25735 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -22,7 +22,7 @@ They are tightly integrated with the existing Async/Task/Coroutine system. In effect threading works by unsetting the sticky flag on a Task, so that it is allowed to run on any thread. This is normally done via the `Threads.@spawn` macro, rather than the `@async` macro. -Interestingly, the `@threads for` macro still remains, and doesn't actually use much of the new machinery. It still uses the old way which is a bit tigher if the loop durations are almost identical. +Interestingly, the `@threads for` macro still remains, and doesn't actually use much of the new machinery. It still uses the old way which is a bit tighter if the loop durations are almost identical. But the new threading stuff is fast, on the order of microseconds to send work off to another thread. Even for uses of `@threads for` the improves have some wins. `IO` is now thread-safe; `ReentrantLock` was added and is the kind of standard lock that you expect to exist, that has notifications on waiting work etc; and a big one: `@threads for` can now be nested without things silently being wrong. @@ -63,8 +63,7 @@ It is interesting to note that for the logging macros introduced in Julia 1.0, t E.g. `@info "message" foo bar` and `@info "message" foo=foo bar=bar` display the same. Which has always felt natural. -## Performance -### References to the Heap from the Stack +## Performance: References to the Heap from the Stack This was promised in 2016 as a feature for 1.0 (released 2018). But we actually didn't get it til 1.5 with [#33886](https://github.com/JuliaLang/julia/issues/33886). Basically, the process of allocating memory from the heap is fairly slow*, where as allocating memory on the stack is basically a non-op. @@ -79,7 +78,37 @@ I find that in practice this often adds up to a 10-30% speed-up in real world co (* Its actually really fast, but it is the kind of thing that rapidly adds up; and its slow vs operations that can happen without touching RAM.) -## Internals +## Time To First Plot +People often complain about the "Time To First Plot" (TTFP) in julia. +I personally have never minded it -- by the time iI am plotting something I have done minutes of thinking so 10 seconds of compilation is nothing. +Plotting turns out is basically a really hard thing for a compiler. +It is many many small methods, most of which are only called once. +And unlike most julia code it doesn't actually benefit all that much from most of the work julia's JIT is normally doing to specialize things -- plotting itself isn't in the hot-loop. +To a long-story short, plotting the postcard example for julia needing to compile things before it can run them. + +As a really hacky way to time this, the following timing. +Timing it twice because need to capture the timing for printing afterwards (which is something very affected by invalidations see below). +Also with precompilation already run +(though see below, precompilation caches often would be deleted in 1.0; and conversely are way faster to create in 1.6 due to parallelization.) + +**Julia 1.0:** +```julia +julia> @time @time using Plots; plot(1:0.1:10, sin.(1:0.1:10)) + 7.168878 seconds (15.87 M allocations: 941.114 MiB, 7.36% gc time) + 9.281561 seconds (25.74 M allocations: 1.393 GiB, 7.88% gc time) +``` +**Julia 1.6:** +```julia +julia> @time @time using Plots; plot(1:0.1:10, sin.(1:0.1:10)) + 4.490434 seconds (8.24 M allocations: 583.285 MiB, 7.13% gc time, 32.80% compilation time) + 4.533302 seconds (8.36 M allocations: 590.949 MiB, 7.06% gc time, 33.41% compilation time) +``` + + +### Per Module Optimization Flags + +TODODOTOTOTOTODODODOD TODO + ### Invalidations Consider a function `foo` with a method `foo(::Number)`. @@ -103,10 +132,13 @@ As a result a lot of user code doesn't trigger invalidations on 1.6, that did on The end result of this is faster compilation after loading packages, since it doesn't have to recompile a ton of invalidated method instances. i.e. decreased time to first plot. +In this has had huge effect on [Revise.jl](https://github.com/timholy/Revise.jl) +which started to take a second or so to load when it gained the dependency on [JuliaInterpretter.jl](https://github.com/JuliaDebug/JuliaInterpreter.jl); which isn't much, but when you do it every time you start julia it is an annoying lack of snappyness. +But thanks to this work, JuliaInterpretter, and thus Revise now load in a flash. A full discussion on the invalidations work can be found in [this blog-post](https://julialang.org/blog/2020/08/invalidations/). -### Manually Created Back-edges for Lowered Code Generated Functions +## Internals: Manually Created Back-edges for Lowered Code Generated Functions This is a very niche and not really at all user-facing feature. To understand why this matters, it's worth understanding how Cassette works. I wrote [blog-post on this a few years ago](https://invenia.github.io/blog/2019/10/30/julialang-features-part-1#making-cassette). @@ -178,8 +210,34 @@ An ideal behavour would be to only show deprecation warning if directly caused b I kind of know what we need to do to the logger to make that possible. But it is not yet something I have had time to do. -### Colored Stack-traces -TODODODOTOTOTOTODODEOEDO TODO + +### Code highlighting for `code_llvm` and `code_native` +This was added in [#36634](https://github.com/JuliaLang/julia/issues/36634). +This functionality was first implemented in the [ColoredLLCodes.jl](https://github.com/kimikage/ColoredLLCodes.jl), where it worked by monkey-patching the InteractiveUtils stdlib. +That package does still work on julia 1.0. + +![Julia 1.0 code_llvm]({{site.url}}/Julia-1.0-1.6-changes/julia1.0-code-llvm.png) +![Julia 1.6 code_llvm]({{site.url}}/Julia-1.0-1.6-changes/julia1.6-code-llvm.png) + +The REPL itself still doesn't have syntax highlighting for Julia code though. +The [OhMyRepl.jl](https://github.com/KristofferC/OhMyREPL.jl) package does provide that, and works all versions of Julia. +It is a lot more than a series of regex though, so I don't think we are going to see it built into julia too soon. +Probably one day, though, as its big dependencies are also required if the parser wants to move to be written in julia. +(Though I also don't expect that any time soon) + +### Clearer Stacktraces + +Just like colored `code_llvm` colored stack-traces also originated in a package that was doing some nasty monkey-patching: [ClearStacktrace.jl](https://github.com/jkrumbiegel/ClearStacktrace.jl). +This was added into Base itself in [#36134](https://github.com/JuliaLang/julia/pull/36134) + +![Julia 1.0 stacktrace]({{site.url}}/Julia-1.0-1.6-changes/julia1.0-stacktrace.png) +![Julia 1.6 stacktrace]({{site.url}}/Julia-1.0-1.6-changes/julia1.6-stacktrace.png) + +The first thing you probably notice is the colored package names, to make it more clear where the error is coming from. +You also should notice the dimming of type-parameters to make complicated types easier to read. +Also the addition of argument names, and showing keyword arguments bearing functions as they are written, rather than as a weird `#DataFrame#654` internal 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.../Julia-1.6-what-has-changed-since-1.0.md | 293 +++++++++++------- 1 file changed, 180 insertions(+), 113 deletions(-) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index f5e25735..66dc3685 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -5,8 +5,29 @@ tags: - julia --- -Julia 1.6 is slated to be the next long-term support release of the Julia programming language. -It has been 2 years since the last LTS, and a lot has changed. +Julia 1.0 came out well over 2 year ago. +Since then a lot has changed; and a lot hasn't. +Since Julia 1.0 was a commitment to no breaking changes; +but that is not to say no new features have been added to the language. + + + +Julia 1.6 is the first feature release since 1.0. +Prior to 1.0 all releases were feature releases; they came out when they were ready. +Since then all releases (except 1.6) have been timed releases. +The release candidate is cut from the main branch every 3 months; and they are released after an [extensive round of additional checking](https://github.com/JuliaLang/julia/pull/35846), which often takes weeks to months. +Julia 1.6 was soft-slated to be the new long-term-support (LTS) version that would have bug-fixes backported to it for the next few years. +The current LTS is julia 1.0, which has now had 5 patch releases made. +Since it was going to be supported for a long-time people wanted to make sure everything good got in; thus it was a feature release. +The core developers have demurred on if 1.6 will actually be selected to be the new LTS (even if it wasn't it won't assend to being LTS til it stops being the current Stable). + +My impression now is that they feel like it has too many cool new things; and that a few things didn't quite make it in even with the extended release cycle. +So it's looking likely to me that 1.7 will actually be the LTS; but that it might also be a feature release -- possibly this time a much shorter release period than usual. +In practice I think for a lot of package maintainer's 1.6 will be a LTS, in that that is the oldest version they will make sure to continue to support. +There has been too many cool new things (as this post will detail) to stay back to only 1.0 features. +Already a lot of packages have dropped support for julia versions older than 1.3. + + This post is kind of a follow-up to my [Julia 1.0 release run-down]({{site.url}}//2018/06/01/Julia-Favourite-New-Things.html). But it's going to be even longer, as it is covering the last 5 releases since then; and I am not skipping the major new features. I am writing this not to break down release by release, @@ -15,23 +36,6 @@ Full details can be found in the NEWS.md, and HISTORY.md TODO: Insert links to the files in the release-1.6 branch. -## Threading -Julia has full support for threading now. -Not just the limited `@threads for` loops, but full GoLang style threads. -They are tightly integrated with the existing Async/Task/Coroutine system. -In effect threading works by unsetting the sticky flag on a Task, so that it is allowed to run on any thread. -This is normally done via the `Threads.@spawn` macro, rather than the `@async` macro. - -Interestingly, the `@threads for` macro still remains, and doesn't actually use much of the new machinery. It still uses the old way which is a bit tighter if the loop durations are almost identical. -But the new threading stuff is fast, on the order of microseconds to send work off to another thread. -Even for uses of `@threads for` the improves have some wins. -`IO` is now thread-safe; `ReentrantLock` was added and is the kind of standard lock that you expect to exist, that has notifications on waiting work etc; and a big one: `@threads for` can now be nested without things silently being wrong. - -A lot of this actually landed in julia 1.2, but julia 1.3 was the release we think of as being for threading, as it gave us `Threads.@spawn`. - -Also in Julia 1.6 we now have `julia -t auto` to start julia with 1 thread per (logical) core. -No more having to remember to set the `JULIA_NUM_THREADS` environment variable before starting it. - ## NamedTuple/keyword arguments automatic naming This feature felt weird when I first read about it, but it has quickly grown on me. How often do you write some code that does some processing and calls some other method passing on some of its keyword arguments? @@ -76,90 +80,7 @@ But with this change now all immutable object can live on the stack, even if som An important consequence of this is that wrapper types, such as the `SubArray` returned from `@view x[1:2]`, now have no overhead to create. I find that in practice this often adds up to a 10-30% speed-up in real world code. -(* Its actually really fast, but it is the kind of thing that rapidly adds up; and its slow vs operations that can happen without touching RAM.) - -## Time To First Plot -People often complain about the "Time To First Plot" (TTFP) in julia. -I personally have never minded it -- by the time iI am plotting something I have done minutes of thinking so 10 seconds of compilation is nothing. -Plotting turns out is basically a really hard thing for a compiler. -It is many many small methods, most of which are only called once. -And unlike most julia code it doesn't actually benefit all that much from most of the work julia's JIT is normally doing to specialize things -- plotting itself isn't in the hot-loop. -To a long-story short, plotting the postcard example for julia needing to compile things before it can run them. - -As a really hacky way to time this, the following timing. -Timing it twice because need to capture the timing for printing afterwards (which is something very affected by invalidations see below). -Also with precompilation already run -(though see below, precompilation caches often would be deleted in 1.0; and conversely are way faster to create in 1.6 due to parallelization.) - -**Julia 1.0:** -```julia -julia> @time @time using Plots; plot(1:0.1:10, sin.(1:0.1:10)) - 7.168878 seconds (15.87 M allocations: 941.114 MiB, 7.36% gc time) - 9.281561 seconds (25.74 M allocations: 1.393 GiB, 7.88% gc time) -``` -**Julia 1.6:** -```julia -julia> @time @time using Plots; plot(1:0.1:10, sin.(1:0.1:10)) - 4.490434 seconds (8.24 M allocations: 583.285 MiB, 7.13% gc time, 32.80% compilation time) - 4.533302 seconds (8.36 M allocations: 590.949 MiB, 7.06% gc time, 33.41% compilation time) -``` - - -### Per Module Optimization Flags - -TODODOTOTOTOTODODODOD TODO - -### Invalidations - -Consider a function `foo` with a method `foo(::Number)`. -If some other function calls it, for example `bar(x::Int) = 2*foo(x)`, the JIT will compile in an instruction for exactly the method instance to call (assuming type-inference work) -- a fast static dispatch, possibly even inlined. -If the user then defines a new more specific method `foo(::Int)`, then the compiled code for `Bar`, need to be invalidated so it will call the new one. -It needs to be recompiled -- which means anything that static dispatches to it needs to be recompiled and so forth. -This is an invalidation. -It's an important feature of the language. -It is key to extensibility. -It doesn't normally cause too many problems. -Since generally basically everything is defined before anything is called, and thus before anything is compiled. - -A notable exception to this is Base an the other standard libraries. -These are compiled into the so-called system image. -Further-more, methods in these standard libraries are some of the most overloaded, thus most likely to have it triggered. - -A bunch of work has gone into dealing with invalidations better. -Not just point-fixes to remove calls that were likely to be invalidated, but several changes to the compiler. -One particular change was not triggering cascading invalidations for methods that couldn't actually be called do the being ambiguous. -As a result a lot of user code doesn't trigger invalidations on 1.6, that did on 1.5. -The end result of this is faster compilation after loading packages, since it doesn't have to recompile a ton of invalidated method instances. -i.e. decreased time to first plot. - -In this has had huge effect on [Revise.jl](https://github.com/timholy/Revise.jl) -which started to take a second or so to load when it gained the dependency on [JuliaInterpretter.jl](https://github.com/JuliaDebug/JuliaInterpreter.jl); which isn't much, but when you do it every time you start julia it is an annoying lack of snappyness. -But thanks to this work, JuliaInterpretter, and thus Revise now load in a flash. - -A full discussion on the invalidations work can be found in [this blog-post](https://julialang.org/blog/2020/08/invalidations/). - -## Internals: Manually Created Back-edges for Lowered Code Generated Functions -This is a very niche and not really at all user-facing feature. -To understand why this matters, it's worth understanding how Cassette works. -I wrote [blog-post on this a few years ago](https://invenia.github.io/blog/2019/10/30/julialang-features-part-1#making-cassette). - -In quiet the opposite, Julia 1.3 allowed for the creation of more invalidations though allowing back-edges to be manually attached to the `CodeInfo` for `@generated` functions that return lowered code. -Backedges are the connection from methods back to each method instance that calls them. -This is what allows invalidations to work, as when a method is redefined, it needs to know what things to recompile. -This change allowed those back-edges to be manually specified for `@generated` functions that were working at the lowered code level. -Which is useful since this technique is primarily used for generating code based on the (lowered) code of existing methods. -For example in [Zygote](https://github.com/FluxML/Zygote.jl), generating the gradient code, from the code of the primal method. -So you want to be able to the regeneration of this code when that original method changes. - - -Basically, end of the day is this allows code that uses [Cassette.jl](https://github.com/jrevels/Cassette.jl), and [IRTools.jl](https://github.com/MikeInnes/IRTools.jl) to not suffer from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. -A particular case of this is for [Zygote](https://github.com/FluxML/Zygote.jl) where redefining a function called by the code that was being differentiated did not result in an updated gradient (unless `Zygote.refresh()`) was run. -This was annoying for - -Other things that this allows is two very weird packages that [Nathan Daly](https://github.com/NHDaly) and I came up with at the JuliaCon 2018 hackathon: [StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) and [Tricks.jl](https://github.com/oxinabox/Tricks.jl/). -[StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) relaxes the restrictions on normal `@generated` functions so that they are also safe from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. -[Tricks.jl](https://github.com/oxinabox/Tricks.jl/) uses this feature to make `hasmethod` etc resolve at compile-time, and then get updated if and when new methods are defined. -Which can allow for defing traits like _"anything that defines a `iterate` method"_. +(* Its actually really fast, but it is the kind of thing that rapidly adds up; and it is slow vs operations that can happen without touching RAM.) ## Front-end changes @@ -179,8 +100,6 @@ But I do see how this causes problems for some interactive work-flows, especiall See the [main github issue](https://github.com/JuliaLang/julia/issues/28789) and the longest [Discourse thread](https://discourse.julialang.org/t/another-possible-solution-to-the-global-scope-debacle/15894), though there were many others. It took years of thinking to workout the solution, particularly because many of the more obvious solutions would be breaking in significant ways. - - ### Deprecations are muted by default. This was me in [this PR](https://github.com/JuliaLang/julia/pull/35362). @@ -238,25 +157,52 @@ You also should notice the dimming of type-parameters to make complicated types Also the addition of argument names, and showing keyword arguments bearing functions as they are written, rather than as a weird `#DataFrame#654` internal function. The whole thing just looks more modern and polished. +### Time Traveling Debugger + +[Julia 1.5 added built in support](https://julialang.org/blog/2020/05/rr/) for [`rr`](https://rr-project.org/) (linux only for now). +This is not useful so much for debugging julia code, but for debugging julia itself. +If you get into one of the very rare situations where the language is doing something truly nonsense; you can send a bug report recorded via `rr`, and someone can debug *exactly* what is happening. +Event to the extent of [diagnosing faulty RAM](https://julialang.org/blog/2020/09/rr-memory-magic/). +`rr` is an impressively cool piece of tech; it boils down to the fact that linux's `fork` is incredibly cheap, because it take advantage of [copy-on-write](https://en.wikipedia.org/wiki/Copy-on-write); so it basically `fork`s before every system call, and serializes what is going on; so you have a full track of everything that happened. +The `rr` integration is really nice easy to use; I had someone who was part of our architecture and operations (i.e. who writes way more CloudFormation than Julia) team run it to submit a support request; and they had no real troubles. ## Pkg stdlib and the General Registry Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the Pkg stdlib; and the general registry is more policy than software. +However, some of the biggest changes have been in maturing out Pkg3, and its surrounds. + TODO: Write bits for these: - Pkg Server: faster updating of registry and download of packages - - Artifacts, BinaryBuilder, Yggdasil, almost no one uses `deps/build.jl` any more - Scratch, and Preferences - - passing arguments to Pkg.test + ### BinaryBuilder, Artifacts, Yggdasil and jll packages. -This story was beginning to be told around julia 1.0 time, +This story was beginning to be told around Julia 1.0 time, but it wasn't really complete nor built into Pkg until Julia 1.3. - -You can read the documentation on [Artifacts](https://julialang.github.io/Pkg.jl/v1/artifacts/), [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl), and [Yggdasil](https://github.com/JuliaPackaging/Yggdrasil) - for full details -TODODODODODODOD +You can read the documentation on [Artifacts](https://julialang.github.io/Pkg.jl/v1/artifacts/), [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl), and [Yggdasil](https://github.com/JuliaPackaging/Yggdrasil) for full details. +This is the story about how julia works with binary dependencies. +As of now, it works really well. + +Sometimes people misunderstand the claims about julia solving the two language problem as saying everything should be rewritten in julia. +Which is far from the truth, ain't noone got time for that. +Julia has always had great foreign function interfacing (FFI); like that [`ccall`](https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/) (as well as [PyCall](https://github.com/JuliaPy/PyCall.jl/), [RCall](https://github.com/JuliaInterop/RCall.jl), [JavaCall](https://github.com/JuliaInterop/JavaCall.jl), and a bunch of others, for non-c-style binaries.) +While FFI makes it easy to call binaries, how about actually getting them on your machine? + +The standard pre-julia 1.0 was to run arbitrary code in the `deps/build.jl` file, which often used [BinDeps.jl](https://github.com/JuliaPackaging/BinDeps.jl/) to download compile things. +Around 1.0 this changed to having the `deps/build.jl` call [BinaryProvider.jl](https://github.com/JuliaPackaging/BinaryProvider.jl/) download a compiled binary built with [BinaryBuilder.jl](https://github.com/JuliaPackaging/BinaryBuilder.jl), and store that alongside the package code. +In Julia 1.3+, the Artifacts system basically broad the BinaryProvider part into the package manager. +Now the package manager controls the downloads, store them in a controlled location more massive duplications, and allows for full compat control. +No more running arbitrary code during installs. + +I feel it is worth really mentioning [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl) here. +While it has existed since before 1.0, it's really grown. +It's a super smooth cross-compilation environment, that can used to build binaries for every platform julia runs on. +This is an amazing tool, built on [containerization](https://en.wikipedia.org/wiki/OS-level_virtualization). +It's the kind of build tool one dreamed on 10 years ago. +It's far more general than julia, I know people have at least experimented with using it with nim. +I hope more things start using it. ### Temporary Environments Julia 1.5 added `pkg> activate --temp` which will create and activate a temporary environment. @@ -412,8 +358,109 @@ For something with a lot of pre-1.0 versions that is a lot of numbers. I think the new version is much cleaner and easier to read. +## Time To First Plot +People often complain about the "Time To First Plot" (TTFP) in julia. +I personally have never minded it -- by the time iI am plotting something I have done minutes of thinking so 10 seconds of compilation is nothing. +Plotting turns out is basically a really hard thing for a compiler. +It is many many small methods, most of which are only called once. +And unlike most julia code it doesn't actually benefit all that much from most of the work julia's JIT is normally doing to specialize things -- plotting itself isn't in the hot-loop. +To a long-story short, plotting the postcard example for julia needing to compile things before it can run them. + +As a really hacky way to time this, the following timing. +Timing it twice because need to capture the timing for printing afterwards (which is something very affected by invalidations see below). +Also with precompilation already run +(though see below, precompilation caches often would be deleted in 1.0; and conversely are way faster to create in 1.6 due to parallelization.) + +**Julia 1.0:** +```julia +julia> @time @time using Plots; plot(1:0.1:10, sin.(1:0.1:10)) + 7.168878 seconds (15.87 M allocations: 941.114 MiB, 7.36% gc time) + 9.281561 seconds (25.74 M allocations: 1.393 GiB, 7.88% gc time) +``` +**Julia 1.6:** +```julia +julia> @time @time using Plots; plot(1:0.1:10, sin.(1:0.1:10)) + 4.490434 seconds (8.24 M allocations: 583.285 MiB, 7.13% gc time, 32.80% compilation time) + 4.533302 seconds (8.36 M allocations: 590.949 MiB, 7.06% gc time, 33.41% compilation time) +``` + + +### Per Module Optimization Flags + +TODODOTOTOTOTODODODOD TODO + +### Invalidations + +Consider a function `foo` with a method `foo(::Number)`. +If some other function calls it, for example `bar(x::Int) = 2*foo(x)`, the JIT will compile in an instruction for exactly the method instance to call (assuming type-inference work) -- a fast static dispatch, possibly even inlined. +If the user then defines a new more specific method `foo(::Int)`, then the compiled code for `Bar`, need to be invalidated so it will call the new one. +It needs to be recompiled -- which means anything that static dispatches to it needs to be recompiled and so forth. +This is an invalidation. +It's an important feature of the language. +It is key to extensibility. +It doesn't normally cause too many problems. +Since generally basically everything is defined before anything is called, and thus before anything is compiled. + +A notable exception to this is Base an the other standard libraries. +These are compiled into the so-called system image. +Further-more, methods in these standard libraries are some of the most overloaded, thus most likely to have it triggered. + +A bunch of work has gone into dealing with invalidations better. +Not just point-fixes to remove calls that were likely to be invalidated, but several changes to the compiler. +One particular change was not triggering cascading invalidations for methods that couldn't actually be called do the being ambiguous. +As a result a lot of user code doesn't trigger invalidations on 1.6, that did on 1.5. +The end result of this is faster compilation after loading packages, since it doesn't have to recompile a ton of invalidated method instances. +i.e. decreased time to first plot. + +In this has had huge effect on [Revise.jl](https://github.com/timholy/Revise.jl) +which started to take a second or so to load when it gained the dependency on [JuliaInterpretter.jl](https://github.com/JuliaDebug/JuliaInterpreter.jl); which isn't much, but when you do it every time you start julia it is an annoying lack of snappyness. +But thanks to this work, JuliaInterpretter, and thus Revise now load in a flash. + +A full discussion on the invalidations work can be found in [this blog-post](https://julialang.org/blog/2020/08/invalidations/). + +## Internals: Manually Created Back-edges for Lowered Code Generated Functions +This is a very niche and not really at all user-facing feature. +To understand why this matters, it's worth understanding how Cassette works. +I wrote [blog-post on this a few years ago](https://invenia.github.io/blog/2019/10/30/julialang-features-part-1#making-cassette). + +In quiet the opposite, Julia 1.3 allowed for the creation of more invalidations though allowing back-edges to be manually attached to the `CodeInfo` for `@generated` functions that return lowered code. +Backedges are the connection from methods back to each method instance that calls them. +This is what allows invalidations to work, as when a method is redefined, it needs to know what things to recompile. +This change allowed those back-edges to be manually specified for `@generated` functions that were working at the lowered code level. +Which is useful since this technique is primarily used for generating code based on the (lowered) code of existing methods. +For example in [Zygote](https://github.com/FluxML/Zygote.jl), generating the gradient code, from the code of the primal method. +So you want to be able to the regeneration of this code when that original method changes. + + +Basically, end of the day is this allows code that uses [Cassette.jl](https://github.com/jrevels/Cassette.jl), and [IRTools.jl](https://github.com/MikeInnes/IRTools.jl) to not suffer from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. +A particular case of this is for [Zygote](https://github.com/FluxML/Zygote.jl) where redefining a function called by the code that was being differentiated did not result in an updated gradient (unless `Zygote.refresh()`) was run. +This was annoying for + +Other things that this allows is two very weird packages that [Nathan Daly](https://github.com/NHDaly) and I came up with at the JuliaCon 2018 hackathon: [StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) and [Tricks.jl](https://github.com/oxinabox/Tricks.jl/). +[StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) relaxes the restrictions on normal `@generated` functions so that they are also safe from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. +[Tricks.jl](https://github.com/oxinabox/Tricks.jl/) uses this feature to make `hasmethod` etc resolve at compile-time, and then get updated if and when new methods are defined. +Which can allow for defing traits like _"anything that defines a `iterate` method"_. + + ## Base and Standard Libraries +### Threading +Julia has full support for threading now. +Not just the limited `@threads for` loops, but full GoLang style threads. +They are tightly integrated with the existing Async/Task/Coroutine system. +In effect threading works by unsetting the sticky flag on a Task, so that it is allowed to run on any thread. +This is normally done via the `Threads.@spawn` macro, rather than the `@async` macro. + +Interestingly, the `@threads for` macro still remains, and doesn't actually use much of the new machinery. It still uses the old way which is a bit tighter if the loop durations are almost identical. +But the new threading stuff is fast, on the order of microseconds to send work off to another thread. +Even for uses of `@threads for` the improves have some wins. +`IO` is now thread-safe; `ReentrantLock` was added and is the kind of standard lock that you expect to exist, that has notifications on waiting work etc; and a big one: `@threads for` can now be nested without things silently being wrong. + +A lot of this actually landed in julia 1.2, but julia 1.3 was the release we think of as being for threading, as it gave us `Threads.@spawn`. + +Also in Julia 1.6 we now have `julia -t auto` to start julia with 1 thread per (logical) core. +No more having to remember to set the `JULIA_NUM_THREADS` environment variable before starting it. + ### 5-arg `mul!`, aka in-place generalized multiplication and addition The 5 arg `mul!(C, A, B, α, β)` performs the operation equivalent to: `C .= A*B*α + C*β`, where `α`, `β` are scalars and `A`, `B` and `C` are compatible mixes of scalars, matrices and vectors. I am still the opinion that it should have been called `muladd!`, but this is the human friendly version of `BLAS.gemm!` (i.e. GEneralized Matrix Multiplication) and it's ilk. @@ -454,6 +501,27 @@ I have [tried getting headers via conditional different commandline download fun It wouldn't be too surprising if eventually we see libcurl swapped out for code extracted from HTTP.jl for a pure julia solution. HTTP.jl works wonderfully to this, but I suspect untangling the client from the server is just a bit annoying right now, particularly as it is still evolving its API. + +## Definable Error Hints +[`Experimental.register_error_hint`](https://docs.julialang.org/en/v1.7-dev/base/base/#Base.Experimental.register_error_hint) +allows packages to define extra information to be shown along with a particular type of error. +A really cool use of it was proposed on [Discourse](https://discourse.julialang.org/t/enforce-interface-implementation/52872/15?u=oxinabox): +you could add an error hint to `MethodError` that says to check that an interface has been implemented correctly. +This has the advantage of pointing the user in what is most likely the right direction. +But it doesn't have the problem of really confounding them if you are wrong, since the original `MethodError` information is still shown (See my earlier [blog-post on the `NotImplementedException` antipattern](https://www.oxinabox.net/2020/04/19/Julia-Antipatterns.html#notimplemented-exceptions)). + +Right now this is not used anywhere in the standard libraries. +At first, I thought that made sense since they can just edit the original `show_error`. +But now I think since it can be used for a subset of a particular type of exception it could well be useful for some of p`Base`'s interfaces](https://docs.julialang.org/en/v1/manual/interfaces/) exactly as described above. +According to [JuliaHub search](https://juliahub.com/ui/CodeSearch?q=register_error_hint&u=all&t=all) right now there is just one package using. +ColorTypes.jl is [using it to explain some _consensual type-piracy_](https://github.com/JuliaGraphics/ColorTypes.jl/blob/bd31741d162361ebd44ed05ae532266998d9ce9f/src/error_hints.jl), where for some functions on its types you need to load another package. + +This is an experimental features, so it's not covered by the SemVer guarantees of the rest of the language. +Still it's neat, I don't expect it to go away, +though I also don't expect it to graduate from experimental status until a bunch of people are using it. +Which they will probably do as people start dropping support for older julia versions. + + ### A bunch of curried functions Julia seems to have ended up with a convention of providing curried methods of functions if that would be useful as the first argument for `filter`. e.g. `filter(isequal(2), [1,2,3,2,1])`, is the same is `filter(x->isequal(x, 2), [1,2,3,2,1]`). @@ -464,7 +532,6 @@ Since then we have added: `isapprox`, `<`,`<=`,`>`, `>=`, `!=`, `startswith`, `endswith`, and `contains`. (I added the last 3 😁) - Aside: `contains` is argument flipped `occursin`, it was a thing in julia 0.6 but was removed in 1.0 and now has been added back. We added it back primarily so we could have the curried form, and to match `startswith` and `endswith`. From a0ea6580b58e4c34f586316c949cd7b0d417b797 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 10 Jan 2021 20:33:07 +0000 Subject: [PATCH 16/62] draft 1 done --- .../Julia-1.6-what-has-changed-since-1.0.md | 144 +++++++++++------- 1 file changed, 87 insertions(+), 57 deletions(-) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md index 66dc3685..0e8a91d2 100644 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/Julia-1.6-what-has-changed-since-1.0.md @@ -32,55 +32,7 @@ This post is kind of a follow-up to my [Julia 1.0 release run-down]({{site.url}} But it's going to be even longer, as it is covering the last 5 releases since then; and I am not skipping the major new features. I am writing this not to break down release by release, but to highlight features that had you only used Julia 1.0, you wouldn't have seen. -Full details can be found in the NEWS.md, and HISTORY.md -TODO: Insert links to the files in the release-1.6 branch. - - -## NamedTuple/keyword arguments automatic naming -This feature felt weird when I first read about it, but it has quickly grown on me. -How often do you write some code that does some processing and calls some other method passing on some of its keyword arguments? -For example -```julia -# Primary method all others redirect to this -foo(x::Bar; a=1, b=2, c=3) = ... - -# Method for if x is given as components -foo(x1, x2; a=10, b=20, c=30, comb=+) = foo(Bar(comp(x1, x2)); a=a, b=b, c=c) -``` -This new feature allows one to avoid writing `(...; a=a, b=b, c=c)`, and instead write `(...; a, b, c)`. -This does come with the requirement to separate keyword arguments from positional arguments by `;`, but I have always done this, and the [BlueStyle guide requires it](https://github.com/invenia/BlueStyle#keyword-arguments). -It feels like we are fully leveraging the distinction of keyword from positional arguments by allowing this. -In contrast, the distinction vs e.g. C# and Python that allow any positional argument to be passed by name, is to make the name of positional arguments not part of the public API, thus avoiding changing it being a breaking change. - -The same syntax can be used to create `NamedTuple`. -```julia -julia> product = ["fries", "burger", "drink"]; - -julia> price = [2, 4, 1]; - -julia> (;product, price) -(product = ["fries", "burger", "drink"], price = [2, 4, 1]) -``` -This is particularly cool for constructing `NamedTuple`s of `Vector`s, which is a valid [Tables.jl Table](https://tables.juliadata.org/stable/#Tables.columntable). - -It is interesting to note that for the logging macros introduced in Julia 1.0, this is how they have always worked. -E.g. `@info "message" foo bar` and `@info "message" foo=foo bar=bar` display the same. -Which has always felt natural. - -## Performance: References to the Heap from the Stack -This was promised in 2016 as a feature for 1.0 (released 2018). -But we actually didn't get it til 1.5 with [#33886](https://github.com/JuliaLang/julia/issues/33886). -Basically, the process of allocating memory from the heap is fairly slow*, where as allocating memory on the stack is basically a non-op. -Indeed Julia benchmarking tools don't count allocations on the stack as allocations at all. -One can find extensive write ups of heap vs stack allocations and how it works in general (though some mix the C specific factors with the CPU details.) -In julia all mutable objects live are allocated on the heap. -Until recently, immutable objects that contained references to heap allocated objects also had to live on the heap. -i.e. immutable objects with immutable fields (with immutable fields with...) could live on the stack. -But with this change now all immutable object can live on the stack, even if some of their fields live on the heap. -An important consequence of this is that wrapper types, such as the `SubArray` returned from `@view x[1:2]`, now have no overhead to create. -I find that in practice this often adds up to a 10-30% speed-up in real world code. - -(* Its actually really fast, but it is the kind of thing that rapidly adds up; and it is slow vs operations that can happen without touching RAM.) +Full details can be found in the [NEWS.md](https://github.com/JuliaLang/julia/blob/backports-release-1.6/NEWS.md), and [HISTORY.md](https://github.com/JuliaLang/julia/blob/backports-release-1.6/HISTORY.md) ## Front-end changes @@ -166,15 +118,82 @@ Event to the extent of [diagnosing faulty RAM](https://julialang.org/blog/2020/0 `rr` is an impressively cool piece of tech; it boils down to the fact that linux's `fork` is incredibly cheap, because it take advantage of [copy-on-write](https://en.wikipedia.org/wiki/Copy-on-write); so it basically `fork`s before every system call, and serializes what is going on; so you have a full track of everything that happened. The `rr` integration is really nice easy to use; I had someone who was part of our architecture and operations (i.e. who writes way more CloudFormation than Julia) team run it to submit a support request; and they had no real troubles. -## Pkg stdlib and the General Registry -Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the Pkg stdlib; and the general registry is more policy than software. -However, some of the biggest changes have been in maturing out Pkg3, and its surrounds. +## Syntax + +### `import as` + +>The syntax import A as B (plus import A: x as y, import A.x as y, and using A: x as y) can now be used to rename imported modules and identifiers ([#1255](https://github.com/JuliaLang/julia/issues/1255)). + +This will be familiar to Python folk who love to do: `import numpy as np`. +I hope we never see it used that ubiquitously in julia. +I am quite happy with `using Foo` which imports into scope everything the author of `Foo` exported. +Python people find that super-weird and scary; but it's fine. +Further, most things you use don't come from the module that defined them anyway, they are overloaded functions e.g. from `Base`. +The real value of `import FooBar as fb` is not to have a short abbreviation so you can do `fb.quux`. +That was already possible via `const fb = FooBar`. +It is to handle cases where the package name itself conflicts with an identifier. +For example (as has often occured) if one has uses a `Pipe` in the REPL, and then later wants to load [Pipe.jl](https://github.com/oxinabox/Pipe.jl/) via `import Pipe` then one gets a name clash before it can be resolved. +Now one can do `import Pipe as PipingPipe` + +### NamedTuple/keyword arguments automatic naming +This feature felt weird when I first read about it, but it has quickly grown on me. +How often do you write some code that does some processing and calls some other method passing on some of its keyword arguments? +For example +```julia +# Primary method all others redirect to this +foo(x::Bar; a=1, b=2, c=3) = ... -TODO: Write bits for these: +# Method for if x is given as components +foo(x1, x2; a=10, b=20, c=30, comb=+) = foo(Bar(comp(x1, x2)); a=a, b=b, c=c) +``` +This new feature allows one to avoid writing `(...; a=a, b=b, c=c)`, and instead write `(...; a, b, c)`. +This does come with the requirement to separate keyword arguments from positional arguments by `;`, but I have always done this, and the [BlueStyle guide requires it](https://github.com/invenia/BlueStyle#keyword-arguments). +It feels like we are fully leveraging the distinction of keyword from positional arguments by allowing this. +In contrast, the distinction vs e.g. C# and Python that allow any positional argument to be passed by name, is to make the name of positional arguments not part of the public API, thus avoiding changing it being a breaking change. - - Pkg Server: faster updating of registry and download of packages - - Scratch, and Preferences +The same syntax can be used to create `NamedTuple`. +```julia +julia> product = ["fries", "burger", "drink"]; + +julia> price = [2, 4, 1]; + +julia> (;product, price) +(product = ["fries", "burger", "drink"], price = [2, 4, 1]) +``` +This is particularly cool for constructing `NamedTuple`s of `Vector`s, which is a valid [Tables.jl Table](https://tables.juliadata.org/stable/#Tables.columntable). + +It is interesting to note that for the logging macros introduced in Julia 1.0, this is how they have always worked. +E.g. `@info "message" foo bar` and `@info "message" foo=foo bar=bar` display the same. +Which has always felt natural. + +### Lowering of `'` +In julia 1.0 `'` was lowered directly into a call to `Base.adjoint`. +This meant it was impossible to redefine what `'` meant in your current module. +Now it lowers to a call to `var"'"`, which is something you can overload. +People often think it is cool to overload this to automatic differentiation so that `f'(x)` gives you the derivative of `f(x)` +Furthermore, it can now have unicode suffixes added to it, to define a new suffix operator. +Such as the one in `Base` which makes `A'ᵀ` give `transpose(A)` (rather than the adjoint/hermitian transpose that `A'` gives). + +## Performance: References to the Heap from the Stack +This was promised in 2016 as a feature for 1.0 (released 2018). +But we actually didn't get it til 1.5 with [#33886](https://github.com/JuliaLang/julia/issues/33886). +Basically, the process of allocating memory from the heap is fairly slow*, where as allocating memory on the stack is basically a non-op. +Indeed Julia benchmarking tools don't count allocations on the stack as allocations at all. +One can find extensive write ups of heap vs stack allocations and how it works in general (though some mix the C specific factors with the CPU details.) +In julia all mutable objects live are allocated on the heap. +Until recently, immutable objects that contained references to heap allocated objects also had to live on the heap. +i.e. immutable objects with immutable fields (with immutable fields with...) could live on the stack. +But with this change now all immutable object can live on the stack, even if some of their fields live on the heap. +An important consequence of this is that wrapper types, such as the `SubArray` returned from `@view x[1:2]`, now have no overhead to create. +I find that in practice this often adds up to a 10-30% speed-up in real world code. + +(* Its actually really fast, but it is the kind of thing that rapidly adds up; and it is slow vs operations that can happen without touching RAM.) + + +## Pkg stdlib and the General Registry +Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the Pkg stdlib; and the general registry is more policy than software. +However, some of the biggest changes have been in maturing out Pkg3, and its surrounds. ### BinaryBuilder, Artifacts, Yggdasil and jll packages. @@ -535,7 +554,11 @@ Since then we have added: Aside: `contains` is argument flipped `occursin`, it was a thing in julia 0.6 but was removed in 1.0 and now has been added back. We added it back primarily so we could have the curried form, and to match `startswith` and `endswith`. -### A ton of other new and improved standard library functions: +### A ton of other new and improved standard library functions + +I am not going to manage to list all of them here. +But I will list some of my standout favorites. + [`@time`])(https://github.com/JuliaLang/julia/pull/37678) now reports how much time was spent on compilation. This is going to help prevent people knew the language from including compilation time in their benchmarks. @@ -555,7 +578,6 @@ Kind of silly we didn't have that, and had been being me at least since 0.6. On things that had been bugging me, I had wanted [`eachslice` and it's special cases: `eachrow` & `eachcol`](https://github.com/JuliaLang/julia/issues/29749) since julia 0.3 when I first started using it. These are super handy when you want to e.g. iterate through vectors of the rows of a matrix. - `redirect_stderr` and `redirect_stdout` now work with `devnull`. So one can run some suppressing output easily as follows: ```julia @@ -573,6 +595,14 @@ I think for Julia 2.0 we should consider making it the default. Also added was a `sort` argument, which I don't see the point of so much, since `sort(readdir(x))` seems cleaner than `readdir(x; sort=true)`; and because I rarely rely on processing files in order. +`ccall` is now available as a macro `@ccall` which lets you specify +There was a [short juliacon talk about this](https://www.youtube.com/watch?v=wofq1DdXM3s) +So now one can do `@ccall(sqrt(4.0::Cdouble)::Cdouble)` +rather than `ccall(:sqrt, Cdouble, (Cdouble,), 4.0,)` +This is what `ccall` always should have been. +In julia 1.7, [`invoke` is getting the same treatment.](https://github.com/JuliaLang/julia/pull/38438). + + ### More "Why didn't it always work that way" than I can count Since 1.0's release there have been so many small improvements to functions that I didn't even know happened, because I assumed they always worked that way. Things like `startswith` supporting regex, (`parse`](https://github.com/JuliaLang/julia/pull/36199) working on `UUID`s, From 742abc3c59dcd02ecf73e45d206f932f72822d4b Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 10 Jan 2021 22:00:47 +0000 Subject: [PATCH 17/62] images --- .../Julia-1.6-what-has-changed-since-1.0.md | 610 ------------------ ....0-conflict.png => julia-1.0-conflict.png} | Bin ....6-conflict.png => julia-1.6-conflict.png} | Bin stylesheets/styles.css | 8 +- 4 files changed, 3 insertions(+), 615 deletions(-) delete mode 100644 _posts/Julia-1.6-what-has-changed-since-1.0.md rename posts_assets/Julia-1.0-1.6-changes/{julia1.0-conflict.png => julia-1.0-conflict.png} (100%) rename posts_assets/Julia-1.0-1.6-changes/{julia1.6-conflict.png => julia-1.6-conflict.png} (100%) diff --git a/_posts/Julia-1.6-what-has-changed-since-1.0.md b/_posts/Julia-1.6-what-has-changed-since-1.0.md deleted file mode 100644 index 0e8a91d2..00000000 --- a/_posts/Julia-1.6-what-has-changed-since-1.0.md +++ /dev/null @@ -1,610 +0,0 @@ ---- -title: Julia 1.6 - what has changed since Julia 1.0? -layout: default -tags: - - julia ---- - -Julia 1.0 came out well over 2 year ago. -Since then a lot has changed; and a lot hasn't. -Since Julia 1.0 was a commitment to no breaking changes; -but that is not to say no new features have been added to the language. - - - -Julia 1.6 is the first feature release since 1.0. -Prior to 1.0 all releases were feature releases; they came out when they were ready. -Since then all releases (except 1.6) have been timed releases. -The release candidate is cut from the main branch every 3 months; and they are released after an [extensive round of additional checking](https://github.com/JuliaLang/julia/pull/35846), which often takes weeks to months. -Julia 1.6 was soft-slated to be the new long-term-support (LTS) version that would have bug-fixes backported to it for the next few years. -The current LTS is julia 1.0, which has now had 5 patch releases made. -Since it was going to be supported for a long-time people wanted to make sure everything good got in; thus it was a feature release. -The core developers have demurred on if 1.6 will actually be selected to be the new LTS (even if it wasn't it won't assend to being LTS til it stops being the current Stable). - -My impression now is that they feel like it has too many cool new things; and that a few things didn't quite make it in even with the extended release cycle. -So it's looking likely to me that 1.7 will actually be the LTS; but that it might also be a feature release -- possibly this time a much shorter release period than usual. -In practice I think for a lot of package maintainer's 1.6 will be a LTS, in that that is the oldest version they will make sure to continue to support. -There has been too many cool new things (as this post will detail) to stay back to only 1.0 features. -Already a lot of packages have dropped support for julia versions older than 1.3. - - -This post is kind of a follow-up to my [Julia 1.0 release run-down]({{site.url}}//2018/06/01/Julia-Favourite-New-Things.html). -But it's going to be even longer, as it is covering the last 5 releases since then; and I am not skipping the major new features. -I am writing this not to break down release by release, -but to highlight features that had you only used Julia 1.0, you wouldn't have seen. -Full details can be found in the [NEWS.md](https://github.com/JuliaLang/julia/blob/backports-release-1.6/NEWS.md), and [HISTORY.md](https://github.com/JuliaLang/julia/blob/backports-release-1.6/HISTORY.md) - - -## Front-end changes -### Soft-scope in the REPL -Julia 1.0 removed the notion of soft-scope from the language. -I was very blasé about the change to for-loop bindings in my [1.0 release post](https://www.oxinabox.net/2018/06/01/Julia-Favourite-New-Things.html#for-loop-variable-binding-changes). -Infact, I didn't even mention this particular change. -It was [#19324](https://github.com/JuliaLang/julia/pull/19324) for reference - -This was undone in in Julia 1.5 with [#33864](https://github.com/JuliaLang/julia/pull/33864) for in the REPL only. -Now in the REPL, assigning to a global variable within a for-loop actually assigns that variable, rather than shadowing it with a new variable in that scope. -The same behavior outside the REPL, now gives a warning. - -Personally, this change never affected me because I never write for-loops at global scope that assign variables. -Indeed basically all I code I write is inside functions. -But I do see how this causes problems for some interactive work-flows, especially e.g. when demonstrating something. -See the [main github issue](https://github.com/JuliaLang/julia/issues/28789) and the longest [Discourse thread](https://discourse.julialang.org/t/another-possible-solution-to-the-global-scope-debacle/15894), though there were many others. -It took years of thinking to workout the solution, particularly because many of the more obvious solutions would be breaking in significant ways. - -### Deprecations are muted by default. - -This was me in [this PR](https://github.com/JuliaLang/julia/pull/35362). -It's not something I am super-happy about. -Though I think it makes sense. -Using deprecated methods is actually fine, as long as your dependencies follow SemVer, and you use `^` (i.e. default) bounding in your Project.toml's `[compat]`. -Which everyone does, because its enforced by the auto-merge script in the General registry. - -Solving deprecations is a thing you should chose to do, actively. -Not casually, when trying to do something else. -In particular, when updating to support a new major release of one of your dependencies, you should run your (integration) tests to see if they succeed. -If they don't succeed, then one removed support for the new version and run julia with deprecation warnings turned on (or set to error); to identify and fix them. -Probably also you are checking the release notes, since you are intentionally updating. - -The core of the reason is because it was actually breaking things. -Irrelevant deprecation warning spam from dependencies of dependencies was causing JuMP and LightGraphs to become too slow to be used. -Since they were from dependencies of dependencies the maintainers of those JuMP and LightGraphs couldn’t even fix them. -Let alone the end users. - -Deprecation warnings are still turned on by default in tests. -Which makes sense since the tests (unlike normal use), is being run by maintainers of the package itself, not its users. -Though it still doesn't make a huge amount of sense to me, since spam from deprecation warnings floods out your actual errors that you want to see during testing. -For this reason Invenia (my employer) has disabled deprecation warnings in the tests for all our closed source packages, and added a new test job that just does deprecation checking (set to error on deprecation and with the job allowed to fail, just so we have notice). - -Hopefully one day we can improve the tooling around deprecation warnings. -An ideal behavour would be to only show deprecation warning if directly caused by a function call made from within the package module of your current active environment. -I kind of know what we need to do to the logger to make that possible. -But it is not yet something I have had time to do. - - -### Code highlighting for `code_llvm` and `code_native` -This was added in [#36634](https://github.com/JuliaLang/julia/issues/36634). -This functionality was first implemented in the [ColoredLLCodes.jl](https://github.com/kimikage/ColoredLLCodes.jl), where it worked by monkey-patching the InteractiveUtils stdlib. -That package does still work on julia 1.0. - -![Julia 1.0 code_llvm]({{site.url}}/Julia-1.0-1.6-changes/julia1.0-code-llvm.png) -![Julia 1.6 code_llvm]({{site.url}}/Julia-1.0-1.6-changes/julia1.6-code-llvm.png) - -The REPL itself still doesn't have syntax highlighting for Julia code though. -The [OhMyRepl.jl](https://github.com/KristofferC/OhMyREPL.jl) package does provide that, and works all versions of Julia. -It is a lot more than a series of regex though, so I don't think we are going to see it built into julia too soon. -Probably one day, though, as its big dependencies are also required if the parser wants to move to be written in julia. -(Though I also don't expect that any time soon) - -### Clearer Stacktraces - -Just like colored `code_llvm` colored stack-traces also originated in a package that was doing some nasty monkey-patching: [ClearStacktrace.jl](https://github.com/jkrumbiegel/ClearStacktrace.jl). -This was added into Base itself in [#36134](https://github.com/JuliaLang/julia/pull/36134) - -![Julia 1.0 stacktrace]({{site.url}}/Julia-1.0-1.6-changes/julia1.0-stacktrace.png) -![Julia 1.6 stacktrace]({{site.url}}/Julia-1.0-1.6-changes/julia1.6-stacktrace.png) - -The first thing you probably notice is the colored package names, to make it more clear where the error is coming from. -You also should notice the dimming of type-parameters to make complicated types easier to read. -Also the addition of argument names, and showing keyword arguments bearing functions as they are written, rather than as a weird `#DataFrame#654` internal function. -The whole thing just looks more modern and polished. - -### Time Traveling Debugger - -[Julia 1.5 added built in support](https://julialang.org/blog/2020/05/rr/) for [`rr`](https://rr-project.org/) (linux only for now). -This is not useful so much for debugging julia code, but for debugging julia itself. -If you get into one of the very rare situations where the language is doing something truly nonsense; you can send a bug report recorded via `rr`, and someone can debug *exactly* what is happening. -Event to the extent of [diagnosing faulty RAM](https://julialang.org/blog/2020/09/rr-memory-magic/). -`rr` is an impressively cool piece of tech; it boils down to the fact that linux's `fork` is incredibly cheap, because it take advantage of [copy-on-write](https://en.wikipedia.org/wiki/Copy-on-write); so it basically `fork`s before every system call, and serializes what is going on; so you have a full track of everything that happened. -The `rr` integration is really nice easy to use; I had someone who was part of our architecture and operations (i.e. who writes way more CloudFormation than Julia) team run it to submit a support request; and they had no real troubles. - - -## Syntax - -### `import as` - ->The syntax import A as B (plus import A: x as y, import A.x as y, and using A: x as y) can now be used to rename imported modules and identifiers ([#1255](https://github.com/JuliaLang/julia/issues/1255)). - -This will be familiar to Python folk who love to do: `import numpy as np`. -I hope we never see it used that ubiquitously in julia. -I am quite happy with `using Foo` which imports into scope everything the author of `Foo` exported. -Python people find that super-weird and scary; but it's fine. -Further, most things you use don't come from the module that defined them anyway, they are overloaded functions e.g. from `Base`. -The real value of `import FooBar as fb` is not to have a short abbreviation so you can do `fb.quux`. -That was already possible via `const fb = FooBar`. -It is to handle cases where the package name itself conflicts with an identifier. -For example (as has often occured) if one has uses a `Pipe` in the REPL, and then later wants to load [Pipe.jl](https://github.com/oxinabox/Pipe.jl/) via `import Pipe` then one gets a name clash before it can be resolved. -Now one can do `import Pipe as PipingPipe` - -### NamedTuple/keyword arguments automatic naming -This feature felt weird when I first read about it, but it has quickly grown on me. -How often do you write some code that does some processing and calls some other method passing on some of its keyword arguments? -For example -```julia -# Primary method all others redirect to this -foo(x::Bar; a=1, b=2, c=3) = ... - -# Method for if x is given as components -foo(x1, x2; a=10, b=20, c=30, comb=+) = foo(Bar(comp(x1, x2)); a=a, b=b, c=c) -``` -This new feature allows one to avoid writing `(...; a=a, b=b, c=c)`, and instead write `(...; a, b, c)`. -This does come with the requirement to separate keyword arguments from positional arguments by `;`, but I have always done this, and the [BlueStyle guide requires it](https://github.com/invenia/BlueStyle#keyword-arguments). -It feels like we are fully leveraging the distinction of keyword from positional arguments by allowing this. -In contrast, the distinction vs e.g. C# and Python that allow any positional argument to be passed by name, is to make the name of positional arguments not part of the public API, thus avoiding changing it being a breaking change. - -The same syntax can be used to create `NamedTuple`. -```julia -julia> product = ["fries", "burger", "drink"]; - -julia> price = [2, 4, 1]; - -julia> (;product, price) -(product = ["fries", "burger", "drink"], price = [2, 4, 1]) -``` -This is particularly cool for constructing `NamedTuple`s of `Vector`s, which is a valid [Tables.jl Table](https://tables.juliadata.org/stable/#Tables.columntable). - -It is interesting to note that for the logging macros introduced in Julia 1.0, this is how they have always worked. -E.g. `@info "message" foo bar` and `@info "message" foo=foo bar=bar` display the same. -Which has always felt natural. - -### Lowering of `'` -In julia 1.0 `'` was lowered directly into a call to `Base.adjoint`. -This meant it was impossible to redefine what `'` meant in your current module. -Now it lowers to a call to `var"'"`, which is something you can overload. -People often think it is cool to overload this to automatic differentiation so that `f'(x)` gives you the derivative of `f(x)` -Furthermore, it can now have unicode suffixes added to it, to define a new suffix operator. -Such as the one in `Base` which makes `A'ᵀ` give `transpose(A)` (rather than the adjoint/hermitian transpose that `A'` gives). - -## Performance: References to the Heap from the Stack -This was promised in 2016 as a feature for 1.0 (released 2018). -But we actually didn't get it til 1.5 with [#33886](https://github.com/JuliaLang/julia/issues/33886). -Basically, the process of allocating memory from the heap is fairly slow*, where as allocating memory on the stack is basically a non-op. -Indeed Julia benchmarking tools don't count allocations on the stack as allocations at all. -One can find extensive write ups of heap vs stack allocations and how it works in general (though some mix the C specific factors with the CPU details.) -In julia all mutable objects live are allocated on the heap. -Until recently, immutable objects that contained references to heap allocated objects also had to live on the heap. -i.e. immutable objects with immutable fields (with immutable fields with...) could live on the stack. -But with this change now all immutable object can live on the stack, even if some of their fields live on the heap. -An important consequence of this is that wrapper types, such as the `SubArray` returned from `@view x[1:2]`, now have no overhead to create. -I find that in practice this often adds up to a 10-30% speed-up in real world code. - -(* Its actually really fast, but it is the kind of thing that rapidly adds up; and it is slow vs operations that can happen without touching RAM.) - - -## Pkg stdlib and the General Registry -Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the Pkg stdlib; and the general registry is more policy than software. -However, some of the biggest changes have been in maturing out Pkg3, and its surrounds. - - -### BinaryBuilder, Artifacts, Yggdasil and jll packages. - -This story was beginning to be told around Julia 1.0 time, -but it wasn't really complete nor built into Pkg until Julia 1.3. -You can read the documentation on [Artifacts](https://julialang.github.io/Pkg.jl/v1/artifacts/), [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl), and [Yggdasil](https://github.com/JuliaPackaging/Yggdrasil) for full details. -This is the story about how julia works with binary dependencies. -As of now, it works really well. - -Sometimes people misunderstand the claims about julia solving the two language problem as saying everything should be rewritten in julia. -Which is far from the truth, ain't noone got time for that. -Julia has always had great foreign function interfacing (FFI); like that [`ccall`](https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/) (as well as [PyCall](https://github.com/JuliaPy/PyCall.jl/), [RCall](https://github.com/JuliaInterop/RCall.jl), [JavaCall](https://github.com/JuliaInterop/JavaCall.jl), and a bunch of others, for non-c-style binaries.) -While FFI makes it easy to call binaries, how about actually getting them on your machine? - -The standard pre-julia 1.0 was to run arbitrary code in the `deps/build.jl` file, which often used [BinDeps.jl](https://github.com/JuliaPackaging/BinDeps.jl/) to download compile things. -Around 1.0 this changed to having the `deps/build.jl` call [BinaryProvider.jl](https://github.com/JuliaPackaging/BinaryProvider.jl/) download a compiled binary built with [BinaryBuilder.jl](https://github.com/JuliaPackaging/BinaryBuilder.jl), and store that alongside the package code. -In Julia 1.3+, the Artifacts system basically broad the BinaryProvider part into the package manager. -Now the package manager controls the downloads, store them in a controlled location more massive duplications, and allows for full compat control. -No more running arbitrary code during installs. - -I feel it is worth really mentioning [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl) here. -While it has existed since before 1.0, it's really grown. -It's a super smooth cross-compilation environment, that can used to build binaries for every platform julia runs on. -This is an amazing tool, built on [containerization](https://en.wikipedia.org/wiki/OS-level_virtualization). -It's the kind of build tool one dreamed on 10 years ago. -It's far more general than julia, I know people have at least experimented with using it with nim. -I hope more things start using it. - -### Temporary Environments -Julia 1.5 added `pkg> activate --temp` which will create and activate a temporary environment. -This environment is deleted when Julia is exited. -This is incredibly handy for: - - - Reproducing an issue - - Answering questions on Stack-Overflow etc - - Checking the behavior of the last release of a package you currently have `dev`ed. - - Quickly trying out an idea - -Remembering that with Pkg3 installing a version of a package you have installed before is incredibly fast. -Pkg doesn't download it again, it basically just points to the existing version on disk. -So using `activate --temp` to quickly try something is indeed quick. - -A more hacky use of it is after loading (via `using`) a package that you have `dev`'ed, you can `activate --temp` and install some of your test-time dependencies to reproduce a test failure without adding them to the main dependencies in the `Project.toml`. -Though there is a better way if you use `test/Project.toml`. - -### Test Dependencies in own Project.toml -In [Julia 1.0 and 1.1, test specific dependencies are listed in the `[extras]` section](https://julialang.github.io/Pkg.jl/v1/creating-packages/#Test-specific-dependencies-in-Julia-1.0-and-1.1), and under `[targets] test=[...]`; with its compatibility listed in the main `[compat]`. -It seemed like this might be in the future be extended for other things, perhaps documentation. -But it was found for documentation in particular that a seperate Project.toml worked well. -As long as you `dev ..` into its `Manifest.toml` so it uses the right version of the package it is documenting. - -The new `tests/Project.toml` extends that idea. -One part of that extension is to remove the need to `dev ..`, and other part is to make available all the main dependencies. -It actually works on a different mechanism to the docs. -It relies on stacked environments, which is a feature julia has had since 1.0 via `LOAD_PATH`, but that is rarely used. - - -One advantage of this is that you can activate that Project.toml, on top of the your existing environment by adding the test directory to your `LOAD_PATH`. -I wrote some more details on exactly how to do that on [Discourse]( -https://discourse.julialang.org/t/activating-test-dependencies/48121/7?u=oxinabox). -It feels kind of hacky, because it is. -At some point there might be a nicer user-interface for stacked environments like this. - - -### Full Transition off METADATA.jl and REQUIRE files, and onto the General Registry -Even though Julia 1.0 had Pkg3 and was supposed to use registries, for a long time the old Pkg2 [METADATA.jl](https://github.com/JuliaLang/METADATA.jl) pseudo-registry was used as the the canonical source of truth. -Registering new releases was made against that, and then a script synchronized [General](https://github.com/JuliaRegistries/General) registry to match it. -This was to allow time for packages to change over to supporting julia 1.0, while still also making releases that supported julia 0.6. -It wasn't until about a year later that this state ended and the General registry became the one source of truth. - -This was kind of sucky, because a lot of the power of Pkg3 was blocked until then. -In particular, since the registries compat was generated from the REQUIRE file, and the REQUIRE files had kind of [gross requirement specification](https://docs.julialang.org/en/v0.6/manual/packages/#Requirements-Specification-1), and everyone basically just lower bounded things, or didn't bound compat at all. -Which made sense because with the only 1 global environment that Pkg2 had, you basically needed the whole ecosystem to be compatible, so restricting was bad. -But Pkg3/Project.toml has the much better default [`^` bounded]](https://julialang.github.io/Pkg.jl/v1/compatibility/#Version-specifier-format-1) to accept only non-breaking changes by SemVer. -(And per project environments, so things don't all have to be compatible -- just things used in a particular project (rather than every project you might ever do). - -#### Automatic Merging -Initially after the change over all PRs to make a new release needed manual review, by one of the very small number of General registry maintainers. - -I am a big fan of [releasing after every non-breaking PR](https://www.oxinabox.net/2019/09/28/Continuous-Delivery-For-Julia-Packages.html). -Why would you not want those bug-fixes and new features released? -It is especially rude to contributors who will make a fix or a feature, but then you don't let them use it because you didn't tag a release. -Plus it makes tracking down bugs easier, if it occurs on a precise version you know the PR that caused it. -But with manual merging it feels bad, to tag 5 releases in a day. -Now they we have automatic merging it is fine. -At time of writing [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl/) was up to 67 releases. - -The big advantage of automatic merging is that it comes with automatic enforcement of standards. -In order to be automerge-able some good standards have to be followed. -One of which is that that no unbounded compat specifiers (e..g `>=1.0`) are permitted; and that everything must have a compat specifier (since unspecified is same as `>=0.0.0`) -That one is particularly great, since if one adds specifiers later that can't be met, then it can trigger downgrades back to the incredible old versions that didn't specify compat and that almost certainly are not compatible. - -To deal with that particular case retro-capping was done to retroactively add bounds to all things that didn't have them. -Which was painful when it was done, since it rewrote compat in the registry, which made it disagree with the `Project.toml`, which is always confusing. -Now that it is done, it is good. - -#### Requirement to have a `Project.toml` -Finally the ability to `pkg> dev` packages that had only a REQUIRE and no `Project.toml` was removed in Julia 1.4. -You can still `pkg> add` them, if they were registered during the transition period. -But to edit the projects they must a `Project.toml` file, which is fine since all the tools to make registering releases also require you to have `Project.toml` now - - - -### Resolver willing to downgrade packages to install new ones (Tiered Resolution) - -Until the [tiered resolver](https://github.com/JuliaLang/Pkg.jl/pull/1330) was added Julia would not change the version of any currently installed package in order to install a new one. -For example consider - - - **Foo.jl** - - v1 - - v2 - - **Bar** - - v1 compatible with Foo v1 - - v2 compatible with Foo v2 - - **Qux.jl** - - v1: compatible only with Foo v1 - -If you did `pkg"add Foo Bar Qux"` you would end up with **Foo** v1, **Bar** v1, and **Qux** v1. -But if you did first: `pkg"add Foo Bar"` (which would install **Foo** v2, and **Bar** v2), -and then did `pkg"add Qux"`, -then on Julia 1.0 you would get an error, as it would refuse to downgrade **Foo** and **Bar** to v1, as is required to allow **Qux** to be installed. -This meant effectly the package manager is stateful, which turns out is really counter intuitive. -The way to resolve this in practice was to delete the `Manifest.toml` and do it again as a single action. -This was a significant problem for [test time dependencies](https://github.com/JuliaLang/Pkg.jl/issues/1352), which if you had a test time dependencies with a indirect dependencies shared with a main dependency of the package, but that was only compatible with an older version of the indirect depenency, you would be unable to run tests as resolving the test time depenency would fail. - -With the new tiered resolved it will try a number of tiers of relaxing existing versions. -It still wants to avoid changing the versions of currently installed packages if possible, but if that is required then it will do so. -It will in turn first attempt to install without any currently installed package being changed, then will try relaximng the constraint to allow changing the version of indirect dependencies, then to allowing changing the versions of direct dependencies. -This ends up far more intuitive: if there is a compatible set of package versions then they will be found. -Regardless of if new packages are added all at once or one at a time. - -### Precompilation - -Precompilation has been enhanced a lot. -That is not the compilation that runs the first time a function is used in a session, but rather the stuff that runs the first time a package is loaded in an environment. - -For a start, the precompilation cache no longer goes stale every time you swap environments. -This was a massive pain in julia 1.0, especially if you worked on more than one thing at a time. -This was fixed in 1.3 to have multiple caches, for different environments. -It's easy to forget this one, but it is actually one of the biggest usability enhancements since 1.0. - -More dramatically, and less easy to overlook, is the parallelism of precompilation added in 1.6. -Precompiling a package requires precompiling all its dependencies first. -This is now done in parallel, and is automatically triggered when you complete Pkg operations. -In contrast, to happening in series the first time a package is loaded. -No more waiting 5 minutes the first time you load an package with a lot of dependencies. -Further the spiffy animated output shows you what is precompiling at a given time; and a progress bar, which makes it feel (even) faster. - - - -### Improved conflict messages -This is one of my own contributions. -Julia 1.0 conflict messages are a terrifying wall of text. - -![Julia 1.0 conflict log]({{site.url}}/Julia-1.0-1.6-changes/julia1.0-conflict.png) -![Julia 1.6 conflict log]({{site.url}}/Julia-1.0-1.6-changes/julia1.6-conflict.png) - -The two main changes are the use of colors, and compressing the version number ranges. -No more giant red wall of numbers. - - -Colors were added to make it easier to see which version numbers are referring to which package. -There is a bit of a problem that it isn't easy to make sure the colors don't get reused, as there are not many in the 16 color list (especially when you skip a few like black, white and error red). -Due to how Pkg constructs its error messages basically every package in the dependency graph gets some message prepared for it, but not displayed, so just assigning them a color in turn gets it to loop around so doesn't reduce change of colors being reused. -There is away to fix that but it is a big change to add structured log messages that are colored only once they are displayed. -We decided after much debate to assign things colors based on the hash of the package name and shortened UUID. -This means things have consistent colors if you fix one and redo it a still have some remaining. -I think this is going to be a subtle improvement on easy of use. -As a cool hack, you can actually change the color list used. -To get the much larger color list I wanted to use originally you can do: -`append!(Pkg.Resolve.CONFLICT_COLORS, [21:51; 55:119; 124:142; 160:184; 196:220])` - - -The other change was consecutive lists of version numbers were compressed to continuous ranges. -In 1.6 these ranges are split only if there is a version that exists between them that is not compatible. -So normally just a single range, since compatibility is typically monotonic. -In contrast in 1.0, they were split if there was a potential version that could exist that is not compatible (even if that version doesn't currently exist). -This mean they split every time the right most nonzero was incremented. -For something with a lot of pre-1.0 versions that is a lot of numbers. -I think the new version is much cleaner and easier to read. - - -## Time To First Plot -People often complain about the "Time To First Plot" (TTFP) in julia. -I personally have never minded it -- by the time iI am plotting something I have done minutes of thinking so 10 seconds of compilation is nothing. -Plotting turns out is basically a really hard thing for a compiler. -It is many many small methods, most of which are only called once. -And unlike most julia code it doesn't actually benefit all that much from most of the work julia's JIT is normally doing to specialize things -- plotting itself isn't in the hot-loop. -To a long-story short, plotting the postcard example for julia needing to compile things before it can run them. - -As a really hacky way to time this, the following timing. -Timing it twice because need to capture the timing for printing afterwards (which is something very affected by invalidations see below). -Also with precompilation already run -(though see below, precompilation caches often would be deleted in 1.0; and conversely are way faster to create in 1.6 due to parallelization.) - -**Julia 1.0:** -```julia -julia> @time @time using Plots; plot(1:0.1:10, sin.(1:0.1:10)) - 7.168878 seconds (15.87 M allocations: 941.114 MiB, 7.36% gc time) - 9.281561 seconds (25.74 M allocations: 1.393 GiB, 7.88% gc time) -``` -**Julia 1.6:** -```julia -julia> @time @time using Plots; plot(1:0.1:10, sin.(1:0.1:10)) - 4.490434 seconds (8.24 M allocations: 583.285 MiB, 7.13% gc time, 32.80% compilation time) - 4.533302 seconds (8.36 M allocations: 590.949 MiB, 7.06% gc time, 33.41% compilation time) -``` - - -### Per Module Optimization Flags - -TODODOTOTOTOTODODODOD TODO - -### Invalidations - -Consider a function `foo` with a method `foo(::Number)`. -If some other function calls it, for example `bar(x::Int) = 2*foo(x)`, the JIT will compile in an instruction for exactly the method instance to call (assuming type-inference work) -- a fast static dispatch, possibly even inlined. -If the user then defines a new more specific method `foo(::Int)`, then the compiled code for `Bar`, need to be invalidated so it will call the new one. -It needs to be recompiled -- which means anything that static dispatches to it needs to be recompiled and so forth. -This is an invalidation. -It's an important feature of the language. -It is key to extensibility. -It doesn't normally cause too many problems. -Since generally basically everything is defined before anything is called, and thus before anything is compiled. - -A notable exception to this is Base an the other standard libraries. -These are compiled into the so-called system image. -Further-more, methods in these standard libraries are some of the most overloaded, thus most likely to have it triggered. - -A bunch of work has gone into dealing with invalidations better. -Not just point-fixes to remove calls that were likely to be invalidated, but several changes to the compiler. -One particular change was not triggering cascading invalidations for methods that couldn't actually be called do the being ambiguous. -As a result a lot of user code doesn't trigger invalidations on 1.6, that did on 1.5. -The end result of this is faster compilation after loading packages, since it doesn't have to recompile a ton of invalidated method instances. -i.e. decreased time to first plot. - -In this has had huge effect on [Revise.jl](https://github.com/timholy/Revise.jl) -which started to take a second or so to load when it gained the dependency on [JuliaInterpretter.jl](https://github.com/JuliaDebug/JuliaInterpreter.jl); which isn't much, but when you do it every time you start julia it is an annoying lack of snappyness. -But thanks to this work, JuliaInterpretter, and thus Revise now load in a flash. - -A full discussion on the invalidations work can be found in [this blog-post](https://julialang.org/blog/2020/08/invalidations/). - -## Internals: Manually Created Back-edges for Lowered Code Generated Functions -This is a very niche and not really at all user-facing feature. -To understand why this matters, it's worth understanding how Cassette works. -I wrote [blog-post on this a few years ago](https://invenia.github.io/blog/2019/10/30/julialang-features-part-1#making-cassette). - -In quiet the opposite, Julia 1.3 allowed for the creation of more invalidations though allowing back-edges to be manually attached to the `CodeInfo` for `@generated` functions that return lowered code. -Backedges are the connection from methods back to each method instance that calls them. -This is what allows invalidations to work, as when a method is redefined, it needs to know what things to recompile. -This change allowed those back-edges to be manually specified for `@generated` functions that were working at the lowered code level. -Which is useful since this technique is primarily used for generating code based on the (lowered) code of existing methods. -For example in [Zygote](https://github.com/FluxML/Zygote.jl), generating the gradient code, from the code of the primal method. -So you want to be able to the regeneration of this code when that original method changes. - - -Basically, end of the day is this allows code that uses [Cassette.jl](https://github.com/jrevels/Cassette.jl), and [IRTools.jl](https://github.com/MikeInnes/IRTools.jl) to not suffer from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. -A particular case of this is for [Zygote](https://github.com/FluxML/Zygote.jl) where redefining a function called by the code that was being differentiated did not result in an updated gradient (unless `Zygote.refresh()`) was run. -This was annoying for - -Other things that this allows is two very weird packages that [Nathan Daly](https://github.com/NHDaly) and I came up with at the JuliaCon 2018 hackathon: [StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) and [Tricks.jl](https://github.com/oxinabox/Tricks.jl/). -[StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) relaxes the restrictions on normal `@generated` functions so that they are also safe from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. -[Tricks.jl](https://github.com/oxinabox/Tricks.jl/) uses this feature to make `hasmethod` etc resolve at compile-time, and then get updated if and when new methods are defined. -Which can allow for defing traits like _"anything that defines a `iterate` method"_. - - -## Base and Standard Libraries - -### Threading -Julia has full support for threading now. -Not just the limited `@threads for` loops, but full GoLang style threads. -They are tightly integrated with the existing Async/Task/Coroutine system. -In effect threading works by unsetting the sticky flag on a Task, so that it is allowed to run on any thread. -This is normally done via the `Threads.@spawn` macro, rather than the `@async` macro. - -Interestingly, the `@threads for` macro still remains, and doesn't actually use much of the new machinery. It still uses the old way which is a bit tighter if the loop durations are almost identical. -But the new threading stuff is fast, on the order of microseconds to send work off to another thread. -Even for uses of `@threads for` the improves have some wins. -`IO` is now thread-safe; `ReentrantLock` was added and is the kind of standard lock that you expect to exist, that has notifications on waiting work etc; and a big one: `@threads for` can now be nested without things silently being wrong. - -A lot of this actually landed in julia 1.2, but julia 1.3 was the release we think of as being for threading, as it gave us `Threads.@spawn`. - -Also in Julia 1.6 we now have `julia -t auto` to start julia with 1 thread per (logical) core. -No more having to remember to set the `JULIA_NUM_THREADS` environment variable before starting it. - -### 5-arg `mul!`, aka in-place generalized multiplication and addition -The 5 arg `mul!(C, A, B, α, β)` performs the operation equivalent to: `C .= A*B*α + C*β`, where `α`, `β` are scalars and `A`, `B` and `C` are compatible mixes of scalars, matrices and vectors. -I am still the opinion that it should have been called `muladd!`, but this is the human friendly version of `BLAS.gemm!` (i.e. GEneralized Matrix Multiplication) and it's ilk. -It promises to alway compute the in-place mul-add in the most efficient, correct, way for any `AbstractArray` subtype. -In contrast, `BLAS.gemm` computes the same thing, but with a bunch of conditions. -It must be a strided array containing only BLAS-scalars, and if one of the inputs is conjugated/transposed you need to input it in non-conjugated/transposed form, and then tell `BLAS.gemm!` via passing in `'C'` or `T` rather than `N`. -5-arg `mul!` takes care of all that for you dispatching to `BLAS.gemm!` or other suitable methods once it has that all sorted. -Further, the existing 3-arg `mul!(C, A, B)` is a special case of it: -`mul!(C, A, B) = mul!(C, A, B, false, true)` (`true` and `false` being 1, and strong 0). -So in general on can just implement the 5-arg form and be done with it. - -I personally didn't care about 5-arg `mul!` at all for a long time. -Yet another in-place function in `LinearAlgebra` that I would never use often enough to remember what it did, and thus wouldn't use it. -But I realized that it is a crucial function for my own area: automatic differentiation. -`mul(C, A, B, true, true)` is the in-place accumulation rule for the reverse mode equivalent of the product rule. - - -### You can now print and interpolate `nothing` into strings. - -This is one of mine [#32148](https://github.com/JuliaLang/julia/pull/32148)., and I find it is such a usability enhancement. -So many small frustrations in Julia 1.0 relating to interpolate a variable containing `nothing` into a string. -Often occurring when you are adding a quick `println` to debug something not being the value you expected; or when building a string for some other error message. -Arguably both of those are better done via other means (`@show`, and values stored in fields in the error type); but we don't always do what is best. -Sometimes it is expedient to just interpolate things into strings without worrying about what type they are. - -### `Base.download` now using libcurl -For a very long time the `download` function which retrieves things over HTTP, was implemented with an amazing hack. -It conditionally shelled out to different programs. -On windows it run a mildly scary powershell script. -On Unixen it first tried to use `curl` then if that wasn't installed it tried to use `wget` and then if that wasn't installed it tried to use `fetch`. -It's low-key amazing that this worked as well as it did -- very few complaints. -But as of 1.6 [it now uses libcurl](https://github.com/JuliaLang/julia/pull/37340). -Using libcurl everywhere gives consistency with proxy settings, and protocol support (beyond HTTP) across all platforms. - -It also has more extensive API via the new [Downloads.jl](https://github.com/JuliaLang/Downloads.jl) standard library. -It can do things like progress logging, and it can retrieve headers. -I have [tried getting headers via conditional different commandline download functions](https://github.com/oxinabox/DataDeps.jl/pull/22) before, it is low-key nightmare fuel; and I ended up swapping out the HTTP.jl for that. -It wouldn't be too surprising if eventually we see libcurl swapped out for code extracted from HTTP.jl for a pure julia solution. -HTTP.jl works wonderfully to this, but I suspect untangling the client from the server is just a bit annoying right now, particularly as it is still evolving its API. - - -## Definable Error Hints -[`Experimental.register_error_hint`](https://docs.julialang.org/en/v1.7-dev/base/base/#Base.Experimental.register_error_hint) -allows packages to define extra information to be shown along with a particular type of error. -A really cool use of it was proposed on [Discourse](https://discourse.julialang.org/t/enforce-interface-implementation/52872/15?u=oxinabox): -you could add an error hint to `MethodError` that says to check that an interface has been implemented correctly. -This has the advantage of pointing the user in what is most likely the right direction. -But it doesn't have the problem of really confounding them if you are wrong, since the original `MethodError` information is still shown (See my earlier [blog-post on the `NotImplementedException` antipattern](https://www.oxinabox.net/2020/04/19/Julia-Antipatterns.html#notimplemented-exceptions)). - -Right now this is not used anywhere in the standard libraries. -At first, I thought that made sense since they can just edit the original `show_error`. -But now I think since it can be used for a subset of a particular type of exception it could well be useful for some of p`Base`'s interfaces](https://docs.julialang.org/en/v1/manual/interfaces/) exactly as described above. -According to [JuliaHub search](https://juliahub.com/ui/CodeSearch?q=register_error_hint&u=all&t=all) right now there is just one package using. -ColorTypes.jl is [using it to explain some _consensual type-piracy_](https://github.com/JuliaGraphics/ColorTypes.jl/blob/bd31741d162361ebd44ed05ae532266998d9ce9f/src/error_hints.jl), where for some functions on its types you need to load another package. - -This is an experimental features, so it's not covered by the SemVer guarantees of the rest of the language. -Still it's neat, I don't expect it to go away, -though I also don't expect it to graduate from experimental status until a bunch of people are using it. -Which they will probably do as people start dropping support for older julia versions. - - -### A bunch of curried functions -Julia seems to have ended up with a convention of providing curried methods of functions if that would be useful as the first argument for `filter`. -e.g. `filter(isequal(2), [1,2,3,2,1])`, is the same is `filter(x->isequal(x, 2), [1,2,3,2,1]`). -In particular these are boolean comparison like functions: -with two arguments, where the thing being compared against is the second. -Julia 1.0 had `isequal`, `==` and `in`. -Since then we have added: -`isapprox`, `<`,`<=`,`>`, `>=`, `!=`, `startswith`, `endswith`, and `contains`. -(I added the last 3 😁) - -Aside: `contains` is argument flipped `occursin`, it was a thing in julia 0.6 but was removed in 1.0 and now has been added back. -We added it back primarily so we could have the curried form, and to match `startswith` and `endswith`. - -### A ton of other new and improved standard library functions - -I am not going to manage to list all of them here. -But I will list some of my standout favorites. - - -[`@time`])(https://github.com/JuliaLang/julia/pull/37678) now reports how much time was spent on compilation. -This is going to help prevent people knew the language from including compilation time in their benchmarks. -It is still better to use [BenchmarkTool.jl's](https://github.com/JuliaCI/BenchmarkTools.jl) `@btime`, since that does multiple samples. -But now that too can report time spend compilation. -It's also useful for identifying if certain functions are taking ages to compile. -Which I guess is its theoretical main point, but I think preventing people benchmarking wrong is going to come up way more often. - -The experimental [`Base.@locals`](https://github.com/JuliaLang/julia/issues/29733 -) was added which returns a dictionary of local variables. -That one surprised me, I though being able to access a dict of local variables would get in the way of the optimizer, since it would prevent it from being able to optimize variables that are used for intermediate values away entirely. -But the compiler folk know better than I do. - -[`splitpath`](https://github.com/JuliaLang/julia/issues/28156) as added, its the opposite of `joinpath`. -Kind of silly we didn't have that, and had been being me at least since 0.6. - -On things that had been bugging me, I had wanted [`eachslice` and it's special cases: `eachrow` & `eachcol`](https://github.com/JuliaLang/julia/issues/29749) since julia 0.3 when I first started using it. -These are super handy when you want to e.g. iterate through vectors of the rows of a matrix. - -`redirect_stderr` and `redirect_stdout` now work with `devnull`. -So one can run some suppressing output easily as follows: -```julia -julia> redirect_stdout(devnull) do - println("You won't see this") - end -``` -This is just handy, doing it without this is seriously annoying. - -`readdir` now accepts a `join=true|false` keyword argument so that it returns paths with the parent dir. -This is good, almost every time I use `readdir` I used it as: -`joinpath.(x, readdir(x))`. -It is slightly cleaner (and faster) to be able to do `readdir(x; join=true)`. -I think for Julia 2.0 we should consider making it the default. -Also added was a `sort` argument, which I don't see the point of so much, since `sort(readdir(x))` seems cleaner than `readdir(x; sort=true)`; and because I rarely rely on processing files in order. - - -`ccall` is now available as a macro `@ccall` which lets you specify -There was a [short juliacon talk about this](https://www.youtube.com/watch?v=wofq1DdXM3s) -So now one can do `@ccall(sqrt(4.0::Cdouble)::Cdouble)` -rather than `ccall(:sqrt, Cdouble, (Cdouble,), 4.0,)` -This is what `ccall` always should have been. -In julia 1.7, [`invoke` is getting the same treatment.](https://github.com/JuliaLang/julia/pull/38438). - - -### More "Why didn't it always work that way" than I can count -Since 1.0's release there have been so many small improvements to functions that I didn't even know happened, because I assumed they always worked that way. -Things like `startswith` supporting regex, (`parse`](https://github.com/JuliaLang/julia/pull/36199) working on `UUID`s, -`accumulate`, `cumsum`, and `cumprod` supporting arbitrary iterators (https://github.com/JuliaLang/julia/pull/34656). -Julia 1.6 is one hell of a more polished language. \ No newline at end of file diff --git a/posts_assets/Julia-1.0-1.6-changes/julia1.0-conflict.png b/posts_assets/Julia-1.0-1.6-changes/julia-1.0-conflict.png similarity index 100% rename from posts_assets/Julia-1.0-1.6-changes/julia1.0-conflict.png rename to posts_assets/Julia-1.0-1.6-changes/julia-1.0-conflict.png diff --git a/posts_assets/Julia-1.0-1.6-changes/julia1.6-conflict.png b/posts_assets/Julia-1.0-1.6-changes/julia-1.6-conflict.png similarity index 100% rename from posts_assets/Julia-1.0-1.6-changes/julia1.6-conflict.png rename to posts_assets/Julia-1.0-1.6-changes/julia-1.6-conflict.png diff --git a/stylesheets/styles.css b/stylesheets/styles.css index 52fa13a0..01aeaab2 100644 --- a/stylesheets/styles.css +++ b/stylesheets/styles.css @@ -277,10 +277,6 @@ em, .em { padding: 0 20px; } -section img { - max-width: 100%; -} - blockquote { border-left: 3px solid #ffcc00; margin: 0; @@ -320,8 +316,10 @@ svg { padding: 16px; } -img { /*Some images are not rendering background for some reason */ +img { + /*Some images are not rendering background for some reason */ background-color: white; + max-width: 100%; } From ae741800ef7f47f5a85b6e3fa3e3305394b10518 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 10 Jan 2021 22:01:24 +0000 Subject: [PATCH 18/62] rename --- ...09-Julia-1.6-what-has-changed-since-1.0.md | 610 ++++++++++++++++++ 1 file changed, 610 insertions(+) create mode 100644 _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md new file mode 100644 index 00000000..a4867c1d --- /dev/null +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -0,0 +1,610 @@ +--- +title: "Julia 1.6: what has changed since Julia 1.0?" +layout: default +tags: + - julia +--- + +Julia 1.0 came out well over 2 year ago. +Since then a lot has changed; and a lot hasn't. +Since Julia 1.0 was a commitment to no breaking changes; +but that is not to say no new features have been added to the language. + + + +Julia 1.6 is the first feature release since 1.0. +Prior to 1.0 all releases were feature releases; they came out when they were ready. +Since then all releases (except 1.6) have been timed releases. +The release candidate is cut from the main branch every 3 months; and they are released after an [extensive round of additional checking](https://github.com/JuliaLang/julia/pull/35846), which often takes weeks to months. +Julia 1.6 was soft-slated to be the new long-term-support (LTS) version that would have bug-fixes backported to it for the next few years. +The current LTS is julia 1.0, which has now had 5 patch releases made. +Since it was going to be supported for a long-time people wanted to make sure everything good got in; thus it was a feature release. +The core developers have demurred on if 1.6 will actually be selected to be the new LTS (even if it wasn't it won't assend to being LTS til it stops being the current Stable). + +My impression now is that they feel like it has too many cool new things; and that a few things didn't quite make it in even with the extended release cycle. +So it's looking likely to me that 1.7 will actually be the LTS; but that it might also be a feature release -- possibly this time a much shorter release period than usual. +In practice I think for a lot of package maintainer's 1.6 will be a LTS, in that that is the oldest version they will make sure to continue to support. +There has been too many cool new things (as this post will detail) to stay back to only 1.0 features. +Already a lot of packages have dropped support for julia versions older than 1.3. + + +This post is kind of a follow-up to my [Julia 1.0 release run-down]({{site.url}}//2018/06/01/Julia-Favourite-New-Things.html). +But it's going to be even longer, as it is covering the last 5 releases since then; and I am not skipping the major new features. +I am writing this not to break down release by release, +but to highlight features that had you only used Julia 1.0, you wouldn't have seen. +Full details can be found in the [NEWS.md](https://github.com/JuliaLang/julia/blob/backports-release-1.6/NEWS.md), and [HISTORY.md](https://github.com/JuliaLang/julia/blob/backports-release-1.6/HISTORY.md) + + +## Front-end changes +### Soft-scope in the REPL +Julia 1.0 removed the notion of soft-scope from the language. +I was very blasé about the change to for-loop bindings in my [1.0 release post](https://www.oxinabox.net/2018/06/01/Julia-Favourite-New-Things.html#for-loop-variable-binding-changes). +Infact, I didn't even mention this particular change. +It was [#19324](https://github.com/JuliaLang/julia/pull/19324) for reference + +This was undone in in Julia 1.5 with [#33864](https://github.com/JuliaLang/julia/pull/33864) for in the REPL only. +Now in the REPL, assigning to a global variable within a for-loop actually assigns that variable, rather than shadowing it with a new variable in that scope. +The same behavior outside the REPL, now gives a warning. + +Personally, this change never affected me because I never write for-loops at global scope that assign variables. +Indeed basically all I code I write is inside functions. +But I do see how this causes problems for some interactive work-flows, especially e.g. when demonstrating something. +See the [main github issue](https://github.com/JuliaLang/julia/issues/28789) and the longest [Discourse thread](https://discourse.julialang.org/t/another-possible-solution-to-the-global-scope-debacle/15894), though there were many others. +It took years of thinking to workout the solution, particularly because many of the more obvious solutions would be breaking in significant ways. + +### Deprecations are muted by default. + +This was me in [this PR](https://github.com/JuliaLang/julia/pull/35362). +It's not something I am super-happy about. +Though I think it makes sense. +Using deprecated methods is actually fine, as long as your dependencies follow SemVer, and you use `^` (i.e. default) bounding in your Project.toml's `[compat]`. +Which everyone does, because its enforced by the auto-merge script in the General registry. + +Solving deprecations is a thing you should chose to do, actively. +Not casually, when trying to do something else. +In particular, when updating to support a new major release of one of your dependencies, you should run your (integration) tests to see if they succeed. +If they don't succeed, then one removed support for the new version and run julia with deprecation warnings turned on (or set to error); to identify and fix them. +Probably also you are checking the release notes, since you are intentionally updating. + +The core of the reason is because it was actually breaking things. +Irrelevant deprecation warning spam from dependencies of dependencies was causing JuMP and LightGraphs to become too slow to be used. +Since they were from dependencies of dependencies the maintainers of those JuMP and LightGraphs couldn’t even fix them. +Let alone the end users. + +Deprecation warnings are still turned on by default in tests. +Which makes sense since the tests (unlike normal use), is being run by maintainers of the package itself, not its users. +Though it still doesn't make a huge amount of sense to me, since spam from deprecation warnings floods out your actual errors that you want to see during testing. +For this reason Invenia (my employer) has disabled deprecation warnings in the tests for all our closed source packages, and added a new test job that just does deprecation checking (set to error on deprecation and with the job allowed to fail, just so we have notice). + +Hopefully one day we can improve the tooling around deprecation warnings. +An ideal behavour would be to only show deprecation warning if directly caused by a function call made from within the package module of your current active environment. +I kind of know what we need to do to the logger to make that possible. +But it is not yet something I have had time to do. + + +### Code highlighting for `code_llvm` and `code_native` +This was added in [#36634](https://github.com/JuliaLang/julia/issues/36634). +This functionality was first implemented in the [ColoredLLCodes.jl](https://github.com/kimikage/ColoredLLCodes.jl), where it worked by monkey-patching the InteractiveUtils stdlib. +That package does still work on julia 1.0. + +![Julia 1.0 code_llvm]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.0-code-llvm.png) +![Julia 1.6 code_llvm]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.6-code-llvm.png) + +The REPL itself still doesn't have syntax highlighting for Julia code though. +The [OhMyRepl.jl](https://github.com/KristofferC/OhMyREPL.jl) package does provide that, and works all versions of Julia. +It is a lot more than a series of regex though, so I don't think we are going to see it built into julia too soon. +Probably one day, though, as its big dependencies are also required if the parser wants to move to be written in julia. +(Though I also don't expect that any time soon) + +### Clearer Stacktraces + +Just like colored `code_llvm` colored stack-traces also originated in a package that was doing some nasty monkey-patching: [ClearStacktrace.jl](https://github.com/jkrumbiegel/ClearStacktrace.jl). +This was added into Base itself in [#36134](https://github.com/JuliaLang/julia/pull/36134) + +![Julia 1.0 stacktrace]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.0-stacktrace.png) +![Julia 1.6 stacktrace]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.6-stacktrace.png) + +The first thing you probably notice is the colored package names, to make it more clear where the error is coming from. +You also should notice the dimming of type-parameters to make complicated types easier to read. +Also the addition of argument names, and showing keyword arguments bearing functions as they are written, rather than as a weird `#DataFrame#654` internal function. +The whole thing just looks more modern and polished. + +### Time Traveling Debugger + +[Julia 1.5 added built in support](https://julialang.org/blog/2020/05/rr/) for [`rr`](https://rr-project.org/) (linux only for now). +This is not useful so much for debugging julia code, but for debugging julia itself. +If you get into one of the very rare situations where the language is doing something truly nonsense; you can send a bug report recorded via `rr`, and someone can debug *exactly* what is happening. +Event to the extent of [diagnosing faulty RAM](https://julialang.org/blog/2020/09/rr-memory-magic/). +`rr` is an impressively cool piece of tech; it boils down to the fact that linux's `fork` is incredibly cheap, because it take advantage of [copy-on-write](https://en.wikipedia.org/wiki/Copy-on-write); so it basically `fork`s before every system call, and serializes what is going on; so you have a full track of everything that happened. +The `rr` integration is really nice easy to use; I had someone who was part of our architecture and operations (i.e. who writes way more CloudFormation than Julia) team run it to submit a support request; and they had no real troubles. + + +## Syntax + +### `import as` + +>The syntax import A as B (plus import A: x as y, import A.x as y, and using A: x as y) can now be used to rename imported modules and identifiers ([#1255](https://github.com/JuliaLang/julia/issues/1255)). + +This will be familiar to Python folk who love to do: `import numpy as np`. +I hope we never see it used that ubiquitously in julia. +I am quite happy with `using Foo` which imports into scope everything the author of `Foo` exported. +Python people find that super-weird and scary; but it's fine. +Further, most things you use don't come from the module that defined them anyway, they are overloaded functions e.g. from `Base`. +The real value of `import FooBar as fb` is not to have a short abbreviation so you can do `fb.quux`. +That was already possible via `const fb = FooBar`. +It is to handle cases where the package name itself conflicts with an identifier. +For example (as has often occured) if one has uses a `Pipe` in the REPL, and then later wants to load [Pipe.jl](https://github.com/oxinabox/Pipe.jl/) via `import Pipe` then one gets a name clash before it can be resolved. +Now one can do `import Pipe as PipingPipe` + +### NamedTuple/keyword arguments automatic naming +This feature felt weird when I first read about it, but it has quickly grown on me. +How often do you write some code that does some processing and calls some other method passing on some of its keyword arguments? +For example +```julia +# Primary method all others redirect to this +foo(x::Bar; a=1, b=2, c=3) = ... + +# Method for if x is given as components +foo(x1, x2; a=10, b=20, c=30, comb=+) = foo(Bar(comp(x1, x2)); a=a, b=b, c=c) +``` +This new feature allows one to avoid writing `(...; a=a, b=b, c=c)`, and instead write `(...; a, b, c)`. +This does come with the requirement to separate keyword arguments from positional arguments by `;`, but I have always done this, and the [BlueStyle guide requires it](https://github.com/invenia/BlueStyle#keyword-arguments). +It feels like we are fully leveraging the distinction of keyword from positional arguments by allowing this. +In contrast, the distinction vs e.g. C# and Python that allow any positional argument to be passed by name, is to make the name of positional arguments not part of the public API, thus avoiding changing it being a breaking change. + +The same syntax can be used to create `NamedTuple`. +```julia +julia> product = ["fries", "burger", "drink"]; + +julia> price = [2, 4, 1]; + +julia> (;product, price) +(product = ["fries", "burger", "drink"], price = [2, 4, 1]) +``` +This is particularly cool for constructing `NamedTuple`s of `Vector`s, which is a valid [Tables.jl Table](https://tables.juliadata.org/stable/#Tables.columntable). + +It is interesting to note that for the logging macros introduced in Julia 1.0, this is how they have always worked. +E.g. `@info "message" foo bar` and `@info "message" foo=foo bar=bar` display the same. +Which has always felt natural. + +### Lowering of `'` +In julia 1.0 `'` was lowered directly into a call to `Base.adjoint`. +This meant it was impossible to redefine what `'` meant in your current module. +Now it lowers to a call to `var"'"`, which is something you can overload. +People often think it is cool to overload this to automatic differentiation so that `f'(x)` gives you the derivative of `f(x)` +Furthermore, it can now have unicode suffixes added to it, to define a new suffix operator. +Such as the one in `Base` which makes `A'ᵀ` give `transpose(A)` (rather than the adjoint/hermitian transpose that `A'` gives). + +## Performance: References to the Heap from the Stack +This was promised in 2016 as a feature for 1.0 (released 2018). +But we actually didn't get it til 1.5 with [#33886](https://github.com/JuliaLang/julia/issues/33886). +Basically, the process of allocating memory from the heap is fairly slow*, where as allocating memory on the stack is basically a non-op. +Indeed Julia benchmarking tools don't count allocations on the stack as allocations at all. +One can find extensive write ups of heap vs stack allocations and how it works in general (though some mix the C specific factors with the CPU details.) +In julia all mutable objects live are allocated on the heap. +Until recently, immutable objects that contained references to heap allocated objects also had to live on the heap. +i.e. immutable objects with immutable fields (with immutable fields with...) could live on the stack. +But with this change now all immutable object can live on the stack, even if some of their fields live on the heap. +An important consequence of this is that wrapper types, such as the `SubArray` returned from `@view x[1:2]`, now have no overhead to create. +I find that in practice this often adds up to a 10-30% speed-up in real world code. + +(* Its actually really fast, but it is the kind of thing that rapidly adds up; and it is slow vs operations that can happen without touching RAM.) + + +## Pkg stdlib and the General Registry +Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the Pkg stdlib; and the general registry is more policy than software. +However, some of the biggest changes have been in maturing out Pkg3, and its surrounds. + + +### BinaryBuilder, Artifacts, Yggdasil and jll packages. + +This story was beginning to be told around Julia 1.0 time, +but it wasn't really complete nor built into Pkg until Julia 1.3. +You can read the documentation on [Artifacts](https://julialang.github.io/Pkg.jl/v1/artifacts/), [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl), and [Yggdasil](https://github.com/JuliaPackaging/Yggdrasil) for full details. +This is the story about how julia works with binary dependencies. +As of now, it works really well. + +Sometimes people misunderstand the claims about julia solving the two language problem as saying everything should be rewritten in julia. +Which is far from the truth, ain't noone got time for that. +Julia has always had great foreign function interfacing (FFI); like that [`ccall`](https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/) (as well as [PyCall](https://github.com/JuliaPy/PyCall.jl/), [RCall](https://github.com/JuliaInterop/RCall.jl), [JavaCall](https://github.com/JuliaInterop/JavaCall.jl), and a bunch of others, for non-c-style binaries.) +While FFI makes it easy to call binaries, how about actually getting them on your machine? + +The standard pre-julia 1.0 was to run arbitrary code in the `deps/build.jl` file, which often used [BinDeps.jl](https://github.com/JuliaPackaging/BinDeps.jl/) to download compile things. +Around 1.0 this changed to having the `deps/build.jl` call [BinaryProvider.jl](https://github.com/JuliaPackaging/BinaryProvider.jl/) download a compiled binary built with [BinaryBuilder.jl](https://github.com/JuliaPackaging/BinaryBuilder.jl), and store that alongside the package code. +In Julia 1.3+, the Artifacts system basically broad the BinaryProvider part into the package manager. +Now the package manager controls the downloads, store them in a controlled location more massive duplications, and allows for full compat control. +No more running arbitrary code during installs. + +I feel it is worth really mentioning [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl) here. +While it has existed since before 1.0, it's really grown. +It's a super smooth cross-compilation environment, that can used to build binaries for every platform julia runs on. +This is an amazing tool, built on [containerization](https://en.wikipedia.org/wiki/OS-level_virtualization). +It's the kind of build tool one dreamed on 10 years ago. +It's far more general than julia, I know people have at least experimented with using it with nim. +I hope more things start using it. + +### Temporary Environments +Julia 1.5 added `pkg> activate --temp` which will create and activate a temporary environment. +This environment is deleted when Julia is exited. +This is incredibly handy for: + + - Reproducing an issue + - Answering questions on Stack-Overflow etc + - Checking the behavior of the last release of a package you currently have `dev`ed. + - Quickly trying out an idea + +Remembering that with Pkg3 installing a version of a package you have installed before is incredibly fast. +Pkg doesn't download it again, it basically just points to the existing version on disk. +So using `activate --temp` to quickly try something is indeed quick. + +A more hacky use of it is after loading (via `using`) a package that you have `dev`'ed, you can `activate --temp` and install some of your test-time dependencies to reproduce a test failure without adding them to the main dependencies in the `Project.toml`. +Though there is a better way if you use `test/Project.toml`. + +### Test Dependencies in own Project.toml +In [Julia 1.0 and 1.1, test specific dependencies are listed in the `[extras]` section](https://julialang.github.io/Pkg.jl/v1/creating-packages/#Test-specific-dependencies-in-Julia-1.0-and-1.1), and under `[targets] test=[...]`; with its compatibility listed in the main `[compat]`. +It seemed like this might be in the future be extended for other things, perhaps documentation. +But it was found for documentation in particular that a seperate Project.toml worked well. +As long as you `dev ..` into its `Manifest.toml` so it uses the right version of the package it is documenting. + +The new `tests/Project.toml` extends that idea. +One part of that extension is to remove the need to `dev ..`, and other part is to make available all the main dependencies. +It actually works on a different mechanism to the docs. +It relies on stacked environments, which is a feature julia has had since 1.0 via `LOAD_PATH`, but that is rarely used. + + +One advantage of this is that you can activate that Project.toml, on top of the your existing environment by adding the test directory to your `LOAD_PATH`. +I wrote some more details on exactly how to do that on [Discourse]( +https://discourse.julialang.org/t/activating-test-dependencies/48121/7?u=oxinabox). +It feels kind of hacky, because it is. +At some point there might be a nicer user-interface for stacked environments like this. + + +### Full Transition off METADATA.jl and REQUIRE files, and onto the General Registry +Even though Julia 1.0 had Pkg3 and was supposed to use registries, for a long time the old Pkg2 [METADATA.jl](https://github.com/JuliaLang/METADATA.jl) pseudo-registry was used as the the canonical source of truth. +Registering new releases was made against that, and then a script synchronized [General](https://github.com/JuliaRegistries/General) registry to match it. +This was to allow time for packages to change over to supporting julia 1.0, while still also making releases that supported julia 0.6. +It wasn't until about a year later that this state ended and the General registry became the one source of truth. + +This was kind of sucky, because a lot of the power of Pkg3 was blocked until then. +In particular, since the registries compat was generated from the REQUIRE file, and the REQUIRE files had kind of [gross requirement specification](https://docs.julialang.org/en/v0.6/manual/packages/#Requirements-Specification-1), and everyone basically just lower bounded things, or didn't bound compat at all. +Which made sense because with the only 1 global environment that Pkg2 had, you basically needed the whole ecosystem to be compatible, so restricting was bad. +But Pkg3/Project.toml has the much better default [`^` bounded]](https://julialang.github.io/Pkg.jl/v1/compatibility/#Version-specifier-format-1) to accept only non-breaking changes by SemVer. +(And per project environments, so things don't all have to be compatible -- just things used in a particular project (rather than every project you might ever do). + +#### Automatic Merging +Initially after the change over all PRs to make a new release needed manual review, by one of the very small number of General registry maintainers. + +I am a big fan of [releasing after every non-breaking PR](https://www.oxinabox.net/2019/09/28/Continuous-Delivery-For-Julia-Packages.html). +Why would you not want those bug-fixes and new features released? +It is especially rude to contributors who will make a fix or a feature, but then you don't let them use it because you didn't tag a release. +Plus it makes tracking down bugs easier, if it occurs on a precise version you know the PR that caused it. +But with manual merging it feels bad, to tag 5 releases in a day. +Now they we have automatic merging it is fine. +At time of writing [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl/) was up to 67 releases. + +The big advantage of automatic merging is that it comes with automatic enforcement of standards. +In order to be automerge-able some good standards have to be followed. +One of which is that that no unbounded compat specifiers (e..g `>=1.0`) are permitted; and that everything must have a compat specifier (since unspecified is same as `>=0.0.0`) +That one is particularly great, since if one adds specifiers later that can't be met, then it can trigger downgrades back to the incredible old versions that didn't specify compat and that almost certainly are not compatible. + +To deal with that particular case retro-capping was done to retroactively add bounds to all things that didn't have them. +Which was painful when it was done, since it rewrote compat in the registry, which made it disagree with the `Project.toml`, which is always confusing. +Now that it is done, it is good. + +#### Requirement to have a `Project.toml` +Finally the ability to `pkg> dev` packages that had only a REQUIRE and no `Project.toml` was removed in Julia 1.4. +You can still `pkg> add` them, if they were registered during the transition period. +But to edit the projects they must a `Project.toml` file, which is fine since all the tools to make registering releases also require you to have `Project.toml` now + + + +### Resolver willing to downgrade packages to install new ones (Tiered Resolution) + +Until the [tiered resolver](https://github.com/JuliaLang/Pkg.jl/pull/1330) was added Julia would not change the version of any currently installed package in order to install a new one. +For example consider + + - **Foo.jl** + - v1 + - v2 + - **Bar** + - v1 compatible with Foo v1 + - v2 compatible with Foo v2 + - **Qux.jl** + - v1: compatible only with Foo v1 + +If you did `pkg"add Foo Bar Qux"` you would end up with **Foo** v1, **Bar** v1, and **Qux** v1. +But if you did first: `pkg"add Foo Bar"` (which would install **Foo** v2, and **Bar** v2), +and then did `pkg"add Qux"`, +then on Julia 1.0 you would get an error, as it would refuse to downgrade **Foo** and **Bar** to v1, as is required to allow **Qux** to be installed. +This meant effectly the package manager is stateful, which turns out is really counter intuitive. +The way to resolve this in practice was to delete the `Manifest.toml` and do it again as a single action. +This was a significant problem for [test time dependencies](https://github.com/JuliaLang/Pkg.jl/issues/1352), which if you had a test time dependencies with a indirect dependencies shared with a main dependency of the package, but that was only compatible with an older version of the indirect depenency, you would be unable to run tests as resolving the test time depenency would fail. + +With the new tiered resolved it will try a number of tiers of relaxing existing versions. +It still wants to avoid changing the versions of currently installed packages if possible, but if that is required then it will do so. +It will in turn first attempt to install without any currently installed package being changed, then will try relaximng the constraint to allow changing the version of indirect dependencies, then to allowing changing the versions of direct dependencies. +This ends up far more intuitive: if there is a compatible set of package versions then they will be found. +Regardless of if new packages are added all at once or one at a time. + +### Precompilation + +Precompilation has been enhanced a lot. +That is not the compilation that runs the first time a function is used in a session, but rather the stuff that runs the first time a package is loaded in an environment. + +For a start, the precompilation cache no longer goes stale every time you swap environments. +This was a massive pain in julia 1.0, especially if you worked on more than one thing at a time. +This was fixed in 1.3 to have multiple caches, for different environments. +It's easy to forget this one, but it is actually one of the biggest usability enhancements since 1.0. + +More dramatically, and less easy to overlook, is the parallelism of precompilation added in 1.6. +Precompiling a package requires precompiling all its dependencies first. +This is now done in parallel, and is automatically triggered when you complete Pkg operations. +In contrast, to happening in series the first time a package is loaded. +No more waiting 5 minutes the first time you load an package with a lot of dependencies. +Further the spiffy animated output shows you what is precompiling at a given time; and a progress bar, which makes it feel (even) faster. + + + +### Improved conflict messages +This is one of my own contributions. +Julia 1.0 conflict messages are a terrifying wall of text. + +![Julia 1.0 conflict log]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.0-conflict.png) +![Julia 1.6 conflict log]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.6-conflict.png) + +The two main changes are the use of colors, and compressing the version number ranges. +No more giant red wall of numbers. + + +Colors were added to make it easier to see which version numbers are referring to which package. +There is a bit of a problem that it isn't easy to make sure the colors don't get reused, as there are not many in the 16 color list (especially when you skip a few like black, white and error red). +Due to how Pkg constructs its error messages basically every package in the dependency graph gets some message prepared for it, but not displayed, so just assigning them a color in turn gets it to loop around so doesn't reduce change of colors being reused. +There is away to fix that but it is a big change to add structured log messages that are colored only once they are displayed. +We decided after much debate to assign things colors based on the hash of the package name and shortened UUID. +This means things have consistent colors if you fix one and redo it a still have some remaining. +I think this is going to be a subtle improvement on easy of use. +As a cool hack, you can actually change the color list used. +To get the much larger color list I wanted to use originally you can do: +`append!(Pkg.Resolve.CONFLICT_COLORS, [21:51; 55:119; 124:142; 160:184; 196:220])` + + +The other change was consecutive lists of version numbers were compressed to continuous ranges. +In 1.6 these ranges are split only if there is a version that exists between them that is not compatible. +So normally just a single range, since compatibility is typically monotonic. +In contrast in 1.0, they were split if there was a potential version that could exist that is not compatible (even if that version doesn't currently exist). +This mean they split every time the right most nonzero was incremented. +For something with a lot of pre-1.0 versions that is a lot of numbers. +I think the new version is much cleaner and easier to read. + + +## Time To First Plot +People often complain about the "Time To First Plot" (TTFP) in julia. +I personally have never minded it -- by the time iI am plotting something I have done minutes of thinking so 10 seconds of compilation is nothing. +Plotting turns out is basically a really hard thing for a compiler. +It is many many small methods, most of which are only called once. +And unlike most julia code it doesn't actually benefit all that much from most of the work julia's JIT is normally doing to specialize things -- plotting itself isn't in the hot-loop. +To a long-story short, plotting the postcard example for julia needing to compile things before it can run them. + +As a really hacky way to time this, the following timing. +Timing it twice because need to capture the timing for printing afterwards (which is something very affected by invalidations see below). +Also with precompilation already run +(though see below, precompilation caches often would be deleted in 1.0; and conversely are way faster to create in 1.6 due to parallelization.) + +**Julia 1.0:** +```julia +julia> @time @time using Plots; plot(1:0.1:10, sin.(1:0.1:10)) + 7.168878 seconds (15.87 M allocations: 941.114 MiB, 7.36% gc time) + 9.281561 seconds (25.74 M allocations: 1.393 GiB, 7.88% gc time) +``` +**Julia 1.6:** +```julia +julia> @time @time using Plots; plot(1:0.1:10, sin.(1:0.1:10)) + 4.490434 seconds (8.24 M allocations: 583.285 MiB, 7.13% gc time, 32.80% compilation time) + 4.533302 seconds (8.36 M allocations: 590.949 MiB, 7.06% gc time, 33.41% compilation time) +``` + + +### Per Module Optimization Flags + +TODODOTOTOTOTODODODOD TODO + +### Invalidations + +Consider a function `foo` with a method `foo(::Number)`. +If some other function calls it, for example `bar(x::Int) = 2*foo(x)`, the JIT will compile in an instruction for exactly the method instance to call (assuming type-inference work) -- a fast static dispatch, possibly even inlined. +If the user then defines a new more specific method `foo(::Int)`, then the compiled code for `Bar`, need to be invalidated so it will call the new one. +It needs to be recompiled -- which means anything that static dispatches to it needs to be recompiled and so forth. +This is an invalidation. +It's an important feature of the language. +It is key to extensibility. +It doesn't normally cause too many problems. +Since generally basically everything is defined before anything is called, and thus before anything is compiled. + +A notable exception to this is Base an the other standard libraries. +These are compiled into the so-called system image. +Further-more, methods in these standard libraries are some of the most overloaded, thus most likely to have it triggered. + +A bunch of work has gone into dealing with invalidations better. +Not just point-fixes to remove calls that were likely to be invalidated, but several changes to the compiler. +One particular change was not triggering cascading invalidations for methods that couldn't actually be called do the being ambiguous. +As a result a lot of user code doesn't trigger invalidations on 1.6, that did on 1.5. +The end result of this is faster compilation after loading packages, since it doesn't have to recompile a ton of invalidated method instances. +i.e. decreased time to first plot. + +In this has had huge effect on [Revise.jl](https://github.com/timholy/Revise.jl) +which started to take a second or so to load when it gained the dependency on [JuliaInterpretter.jl](https://github.com/JuliaDebug/JuliaInterpreter.jl); which isn't much, but when you do it every time you start julia it is an annoying lack of snappyness. +But thanks to this work, JuliaInterpretter, and thus Revise now load in a flash. + +A full discussion on the invalidations work can be found in [this blog-post](https://julialang.org/blog/2020/08/invalidations/). + +## Internals: Manually Created Back-edges for Lowered Code Generated Functions +This is a very niche and not really at all user-facing feature. +To understand why this matters, it's worth understanding how Cassette works. +I wrote [blog-post on this a few years ago](https://invenia.github.io/blog/2019/10/30/julialang-features-part-1#making-cassette). + +In quiet the opposite, Julia 1.3 allowed for the creation of more invalidations though allowing back-edges to be manually attached to the `CodeInfo` for `@generated` functions that return lowered code. +Backedges are the connection from methods back to each method instance that calls them. +This is what allows invalidations to work, as when a method is redefined, it needs to know what things to recompile. +This change allowed those back-edges to be manually specified for `@generated` functions that were working at the lowered code level. +Which is useful since this technique is primarily used for generating code based on the (lowered) code of existing methods. +For example in [Zygote](https://github.com/FluxML/Zygote.jl), generating the gradient code, from the code of the primal method. +So you want to be able to the regeneration of this code when that original method changes. + + +Basically, end of the day is this allows code that uses [Cassette.jl](https://github.com/jrevels/Cassette.jl), and [IRTools.jl](https://github.com/MikeInnes/IRTools.jl) to not suffer from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. +A particular case of this is for [Zygote](https://github.com/FluxML/Zygote.jl) where redefining a function called by the code that was being differentiated did not result in an updated gradient (unless `Zygote.refresh()`) was run. +This was annoying for + +Other things that this allows is two very weird packages that [Nathan Daly](https://github.com/NHDaly) and I came up with at the JuliaCon 2018 hackathon: [StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) and [Tricks.jl](https://github.com/oxinabox/Tricks.jl/). +[StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) relaxes the restrictions on normal `@generated` functions so that they are also safe from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. +[Tricks.jl](https://github.com/oxinabox/Tricks.jl/) uses this feature to make `hasmethod` etc resolve at compile-time, and then get updated if and when new methods are defined. +Which can allow for defing traits like _"anything that defines a `iterate` method"_. + + +## Base and Standard Libraries + +### Threading +Julia has full support for threading now. +Not just the limited `@threads for` loops, but full GoLang style threads. +They are tightly integrated with the existing Async/Task/Coroutine system. +In effect threading works by unsetting the sticky flag on a Task, so that it is allowed to run on any thread. +This is normally done via the `Threads.@spawn` macro, rather than the `@async` macro. + +Interestingly, the `@threads for` macro still remains, and doesn't actually use much of the new machinery. It still uses the old way which is a bit tighter if the loop durations are almost identical. +But the new threading stuff is fast, on the order of microseconds to send work off to another thread. +Even for uses of `@threads for` the improves have some wins. +`IO` is now thread-safe; `ReentrantLock` was added and is the kind of standard lock that you expect to exist, that has notifications on waiting work etc; and a big one: `@threads for` can now be nested without things silently being wrong. + +A lot of this actually landed in julia 1.2, but julia 1.3 was the release we think of as being for threading, as it gave us `Threads.@spawn`. + +Also in Julia 1.6 we now have `julia -t auto` to start julia with 1 thread per (logical) core. +No more having to remember to set the `JULIA_NUM_THREADS` environment variable before starting it. + +### 5-arg `mul!`, aka in-place generalized multiplication and addition +The 5 arg `mul!(C, A, B, α, β)` performs the operation equivalent to: `C .= A*B*α + C*β`, where `α`, `β` are scalars and `A`, `B` and `C` are compatible mixes of scalars, matrices and vectors. +I am still the opinion that it should have been called `muladd!`, but this is the human friendly version of `BLAS.gemm!` (i.e. GEneralized Matrix Multiplication) and it's ilk. +It promises to alway compute the in-place mul-add in the most efficient, correct, way for any `AbstractArray` subtype. +In contrast, `BLAS.gemm` computes the same thing, but with a bunch of conditions. +It must be a strided array containing only BLAS-scalars, and if one of the inputs is conjugated/transposed you need to input it in non-conjugated/transposed form, and then tell `BLAS.gemm!` via passing in `'C'` or `T` rather than `N`. +5-arg `mul!` takes care of all that for you dispatching to `BLAS.gemm!` or other suitable methods once it has that all sorted. +Further, the existing 3-arg `mul!(C, A, B)` is a special case of it: +`mul!(C, A, B) = mul!(C, A, B, false, true)` (`true` and `false` being 1, and strong 0). +So in general on can just implement the 5-arg form and be done with it. + +I personally didn't care about 5-arg `mul!` at all for a long time. +Yet another in-place function in `LinearAlgebra` that I would never use often enough to remember what it did, and thus wouldn't use it. +But I realized that it is a crucial function for my own area: automatic differentiation. +`mul(C, A, B, true, true)` is the in-place accumulation rule for the reverse mode equivalent of the product rule. + + +### You can now print and interpolate `nothing` into strings. + +This is one of mine [#32148](https://github.com/JuliaLang/julia/pull/32148)., and I find it is such a usability enhancement. +So many small frustrations in Julia 1.0 relating to interpolate a variable containing `nothing` into a string. +Often occurring when you are adding a quick `println` to debug something not being the value you expected; or when building a string for some other error message. +Arguably both of those are better done via other means (`@show`, and values stored in fields in the error type); but we don't always do what is best. +Sometimes it is expedient to just interpolate things into strings without worrying about what type they are. + +### `Base.download` now using libcurl +For a very long time the `download` function which retrieves things over HTTP, was implemented with an amazing hack. +It conditionally shelled out to different programs. +On windows it run a mildly scary powershell script. +On Unixen it first tried to use `curl` then if that wasn't installed it tried to use `wget` and then if that wasn't installed it tried to use `fetch`. +It's low-key amazing that this worked as well as it did -- very few complaints. +But as of 1.6 [it now uses libcurl](https://github.com/JuliaLang/julia/pull/37340). +Using libcurl everywhere gives consistency with proxy settings, and protocol support (beyond HTTP) across all platforms. + +It also has more extensive API via the new [Downloads.jl](https://github.com/JuliaLang/Downloads.jl) standard library. +It can do things like progress logging, and it can retrieve headers. +I have [tried getting headers via conditional different commandline download functions](https://github.com/oxinabox/DataDeps.jl/pull/22) before, it is low-key nightmare fuel; and I ended up swapping out the HTTP.jl for that. +It wouldn't be too surprising if eventually we see libcurl swapped out for code extracted from HTTP.jl for a pure julia solution. +HTTP.jl works wonderfully to this, but I suspect untangling the client from the server is just a bit annoying right now, particularly as it is still evolving its API. + + +## Definable Error Hints +[`Experimental.register_error_hint`](https://docs.julialang.org/en/v1.7-dev/base/base/#Base.Experimental.register_error_hint) +allows packages to define extra information to be shown along with a particular type of error. +A really cool use of it was proposed on [Discourse](https://discourse.julialang.org/t/enforce-interface-implementation/52872/15?u=oxinabox): +you could add an error hint to `MethodError` that says to check that an interface has been implemented correctly. +This has the advantage of pointing the user in what is most likely the right direction. +But it doesn't have the problem of really confounding them if you are wrong, since the original `MethodError` information is still shown (See my earlier [blog-post on the `NotImplementedException` antipattern](https://www.oxinabox.net/2020/04/19/Julia-Antipatterns.html#notimplemented-exceptions)). + +Right now this is not used anywhere in the standard libraries. +At first, I thought that made sense since they can just edit the original `show_error`. +But now I think since it can be used for a subset of a particular type of exception it could well be useful for some of p`Base`'s interfaces](https://docs.julialang.org/en/v1/manual/interfaces/) exactly as described above. +According to [JuliaHub search](https://juliahub.com/ui/CodeSearch?q=register_error_hint&u=all&t=all) right now there is just one package using. +ColorTypes.jl is [using it to explain some _consensual type-piracy_](https://github.com/JuliaGraphics/ColorTypes.jl/blob/bd31741d162361ebd44ed05ae532266998d9ce9f/src/error_hints.jl), where for some functions on its types you need to load another package. + +This is an experimental features, so it's not covered by the SemVer guarantees of the rest of the language. +Still it's neat, I don't expect it to go away, +though I also don't expect it to graduate from experimental status until a bunch of people are using it. +Which they will probably do as people start dropping support for older julia versions. + + +### A bunch of curried functions +Julia seems to have ended up with a convention of providing curried methods of functions if that would be useful as the first argument for `filter`. +e.g. `filter(isequal(2), [1,2,3,2,1])`, is the same is `filter(x->isequal(x, 2), [1,2,3,2,1]`). +In particular these are boolean comparison like functions: +with two arguments, where the thing being compared against is the second. +Julia 1.0 had `isequal`, `==` and `in`. +Since then we have added: +`isapprox`, `<`,`<=`,`>`, `>=`, `!=`, `startswith`, `endswith`, and `contains`. +(I added the last 3 😁) + +Aside: `contains` is argument flipped `occursin`, it was a thing in julia 0.6 but was removed in 1.0 and now has been added back. +We added it back primarily so we could have the curried form, and to match `startswith` and `endswith`. + +### A ton of other new and improved standard library functions + +I am not going to manage to list all of them here. +But I will list some of my standout favorites. + + +[`@time`])(https://github.com/JuliaLang/julia/pull/37678) now reports how much time was spent on compilation. +This is going to help prevent people knew the language from including compilation time in their benchmarks. +It is still better to use [BenchmarkTool.jl's](https://github.com/JuliaCI/BenchmarkTools.jl) `@btime`, since that does multiple samples. +But now that too can report time spend compilation. +It's also useful for identifying if certain functions are taking ages to compile. +Which I guess is its theoretical main point, but I think preventing people benchmarking wrong is going to come up way more often. + +The experimental [`Base.@locals`](https://github.com/JuliaLang/julia/issues/29733 +) was added which returns a dictionary of local variables. +That one surprised me, I though being able to access a dict of local variables would get in the way of the optimizer, since it would prevent it from being able to optimize variables that are used for intermediate values away entirely. +But the compiler folk know better than I do. + +[`splitpath`](https://github.com/JuliaLang/julia/issues/28156) as added, its the opposite of `joinpath`. +Kind of silly we didn't have that, and had been being me at least since 0.6. + +On things that had been bugging me, I had wanted [`eachslice` and it's special cases: `eachrow` & `eachcol`](https://github.com/JuliaLang/julia/issues/29749) since julia 0.3 when I first started using it. +These are super handy when you want to e.g. iterate through vectors of the rows of a matrix. + +`redirect_stderr` and `redirect_stdout` now work with `devnull`. +So one can run some suppressing output easily as follows: +```julia +julia> redirect_stdout(devnull) do + println("You won't see this") + end +``` +This is just handy, doing it without this is seriously annoying. + +`readdir` now accepts a `join=true|false` keyword argument so that it returns paths with the parent dir. +This is good, almost every time I use `readdir` I used it as: +`joinpath.(x, readdir(x))`. +It is slightly cleaner (and faster) to be able to do `readdir(x; join=true)`. +I think for Julia 2.0 we should consider making it the default. +Also added was a `sort` argument, which I don't see the point of so much, since `sort(readdir(x))` seems cleaner than `readdir(x; sort=true)`; and because I rarely rely on processing files in order. + + +`ccall` is now available as a macro `@ccall` which lets you specify +There was a [short juliacon talk about this](https://www.youtube.com/watch?v=wofq1DdXM3s) +So now one can do `@ccall(sqrt(4.0::Cdouble)::Cdouble)` +rather than `ccall(:sqrt, Cdouble, (Cdouble,), 4.0,)` +This is what `ccall` always should have been. +In julia 1.7, [`invoke` is getting the same treatment.](https://github.com/JuliaLang/julia/pull/38438). + + +### More "Why didn't it always work that way" than I can count +Since 1.0's release there have been so many small improvements to functions that I didn't even know happened, because I assumed they always worked that way. +Things like `startswith` supporting regex, (`parse`](https://github.com/JuliaLang/julia/pull/36199) working on `UUID`s, +`accumulate`, `cumsum`, and `cumprod` supporting arbitrary iterators (https://github.com/JuliaLang/julia/pull/34656). +Julia 1.6 is one hell of a more polished language. \ No newline at end of file From 5c13815f8e073bbbb6d0d3e623c5d89f5f9fe918 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 10 Jan 2021 22:15:15 +0000 Subject: [PATCH 19/62] editting wip --- ...-09-Julia-1.6-what-has-changed-since-1.0.md | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index a4867c1d..c0612621 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -87,7 +87,13 @@ This was added in [#36634](https://github.com/JuliaLang/julia/issues/36634). This functionality was first implemented in the [ColoredLLCodes.jl](https://github.com/kimikage/ColoredLLCodes.jl), where it worked by monkey-patching the InteractiveUtils stdlib. That package does still work on julia 1.0. + +**Julia 1.0:** + ![Julia 1.0 code_llvm]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.0-code-llvm.png) + +**Julia 1.6:** + ![Julia 1.6 code_llvm]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.6-code-llvm.png) The REPL itself still doesn't have syntax highlighting for Julia code though. @@ -101,7 +107,12 @@ Probably one day, though, as its big dependencies are also required if the parse Just like colored `code_llvm` colored stack-traces also originated in a package that was doing some nasty monkey-patching: [ClearStacktrace.jl](https://github.com/jkrumbiegel/ClearStacktrace.jl). This was added into Base itself in [#36134](https://github.com/JuliaLang/julia/pull/36134) +**Julia 1.0:** + ![Julia 1.0 stacktrace]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.0-stacktrace.png) + +**Julia 1.6:** + ![Julia 1.6 stacktrace]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.6-stacktrace.png) The first thing you probably notice is the colored package names, to make it more clear where the error is coming from. @@ -349,7 +360,12 @@ Further the spiffy animated output shows you what is precompiling at a given tim This is one of my own contributions. Julia 1.0 conflict messages are a terrifying wall of text. +**Julia 1.0:** + ![Julia 1.0 conflict log]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.0-conflict.png) + +**Julia 1.6:** + ![Julia 1.6 conflict log]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.6-conflict.png) The two main changes are the use of colors, and compressing the version number ranges. @@ -606,5 +622,5 @@ In julia 1.7, [`invoke` is getting the same treatment.](https://github.com/Julia ### More "Why didn't it always work that way" than I can count Since 1.0's release there have been so many small improvements to functions that I didn't even know happened, because I assumed they always worked that way. Things like `startswith` supporting regex, (`parse`](https://github.com/JuliaLang/julia/pull/36199) working on `UUID`s, -`accumulate`, `cumsum`, and `cumprod` supporting arbitrary iterators (https://github.com/JuliaLang/julia/pull/34656). +`accumulate`, `cumsum`, and `cumprod` supporting arbitrary iterators ([#32656](https://github.com/JuliaLang/julia/pull/34656)). Julia 1.6 is one hell of a more polished language. \ No newline at end of file From 1edea1e9685da6014cd54e80edf7d5ab90bf035d Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 10 Jan 2021 23:47:51 +0000 Subject: [PATCH 20/62] draft 1 done --- ...09-Julia-1.6-what-has-changed-since-1.0.md | 29 ++++++++++++++----- 1 file changed, 21 insertions(+), 8 deletions(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index c0612621..00ceec0a 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -402,9 +402,10 @@ And unlike most julia code it doesn't actually benefit all that much from most o To a long-story short, plotting the postcard example for julia needing to compile things before it can run them. As a really hacky way to time this, the following timing. -Timing it twice because need to capture the timing for printing afterwards (which is something very affected by invalidations see below). -Also with precompilation already run -(though see below, precompilation caches often would be deleted in 1.0; and conversely are way faster to create in 1.6 due to parallelization.) +You can see did `@time @time ...` and two number were displayed. +The first number is how long from the start of the command until the `using Plots` and `plot(...)` command finished. +The second is that, plus how long it took before it could show the output of the first. +It's worth looking at this later because it turns on invalidations (see below) made the REPL have to recompile a bunch of display code. **Julia 1.0:** ```julia @@ -418,11 +419,11 @@ julia> @time @time using Plots; plot(1:0.1:10, sin.(1:0.1:10)) 4.490434 seconds (8.24 M allocations: 583.285 MiB, 7.13% gc time, 32.80% compilation time) 4.533302 seconds (8.36 M allocations: 590.949 MiB, 7.06% gc time, 33.41% compilation time) ``` +So it's a bit better than 2 times faster on my machine. - -### Per Module Optimization Flags - -TODODOTOTOTOTODODODOD TODO +Note also that in the above timing I had already run precompilation. +Which seems fairest. +Though see below, precompilation caches often would be deleted in 1.0; and conversely are created much faster in 1.6 due to parallelization. ### Invalidations @@ -453,7 +454,19 @@ But thanks to this work, JuliaInterpretter, and thus Revise now load in a flash. A full discussion on the invalidations work can be found in [this blog-post](https://julialang.org/blog/2020/08/invalidations/). -## Internals: Manually Created Back-edges for Lowered Code Generated Functions +### Per Module Optimization Flags +>The compiler optimization level can now be set per-module using the experimental macro Base.Experimental.@optlevel n. For code that is not performance-critical, setting this to 0 or 1 can provide significant latency improvements ([#34896](https://github.com/JuliaLang/julia/issues/34896)). + +> Compilation and type inference can now be enabled or disabled at the module level using the experimental macro Base.Experimental.@compiler_options ([#37041](https://github.com/JuliaLang/julia/issues/37041)). + +Recall I said most of julia compilation isn't even that useful for plotting, since making it run fast isn't a priority (but loading fast is) + +`@optlevel` only controls which LLVM optimization passes run; which is right at the end of the compilation pipeline. +Turning off compilation and type inference on the other-hand turns off a ton more. + + +## Internals +### Manually Created Back-edges for Lowered Code Generated Functions This is a very niche and not really at all user-facing feature. To understand why this matters, it's worth understanding how Cassette works. I wrote [blog-post on this a few years ago](https://invenia.github.io/blog/2019/10/30/julialang-features-part-1#making-cassette). From e4ef0a81aea1cc2bd223fd90ad3dde571de1ff3c Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Mon, 11 Jan 2021 23:00:10 +0000 Subject: [PATCH 21/62] Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Apply suggestions from code review Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf --- ...09-Julia-1.6-what-has-changed-since-1.0.md | 55 +++++++++---------- 1 file changed, 27 insertions(+), 28 deletions(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index 00ceec0a..2046de43 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -17,15 +17,15 @@ Prior to 1.0 all releases were feature releases; they came out when they were re Since then all releases (except 1.6) have been timed releases. The release candidate is cut from the main branch every 3 months; and they are released after an [extensive round of additional checking](https://github.com/JuliaLang/julia/pull/35846), which often takes weeks to months. Julia 1.6 was soft-slated to be the new long-term-support (LTS) version that would have bug-fixes backported to it for the next few years. -The current LTS is julia 1.0, which has now had 5 patch releases made. +The current LTS is Julia 1.0, which has now had 5 patch releases made. Since it was going to be supported for a long-time people wanted to make sure everything good got in; thus it was a feature release. -The core developers have demurred on if 1.6 will actually be selected to be the new LTS (even if it wasn't it won't assend to being LTS til it stops being the current Stable). +The core developers have demurred on if 1.6 will actually be selected to be the new LTS (even if it wasn't it won't ascend to being LTS til it stops being the current Stable). My impression now is that they feel like it has too many cool new things; and that a few things didn't quite make it in even with the extended release cycle. So it's looking likely to me that 1.7 will actually be the LTS; but that it might also be a feature release -- possibly this time a much shorter release period than usual. -In practice I think for a lot of package maintainer's 1.6 will be a LTS, in that that is the oldest version they will make sure to continue to support. -There has been too many cool new things (as this post will detail) to stay back to only 1.0 features. -Already a lot of packages have dropped support for julia versions older than 1.3. +In practice I think for a lot of package maintainers 1.6 will be a LTS, in that that is the oldest version they will make sure to continue to support. +There have been too many cool new things (as this post will detail) to stay back to only 1.0 features. +Already a lot of packages have dropped support for Julia versions older than 1.3. This post is kind of a follow-up to my [Julia 1.0 release run-down]({{site.url}}//2018/06/01/Julia-Favourite-New-Things.html). @@ -39,17 +39,17 @@ Full details can be found in the [NEWS.md](https://github.com/JuliaLang/julia/bl ### Soft-scope in the REPL Julia 1.0 removed the notion of soft-scope from the language. I was very blasé about the change to for-loop bindings in my [1.0 release post](https://www.oxinabox.net/2018/06/01/Julia-Favourite-New-Things.html#for-loop-variable-binding-changes). -Infact, I didn't even mention this particular change. -It was [#19324](https://github.com/JuliaLang/julia/pull/19324) for reference +In fact, I didn't even mention this particular change. +It was [#19324](https://github.com/JuliaLang/julia/pull/19324) for reference. This was undone in in Julia 1.5 with [#33864](https://github.com/JuliaLang/julia/pull/33864) for in the REPL only. Now in the REPL, assigning to a global variable within a for-loop actually assigns that variable, rather than shadowing it with a new variable in that scope. -The same behavior outside the REPL, now gives a warning. +The same behavior outside the REPL now gives a warning. -Personally, this change never affected me because I never write for-loops at global scope that assign variables. +Personally, this change never affected me because I never write for-loops that assign variables at global scope. Indeed basically all I code I write is inside functions. -But I do see how this causes problems for some interactive work-flows, especially e.g. when demonstrating something. -See the [main github issue](https://github.com/JuliaLang/julia/issues/28789) and the longest [Discourse thread](https://discourse.julialang.org/t/another-possible-solution-to-the-global-scope-debacle/15894), though there were many others. +But I do see how this causes problems for some interactive workflows, especially e.g. when demonstrating something. +See the [main GitHub issue](https://github.com/JuliaLang/julia/issues/28789) and the longest [Discourse thread](https://discourse.julialang.org/t/another-possible-solution-to-the-global-scope-debacle/15894), though there were many others. It took years of thinking to workout the solution, particularly because many of the more obvious solutions would be breaking in significant ways. ### Deprecations are muted by default. @@ -68,8 +68,7 @@ Probably also you are checking the release notes, since you are intentionally up The core of the reason is because it was actually breaking things. Irrelevant deprecation warning spam from dependencies of dependencies was causing JuMP and LightGraphs to become too slow to be used. -Since they were from dependencies of dependencies the maintainers of those JuMP and LightGraphs couldn’t even fix them. -Let alone the end users. +Since they were from dependencies of dependencies the maintainers of JuMP and LightGraphs couldn’t even fix them, let alone the end users. Deprecation warnings are still turned on by default in tests. Which makes sense since the tests (unlike normal use), is being run by maintainers of the package itself, not its users. @@ -117,17 +116,17 @@ This was added into Base itself in [#36134](https://github.com/JuliaLang/julia/p The first thing you probably notice is the colored package names, to make it more clear where the error is coming from. You also should notice the dimming of type-parameters to make complicated types easier to read. -Also the addition of argument names, and showing keyword arguments bearing functions as they are written, rather than as a weird `#DataFrame#654` internal function. +Also the addition of argument names, and showing functions with keyword arguments as they are written, rather than as a weird `#DataFrame#654` internal function. The whole thing just looks more modern and polished. ### Time Traveling Debugger -[Julia 1.5 added built in support](https://julialang.org/blog/2020/05/rr/) for [`rr`](https://rr-project.org/) (linux only for now). -This is not useful so much for debugging julia code, but for debugging julia itself. +[Julia 1.5 added built-in support](https://julialang.org/blog/2020/05/rr/) for [`rr`](https://rr-project.org/) (Linux only for now). +This is not useful so much for debugging Julia code, but for debugging Julia itself. If you get into one of the very rare situations where the language is doing something truly nonsense; you can send a bug report recorded via `rr`, and someone can debug *exactly* what is happening. Event to the extent of [diagnosing faulty RAM](https://julialang.org/blog/2020/09/rr-memory-magic/). -`rr` is an impressively cool piece of tech; it boils down to the fact that linux's `fork` is incredibly cheap, because it take advantage of [copy-on-write](https://en.wikipedia.org/wiki/Copy-on-write); so it basically `fork`s before every system call, and serializes what is going on; so you have a full track of everything that happened. -The `rr` integration is really nice easy to use; I had someone who was part of our architecture and operations (i.e. who writes way more CloudFormation than Julia) team run it to submit a support request; and they had no real troubles. +`rr` is an impressively cool piece of tech; it boils down to the fact that linux's `fork` is incredibly cheap, because it takes advantage of [copy-on-write](https://en.wikipedia.org/wiki/Copy-on-write); so it basically `fork`s before every system call, and serializes what is going on; so you have a full track of everything that happened. +The `rr` integration is really nice and easy to use; I had someone who was part of our architecture and operations team (i.e. who writes way more CloudFormation than Julia) run it to submit a support request; and they had no real troubles. ## Syntax @@ -136,21 +135,21 @@ The `rr` integration is really nice easy to use; I had someone who was part of o >The syntax import A as B (plus import A: x as y, import A.x as y, and using A: x as y) can now be used to rename imported modules and identifiers ([#1255](https://github.com/JuliaLang/julia/issues/1255)). -This will be familiar to Python folk who love to do: `import numpy as np`. +This will be familiar to Python folks who love to do: `import numpy as np`. I hope we never see it used that ubiquitously in julia. I am quite happy with `using Foo` which imports into scope everything the author of `Foo` exported. -Python people find that super-weird and scary; but it's fine. -Further, most things you use don't come from the module that defined them anyway, they are overloaded functions e.g. from `Base`. +Python people find that super weird and scary; but it's fine. +Further, most things you use don't come from the module that defined them anyways; they are overloaded functions e.g. from `Base`. The real value of `import FooBar as fb` is not to have a short abbreviation so you can do `fb.quux`. That was already possible via `const fb = FooBar`. It is to handle cases where the package name itself conflicts with an identifier. For example (as has often occured) if one has uses a `Pipe` in the REPL, and then later wants to load [Pipe.jl](https://github.com/oxinabox/Pipe.jl/) via `import Pipe` then one gets a name clash before it can be resolved. -Now one can do `import Pipe as PipingPipe` +Now one can do `import Pipe as PipingPipe`. ### NamedTuple/keyword arguments automatic naming This feature felt weird when I first read about it, but it has quickly grown on me. How often do you write some code that does some processing and calls some other method passing on some of its keyword arguments? -For example +For example: ```julia # Primary method all others redirect to this foo(x::Bar; a=1, b=2, c=3) = ... @@ -163,7 +162,7 @@ This does come with the requirement to separate keyword arguments from positiona It feels like we are fully leveraging the distinction of keyword from positional arguments by allowing this. In contrast, the distinction vs e.g. C# and Python that allow any positional argument to be passed by name, is to make the name of positional arguments not part of the public API, thus avoiding changing it being a breaking change. -The same syntax can be used to create `NamedTuple`. +The same syntax can be used to create `NamedTuple`s. ```julia julia> product = ["fries", "burger", "drink"]; @@ -608,7 +607,7 @@ On things that had been bugging me, I had wanted [`eachslice` and it's special c These are super handy when you want to e.g. iterate through vectors of the rows of a matrix. `redirect_stderr` and `redirect_stdout` now work with `devnull`. -So one can run some suppressing output easily as follows: +So one can run some code while suppressing output easily as follows: ```julia julia> redirect_stdout(devnull) do println("You won't see this") @@ -633,7 +632,7 @@ In julia 1.7, [`invoke` is getting the same treatment.](https://github.com/Julia ### More "Why didn't it always work that way" than I can count -Since 1.0's release there have been so many small improvements to functions that I didn't even know happened, because I assumed they always worked that way. -Things like `startswith` supporting regex, (`parse`](https://github.com/JuliaLang/julia/pull/36199) working on `UUID`s, +Since 1.0's release, there have been so many small improvements to functions that I didn't even know happened, because I assumed they always worked that way. +Things like `startswith` supporting regex, [`parse`](https://github.com/JuliaLang/julia/pull/36199) working on `UUID`s, `accumulate`, `cumsum`, and `cumprod` supporting arbitrary iterators ([#32656](https://github.com/JuliaLang/julia/pull/34656)). -Julia 1.6 is one hell of a more polished language. \ No newline at end of file +Julia 1.6 is one hell of a more polished language. From 09af888bb4bb4fdbe6d054d9c24e402e4c7eeab2 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Mon, 11 Jan 2021 23:01:01 +0000 Subject: [PATCH 22/62] Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf --- _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index 2046de43..1e082891 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -174,8 +174,7 @@ julia> (;product, price) This is particularly cool for constructing `NamedTuple`s of `Vector`s, which is a valid [Tables.jl Table](https://tables.juliadata.org/stable/#Tables.columntable). It is interesting to note that for the logging macros introduced in Julia 1.0, this is how they have always worked. -E.g. `@info "message" foo bar` and `@info "message" foo=foo bar=bar` display the same. -Which has always felt natural. +E.g. `@info "message" foo bar` and `@info "message" foo=foo bar=bar` display the same, which has always felt natural. ### Lowering of `'` In julia 1.0 `'` was lowered directly into a call to `Base.adjoint`. From 44cb8c21fe7b64ebbe561b4cef9e1a94b47cb751 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Mon, 11 Jan 2021 23:43:36 +0000 Subject: [PATCH 23/62] editting --- ...09-Julia-1.6-what-has-changed-since-1.0.md | 78 +++++++++---------- 1 file changed, 36 insertions(+), 42 deletions(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index 1e082891..ff896a2f 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -50,41 +50,38 @@ Personally, this change never affected me because I never write for-loops that a Indeed basically all I code I write is inside functions. But I do see how this causes problems for some interactive workflows, especially e.g. when demonstrating something. See the [main GitHub issue](https://github.com/JuliaLang/julia/issues/28789) and the longest [Discourse thread](https://discourse.julialang.org/t/another-possible-solution-to-the-global-scope-debacle/15894), though there were many others. -It took years of thinking to workout the solution, particularly because many of the more obvious solutions would be breaking in significant ways. +It took over a year of discussion to work out the solution, particularly because many of the more obvious solutions would be breaking in significant ways. -### Deprecations are muted by default. +### Deprecations are muted by default This was me in [this PR](https://github.com/JuliaLang/julia/pull/35362). -It's not something I am super-happy about. +It's not something I am super happy about. Though I think it makes sense. -Using deprecated methods is actually fine, as long as your dependencies follow SemVer, and you use `^` (i.e. default) bounding in your Project.toml's `[compat]`. -Which everyone does, because its enforced by the auto-merge script in the General registry. +Using deprecated methods is actually fine, as long as your dependencies follow SemVer, and you use `^` (i.e. default) bounding in your Project.toml's `[compat]`, which everyone does, because it's enforced by the auto-merge script in the General registry. Solving deprecations is a thing you should chose to do, actively. Not casually, when trying to do something else. In particular, when updating to support a new major release of one of your dependencies, you should run your (integration) tests to see if they succeed. -If they don't succeed, then one removed support for the new version and run julia with deprecation warnings turned on (or set to error); to identify and fix them. +If they don't succeed, then one removed support for the new version and run Julia with deprecation warnings turned on (or set to error); to identify and fix them. Probably also you are checking the release notes, since you are intentionally updating. The core of the reason is because it was actually breaking things. Irrelevant deprecation warning spam from dependencies of dependencies was causing JuMP and LightGraphs to become too slow to be used. Since they were from dependencies of dependencies the maintainers of JuMP and LightGraphs couldn’t even fix them, let alone the end users. -Deprecation warnings are still turned on by default in tests. -Which makes sense since the tests (unlike normal use), is being run by maintainers of the package itself, not its users. +Deprecation warnings are still turned on by default in tests, which makes sense since the tests (unlike normal use), are being run by maintainers of the package itself, not its users. Though it still doesn't make a huge amount of sense to me, since spam from deprecation warnings floods out your actual errors that you want to see during testing. -For this reason Invenia (my employer) has disabled deprecation warnings in the tests for all our closed source packages, and added a new test job that just does deprecation checking (set to error on deprecation and with the job allowed to fail, just so we have notice). +For this reason Invenia (my employer) has disabled deprecation warnings in the tests for all our closed source packages, and added a new test job that just does deprecation checking (set to error on deprecation and with the job allowed to fail, just so we are informed). Hopefully one day we can improve the tooling around deprecation warnings. An ideal behavour would be to only show deprecation warning if directly caused by a function call made from within the package module of your current active environment. -I kind of know what we need to do to the logger to make that possible. -But it is not yet something I have had time to do. +I kind of know what we need to do to the logger to make that possible, but it is not yet something I have had time to do. ### Code highlighting for `code_llvm` and `code_native` This was added in [#36634](https://github.com/JuliaLang/julia/issues/36634). -This functionality was first implemented in the [ColoredLLCodes.jl](https://github.com/kimikage/ColoredLLCodes.jl), where it worked by monkey-patching the InteractiveUtils stdlib. -That package does still work on julia 1.0. +This functionality was first implemented in [ColoredLLCodes.jl](https://github.com/kimikage/ColoredLLCodes.jl), where it worked by monkey-patching the InteractiveUtils stdlib. +That package does still work on Julia 1.0. **Julia 1.0:** @@ -96,15 +93,14 @@ That package does still work on julia 1.0. ![Julia 1.6 code_llvm]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.6-code-llvm.png) The REPL itself still doesn't have syntax highlighting for Julia code though. -The [OhMyRepl.jl](https://github.com/KristofferC/OhMyREPL.jl) package does provide that, and works all versions of Julia. -It is a lot more than a series of regex though, so I don't think we are going to see it built into julia too soon. -Probably one day, though, as its big dependencies are also required if the parser wants to move to be written in julia. -(Though I also don't expect that any time soon) +The [OhMyRepl](https://github.com/KristofferC/OhMyREPL.jl) package does provide that, and works all versions of Julia. +It is a lot more than a series of regexes though, so I don't think we are going to see it built into Julia too soon. +Probably one day, though, as its big dependencies are also required if the parser wants to move to be written in Julia (though I also don't expect that any time soon). ### Clearer Stacktraces Just like colored `code_llvm` colored stack-traces also originated in a package that was doing some nasty monkey-patching: [ClearStacktrace.jl](https://github.com/jkrumbiegel/ClearStacktrace.jl). -This was added into Base itself in [#36134](https://github.com/JuliaLang/julia/pull/36134) +This was added into Base itself in [#36134](https://github.com/JuliaLang/julia/pull/36134). **Julia 1.0:** @@ -115,7 +111,7 @@ This was added into Base itself in [#36134](https://github.com/JuliaLang/julia/p ![Julia 1.6 stacktrace]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.6-stacktrace.png) The first thing you probably notice is the colored package names, to make it more clear where the error is coming from. -You also should notice the dimming of type-parameters to make complicated types easier to read. +You also should notice the dimming of type parameters to make complicated types easier to read. Also the addition of argument names, and showing functions with keyword arguments as they are written, rather than as a weird `#DataFrame#654` internal function. The whole thing just looks more modern and polished. @@ -177,27 +173,25 @@ It is interesting to note that for the logging macros introduced in Julia 1.0, t E.g. `@info "message" foo bar` and `@info "message" foo=foo bar=bar` display the same, which has always felt natural. ### Lowering of `'` -In julia 1.0 `'` was lowered directly into a call to `Base.adjoint`. +In Julia 1.0 `'` was lowered directly into a call to `Base.adjoint`. This meant it was impossible to redefine what `'` meant in your current module. Now it lowers to a call to `var"'"`, which is something you can overload. -People often think it is cool to overload this to automatic differentiation so that `f'(x)` gives you the derivative of `f(x)` -Furthermore, it can now have unicode suffixes added to it, to define a new suffix operator. -Such as the one in `Base` which makes `A'ᵀ` give `transpose(A)` (rather than the adjoint/hermitian transpose that `A'` gives). +People often think it is cool to overload this to automatic differentiation so that `f'(x)` gives you the derivative of `f(x)`. +Furthermore, it can now have Unicode suffixes added to it, to define a new suffix operator. +For example, the one in `Base` which makes `A'ᵀ` give `transpose(A)` (rather than the adjoint/hermitian transpose which is returned by `A'`). ## Performance: References to the Heap from the Stack -This was promised in 2016 as a feature for 1.0 (released 2018). -But we actually didn't get it til 1.5 with [#33886](https://github.com/JuliaLang/julia/issues/33886). -Basically, the process of allocating memory from the heap is fairly slow*, where as allocating memory on the stack is basically a non-op. -Indeed Julia benchmarking tools don't count allocations on the stack as allocations at all. -One can find extensive write ups of heap vs stack allocations and how it works in general (though some mix the C specific factors with the CPU details.) -In julia all mutable objects live are allocated on the heap. -Until recently, immutable objects that contained references to heap allocated objects also had to live on the heap. -i.e. immutable objects with immutable fields (with immutable fields with...) could live on the stack. -But with this change now all immutable object can live on the stack, even if some of their fields live on the heap. +This was promised in 2016 as a feature for 1.0 (released 2018), but we actually didn't get it until 1.5 (released 2020) with [#33886](https://github.com/JuliaLang/julia/issues/33886). +In short, the process of allocating memory from the heap is fairly slow*, whereas allocating memory on the stack is basically a no-op. +Indeed, Julia benchmarking tools don't count allocations on the stack as allocations at all. +One can find extensive write ups of heap vs. stack allocations and how it works in general (though some mix the C specific factors with the CPU details). +In Julia all mutable objects live are allocated on the heap. +Until recently, immutable objects that contained references to heap allocated objects also had to live on the heap, i.e. only immutable objects with immutable fields (with immutable fields with...) could live on the stack. +But with this change, all immutable objects can live on the stack, even if some of their fields live on the heap. An important consequence of this is that wrapper types, such as the `SubArray` returned from `@view x[1:2]`, now have no overhead to create. I find that in practice this often adds up to a 10-30% speed-up in real world code. -(* Its actually really fast, but it is the kind of thing that rapidly adds up; and it is slow vs operations that can happen without touching RAM.) +(* It's actually really fast, but it is the kind of thing that rapidly adds up; and it is slow vs. operations that can happen without touching RAM.) ## Pkg stdlib and the General Registry @@ -209,7 +203,7 @@ However, some of the biggest changes have been in maturing out Pkg3, and its sur This story was beginning to be told around Julia 1.0 time, but it wasn't really complete nor built into Pkg until Julia 1.3. -You can read the documentation on [Artifacts](https://julialang.github.io/Pkg.jl/v1/artifacts/), [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl), and [Yggdasil](https://github.com/JuliaPackaging/Yggdrasil) for full details. +You can read the documentation on [Artifact](https://julialang.github.io/Pkg.jl/v1/artifacts/), [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl), and [Yggdasil](https://github.com/JuliaPackaging/Yggdrasil) for full details. This is the story about how julia works with binary dependencies. As of now, it works really well. @@ -218,7 +212,7 @@ Which is far from the truth, ain't noone got time for that. Julia has always had great foreign function interfacing (FFI); like that [`ccall`](https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/) (as well as [PyCall](https://github.com/JuliaPy/PyCall.jl/), [RCall](https://github.com/JuliaInterop/RCall.jl), [JavaCall](https://github.com/JuliaInterop/JavaCall.jl), and a bunch of others, for non-c-style binaries.) While FFI makes it easy to call binaries, how about actually getting them on your machine? -The standard pre-julia 1.0 was to run arbitrary code in the `deps/build.jl` file, which often used [BinDeps.jl](https://github.com/JuliaPackaging/BinDeps.jl/) to download compile things. +The standard pre-Julia 1.0 was to run arbitrary code in the `deps/build.jl` file, which often used [BinDeps.jl](https://github.com/JuliaPackaging/BinDeps.jl/) to download compile things. Around 1.0 this changed to having the `deps/build.jl` call [BinaryProvider.jl](https://github.com/JuliaPackaging/BinaryProvider.jl/) download a compiled binary built with [BinaryBuilder.jl](https://github.com/JuliaPackaging/BinaryBuilder.jl), and store that alongside the package code. In Julia 1.3+, the Artifacts system basically broad the BinaryProvider part into the package manager. Now the package manager controls the downloads, store them in a controlled location more massive duplications, and allows for full compat control. @@ -261,7 +255,7 @@ It actually works on a different mechanism to the docs. It relies on stacked environments, which is a feature julia has had since 1.0 via `LOAD_PATH`, but that is rarely used. -One advantage of this is that you can activate that Project.toml, on top of the your existing environment by adding the test directory to your `LOAD_PATH`. +One advantage of this is that you can activate that Project.toml, on top of the your existing environment by adding the test directory to your `LOAD_PATH`. I wrote some more details on exactly how to do that on [Discourse]( https://discourse.julialang.org/t/activating-test-dependencies/48121/7?u=oxinabox). It feels kind of hacky, because it is. @@ -271,7 +265,7 @@ At some point there might be a nicer user-interface for stacked environments lik ### Full Transition off METADATA.jl and REQUIRE files, and onto the General Registry Even though Julia 1.0 had Pkg3 and was supposed to use registries, for a long time the old Pkg2 [METADATA.jl](https://github.com/JuliaLang/METADATA.jl) pseudo-registry was used as the the canonical source of truth. Registering new releases was made against that, and then a script synchronized [General](https://github.com/JuliaRegistries/General) registry to match it. -This was to allow time for packages to change over to supporting julia 1.0, while still also making releases that supported julia 0.6. +This was to allow time for packages to change over to supporting Julia 1.0, while still also making releases that supported julia 0.6. It wasn't until about a year later that this state ended and the General registry became the one source of truth. This was kind of sucky, because a lot of the power of Pkg3 was blocked until then. @@ -341,7 +335,7 @@ Precompilation has been enhanced a lot. That is not the compilation that runs the first time a function is used in a session, but rather the stuff that runs the first time a package is loaded in an environment. For a start, the precompilation cache no longer goes stale every time you swap environments. -This was a massive pain in julia 1.0, especially if you worked on more than one thing at a time. +This was a massive pain in Julia 1.0, especially if you worked on more than one thing at a time. This was fixed in 1.3 to have multiple caches, for different environments. It's easy to forget this one, but it is actually one of the biggest usability enhancements since 1.0. @@ -480,7 +474,7 @@ So you want to be able to the regeneration of this code when that original metho Basically, end of the day is this allows code that uses [Cassette.jl](https://github.com/jrevels/Cassette.jl), and [IRTools.jl](https://github.com/MikeInnes/IRTools.jl) to not suffer from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. A particular case of this is for [Zygote](https://github.com/FluxML/Zygote.jl) where redefining a function called by the code that was being differentiated did not result in an updated gradient (unless `Zygote.refresh()`) was run. -This was annoying for +This was annoying for Other things that this allows is two very weird packages that [Nathan Daly](https://github.com/NHDaly) and I came up with at the JuliaCon 2018 hackathon: [StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) and [Tricks.jl](https://github.com/oxinabox/Tricks.jl/). [StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) relaxes the restrictions on normal `@generated` functions so that they are also safe from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. @@ -502,7 +496,7 @@ But the new threading stuff is fast, on the order of microseconds to send work o Even for uses of `@threads for` the improves have some wins. `IO` is now thread-safe; `ReentrantLock` was added and is the kind of standard lock that you expect to exist, that has notifications on waiting work etc; and a big one: `@threads for` can now be nested without things silently being wrong. -A lot of this actually landed in julia 1.2, but julia 1.3 was the release we think of as being for threading, as it gave us `Threads.@spawn`. +A lot of this actually landed in Julia 1.2, but Julia 1.3 was the release we think of as being for threading, as it gave us `Threads.@spawn`. Also in Julia 1.6 we now have `julia -t auto` to start julia with 1 thread per (logical) core. No more having to remember to set the `JULIA_NUM_THREADS` environment variable before starting it. @@ -622,12 +616,12 @@ I think for Julia 2.0 we should consider making it the default. Also added was a `sort` argument, which I don't see the point of so much, since `sort(readdir(x))` seems cleaner than `readdir(x; sort=true)`; and because I rarely rely on processing files in order. -`ccall` is now available as a macro `@ccall` which lets you specify +`ccall` is now available as a macro `@ccall` which lets you specify There was a [short juliacon talk about this](https://www.youtube.com/watch?v=wofq1DdXM3s) So now one can do `@ccall(sqrt(4.0::Cdouble)::Cdouble)` rather than `ccall(:sqrt, Cdouble, (Cdouble,), 4.0,)` This is what `ccall` always should have been. -In julia 1.7, [`invoke` is getting the same treatment.](https://github.com/JuliaLang/julia/pull/38438). +In Julia 1.7, [`invoke` is getting the same treatment.](https://github.com/JuliaLang/julia/pull/38438). ### More "Why didn't it always work that way" than I can count From 0f64fef5c06cdd3d77ec64c2f162cbaf781a81ce Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Mon, 11 Jan 2021 23:48:39 +0000 Subject: [PATCH 24/62] Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf --- _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index ff896a2f..a7fd65d0 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -196,7 +196,7 @@ I find that in practice this often adds up to a 10-30% speed-up in real world co ## Pkg stdlib and the General Registry Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the Pkg stdlib; and the general registry is more policy than software. -However, some of the biggest changes have been in maturing out Pkg3, and its surrounds. +However, some of the biggest changes have been in maturing out Pkg3, and its surroundings. ### BinaryBuilder, Artifacts, Yggdasil and jll packages. From 42fd8234e80bc6d057ab93798bb703b10f0cb260 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Mon, 11 Jan 2021 23:48:56 +0000 Subject: [PATCH 25/62] Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf --- _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index a7fd65d0..fdec163f 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -199,7 +199,7 @@ Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the However, some of the biggest changes have been in maturing out Pkg3, and its surroundings. -### BinaryBuilder, Artifacts, Yggdasil and jll packages. +### BinaryBuilder, Artifacts, Yggdasil and JLL packages This story was beginning to be told around Julia 1.0 time, but it wasn't really complete nor built into Pkg until Julia 1.3. From a5f37d913575bce900f478c7fd5d8bc9ac6b1b32 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Mon, 11 Jan 2021 23:49:23 +0000 Subject: [PATCH 26/62] Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf --- _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index fdec163f..57b8fa57 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -204,7 +204,7 @@ However, some of the biggest changes have been in maturing out Pkg3, and its sur This story was beginning to be told around Julia 1.0 time, but it wasn't really complete nor built into Pkg until Julia 1.3. You can read the documentation on [Artifact](https://julialang.github.io/Pkg.jl/v1/artifacts/), [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl), and [Yggdasil](https://github.com/JuliaPackaging/Yggdrasil) for full details. -This is the story about how julia works with binary dependencies. +This is the story about how Julia works with binary dependencies. As of now, it works really well. Sometimes people misunderstand the claims about julia solving the two language problem as saying everything should be rewritten in julia. From e9cd01667e820c9beb8c30635600df925461cfae Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Mon, 11 Jan 2021 23:49:40 +0000 Subject: [PATCH 27/62] Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf --- _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index 57b8fa57..1aebb6b9 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -207,7 +207,7 @@ You can read the documentation on [Artifact](https://julialang.github.io/Pkg.jl/ This is the story about how Julia works with binary dependencies. As of now, it works really well. -Sometimes people misunderstand the claims about julia solving the two language problem as saying everything should be rewritten in julia. +Sometimes people misunderstand the claims about Julia solving the two language problem as saying everything should be rewritten in Julia. Which is far from the truth, ain't noone got time for that. Julia has always had great foreign function interfacing (FFI); like that [`ccall`](https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/) (as well as [PyCall](https://github.com/JuliaPy/PyCall.jl/), [RCall](https://github.com/JuliaInterop/RCall.jl), [JavaCall](https://github.com/JuliaInterop/JavaCall.jl), and a bunch of others, for non-c-style binaries.) While FFI makes it easy to call binaries, how about actually getting them on your machine? From 1df14cbc513040169bba9c01aae09c8451e9ead7 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Mon, 11 Jan 2021 23:50:25 +0000 Subject: [PATCH 28/62] Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf --- _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index 1aebb6b9..3355107b 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -209,7 +209,7 @@ As of now, it works really well. Sometimes people misunderstand the claims about Julia solving the two language problem as saying everything should be rewritten in Julia. Which is far from the truth, ain't noone got time for that. -Julia has always had great foreign function interfacing (FFI); like that [`ccall`](https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/) (as well as [PyCall](https://github.com/JuliaPy/PyCall.jl/), [RCall](https://github.com/JuliaInterop/RCall.jl), [JavaCall](https://github.com/JuliaInterop/JavaCall.jl), and a bunch of others, for non-c-style binaries.) +Julia has always had great foreign function interfacing (FFI); like [`ccall`](https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/) (as well as [PyCall](https://github.com/JuliaPy/PyCall.jl/), [RCall](https://github.com/JuliaInterop/RCall.jl), [JavaCall](https://github.com/JuliaInterop/JavaCall.jl), and a bunch of others, for non-C-style binaries). While FFI makes it easy to call binaries, how about actually getting them on your machine? The standard pre-Julia 1.0 was to run arbitrary code in the `deps/build.jl` file, which often used [BinDeps.jl](https://github.com/JuliaPackaging/BinDeps.jl/) to download compile things. From 3c4e02549a2a947e501e2a78531e29312953e30e Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Mon, 11 Jan 2021 23:50:54 +0000 Subject: [PATCH 29/62] Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Chris de Graaf --- _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index 3355107b..0213d698 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -210,7 +210,7 @@ As of now, it works really well. Sometimes people misunderstand the claims about Julia solving the two language problem as saying everything should be rewritten in Julia. Which is far from the truth, ain't noone got time for that. Julia has always had great foreign function interfacing (FFI); like [`ccall`](https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/) (as well as [PyCall](https://github.com/JuliaPy/PyCall.jl/), [RCall](https://github.com/JuliaInterop/RCall.jl), [JavaCall](https://github.com/JuliaInterop/JavaCall.jl), and a bunch of others, for non-C-style binaries). -While FFI makes it easy to call binaries, how about actually getting them on your machine? +While FFI makes it easy to call binaries, how about actually getting them onto your machine? The standard pre-Julia 1.0 was to run arbitrary code in the `deps/build.jl` file, which often used [BinDeps.jl](https://github.com/JuliaPackaging/BinDeps.jl/) to download compile things. Around 1.0 this changed to having the `deps/build.jl` call [BinaryProvider.jl](https://github.com/JuliaPackaging/BinaryProvider.jl/) download a compiled binary built with [BinaryBuilder.jl](https://github.com/JuliaPackaging/BinaryBuilder.jl), and store that alongside the package code. From 577f0cfd4445f6956f41126c6c271d3701c8ac04 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Mon, 11 Jan 2021 23:57:36 +0000 Subject: [PATCH 30/62] editting --- ...-09-Julia-1.6-what-has-changed-since-1.0.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index 0213d698..7ce55770 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -212,31 +212,31 @@ Which is far from the truth, ain't noone got time for that. Julia has always had great foreign function interfacing (FFI); like [`ccall`](https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/) (as well as [PyCall](https://github.com/JuliaPy/PyCall.jl/), [RCall](https://github.com/JuliaInterop/RCall.jl), [JavaCall](https://github.com/JuliaInterop/JavaCall.jl), and a bunch of others, for non-C-style binaries). While FFI makes it easy to call binaries, how about actually getting them onto your machine? -The standard pre-Julia 1.0 was to run arbitrary code in the `deps/build.jl` file, which often used [BinDeps.jl](https://github.com/JuliaPackaging/BinDeps.jl/) to download compile things. -Around 1.0 this changed to having the `deps/build.jl` call [BinaryProvider.jl](https://github.com/JuliaPackaging/BinaryProvider.jl/) download a compiled binary built with [BinaryBuilder.jl](https://github.com/JuliaPackaging/BinaryBuilder.jl), and store that alongside the package code. -In Julia 1.3+, the Artifacts system basically broad the BinaryProvider part into the package manager. -Now the package manager controls the downloads, store them in a controlled location more massive duplications, and allows for full compat control. +Before Julia 1.0, the standard was to run arbitrary code in the `deps/build.jl` file, which often used [BinDeps.jl](https://github.com/JuliaPackaging/BinDeps.jl/) to download and compile things. +Around 1.0, this changed to having the `deps/build.jl` call [BinaryProvider.jl](https://github.com/JuliaPackaging/BinaryProvider.jl/) to download a compiled binary built with [BinaryBuilder.jl](https://github.com/JuliaPackaging/BinaryBuilder.jl), and store that alongside the package code. +In Julia 1.3+, the Artifacts system basically brought the BinaryProvider part into the package manager. +Now the package manager controls the downloads, stores them in a controlled location to avoid massive duplication, and allows for full compat control. No more running arbitrary code during installs. I feel it is worth really mentioning [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl) here. While it has existed since before 1.0, it's really grown. -It's a super smooth cross-compilation environment, that can used to build binaries for every platform julia runs on. +It's a super smooth cross-compilation environment, that can used to build binaries for every platform Julia runs on. This is an amazing tool, built on [containerization](https://en.wikipedia.org/wiki/OS-level_virtualization). It's the kind of build tool one dreamed on 10 years ago. -It's far more general than julia, I know people have at least experimented with using it with nim. +It's far more general than Julia, I know people have at least experimented with using it with Nim. I hope more things start using it. ### Temporary Environments Julia 1.5 added `pkg> activate --temp` which will create and activate a temporary environment. -This environment is deleted when Julia is exited. +This environment is deleted when Julia exists. This is incredibly handy for: - Reproducing an issue - - Answering questions on Stack-Overflow etc + - Answering questions on Stack Overflow etc - Checking the behavior of the last release of a package you currently have `dev`ed. - Quickly trying out an idea -Remembering that with Pkg3 installing a version of a package you have installed before is incredibly fast. +Remembering that with Pkg, installing a version of a package you have installed before is incredibly fast. Pkg doesn't download it again, it basically just points to the existing version on disk. So using `activate --temp` to quickly try something is indeed quick. From adc1288da4b7c074b02b89126e214e979d89183e Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Tue, 12 Jan 2021 00:25:12 +0000 Subject: [PATCH 31/62] Apply suggestions from code review Co-authored-by: Chris de Graaf --- ...09-Julia-1.6-what-has-changed-since-1.0.md | 107 +++++++++--------- 1 file changed, 53 insertions(+), 54 deletions(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index 7ce55770..71170d36 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -243,73 +243,72 @@ So using `activate --temp` to quickly try something is indeed quick. A more hacky use of it is after loading (via `using`) a package that you have `dev`'ed, you can `activate --temp` and install some of your test-time dependencies to reproduce a test failure without adding them to the main dependencies in the `Project.toml`. Though there is a better way if you use `test/Project.toml`. -### Test Dependencies in own Project.toml -In [Julia 1.0 and 1.1, test specific dependencies are listed in the `[extras]` section](https://julialang.github.io/Pkg.jl/v1/creating-packages/#Test-specific-dependencies-in-Julia-1.0-and-1.1), and under `[targets] test=[...]`; with its compatibility listed in the main `[compat]`. -It seemed like this might be in the future be extended for other things, perhaps documentation. -But it was found for documentation in particular that a seperate Project.toml worked well. -As long as you `dev ..` into its `Manifest.toml` so it uses the right version of the package it is documenting. +### Test Dependencies in their own Project.toml +In Julia 1.0 and 1.1, [test specific dependencies are listed in the `[extras]` section](https://julialang.github.io/Pkg.jl/v1/creating-packages/#Test-specific-dependencies-in-Julia-1.0-and-1.1), and under `[targets] test=[...]`; with their compatibility listed in the main `[compat]`. +It seemed like this might be extended for other things in the future, perhaps documentation. +But it was found for documentation in particular that a separate `Project.toml` worked well (as long as you `dev ..` into its `Manifest.toml` so it uses the right version of the package it is documenting). -The new `tests/Project.toml` extends that idea. +The new `test/Project.toml` extends that idea. One part of that extension is to remove the need to `dev ..`, and other part is to make available all the main dependencies. -It actually works on a different mechanism to the docs. -It relies on stacked environments, which is a feature julia has had since 1.0 via `LOAD_PATH`, but that is rarely used. +It actually works on a different mechanism than the docs. +It relies on stacked environments, which is a feature Julia has had since 1.0 via `LOAD_PATH`, but that is rarely used. One advantage of this is that you can activate that Project.toml, on top of the your existing environment by adding the test directory to your `LOAD_PATH`. I wrote some more details on exactly how to do that on [Discourse]( https://discourse.julialang.org/t/activating-test-dependencies/48121/7?u=oxinabox). It feels kind of hacky, because it is. -At some point there might be a nicer user-interface for stacked environments like this. +At some point there might be a nicer user interface for stacked environments like this. ### Full Transition off METADATA.jl and REQUIRE files, and onto the General Registry Even though Julia 1.0 had Pkg3 and was supposed to use registries, for a long time the old Pkg2 [METADATA.jl](https://github.com/JuliaLang/METADATA.jl) pseudo-registry was used as the the canonical source of truth. -Registering new releases was made against that, and then a script synchronized [General](https://github.com/JuliaRegistries/General) registry to match it. +Registering new releases was made against that, and then a script synchronized the [General](https://github.com/JuliaRegistries/General) registry to match it. This was to allow time for packages to change over to supporting Julia 1.0, while still also making releases that supported julia 0.6. It wasn't until about a year later that this state ended and the General registry became the one source of truth. This was kind of sucky, because a lot of the power of Pkg3 was blocked until then. -In particular, since the registries compat was generated from the REQUIRE file, and the REQUIRE files had kind of [gross requirement specification](https://docs.julialang.org/en/v0.6/manual/packages/#Requirements-Specification-1), and everyone basically just lower bounded things, or didn't bound compat at all. -Which made sense because with the only 1 global environment that Pkg2 had, you basically needed the whole ecosystem to be compatible, so restricting was bad. -But Pkg3/Project.toml has the much better default [`^` bounded]](https://julialang.github.io/Pkg.jl/v1/compatibility/#Version-specifier-format-1) to accept only non-breaking changes by SemVer. -(And per project environments, so things don't all have to be compatible -- just things used in a particular project (rather than every project you might ever do). +In particular, since the registries compat was generated from the REQUIRE file, and the REQUIRE files had kind of [gross requirement specification](https://docs.julialang.org/en/v0.6/manual/packages/#Requirements-Specification-1), and everyone basically just lower-bounded things, or didn't bound compat at all. +Which made sense because with the single global environment that Pkg2 had, you basically needed the whole ecosystem to be compatible, so restricting was bad. +But Pkg3/Project.toml has the much better default [`^`-bounds](https://julialang.github.io/Pkg.jl/v1/compatibility/#Version-specifier-format-1) to accept only non-breaking changes by SemVer. +And per project environments, things don't all have to be compatible -- just things used in a particular project (rather than every project you might ever do). #### Automatic Merging -Initially after the change over all PRs to make a new release needed manual review, by one of the very small number of General registry maintainers. +Initially after the transition, all registry PRs to make a new release needed manual review by one of the very small number of General registry maintainers. I am a big fan of [releasing after every non-breaking PR](https://www.oxinabox.net/2019/09/28/Continuous-Delivery-For-Julia-Packages.html). Why would you not want those bug-fixes and new features released? It is especially rude to contributors who will make a fix or a feature, but then you don't let them use it because you didn't tag a release. Plus it makes tracking down bugs easier, if it occurs on a precise version you know the PR that caused it. -But with manual merging it feels bad, to tag 5 releases in a day. -Now they we have automatic merging it is fine. -At time of writing [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl/) was up to 67 releases. +But with manual merging it feels bad to tag 5 releases in a day. +Now we have automatic merging, so it is fine. +At time of writing, [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl/) was up to 67 releases. The big advantage of automatic merging is that it comes with automatic enforcement of standards. -In order to be automerge-able some good standards have to be followed. -One of which is that that no unbounded compat specifiers (e..g `>=1.0`) are permitted; and that everything must have a compat specifier (since unspecified is same as `>=0.0.0`) -That one is particularly great, since if one adds specifiers later that can't be met, then it can trigger downgrades back to the incredible old versions that didn't specify compat and that almost certainly are not compatible. +In order to be auto-mergeable, some good standards have to be followed. +One of which is that that no unbounded compat specifiers (e.g. `>=1.0`) are permitted; and that everything must have a compat specifier (since unspecified is same as `>=0.0.0`) +That one is particularly great, since if one adds specifiers later that can't be met, then it can trigger downgrades back to incredibly old versions that didn't specify compat and that almost certainly are not compatible. -To deal with that particular case retro-capping was done to retroactively add bounds to all things that didn't have them. -Which was painful when it was done, since it rewrote compat in the registry, which made it disagree with the `Project.toml`, which is always confusing. -Now that it is done, it is good. +To deal with that particular case, retro-capping was done to retroactively add bounds to all things that didn't have them. +This was painful when it was done, since it rewrote compat in the registry, which made it disagree with the `Project.toml` in package repositories, which is always confusing. +But now that it is done, it is good. #### Requirement to have a `Project.toml` Finally the ability to `pkg> dev` packages that had only a REQUIRE and no `Project.toml` was removed in Julia 1.4. -You can still `pkg> add` them, if they were registered during the transition period. -But to edit the projects they must a `Project.toml` file, which is fine since all the tools to make registering releases also require you to have `Project.toml` now +You can still `pkg> add` them if they were registered during the transition period. +But to edit the projects they must a `Project.toml` file, which is fine since all the tools to register releases also require you to have `Project.toml` now. ### Resolver willing to downgrade packages to install new ones (Tiered Resolution) -Until the [tiered resolver](https://github.com/JuliaLang/Pkg.jl/pull/1330) was added Julia would not change the version of any currently installed package in order to install a new one. -For example consider +Until the [tiered resolver](https://github.com/JuliaLang/Pkg.jl/pull/1330) was added, Julia would not change the version of any currently installed package in order to install a new one. +For example, consider: - **Foo.jl** - v1 - v2 - - **Bar** + - **Bar.jl** - v1 compatible with Foo v1 - v2 compatible with Foo v2 - **Qux.jl** @@ -319,15 +318,14 @@ If you did `pkg"add Foo Bar Qux"` you would end up with **Foo** v1, **Bar** v1, But if you did first: `pkg"add Foo Bar"` (which would install **Foo** v2, and **Bar** v2), and then did `pkg"add Qux"`, then on Julia 1.0 you would get an error, as it would refuse to downgrade **Foo** and **Bar** to v1, as is required to allow **Qux** to be installed. -This meant effectly the package manager is stateful, which turns out is really counter intuitive. +This meant that the package manager is effectively stateful, which turns out to be really counter intuitive. The way to resolve this in practice was to delete the `Manifest.toml` and do it again as a single action. -This was a significant problem for [test time dependencies](https://github.com/JuliaLang/Pkg.jl/issues/1352), which if you had a test time dependencies with a indirect dependencies shared with a main dependency of the package, but that was only compatible with an older version of the indirect depenency, you would be unable to run tests as resolving the test time depenency would fail. +This was a significant problem for [test-time dependencies](https://github.com/JuliaLang/Pkg.jl/issues/1352), where if you had a test-time dependency with an indirect dependency shared with a main dependency of the package, but that was only compatible with an older version of the indirect dependency, you would be unable to run tests as resolving the test-time dependency would fail. -With the new tiered resolved it will try a number of tiers of relaxing existing versions. +With the new tiered resolved, it will try a number of methods to relax existing versions. It still wants to avoid changing the versions of currently installed packages if possible, but if that is required then it will do so. -It will in turn first attempt to install without any currently installed package being changed, then will try relaximng the constraint to allow changing the version of indirect dependencies, then to allowing changing the versions of direct dependencies. -This ends up far more intuitive: if there is a compatible set of package versions then they will be found. -Regardless of if new packages are added all at once or one at a time. +It will first attempt to install without any currently installed package versions being changed, then will try relaxing the constraints to allow changing the versions of indirect dependencies, then to allowing changing the versions of direct dependencies. +This ends up being far more intuitive: if there is a compatible set of package versions then they will be found, regardless of whether new packages are added all at once or one at a time. ### Precompilation @@ -336,15 +334,14 @@ That is not the compilation that runs the first time a function is used in a ses For a start, the precompilation cache no longer goes stale every time you swap environments. This was a massive pain in Julia 1.0, especially if you worked on more than one thing at a time. -This was fixed in 1.3 to have multiple caches, for different environments. +This was fixed in 1.3 to have a separate cache for each environment. It's easy to forget this one, but it is actually one of the biggest usability enhancements since 1.0. -More dramatically, and less easy to overlook, is the parallelism of precompilation added in 1.6. +More dramatic, and less easy to overlook, is the parallelism of precompilation added in 1.6. Precompiling a package requires precompiling all its dependencies first. -This is now done in parallel, and is automatically triggered when you complete Pkg operations. -In contrast, to happening in series the first time a package is loaded. -No more waiting 5 minutes the first time you load an package with a lot of dependencies. -Further the spiffy animated output shows you what is precompiling at a given time; and a progress bar, which makes it feel (even) faster. +This is now done in parallel, and is automatically triggered when you complete Pkg operations, in contrast to happening serially the first time a package is loaded. +No more waiting 5 minutes the first time you load a package with a lot of dependencies. +Further, the spiffy animated output shows you what is precompiling at any given time, as well as a progress bar, which makes it feel (even) faster. @@ -365,24 +362,26 @@ No more giant red wall of numbers. Colors were added to make it easier to see which version numbers are referring to which package. -There is a bit of a problem that it isn't easy to make sure the colors don't get reused, as there are not many in the 16 color list (especially when you skip a few like black, white and error red). -Due to how Pkg constructs its error messages basically every package in the dependency graph gets some message prepared for it, but not displayed, so just assigning them a color in turn gets it to loop around so doesn't reduce change of colors being reused. -There is away to fix that but it is a big change to add structured log messages that are colored only once they are displayed. -We decided after much debate to assign things colors based on the hash of the package name and shortened UUID. -This means things have consistent colors if you fix one and redo it a still have some remaining. -I think this is going to be a subtle improvement on easy of use. +There is a bit of a problem in that it isn't easy to make sure the colors don't get reused, as there are not many in the 16 color list (especially when you skip a few like black, white and error red). +Due to how Pkg constructs its error messages, basically every package in the dependency graph gets some message prepared for it, but not displayed, so just assigning them a color in turn gets it to loop around which still results in colors being reused. +There is a way to fix that, but it is a big change to add structured log messages that are colored only once when they are displayed. +We decided after much debate to assign colors based on the hash of the package name and shortened UUID. +This means things have consistent colors for any packages still listed, even after you have resolves some conflicts. +I think this is going to be a subtle improvement on ease of use. As a cool hack, you can actually change the color list used. To get the much larger color list I wanted to use originally you can do: -`append!(Pkg.Resolve.CONFLICT_COLORS, [21:51; 55:119; 124:142; 160:184; 196:220])` +```julia +append!(Pkg.Resolve.CONFLICT_COLORS, [21:51; 55:119; 124:142; 160:184; 196:220]) +``` The other change was consecutive lists of version numbers were compressed to continuous ranges. -In 1.6 these ranges are split only if there is a version that exists between them that is not compatible. -So normally just a single range, since compatibility is typically monotonic. -In contrast in 1.0, they were split if there was a potential version that could exist that is not compatible (even if that version doesn't currently exist). -This mean they split every time the right most nonzero was incremented. -For something with a lot of pre-1.0 versions that is a lot of numbers. -I think the new version is much cleaner and easier to read. +In 1.6, these ranges are split only if there is a version that exists between them that is not compatible. +So normally we get just a single range, since compatibility is typically monotonic. +In contrast, in 1.0, they were split if there was a potential version that could exist that is not compatible (even if that version doesn't currently exist). +This means they split every time the rightmost nonzero part of the version number was incremented. +For something with a lot of pre-1.0 versions, that is a lot of numbers. +I think the new method is much cleaner and easier to read. ## Time To First Plot From bf9444854905b12fe464067448d818ea3b82ebb2 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Tue, 12 Jan 2021 00:41:23 +0000 Subject: [PATCH 32/62] Apply suggestions from code review Co-authored-by: Chris de Graaf --- ...09-Julia-1.6-what-has-changed-since-1.0.md | 157 +++++++++--------- 1 file changed, 78 insertions(+), 79 deletions(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index 71170d36..b5165720 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -385,18 +385,18 @@ I think the new method is much cleaner and easier to read. ## Time To First Plot -People often complain about the "Time To First Plot" (TTFP) in julia. -I personally have never minded it -- by the time iI am plotting something I have done minutes of thinking so 10 seconds of compilation is nothing. -Plotting turns out is basically a really hard thing for a compiler. -It is many many small methods, most of which are only called once. -And unlike most julia code it doesn't actually benefit all that much from most of the work julia's JIT is normally doing to specialize things -- plotting itself isn't in the hot-loop. -To a long-story short, plotting the postcard example for julia needing to compile things before it can run them. - -As a really hacky way to time this, the following timing. -You can see did `@time @time ...` and two number were displayed. +People often complain about the "Time To First Plot" (TTFP) in Julia. +I personally have never minded it -- by the time I am plotting something, I have done minutes of thinking so 10 seconds of compilation is nothing. +Plotting, it turns out, is basically a really hard thing for a compiler. +It is many, many, small methods, most of which are only called once. +And unlike most Julia code, it doesn't actually benefit all that much from most of the work Julia's JIT is normally doing to specialize things -- plotting itself isn't in the hot-loop. +To make a long-story short, plotting is the postcard example for Julia needing to compile things before it can run them. + +As a really hacky way to time this, consider the following timing. +You can see I did `@time @time ...` and two numbers were displayed. The first number is how long from the start of the command until the `using Plots` and `plot(...)` command finished. The second is that, plus how long it took before it could show the output of the first. -It's worth looking at this later because it turns on invalidations (see below) made the REPL have to recompile a bunch of display code. +It's worth looking at this later because it turns out that invalidations (see below) made the REPL have to recompile a bunch of display code. **Julia 1.0:** ```julia @@ -412,115 +412,114 @@ julia> @time @time using Plots; plot(1:0.1:10, sin.(1:0.1:10)) ``` So it's a bit better than 2 times faster on my machine. -Note also that in the above timing I had already run precompilation. -Which seems fairest. +Note also that in the above timing I had already run precompilation, which seems fairest. Though see below, precompilation caches often would be deleted in 1.0; and conversely are created much faster in 1.6 due to parallelization. ### Invalidations Consider a function `foo` with a method `foo(::Number)`. -If some other function calls it, for example `bar(x::Int) = 2*foo(x)`, the JIT will compile in an instruction for exactly the method instance to call (assuming type-inference work) -- a fast static dispatch, possibly even inlined. -If the user then defines a new more specific method `foo(::Int)`, then the compiled code for `Bar`, need to be invalidated so it will call the new one. -It needs to be recompiled -- which means anything that static dispatches to it needs to be recompiled and so forth. +If some other function calls it, for example `bar(x::Int) = 2*foo(x)`, the JIT will compile in an instruction for exactly the method instance to call (assuming type inference works) -- a fast static dispatch, possibly even inlined. +If the user then defines a new, more specific method `foo(::Int)`, then the compiled code for `Bar` need to be invalidated so it will call the new one. +It needs to be recompiled -- which means anything that statically dispatches to it needs to be recompiled, and so forth. This is an invalidation. It's an important feature of the language. It is key to extensibility. -It doesn't normally cause too many problems. -Since generally basically everything is defined before anything is called, and thus before anything is compiled. +It doesn't normally cause too many problems, since generally, basically everything is defined before anything is called, and thus before anything is compiled. A notable exception to this is Base an the other standard libraries. These are compiled into the so-called system image. -Further-more, methods in these standard libraries are some of the most overloaded, thus most likely to have it triggered. +Furthermore, methods in these standard libraries are some of the most overloaded, thus most likely to be invalidated. A bunch of work has gone into dealing with invalidations better. Not just point-fixes to remove calls that were likely to be invalidated, but several changes to the compiler. -One particular change was not triggering cascading invalidations for methods that couldn't actually be called do the being ambiguous. -As a result a lot of user code doesn't trigger invalidations on 1.6, that did on 1.5. +One particular change was not triggering cascading invalidations for methods that couldn't actually be called due the being ambiguous. +As a result, a lot of user code that triggered invalidations on 1.5 no longer does so on 1.6. The end result of this is faster compilation after loading packages, since it doesn't have to recompile a ton of invalidated method instances. i.e. decreased time to first plot. -In this has had huge effect on [Revise.jl](https://github.com/timholy/Revise.jl) -which started to take a second or so to load when it gained the dependency on [JuliaInterpretter.jl](https://github.com/JuliaDebug/JuliaInterpreter.jl); which isn't much, but when you do it every time you start julia it is an annoying lack of snappyness. -But thanks to this work, JuliaInterpretter, and thus Revise now load in a flash. +This has had a huge effect on [Revise.jl](https://github.com/timholy/Revise.jl) +which started to take a second or so to load when it gained the dependency on [JuliaInterpreter.jl](https://github.com/JuliaDebug/JuliaInterpreter.jl); which isn't much, but when you do it every time you start Julia it is an annoying lack of snappiness. +But thanks to this work, JuliaInterpreter, and thus Revise, now load in a flash. -A full discussion on the invalidations work can be found in [this blog-post](https://julialang.org/blog/2020/08/invalidations/). +A full discussion on the invalidations work can be found in [this blog post](https://julialang.org/blog/2020/08/invalidations/). -### Per Module Optimization Flags +### Per-Module Optimization Flags >The compiler optimization level can now be set per-module using the experimental macro Base.Experimental.@optlevel n. For code that is not performance-critical, setting this to 0 or 1 can provide significant latency improvements ([#34896](https://github.com/JuliaLang/julia/issues/34896)). > Compilation and type inference can now be enabled or disabled at the module level using the experimental macro Base.Experimental.@compiler_options ([#37041](https://github.com/JuliaLang/julia/issues/37041)). -Recall I said most of julia compilation isn't even that useful for plotting, since making it run fast isn't a priority (but loading fast is) +Recall that I said most of Julia compilation isn't even that useful for plotting, since making it run fast isn't a priority (but loading fast is). `@optlevel` only controls which LLVM optimization passes run; which is right at the end of the compilation pipeline. -Turning off compilation and type inference on the other-hand turns off a ton more. +Turning off compilation and type inference on the other hand turns off a ton more. ## Internals ### Manually Created Back-edges for Lowered Code Generated Functions This is a very niche and not really at all user-facing feature. To understand why this matters, it's worth understanding how Cassette works. -I wrote [blog-post on this a few years ago](https://invenia.github.io/blog/2019/10/30/julialang-features-part-1#making-cassette). +I wrote a [blog post on this a few years ago](https://invenia.github.io/blog/2019/10/30/julialang-features-part-1#making-cassette). -In quiet the opposite, Julia 1.3 allowed for the creation of more invalidations though allowing back-edges to be manually attached to the `CodeInfo` for `@generated` functions that return lowered code. -Backedges are the connection from methods back to each method instance that calls them. +In quite the opposite, Julia 1.3 allowed for the creation of more invalidations through allowing back-edges to be manually attached to the `CodeInfo` for `@generated` functions that return lowered code. +Back-edges are the connections from methods back to each method instance that calls them. This is what allows invalidations to work, as when a method is redefined, it needs to know what things to recompile. This change allowed those back-edges to be manually specified for `@generated` functions that were working at the lowered code level. -Which is useful since this technique is primarily used for generating code based on the (lowered) code of existing methods. -For example in [Zygote](https://github.com/FluxML/Zygote.jl), generating the gradient code, from the code of the primal method. -So you want to be able to the regeneration of this code when that original method changes. +This is useful since this technique is primarily used for generating code based on the (lowered) code of existing methods. +For example, in [Zygote](https://github.com/FluxML/Zygote.jl), generating the gradient code from the code of the primal method. +So you want to be able to trigger the regeneration of this code when that original method changes. -Basically, end of the day is this allows code that uses [Cassette.jl](https://github.com/jrevels/Cassette.jl), and [IRTools.jl](https://github.com/MikeInnes/IRTools.jl) to not suffer from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. -A particular case of this is for [Zygote](https://github.com/FluxML/Zygote.jl) where redefining a function called by the code that was being differentiated did not result in an updated gradient (unless `Zygote.refresh()`) was run. +Basically, this allows code that uses [Cassette.jl](https://github.com/JuliaLabs/Cassette.jl), and [IRTools.jl](https://github.com/MikeInnes/IRTools.jl) to not suffer from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. +A particular case of this is for [Zygote](https://github.com/FluxML/Zygote.jl), where redefining a function called by the code that was being differentiated did not result in an updated gradient (unless `Zygote.refresh()`) was run. This was annoying for -Other things that this allows is two very weird packages that [Nathan Daly](https://github.com/NHDaly) and I came up with at the JuliaCon 2018 hackathon: [StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) and [Tricks.jl](https://github.com/oxinabox/Tricks.jl/). +Other things that this allows are two very weird packages that [Nathan Daly](https://github.com/NHDaly) and I came up with at the JuliaCon 2018 hackathon: [StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) and [Tricks.jl](https://github.com/oxinabox/Tricks.jl/). [StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) relaxes the restrictions on normal `@generated` functions so that they are also safe from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. -[Tricks.jl](https://github.com/oxinabox/Tricks.jl/) uses this feature to make `hasmethod` etc resolve at compile-time, and then get updated if and when new methods are defined. -Which can allow for defing traits like _"anything that defines a `iterate` method"_. +[Tricks.jl](https://github.com/oxinabox/Tricks.jl/) uses this feature to make `hasmethod`, etc. resolve at compile-time, and then get updated if and when new methods are defined. +This can allow for defining traits like _"anything that defines a `iterate` method"_. ## Base and Standard Libraries ### Threading Julia has full support for threading now. -Not just the limited `@threads for` loops, but full GoLang style threads. +Not just the limited `@threads for` loops, but full Go-style threads. They are tightly integrated with the existing Async/Task/Coroutine system. -In effect threading works by unsetting the sticky flag on a Task, so that it is allowed to run on any thread. +In effect, threading works by unsetting the sticky flag on a Task, so that it is allowed to run on any thread. This is normally done via the `Threads.@spawn` macro, rather than the `@async` macro. Interestingly, the `@threads for` macro still remains, and doesn't actually use much of the new machinery. It still uses the old way which is a bit tighter if the loop durations are almost identical. But the new threading stuff is fast, on the order of microseconds to send work off to another thread. -Even for uses of `@threads for` the improves have some wins. -`IO` is now thread-safe; `ReentrantLock` was added and is the kind of standard lock that you expect to exist, that has notifications on waiting work etc; and a big one: `@threads for` can now be nested without things silently being wrong. +Even for uses of `@threads for`, we get some wins from the improvements. +`IO` is now thread-safe; `ReentrantLock` was added and is the kind of standard lock that you expect to exist. +It has notifications on waiting work, etc.; and a big one: `@threads for` can now be nested without things silently being wrong. A lot of this actually landed in Julia 1.2, but Julia 1.3 was the release we think of as being for threading, as it gave us `Threads.@spawn`. -Also in Julia 1.6 we now have `julia -t auto` to start julia with 1 thread per (logical) core. +Also, in Julia 1.6, we now have `julia -t auto` to start Julia with 1 thread per (logical) core. No more having to remember to set the `JULIA_NUM_THREADS` environment variable before starting it. ### 5-arg `mul!`, aka in-place generalized multiplication and addition The 5 arg `mul!(C, A, B, α, β)` performs the operation equivalent to: `C .= A*B*α + C*β`, where `α`, `β` are scalars and `A`, `B` and `C` are compatible mixes of scalars, matrices and vectors. -I am still the opinion that it should have been called `muladd!`, but this is the human friendly version of `BLAS.gemm!` (i.e. GEneralized Matrix Multiplication) and it's ilk. -It promises to alway compute the in-place mul-add in the most efficient, correct, way for any `AbstractArray` subtype. -In contrast, `BLAS.gemm` computes the same thing, but with a bunch of conditions. -It must be a strided array containing only BLAS-scalars, and if one of the inputs is conjugated/transposed you need to input it in non-conjugated/transposed form, and then tell `BLAS.gemm!` via passing in `'C'` or `T` rather than `N`. +I am still of the opinion that it should have been called `muladd!`, but this is the human-friendly version of `BLAS.gemm!` (i.e. GEneralized Matrix Multiplication) and it's ilk. +It promises to alway compute the in-place mul-add in the most efficient, correct way for any `AbstractArray` subtype. +In contrast, `BLAS.gemm!` computes the same thing, but with a bunch of conditions. +It must be a strided array containing only BLAS scalars, and if one of the inputs is conjugated/transposed you need to input it in non-conjugated/transposed form, and then tell `BLAS.gemm!` via passing in `'C'` or `T` rather than `N`. 5-arg `mul!` takes care of all that for you dispatching to `BLAS.gemm!` or other suitable methods once it has that all sorted. Further, the existing 3-arg `mul!(C, A, B)` is a special case of it: `mul!(C, A, B) = mul!(C, A, B, false, true)` (`true` and `false` being 1, and strong 0). -So in general on can just implement the 5-arg form and be done with it. +So in general, one can just implement the 5-arg form and be done with it. I personally didn't care about 5-arg `mul!` at all for a long time. -Yet another in-place function in `LinearAlgebra` that I would never use often enough to remember what it did, and thus wouldn't use it. +It was yet another in-place function in `LinearAlgebra` that I would never use often enough to remember what it did, and thus wouldn't use. But I realized that it is a crucial function for my own area: automatic differentiation. `mul(C, A, B, true, true)` is the in-place accumulation rule for the reverse mode equivalent of the product rule. ### You can now print and interpolate `nothing` into strings. -This is one of mine [#32148](https://github.com/JuliaLang/julia/pull/32148)., and I find it is such a usability enhancement. -So many small frustrations in Julia 1.0 relating to interpolate a variable containing `nothing` into a string. +This is one of mine [#32148](https://github.com/JuliaLang/julia/pull/32148), and I find it is such a usability enhancement. +So many small frustrations in Julia 1.0 related to interpolating a variable containing `nothing` into a string. Often occurring when you are adding a quick `println` to debug something not being the value you expected; or when building a string for some other error message. Arguably both of those are better done via other means (`@show`, and values stored in fields in the error type); but we don't always do what is best. Sometimes it is expedient to just interpolate things into strings without worrying about what type they are. @@ -528,16 +527,16 @@ Sometimes it is expedient to just interpolate things into strings without worryi ### `Base.download` now using libcurl For a very long time the `download` function which retrieves things over HTTP, was implemented with an amazing hack. It conditionally shelled out to different programs. -On windows it run a mildly scary powershell script. -On Unixen it first tried to use `curl` then if that wasn't installed it tried to use `wget` and then if that wasn't installed it tried to use `fetch`. +On Windows it ran a mildly scary PowerShell script. +On Unixen it first tried to use `curl`, then if that wasn't installed it tried to use `wget`, and then if that wasn't installed it tried to use `fetch`. It's low-key amazing that this worked as well as it did -- very few complaints. -But as of 1.6 [it now uses libcurl](https://github.com/JuliaLang/julia/pull/37340). +But as of 1.6, [it now uses libcurl](https://github.com/JuliaLang/julia/pull/37340). Using libcurl everywhere gives consistency with proxy settings, and protocol support (beyond HTTP) across all platforms. -It also has more extensive API via the new [Downloads.jl](https://github.com/JuliaLang/Downloads.jl) standard library. +It also has a more extensive API via the new [Downloads.jl](https://github.com/JuliaLang/Downloads.jl) standard library. It can do things like progress logging, and it can retrieve headers. -I have [tried getting headers via conditional different commandline download functions](https://github.com/oxinabox/DataDeps.jl/pull/22) before, it is low-key nightmare fuel; and I ended up swapping out the HTTP.jl for that. -It wouldn't be too surprising if eventually we see libcurl swapped out for code extracted from HTTP.jl for a pure julia solution. +I have [tried getting headers via conditional different command-line download functions](https://github.com/oxinabox/DataDeps.jl/pull/22) before, it is low-key nightmare fuel; and I ended up swapping out to HTTP.jl for that. +It wouldn't be too surprising if eventually we see libcurl swapped out for code extracted from HTTP.jl for a pure Julia solution. HTTP.jl works wonderfully to this, but I suspect untangling the client from the server is just a bit annoying right now, particularly as it is still evolving its API. @@ -547,31 +546,31 @@ allows packages to define extra information to be shown along with a particular A really cool use of it was proposed on [Discourse](https://discourse.julialang.org/t/enforce-interface-implementation/52872/15?u=oxinabox): you could add an error hint to `MethodError` that says to check that an interface has been implemented correctly. This has the advantage of pointing the user in what is most likely the right direction. -But it doesn't have the problem of really confounding them if you are wrong, since the original `MethodError` information is still shown (See my earlier [blog-post on the `NotImplementedException` antipattern](https://www.oxinabox.net/2020/04/19/Julia-Antipatterns.html#notimplemented-exceptions)). +But it doesn't have the problem of really confounding them if you are wrong, since the original `MethodError` information is still shown (See my earlier [blog post on the `NotImplementedException` antipattern](https://www.oxinabox.net/2020/04/19/Julia-Antipatterns.html#notimplemented-exceptions)). Right now this is not used anywhere in the standard libraries. At first, I thought that made sense since they can just edit the original `show_error`. -But now I think since it can be used for a subset of a particular type of exception it could well be useful for some of p`Base`'s interfaces](https://docs.julialang.org/en/v1/manual/interfaces/) exactly as described above. -According to [JuliaHub search](https://juliahub.com/ui/CodeSearch?q=register_error_hint&u=all&t=all) right now there is just one package using. +But now I think since it can be used for a subset of a particular type of exception it could well be useful for some of [`Base`'s interfaces](https://docs.julialang.org/en/v1/manual/interfaces/) exactly as described above. +According to [JuliaHub search](https://juliahub.com/ui/CodeSearch?q=register_error_hint&u=all&t=all), right now there is just one package using it. ColorTypes.jl is [using it to explain some _consensual type-piracy_](https://github.com/JuliaGraphics/ColorTypes.jl/blob/bd31741d162361ebd44ed05ae532266998d9ce9f/src/error_hints.jl), where for some functions on its types you need to load another package. This is an experimental features, so it's not covered by the SemVer guarantees of the rest of the language. -Still it's neat, I don't expect it to go away, +Still it's neat, and I don't expect it to go away, though I also don't expect it to graduate from experimental status until a bunch of people are using it. -Which they will probably do as people start dropping support for older julia versions. +Which they will probably do as people start dropping support for older Julia versions. ### A bunch of curried functions Julia seems to have ended up with a convention of providing curried methods of functions if that would be useful as the first argument for `filter`. -e.g. `filter(isequal(2), [1,2,3,2,1])`, is the same is `filter(x->isequal(x, 2), [1,2,3,2,1]`). -In particular these are boolean comparison like functions: +For example, `filter(isequal(2), [1,2,3,2,1])` is the same is `filter(x->isequal(x, 2), [1,2,3,2,1])`. +In particular these are boolean comparison-like functions: with two arguments, where the thing being compared against is the second. Julia 1.0 had `isequal`, `==` and `in`. Since then we have added: -`isapprox`, `<`,`<=`,`>`, `>=`, `!=`, `startswith`, `endswith`, and `contains`. -(I added the last 3 😁) +`isapprox`, `<`,`<=`,`>`, `>=`, `!=`, `startswith`, `endswith`, and `contains` (I added the last 3 😁). -Aside: `contains` is argument flipped `occursin`, it was a thing in julia 0.6 but was removed in 1.0 and now has been added back. +Aside: `contains` is argument-flipped `occursin`. +It was a thing in Julia 0.6, but was removed in 1.0 and now has been added back. We added it back primarily so we could have the curried form, and to match `startswith` and `endswith`. ### A ton of other new and improved standard library functions @@ -581,22 +580,22 @@ But I will list some of my standout favorites. [`@time`])(https://github.com/JuliaLang/julia/pull/37678) now reports how much time was spent on compilation. -This is going to help prevent people knew the language from including compilation time in their benchmarks. -It is still better to use [BenchmarkTool.jl's](https://github.com/JuliaCI/BenchmarkTools.jl) `@btime`, since that does multiple samples. -But now that too can report time spend compilation. +This is going to help prevent people new to the language from including compilation time in their benchmarks. +It is still better to use [BenchmarkTools.jl's](https://github.com/JuliaCI/BenchmarkTools.jl) `@btime`, since that does multiple samples. +But now that too can report time spent on compilation. It's also useful for identifying if certain functions are taking ages to compile. -Which I guess is its theoretical main point, but I think preventing people benchmarking wrong is going to come up way more often. +Which I guess is its theoretical main point, but I think preventing people from benchmarking wrong is going to come up way more often. -The experimental [`Base.@locals`](https://github.com/JuliaLang/julia/issues/29733 -) was added which returns a dictionary of local variables. -That one surprised me, I though being able to access a dict of local variables would get in the way of the optimizer, since it would prevent it from being able to optimize variables that are used for intermediate values away entirely. -But the compiler folk know better than I do. +The experimental [`Base.@locals`](https://github.com/JuliaLang/julia/issues/29733) +was added, which returns a dictionary of local variables. +That one surprised me; I though being able to access a dict of local variables would get in the way of the optimizer, since it would prevent it from being able to optimize variables that are used for intermediate values away entirely. +But the compiler folks know better than I do. -[`splitpath`](https://github.com/JuliaLang/julia/issues/28156) as added, its the opposite of `joinpath`. -Kind of silly we didn't have that, and had been being me at least since 0.6. +[`splitpath`](https://github.com/JuliaLang/julia/issues/28156) is added, it's the opposite of `joinpath`. +Kind of silly we didn't have that, and had been bugging me at least since 0.6. -On things that had been bugging me, I had wanted [`eachslice` and it's special cases: `eachrow` & `eachcol`](https://github.com/JuliaLang/julia/issues/29749) since julia 0.3 when I first started using it. -These are super handy when you want to e.g. iterate through vectors of the rows of a matrix. +On things that had been bugging me, I had wanted [`eachslice` and its special cases: `eachrow` & `eachcol`](https://github.com/JuliaLang/julia/issues/29749) since Julia 0.3 when I first started using it. +These are super handy, for example when you want to iterate through vectors of the rows of a matrix. `redirect_stderr` and `redirect_stdout` now work with `devnull`. So one can run some code while suppressing output easily as follows: From ee5f230be4c4c1657864adab452e78db4371acd7 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Tue, 12 Jan 2021 23:03:07 +0000 Subject: [PATCH 33/62] Update _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md --- _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index b5165720..fd0b01e0 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -471,7 +471,8 @@ So you want to be able to trigger the regeneration of this code when that origin Basically, this allows code that uses [Cassette.jl](https://github.com/JuliaLabs/Cassette.jl), and [IRTools.jl](https://github.com/MikeInnes/IRTools.jl) to not suffer from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. A particular case of this is for [Zygote](https://github.com/FluxML/Zygote.jl), where redefining a function called by the code that was being differentiated did not result in an updated gradient (unless `Zygote.refresh()`) was run. -This was annoying for +This was annoying for working in the REPL, where you might e.g. change your neural nets loss function without redefining the network. +In that case, if you forgot to run `Zyote.refresh()` it would just train wrong. Other things that this allows are two very weird packages that [Nathan Daly](https://github.com/NHDaly) and I came up with at the JuliaCon 2018 hackathon: [StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) and [Tricks.jl](https://github.com/oxinabox/Tricks.jl/). [StagedFunctions.jl](https://github.com/NHDaly/StagedFunctions.jl) relaxes the restrictions on normal `@generated` functions so that they are also safe from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. From ac443debf575ebb0e5518446f70421375183048d Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sat, 16 Jan 2021 17:47:15 +0000 Subject: [PATCH 34/62] wiop --- ...09-Julia-1.6-what-has-changed-since-1.0.md | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index fd0b01e0..774f7185 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -322,10 +322,21 @@ This meant that the package manager is effectively stateful, which turns out to The way to resolve this in practice was to delete the `Manifest.toml` and do it again as a single action. This was a significant problem for [test-time dependencies](https://github.com/JuliaLang/Pkg.jl/issues/1352), where if you had a test-time dependency with an indirect dependency shared with a main dependency of the package, but that was only compatible with an older version of the indirect dependency, you would be unable to run tests as resolving the test-time dependency would fail. -With the new tiered resolved, it will try a number of methods to relax existing versions. -It still wants to avoid changing the versions of currently installed packages if possible, but if that is required then it will do so. -It will first attempt to install without any currently installed package versions being changed, then will try relaxing the constraints to allow changing the versions of indirect dependencies, then to allowing changing the versions of direct dependencies. -This ends up being far more intuitive: if there is a compatible set of package versions then they will be found, regardless of whether new packages are added all at once or one at a time. +With the new tiered resolved, `pkg> add` will try a number of increasingly relaxed strategies to try and install the new package while making minimal changes to the versions of the existing packages that are already installed. +You can see the full description of the [`--preserve` options in the Pkg docs](https://julialang.github.io/Pkg.jl/v1/repl/#repl-add). + + - First it will try and avoid all changes. + - Then it will try and avoid changing the version of direct dependencies, but allow changes to the version of indirect dependencies. + - Then it will allow changes to direct dependencies but will avoid changing the major (or minor pre-1.0) version, even if `Project.toml`’s `[compat]` says it is allowed to. + - Then it will allow changes to everything (as long as permitted by `Project.toml`’s `[compat]` section) + - Finally it will give up and error if it still can’t find a compatible set of package that let it add the new one. + +For a package with a fully filled in `[compat]` section in the `Project.toml`, then it is always fine to allow changes to everything -- which is what this will do if it has to. +If one hasn't setup the `[compat]` section then this can be nice to minimize changes. +(But also: please setup your `[compat]` bounds, for your own sake.) + +The net result of this is far more intuitive behavior for `pkg> add`. +If there is a compatible set of package versions then they will be found, regardless of whether new packages are added all at once or one at a time. ### Precompilation From 6bd581fe5788cf5f26c0b3082d30b5ee00b4f538 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 24 Jan 2021 16:12:57 +0000 Subject: [PATCH 35/62] update ttfp --- ...09-Julia-1.6-what-has-changed-since-1.0.md | 20 +++++++------------ 1 file changed, 7 insertions(+), 13 deletions(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index 774f7185..9dad15d8 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -403,25 +403,19 @@ It is many, many, small methods, most of which are only called once. And unlike most Julia code, it doesn't actually benefit all that much from most of the work Julia's JIT is normally doing to specialize things -- plotting itself isn't in the hot-loop. To make a long-story short, plotting is the postcard example for Julia needing to compile things before it can run them. -As a really hacky way to time this, consider the following timing. -You can see I did `@time @time ...` and two numbers were displayed. -The first number is how long from the start of the command until the `using Plots` and `plot(...)` command finished. -The second is that, plus how long it took before it could show the output of the first. -It's worth looking at this later because it turns out that invalidations (see below) made the REPL have to recompile a bunch of display code. - +TODO: Replace this with numbers from someones system that is not behavoiu **Julia 1.0:** ```julia -julia> @time @time using Plots; plot(1:0.1:10, sin.(1:0.1:10)) - 7.168878 seconds (15.87 M allocations: 941.114 MiB, 7.36% gc time) - 9.281561 seconds (25.74 M allocations: 1.393 GiB, 7.88% gc time) +julia> @time (using Plots; display(plot(1:0.1:10, sin.(1:0.1:10)))) + 16.780888 seconds (29.55 M allocations: 1.609 GiB, 3.79% gc time) ``` **Julia 1.6:** ```julia -julia> @time @time using Plots; plot(1:0.1:10, sin.(1:0.1:10)) - 4.490434 seconds (8.24 M allocations: 583.285 MiB, 7.13% gc time, 32.80% compilation time) - 4.533302 seconds (8.36 M allocations: 590.949 MiB, 7.06% gc time, 33.41% compilation time) +julia> @time (using Plots; display(plot(1:0.1:10, sin.(1:0.1:10)))) + 9.694037 seconds (18.29 M allocations: 1.164 GiB, 4.17% gc time, 0.40% compilation time) ``` -So it's a bit better than 2 times faster on my machine. + +Sure it is still not instant, but it's a lot faster than it was. Note also that in the above timing I had already run precompilation, which seems fairest. Though see below, precompilation caches often would be deleted in 1.0; and conversely are created much faster in 1.6 due to parallelization. From bd74c20901918b51e4003911c1aff011f29b294b Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 24 Jan 2021 16:13:47 +0000 Subject: [PATCH 36/62] update binary builder link --- _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index 9dad15d8..ee273fba 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -218,7 +218,7 @@ In Julia 1.3+, the Artifacts system basically brought the BinaryProvider part in Now the package manager controls the downloads, stores them in a controlled location to avoid massive duplication, and allows for full compat control. No more running arbitrary code during installs. -I feel it is worth really mentioning [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl) here. +I feel it is worth really mentioning [BinaryBuilder](https://binarybuilder.org/) here. While it has existed since before 1.0, it's really grown. It's a super smooth cross-compilation environment, that can used to build binaries for every platform Julia runs on. This is an amazing tool, built on [containerization](https://en.wikipedia.org/wiki/OS-level_virtualization). From 31e56d24043fd4967f00f268297db1537401664b Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sat, 13 Feb 2021 13:36:49 +0000 Subject: [PATCH 37/62] precompile video --- _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index ee273fba..e8b49d6a 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -354,7 +354,13 @@ This is now done in parallel, and is automatically triggered when you complete P No more waiting 5 minutes the first time you load a package with a lot of dependencies. Further, the spiffy animated output shows you what is precompiling at any given time, as well as a progress bar, which makes it feel (even) faster. - + +
        + +
        ### Improved conflict messages This is one of my own contributions. From 1e10ae23a17433cda45a18fb19a9d6030d4673e6 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sat, 13 Feb 2021 13:39:27 +0000 Subject: [PATCH 38/62] thank people --- _posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index e8b49d6a..0e493c83 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -639,3 +639,9 @@ Since 1.0's release, there have been so many small improvements to functions tha Things like `startswith` supporting regex, [`parse`](https://github.com/JuliaLang/julia/pull/36199) working on `UUID`s, `accumulate`, `cumsum`, and `cumprod` supporting arbitrary iterators ([#32656](https://github.com/JuliaLang/julia/pull/34656)). Julia 1.6 is one hell of a more polished language. + + +--- + +Thanks to all the people who contributed to editing the post. +Especially: [Mosè Giordano](https://giordano.github.io/), and [Chris de Graaf](https://cdg.dev/). From ba415b3ded11be832b8dea460555638084512ce2 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sat, 13 Feb 2021 15:15:18 +0000 Subject: [PATCH 39/62] PkgServer --- ...09-Julia-1.6-what-has-changed-since-1.0.md | 34 +++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index 0e493c83..0b7aa366 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -400,6 +400,40 @@ This means they split every time the rightmost nonzero part of the version numbe For something with a lot of pre-1.0 versions, that is a lot of numbers. I think the new method is much cleaner and easier to read. +### PkgServer +One of the big advancements between julia 0.6 and 1.0 was switching Pkg to special-case `pkg> add` for repos hosted on GitHub and fetch tarballs (available via GitHub's API), rather than clone the repo with full history. +At JuliaCon 2018 everyone was raving about how much faster it was. +Except me and the 2 other Australians present. +We were like _"Still slow, what are you talking about?"_. +Turns out that GitHub's speed to Australia (at least for that API), is slow. Australian Internet has infamously high latency, downloading dozens of packages is often slow. +GitHub wasn't optimized for this kind of use, its not a content distribution network. +Its optimized for cloning things you want to develop, not downloading things you want to use. + +The new PkgServer solves this by being a setup that *is* designed for distributing packages. +This was introduced as opt-in in Julia 1.4, and made the default in 1.5. +For [full details see the original issue](https://github.com/JuliaLang/Pkg.jl/issues/1377) that introduced the Pkg protocol, it has a ton of info at the top explaining how it all works. +There are [distributed PkgServers around the world](https://status.julialang.org/). + +Not only does this solve the latency problems of connecting to some GitHub server that is on the other side of the world, it also improves support for non-GitHub hosting. +Its always been possible to host Julia packages on anything supporting git. +There are [over a dozen packages in General Registry hosted on GitLab](https://github.com/JuliaRegistries/General/search?q=gitlab) for example. +My employer, [Invenia](https://www.invenia.ca/), has all our internal packages hosted on GitLab, and registered on our internal registry. +But in effect, prior to PkgServer, GitHub was 1st class in Pkg, due to its download as a tarball feature. +It makes sense, GitLab and BitBucket don't even provide an API for downloading a repo via content (tree) hash (only commit hash), so it can't be implemented inside Pkg itself. +But by going via PkgServer, everything can be consistently served. + +PkgServer has not been without struggles. +Early versions of the system stored some metrics, so that package authors could be informed of how many users their package had, and over what periods users kepts using their packages. +It was concluded that this might be too invasive, and it was removed before the Julia 1.5 release. +It's also had some struggles with keeping up with the load, with some servers becoming overloaded. +More recently its had some issues (hopefully now fixed) where it has not been able to see the latest releases of packages because it is still seeing an out-of-date copy of the registry. + +As [Jeff Atwood said](https://twitter.com/codinghorror/status/506010907021828096): + +> There are two hard things in computer science: cache invalidation, naming things, and off-by-one errors. + +[![XKCD 1854](https://imgs.xkcd.com/comics/refresh_types.png) +(XKCD 1845](https://xkcd.com/1854/)) ## Time To First Plot People often complain about the "Time To First Plot" (TTFP) in Julia. From 68ce9599caa070fdf41a819cbeb455784a747eb0 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sat, 13 Feb 2021 17:32:37 +0000 Subject: [PATCH 40/62] eddited up to line 225 (Pkg section) --- ...09-Julia-1.6-what-has-changed-since-1.0.md | 272 ++++++++++-------- 1 file changed, 157 insertions(+), 115 deletions(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index 0b7aa366..b5a6ff20 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -12,7 +12,15 @@ but that is not to say no new features have been added to the language. -Julia 1.6 is the first feature release since 1.0. +Julia 1.6 is a huge release and it is coming out relatively soon. +RC-1 was [released recently](https://discourse.julialang.org/t/julia-v1-6-0-rc1-is-now-available/54775). +I suspect we have at least a few more weeks before the final release. +The Julia Core team take a huge amount of care in not breaking any packages. +So while that is all being checked and corner cases fixed, I think we have some time. +Thus this post, reflecting not just on 1.6, but on everything that has happened since the 1.0 release. + + +Julia 1.6 will be the first "feature" release since 1.0. Prior to 1.0 all releases were feature releases; they came out when they were ready. Since then all releases (except 1.6) have been timed releases. The release candidate is cut from the main branch every 3 months; and they are released after an [extensive round of additional checking](https://github.com/JuliaLang/julia/pull/35846), which often takes weeks to months. @@ -27,7 +35,6 @@ In practice I think for a lot of package maintainers 1.6 will be a LTS, in that There have been too many cool new things (as this post will detail) to stay back to only 1.0 features. Already a lot of packages have dropped support for Julia versions older than 1.3. - This post is kind of a follow-up to my [Julia 1.0 release run-down]({{site.url}}//2018/06/01/Julia-Favourite-New-Things.html). But it's going to be even longer, as it is covering the last 5 releases since then; and I am not skipping the major new features. I am writing this not to break down release by release, @@ -54,36 +61,43 @@ It took over a year of discussion to work out the solution, particularly because ### Deprecations are muted by default +Julia 1.5+ doesn't display deprecation warnings, unless you run julia with the `--depwarn=yes` flag. This was me in [this PR](https://github.com/JuliaLang/julia/pull/35362). It's not something I am super happy about. Though I think it makes sense. Using deprecated methods is actually fine, as long as your dependencies follow SemVer, and you use `^` (i.e. default) bounding in your Project.toml's `[compat]`, which everyone does, because it's enforced by the auto-merge script in the General registry. +Often it is even necessary if you want to keep compatibility for a while. -Solving deprecations is a thing you should chose to do, actively. +Solving deprecations is a thing you should actively chose to do. Not casually, when trying to do something else. -In particular, when updating to support a new major release of one of your dependencies, you should run your (integration) tests to see if they succeed. -If they don't succeed, then one removed support for the new version and run Julia with deprecation warnings turned on (or set to error); to identify and fix them. -Probably also you are checking the release notes, since you are intentionally updating. +In particular, when updating to support a new major release of one of your dependencies, you should follow a process. +Something like: -The core of the reason is because it was actually breaking things. + 1. Check the release notes. + 2. Relax compat bounds + 3. Run your integration tests, if everything passes you are done. + 4. Of failed revert change to compat bounds rerun your integration tests paying attention to deprecation warnings. + +Updating your dependencies should be an active choice. +Potentially one that is automated, but not one that you do while adding another feature (if you can help it). + +The core of the reason we disabled them is because they was actually breaking things. Irrelevant deprecation warning spam from dependencies of dependencies was causing JuMP and LightGraphs to become too slow to be used. -Since they were from dependencies of dependencies the maintainers of JuMP and LightGraphs couldn’t even fix them, let alone the end users. +Further, since they were from dependencies of dependencies the maintainers of JuMP and LightGraphs couldn’t even fix them, let alone the end users. Deprecation warnings are still turned on by default in tests, which makes sense since the tests (unlike normal use), are being run by maintainers of the package itself, not its users. Though it still doesn't make a huge amount of sense to me, since spam from deprecation warnings floods out your actual errors that you want to see during testing. -For this reason Invenia (my employer) has disabled deprecation warnings in the tests for all our closed source packages, and added a new test job that just does deprecation checking (set to error on deprecation and with the job allowed to fail, just so we are informed). +For this reason [Invenia](https://invenia.ca) (my employer) has disabled deprecation warnings in the CI tests for all our closed source packages, and added a new test job that just does deprecation checking (set to error on deprecation and with the job allowed to fail, just so we are informed). Hopefully one day we can improve the tooling around deprecation warnings. -An ideal behavour would be to only show deprecation warning if directly caused by a function call made from within the package module of your current active environment. +An ideal behavior would be to only show deprecation warning if directly caused by a function call made from within the package module of your current active environment. I kind of know what we need to do to the logger to make that possible, but it is not yet something I have had time to do. - ### Code highlighting for `code_llvm` and `code_native` This was added in [#36634](https://github.com/JuliaLang/julia/issues/36634). This functionality was first implemented in [ColoredLLCodes.jl](https://github.com/kimikage/ColoredLLCodes.jl), where it worked by monkey-patching the InteractiveUtils stdlib. That package does still work on Julia 1.0. - **Julia 1.0:** ![Julia 1.0 code_llvm]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.0-code-llvm.png) @@ -121,25 +135,31 @@ The whole thing just looks more modern and polished. This is not useful so much for debugging Julia code, but for debugging Julia itself. If you get into one of the very rare situations where the language is doing something truly nonsense; you can send a bug report recorded via `rr`, and someone can debug *exactly* what is happening. Event to the extent of [diagnosing faulty RAM](https://julialang.org/blog/2020/09/rr-memory-magic/). -`rr` is an impressively cool piece of tech; it boils down to the fact that linux's `fork` is incredibly cheap, because it takes advantage of [copy-on-write](https://en.wikipedia.org/wiki/Copy-on-write); so it basically `fork`s before every system call, and serializes what is going on; so you have a full track of everything that happened. +`rr` is an impressively cool piece of tech. +It leverages the fact that linux's `fork` is incredibly cheap, because it takes advantage of [copy-on-write](https://en.wikipedia.org/wiki/Copy-on-write). +`rr` basically `fork`s before every system call, and serializes what is going on; so you have a full track of everything that happened. The `rr` integration is really nice and easy to use; I had someone who was part of our architecture and operations team (i.e. who writes way more CloudFormation than Julia) run it to submit a support request; and they had no real troubles. - ## Syntax ### `import as` >The syntax import A as B (plus import A: x as y, import A.x as y, and using A: x as y) can now be used to rename imported modules and identifiers ([#1255](https://github.com/JuliaLang/julia/issues/1255)). -This will be familiar to Python folks who love to do: `import numpy as np`. +This will be familiar to Python folks who love to write things like: `import numpy as np`. I hope we never see it used that ubiquitously in julia. I am quite happy with `using Foo` which imports into scope everything the author of `Foo` exported. Python people find that super weird and scary; but it's fine. -Further, most things you use don't come from the module that defined them anyways; they are overloaded functions e.g. from `Base`. +Note that in Julia, many (most?) things you use don't belong to the scope of module that defined them anyways; they are overloaded functions e.g. from `Base`. +So what you import doesn't matter as much as you might think. +I recently learned that Haskell is like Julia with a default of importing all exports. +It has been fine for julia, and find for Haskell for ages. +Though in neither is it required. + The real value of `import FooBar as fb` is not to have a short abbreviation so you can do `fb.quux`. That was already possible via `const fb = FooBar`. It is to handle cases where the package name itself conflicts with an identifier. -For example (as has often occured) if one has uses a `Pipe` in the REPL, and then later wants to load [Pipe.jl](https://github.com/oxinabox/Pipe.jl/) via `import Pipe` then one gets a name clash before it can be resolved. +For example (as has often occurred) if one has uses a `Pipe` in the REPL, and then later wants to load [Pipe.jl](https://github.com/oxinabox/Pipe.jl/) via `import Pipe` then one gets a name clash before it can be even be loaded. Now one can do `import Pipe as PipingPipe`. ### NamedTuple/keyword arguments automatic naming @@ -151,12 +171,18 @@ For example: foo(x::Bar; a=1, b=2, c=3) = ... # Method for if x is given as components -foo(x1, x2; a=10, b=20, c=30, comb=+) = foo(Bar(comp(x1, x2)); a=a, b=b, c=c) +foo(x1, x2; a=10, b=20, c=30, op=+) = foo(Bar(op(x1, x2)); a=a, b=b, c=c) +``` +This new feature allows one to avoid writing `(...; a=a, b=b, c=c)`, and instead write `(...; a, b, c)`, where `a`, `b`, `c` are the name of local variables that align with the name of the keyword arguments. +```julia +# Method for if x is given as components +foo(x1, x2; a=10, b=20, c=30, op=+) = foo(Bar(op(x1, x2)); a, b, c) ``` -This new feature allows one to avoid writing `(...; a=a, b=b, c=c)`, and instead write `(...; a, b, c)`. + This does come with the requirement to separate keyword arguments from positional arguments by `;`, but I have always done this, and the [BlueStyle guide requires it](https://github.com/invenia/BlueStyle#keyword-arguments). It feels like we are fully leveraging the distinction of keyword from positional arguments by allowing this. -In contrast, the distinction vs e.g. C# and Python that allow any positional argument to be passed by name, is to make the name of positional arguments not part of the public API, thus avoiding changing it being a breaking change. +Where as the previous motivation for this distinction (vs e.g. C# and Python that allow any positional argument to be passed by name), was to make the name of positional arguments not part of the public API, thus avoiding changing it being a breaking change. +This new feature feels more ... featureful. The same syntax can be used to create `NamedTuple`s. ```julia @@ -176,13 +202,15 @@ E.g. `@info "message" foo bar` and `@info "message" foo=foo bar=bar` display the In Julia 1.0 `'` was lowered directly into a call to `Base.adjoint`. This meant it was impossible to redefine what `'` meant in your current module. Now it lowers to a call to `var"'"`, which is something you can overload. -People often think it is cool to overload this to automatic differentiation so that `f'(x)` gives you the derivative of `f(x)`. -Furthermore, it can now have Unicode suffixes added to it, to define a new suffix operator. +In `Base` it is overloaded to `Base.adjoint` so nothing has changed. +People often think it is cool to overload this in automatic differentiation packages so that `f'(x)` gives you the derivative of `f(x)`. + +Furthermore, `'` can now have Unicode suffixes added to it, to define a new suffix operator. For example, the one in `Base` which makes `A'ᵀ` give `transpose(A)` (rather than the adjoint/hermitian transpose which is returned by `A'`). ## Performance: References to the Heap from the Stack This was promised in 2016 as a feature for 1.0 (released 2018), but we actually didn't get it until 1.5 (released 2020) with [#33886](https://github.com/JuliaLang/julia/issues/33886). -In short, the process of allocating memory from the heap is fairly slow*, whereas allocating memory on the stack is basically a no-op. +In short, the process of allocating memory from the heap is fairly slow\*, whereas allocating memory on the stack is basically a no-op. Indeed, Julia benchmarking tools don't count allocations on the stack as allocations at all. One can find extensive write ups of heap vs. stack allocations and how it works in general (though some mix the C specific factors with the CPU details). In Julia all mutable objects live are allocated on the heap. @@ -191,41 +219,13 @@ But with this change, all immutable objects can live on the stack, even if some An important consequence of this is that wrapper types, such as the `SubArray` returned from `@view x[1:2]`, now have no overhead to create. I find that in practice this often adds up to a 10-30% speed-up in real world code. -(* It's actually really fast, but it is the kind of thing that rapidly adds up; and it is slow vs. operations that can happen without touching RAM.) +(\* It's actually pretty fast, but it is the kind of thing that rapidly adds up; and it is slow vs operations that can happen without dynamic allocations.) ## Pkg stdlib and the General Registry -Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the Pkg stdlib; and the general registry is more policy than software. +Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the Pkg stdlib; and the General registry is more policy than software. However, some of the biggest changes have been in maturing out Pkg3, and its surroundings. - -### BinaryBuilder, Artifacts, Yggdasil and JLL packages - -This story was beginning to be told around Julia 1.0 time, -but it wasn't really complete nor built into Pkg until Julia 1.3. -You can read the documentation on [Artifact](https://julialang.github.io/Pkg.jl/v1/artifacts/), [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl), and [Yggdasil](https://github.com/JuliaPackaging/Yggdrasil) for full details. -This is the story about how Julia works with binary dependencies. -As of now, it works really well. - -Sometimes people misunderstand the claims about Julia solving the two language problem as saying everything should be rewritten in Julia. -Which is far from the truth, ain't noone got time for that. -Julia has always had great foreign function interfacing (FFI); like [`ccall`](https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/) (as well as [PyCall](https://github.com/JuliaPy/PyCall.jl/), [RCall](https://github.com/JuliaInterop/RCall.jl), [JavaCall](https://github.com/JuliaInterop/JavaCall.jl), and a bunch of others, for non-C-style binaries). -While FFI makes it easy to call binaries, how about actually getting them onto your machine? - -Before Julia 1.0, the standard was to run arbitrary code in the `deps/build.jl` file, which often used [BinDeps.jl](https://github.com/JuliaPackaging/BinDeps.jl/) to download and compile things. -Around 1.0, this changed to having the `deps/build.jl` call [BinaryProvider.jl](https://github.com/JuliaPackaging/BinaryProvider.jl/) to download a compiled binary built with [BinaryBuilder.jl](https://github.com/JuliaPackaging/BinaryBuilder.jl), and store that alongside the package code. -In Julia 1.3+, the Artifacts system basically brought the BinaryProvider part into the package manager. -Now the package manager controls the downloads, stores them in a controlled location to avoid massive duplication, and allows for full compat control. -No more running arbitrary code during installs. - -I feel it is worth really mentioning [BinaryBuilder](https://binarybuilder.org/) here. -While it has existed since before 1.0, it's really grown. -It's a super smooth cross-compilation environment, that can used to build binaries for every platform Julia runs on. -This is an amazing tool, built on [containerization](https://en.wikipedia.org/wiki/OS-level_virtualization). -It's the kind of build tool one dreamed on 10 years ago. -It's far more general than Julia, I know people have at least experimented with using it with Nim. -I hope more things start using it. - ### Temporary Environments Julia 1.5 added `pkg> activate --temp` which will create and activate a temporary environment. This environment is deleted when Julia exists. @@ -243,62 +243,6 @@ So using `activate --temp` to quickly try something is indeed quick. A more hacky use of it is after loading (via `using`) a package that you have `dev`'ed, you can `activate --temp` and install some of your test-time dependencies to reproduce a test failure without adding them to the main dependencies in the `Project.toml`. Though there is a better way if you use `test/Project.toml`. -### Test Dependencies in their own Project.toml -In Julia 1.0 and 1.1, [test specific dependencies are listed in the `[extras]` section](https://julialang.github.io/Pkg.jl/v1/creating-packages/#Test-specific-dependencies-in-Julia-1.0-and-1.1), and under `[targets] test=[...]`; with their compatibility listed in the main `[compat]`. -It seemed like this might be extended for other things in the future, perhaps documentation. -But it was found for documentation in particular that a separate `Project.toml` worked well (as long as you `dev ..` into its `Manifest.toml` so it uses the right version of the package it is documenting). - -The new `test/Project.toml` extends that idea. -One part of that extension is to remove the need to `dev ..`, and other part is to make available all the main dependencies. -It actually works on a different mechanism than the docs. -It relies on stacked environments, which is a feature Julia has had since 1.0 via `LOAD_PATH`, but that is rarely used. - - -One advantage of this is that you can activate that Project.toml, on top of the your existing environment by adding the test directory to your `LOAD_PATH`. -I wrote some more details on exactly how to do that on [Discourse]( -https://discourse.julialang.org/t/activating-test-dependencies/48121/7?u=oxinabox). -It feels kind of hacky, because it is. -At some point there might be a nicer user interface for stacked environments like this. - - -### Full Transition off METADATA.jl and REQUIRE files, and onto the General Registry -Even though Julia 1.0 had Pkg3 and was supposed to use registries, for a long time the old Pkg2 [METADATA.jl](https://github.com/JuliaLang/METADATA.jl) pseudo-registry was used as the the canonical source of truth. -Registering new releases was made against that, and then a script synchronized the [General](https://github.com/JuliaRegistries/General) registry to match it. -This was to allow time for packages to change over to supporting Julia 1.0, while still also making releases that supported julia 0.6. -It wasn't until about a year later that this state ended and the General registry became the one source of truth. - -This was kind of sucky, because a lot of the power of Pkg3 was blocked until then. -In particular, since the registries compat was generated from the REQUIRE file, and the REQUIRE files had kind of [gross requirement specification](https://docs.julialang.org/en/v0.6/manual/packages/#Requirements-Specification-1), and everyone basically just lower-bounded things, or didn't bound compat at all. -Which made sense because with the single global environment that Pkg2 had, you basically needed the whole ecosystem to be compatible, so restricting was bad. -But Pkg3/Project.toml has the much better default [`^`-bounds](https://julialang.github.io/Pkg.jl/v1/compatibility/#Version-specifier-format-1) to accept only non-breaking changes by SemVer. -And per project environments, things don't all have to be compatible -- just things used in a particular project (rather than every project you might ever do). - -#### Automatic Merging -Initially after the transition, all registry PRs to make a new release needed manual review by one of the very small number of General registry maintainers. - -I am a big fan of [releasing after every non-breaking PR](https://www.oxinabox.net/2019/09/28/Continuous-Delivery-For-Julia-Packages.html). -Why would you not want those bug-fixes and new features released? -It is especially rude to contributors who will make a fix or a feature, but then you don't let them use it because you didn't tag a release. -Plus it makes tracking down bugs easier, if it occurs on a precise version you know the PR that caused it. -But with manual merging it feels bad to tag 5 releases in a day. -Now we have automatic merging, so it is fine. -At time of writing, [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl/) was up to 67 releases. - -The big advantage of automatic merging is that it comes with automatic enforcement of standards. -In order to be auto-mergeable, some good standards have to be followed. -One of which is that that no unbounded compat specifiers (e.g. `>=1.0`) are permitted; and that everything must have a compat specifier (since unspecified is same as `>=0.0.0`) -That one is particularly great, since if one adds specifiers later that can't be met, then it can trigger downgrades back to incredibly old versions that didn't specify compat and that almost certainly are not compatible. - -To deal with that particular case, retro-capping was done to retroactively add bounds to all things that didn't have them. -This was painful when it was done, since it rewrote compat in the registry, which made it disagree with the `Project.toml` in package repositories, which is always confusing. -But now that it is done, it is good. - -#### Requirement to have a `Project.toml` -Finally the ability to `pkg> dev` packages that had only a REQUIRE and no `Project.toml` was removed in Julia 1.4. -You can still `pkg> add` them if they were registered during the transition period. -But to edit the projects they must a `Project.toml` file, which is fine since all the tools to register releases also require you to have `Project.toml` now. - - ### Resolver willing to downgrade packages to install new ones (Tiered Resolution) @@ -306,13 +250,13 @@ Until the [tiered resolver](https://github.com/JuliaLang/Pkg.jl/pull/1330) was a For example, consider: - **Foo.jl** - - v1 - - v2 + - v1 + - v2 - **Bar.jl** - - v1 compatible with Foo v1 - - v2 compatible with Foo v2 + - v1 compatible with Foo v1 + - v2 compatible with Foo v2 - **Qux.jl** - - v1: compatible only with Foo v1 + - v1: compatible only with Foo v1 If you did `pkg"add Foo Bar Qux"` you would end up with **Foo** v1, **Bar** v1, and **Qux** v1. But if you did first: `pkg"add Foo Bar"` (which would install **Foo** v2, and **Bar** v2), @@ -364,6 +308,7 @@ Further, the spiffy animated output shows you what is precompiling at any given ### Improved conflict messages This is one of my own contributions. +I am honestly really satisfied with it. Julia 1.0 conflict messages are a terrifying wall of text. **Julia 1.0:** @@ -400,6 +345,103 @@ This means they split every time the rightmost nonzero part of the version numbe For something with a lot of pre-1.0 versions, that is a lot of numbers. I think the new method is much cleaner and easier to read. +### Test Dependencies in their own Project.toml +In Julia 1.0 and 1.1, [test specific dependencies are listed in the `[extras]` section](https://julialang.github.io/Pkg.jl/v1/creating-packages/#Test-specific-dependencies-in-Julia-1.0-and-1.1), and under `[targets] test=[...]`; with their compatibility listed in the main `[compat]`. +It seemed like this might be extended for other things in the future, perhaps documentation. +But it was found for documentation in particular that a separate `Project.toml` worked well (as long as you `dev ..` into its `Manifest.toml` so it uses the right version of the package it is documenting). + +The new `test/Project.toml` extends that idea. +One part of that extension is to remove the need to `dev ..`, and other part is to make available all the main dependencies. +It actually works on a different mechanism than the docs. +It relies on stacked environments, which is a feature Julia has had since 1.0 via `LOAD_PATH`, but that is rarely used. + + +One advantage of this is that you can activate that Project.toml, on top of the your existing environment by adding the test directory to your `LOAD_PATH`. +I wrote some more details on exactly how to do that on [Discourse]( +https://discourse.julialang.org/t/activating-test-dependencies/48121/7?u=oxinabox). +It feels kind of hacky, because it is. +At some point there might be a nicer user interface for stacked environments like this. + + + +### BinaryBuilder, Artifacts, Yggdasil and JLL packages + +This story was beginning to be told around Julia 1.0 time, +but it wasn't really complete nor built into Pkg until Julia 1.3. +You can read the documentation on [Artifact](https://julialang.github.io/Pkg.jl/v1/artifacts/), [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl), and [Yggdasil](https://github.com/JuliaPackaging/Yggdrasil) for full details. +This is the story about how Julia works with binary dependencies. +As of now, it works really well. + +Sometimes people misunderstand the claims about Julia solving the two language problem as saying everything should be rewritten in Julia. +Which is far from the truth, ain't noone got time for that. +Julia has always had great foreign function interfacing (FFI); like [`ccall`](https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/) (as well as [PyCall](https://github.com/JuliaPy/PyCall.jl/), [RCall](https://github.com/JuliaInterop/RCall.jl), [JavaCall](https://github.com/JuliaInterop/JavaCall.jl), and a bunch of others, for non-C-style binaries). +While FFI makes it easy to call binaries, how about actually getting them onto your machine? + +Before Julia 1.0, the standard was to run arbitrary code in the `deps/build.jl` file, which often used [BinDeps.jl](https://github.com/JuliaPackaging/BinDeps.jl/) to download and compile things. +Around 1.0, this changed to having the `deps/build.jl` call [BinaryProvider.jl](https://github.com/JuliaPackaging/BinaryProvider.jl/) to download a compiled binary built with [BinaryBuilder.jl](https://github.com/JuliaPackaging/BinaryBuilder.jl), and store that alongside the package code. +In Julia 1.3+, the Artifacts system basically brought the BinaryProvider part into the package manager. +Now the package manager controls the downloads, stores them in a controlled location to avoid massive duplication, and allows for full compat control. +No more running arbitrary code during installs. + +The integration of installing the binaries into Pkg for Julia 1.3 is a major reason why people have dropped Julia 1.0 support. +While you can still use BinaryProvider.jl for the download, it is much cleaner and easier to just use Julia 1.3+ and have the Artifact system take care of it. + +I feel it is worth really mentioning [BinaryBuilder](https://binarybuilder.org/) here. +While it has existed since before 1.0, it's really grown. +It's a super smooth cross-compilation environment, that can used to build binaries for every platform Julia runs on. +This is an amazing tool, built on [containerization](https://en.wikipedia.org/wiki/OS-level_virtualization). +It's the kind of build tool one dreamed on 10 years ago. +It's far more general than Julia, I know people have at least experimented with using it with Nim. +I hope more things start using it. + +A novel use of this infrastructure is [`ygg`](https://discourse.julialang.org/t/announcing-ygg/54687), a OS independent package manager (some-what like Conda or HomeBrew), that is backed by Yggdasil. +This is really neat, not simply because it is making use of all that work into cross-compiling the universe, but because its actually really useful. +`ygg` installs everything to the normal julia artifact storage location, and doesn't require any kind of admin privileges. +This is huge for working on systems with restricted privileges, like university PCs. +I used to use [junest](https://github.com/fsquillace/junest) for this, but it was a bit flakey because it used scary `proot` hacks. +When it broke, I often resorted to compiling things from source, so I could install them to my home directory. +I have had to bootstrap a lot of things following [Linux From Scratch](http://www.linuxfromscratch.org/blfs/). +`ygg` leverages the fact that the BinaryBuilder cross-compilation setup has already sorted out how to make portable binaries. + +### Full Transition off METADATA.jl and REQUIRE files, and onto the General Registry +Even though Julia 1.0 had Pkg3 and was supposed to use registries, for a long time the old Pkg2 [METADATA.jl](https://github.com/JuliaLang/METADATA.jl) pseudo-registry was used as the the canonical source of truth. +Registering new releases was made against that, and then a script synchronized the [General](https://github.com/JuliaRegistries/General) registry to match it. +This was to allow time for packages to change over to supporting Julia 1.0, while still also making releases that supported Julia 0.6. +It wasn't until about a year later that General registry became the one source of truth, and METADATA.jl was retired. + +This was kind of sucky, because a lot of the power of Pkg3 was blocked until then. +In particular, since the registries compat was generated from the REQUIRE file, and the `REQUIRE` files had kind of [gross requirement specification](https://docs.julialang.org/en/v0.6/manual/packages/#Requirements-Specification-1), and everyone basically just lower-bounded things, or didn't bound compat at all. +Which made sense because with the single global environment that Pkg2 had, you basically needed the whole ecosystem to be compatible, so restricting was bad. +But `Project.toml` has the much better default [`^`-bounds](https://julialang.github.io/Pkg.jl/v1/compatibility/#Version-specifier-format-1) to accept only non-breaking changes by SemVer. +And per project environments, things don't all have to be compatible -- just things used in a particular project (rather than every project you might ever do). + +#### Automatic Merging +Initially after the transition, all registry PRs to make a new release needed manual review by one of the very small number of General registry maintainers. + +I am a big fan of [releasing after every non-breaking PR](https://www.oxinabox.net/2019/09/28/Continuous-Delivery-For-Julia-Packages.html). +Why would you not want those bug-fixes and new features released? +It is especially rude to contributors who will make a fix or a feature, but then you don't let them use it because you didn't tag a release. +Plus it makes tracking down bugs easier, if it occurs on a precise version you know the PR that caused it. +But with manual merging it feels bad to tag 5 releases in a day. +Now we have automatic merging, so it is fine. +At time of writing, [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl/) was up to 67 releases. + +The big advantage of automatic merging is that it comes with automatic enforcement of standards. +In order to be auto-mergeable, some good standards have to be followed. +One of which is that that no unbounded compat specifiers (e.g. `>=1.0`) are permitted; and that everything must have a compat specifier (since unspecified is same as `>=0.0.0`) +That one is particularly great, since if one adds specifiers later that can't be met, then it can trigger downgrades back to incredibly old versions that didn't specify compat and that almost certainly are not compatible. + +To deal with that particular case, retro-capping was done to retroactively add bounds to all things that didn't have them. +This was painful when it was done, since it rewrote compat in the registry, which made it disagree with the `Project.toml` in package repositories, which is always confusing. +But now that it is done, it is good. + +#### Requirement to have a `Project.toml` +Finally the legacy ability to `pkg> dev` packages that had only a REQUIRE and no `Project.toml` was removed in Julia 1.4. +You can still `pkg> add` them if they were registered during the transition period. +But to edit the projects they must a `Project.toml` file, which is fine since all the tools to register releases also require you to have `Project.toml` now. + + + ### PkgServer One of the big advancements between julia 0.6 and 1.0 was switching Pkg to special-case `pkg> add` for repos hosted on GitHub and fetch tarballs (available via GitHub's API), rather than clone the repo with full history. At JuliaCon 2018 everyone was raving about how much faster it was. @@ -625,7 +667,7 @@ I am not going to manage to list all of them here. But I will list some of my standout favorites. -[`@time`])(https://github.com/JuliaLang/julia/pull/37678) now reports how much time was spent on compilation. +[`@time`](https://github.com/JuliaLang/julia/pull/37678) now reports how much time was spent on compilation. This is going to help prevent people new to the language from including compilation time in their benchmarks. It is still better to use [BenchmarkTools.jl's](https://github.com/JuliaCI/BenchmarkTools.jl) `@btime`, since that does multiple samples. But now that too can report time spent on compilation. From ca14d07154aedccca1a1b9c7c398b091a37028c2 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sat, 13 Feb 2021 20:42:37 +0000 Subject: [PATCH 41/62] editting pass done --- ...09-Julia-1.6-what-has-changed-since-1.0.md | 158 ++++++++++-------- 1 file changed, 86 insertions(+), 72 deletions(-) diff --git a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md index b5a6ff20..bec14482 100644 --- a/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-01-09-Julia-1.6-what-has-changed-since-1.0.md @@ -223,31 +223,14 @@ I find that in practice this often adds up to a 10-30% speed-up in real world co ## Pkg stdlib and the General Registry -Writing this section is a bit hard as there is no NEWS.md nor HISTORY.md for the Pkg stdlib; and the General registry is more policy than software. -However, some of the biggest changes have been in maturing out Pkg3, and its surroundings. - -### Temporary Environments -Julia 1.5 added `pkg> activate --temp` which will create and activate a temporary environment. -This environment is deleted when Julia exists. -This is incredibly handy for: - - - Reproducing an issue - - Answering questions on Stack Overflow etc - - Checking the behavior of the last release of a package you currently have `dev`ed. - - Quickly trying out an idea - -Remembering that with Pkg, installing a version of a package you have installed before is incredibly fast. -Pkg doesn't download it again, it basically just points to the existing version on disk. -So using `activate --temp` to quickly try something is indeed quick. - -A more hacky use of it is after loading (via `using`) a package that you have `dev`'ed, you can `activate --temp` and install some of your test-time dependencies to reproduce a test failure without adding them to the main dependencies in the `Project.toml`. -Though there is a better way if you use `test/Project.toml`. +Some of the biggest changes have been in maturing out Pkg3, and its surroundings. +Writing this section is a bit hard as there is no `NEWS.md` nor `HISTORY.md` for the Pkg stdlib; and the General registry is more policy than software. ### Resolver willing to downgrade packages to install new ones (Tiered Resolution) Until the [tiered resolver](https://github.com/JuliaLang/Pkg.jl/pull/1330) was added, Julia would not change the version of any currently installed package in order to install a new one. -For example, consider: +For example, consider the following set of packages with different versions and compatibilities: - **Foo.jl** - v1 @@ -263,15 +246,15 @@ But if you did first: `pkg"add Foo Bar"` (which would install **Foo** v2, and * and then did `pkg"add Qux"`, then on Julia 1.0 you would get an error, as it would refuse to downgrade **Foo** and **Bar** to v1, as is required to allow **Qux** to be installed. This meant that the package manager is effectively stateful, which turns out to be really counter intuitive. -The way to resolve this in practice was to delete the `Manifest.toml` and do it again as a single action. +The way to resolve this in practice was to delete the `Manifest.toml`, which is where the state is stored, and do it again as a single action. This was a significant problem for [test-time dependencies](https://github.com/JuliaLang/Pkg.jl/issues/1352), where if you had a test-time dependency with an indirect dependency shared with a main dependency of the package, but that was only compatible with an older version of the indirect dependency, you would be unable to run tests as resolving the test-time dependency would fail. -With the new tiered resolved, `pkg> add` will try a number of increasingly relaxed strategies to try and install the new package while making minimal changes to the versions of the existing packages that are already installed. +With the new tiered resolved, `pkg"add ..."` will try a number of increasingly relaxed strategies to try and install the new package while making minimal changes to the versions of the existing packages that are already installed. You can see the full description of the [`--preserve` options in the Pkg docs](https://julialang.github.io/Pkg.jl/v1/repl/#repl-add). - First it will try and avoid all changes. - Then it will try and avoid changing the version of direct dependencies, but allow changes to the version of indirect dependencies. - - Then it will allow changes to direct dependencies but will avoid changing the major (or minor pre-1.0) version, even if `Project.toml`’s `[compat]` says it is allowed to. + - Then it will allow changes to direct dependencies, but will avoid changing the major (or minor pre-1.0) version, even if `Project.toml`’s `[compat]` says it is allowed to. - Then it will allow changes to everything (as long as permitted by `Project.toml`’s `[compat]` section) - Finally it will give up and error if it still can’t find a compatible set of package that let it add the new one. @@ -279,25 +262,28 @@ For a package with a fully filled in `[compat]` section in the `Project.toml`, If one hasn't setup the `[compat]` section then this can be nice to minimize changes. (But also: please setup your `[compat]` bounds, for your own sake.) -The net result of this is far more intuitive behavior for `pkg> add`. +The net result of this is far more intuitive behavior for `pkg"add ..."`. If there is a compatible set of package versions then they will be found, regardless of whether new packages are added all at once or one at a time. ### Precompilation Precompilation has been enhanced a lot. That is not the compilation that runs the first time a function is used in a session, but rather the stuff that runs the first time a package is loaded in an environment. +Or that can be run manually via `pkg"precompile"`, or in 1.6+ that is run automatically when you exit Pkg mode in the REPL. For a start, the precompilation cache no longer goes stale every time you swap environments. This was a massive pain in Julia 1.0, especially if you worked on more than one thing at a time. This was fixed in 1.3 to have a separate cache for each environment. It's easy to forget this one, but it is actually one of the biggest usability enhancements since 1.0. -More dramatic, and less easy to overlook, is the parallelism of precompilation added in 1.6. -Precompiling a package requires precompiling all its dependencies first. -This is now done in parallel, and is automatically triggered when you complete Pkg operations, in contrast to happening serially the first time a package is loaded. +More dramatic, is the parallelism of precompilation added in 1.6. +Precompiling a package requires precompiling all its dependencies first (and so forth). +This is now done in parallel, and (as mentioned) is automatically triggered when you complete Pkg operations. +In contrast to happening serially the first time a package is loaded. No more waiting 5 minutes the first time you load a package with a lot of dependencies. Further, the spiffy animated output shows you what is precompiling at any given time, as well as a progress bar, which makes it feel (even) faster. +You can see in the video below how much faster this makes precompilation. @@ -21,12 +21,12 @@ Thus this post, reflecting not just on 1.6, but on everything that has happened Julia 1.6 will be the first "feature" release since 1.0. -Prior to 1.0 all releases were feature releases; they came out when they were ready. -Since then all releases (except 1.6) have been timed releases. +Prior to 1.0, all releases were feature releases; they came out when they were ready. +Since then, all releases (except 1.6) have been timed releases. The release candidate is cut from the main branch every 3 months; and they are released after an [extensive round of additional checking](https://github.com/JuliaLang/julia/pull/35846), which often takes weeks to months. Julia 1.6 was soft-slated to be the new long-term-support (LTS) version that would have bug-fixes backported to it for the next few years. The current LTS is Julia 1.0, which has now had 5 patch releases made. -Since it was going to be supported for a long-time people wanted to make sure everything good got in; thus it was a feature release. +Since it was going to be supported for a long-time, people wanted to make sure everything good got in; thus it was a feature release. The core developers have demurred on if 1.6 will actually be selected to be the new LTS (even if it is selected, it won't ascend to being the LTS til it stops being the current Stable). My impression now is that they feel like it has too many cool new things; and that a few things didn't quite make it in even with the extended release cycle. From 8eba5dad8255e887189308959876814cc9675532 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 11:07:58 +0000 Subject: [PATCH 48/62] Update _posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Nils --- _posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md index 235422f5..5f900f1d 100644 --- a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md @@ -36,7 +36,7 @@ There have been too many cool new things (as this post will detail) to stay back Already a lot of packages have dropped support for Julia versions older than 1.3. This post is kind of a follow-up to my [Julia 1.0 release run-down]({{site.url}}//2018/06/01/Julia-Favourite-New-Things.html). -But it's going to be even longer, as it is covering the last 5 releases since then; and I am not skipping the major new features. +But it's going to be even longer, as it is covering the last 5 releases since then and I am not skipping the major new features. I am writing this not to break down release by release, but to highlight features that had you only used Julia 1.0, you wouldn't have seen. Full details can be found in the [NEWS.md](https://github.com/JuliaLang/julia/blob/backports-release-1.6/NEWS.md), and [HISTORY.md](https://github.com/JuliaLang/julia/blob/backports-release-1.6/HISTORY.md) From 3d05791803eb62ae01bb0c1d0e06398f70efd7ed Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 11:09:54 +0000 Subject: [PATCH 49/62] Update _posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md Co-authored-by: Vaibhav Kumar Dixit --- _posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md index 5f900f1d..7b775167 100644 --- a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md @@ -38,7 +38,7 @@ Already a lot of packages have dropped support for Julia versions older than 1.3 This post is kind of a follow-up to my [Julia 1.0 release run-down]({{site.url}}//2018/06/01/Julia-Favourite-New-Things.html). But it's going to be even longer, as it is covering the last 5 releases since then and I am not skipping the major new features. I am writing this not to break down release by release, -but to highlight features that had you only used Julia 1.0, you wouldn't have seen. +but to highlight features that, had you only used Julia 1.0, you wouldn't have seen. Full details can be found in the [NEWS.md](https://github.com/JuliaLang/julia/blob/backports-release-1.6/NEWS.md), and [HISTORY.md](https://github.com/JuliaLang/julia/blob/backports-release-1.6/HISTORY.md) From eb0cead6fd5d787f8a5fa9f7579fcc6decc98bb8 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 11:11:19 +0000 Subject: [PATCH 50/62] Apply suggestions from code review Co-authored-by: Nils --- _posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md index 7b775167..c2bd6f13 100644 --- a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md @@ -55,7 +55,7 @@ The same behavior outside the REPL now gives a warning. Personally, this change never affected me because I never write for-loops that assign variables at global scope. Indeed basically all I code I write is inside functions. -But I do see how this causes problems for some interactive workflows, especially e.g. when demonstrating something. +But I do see how this causes problems for some interactive workflows, e.g. when demonstrating something. See the [main GitHub issue](https://github.com/JuliaLang/julia/issues/28789) and the longest [Discourse thread](https://discourse.julialang.org/t/another-possible-solution-to-the-global-scope-debacle/15894), though there were many others. It took over a year of discussion to work out the solution, particularly because many of the more obvious solutions would be breaking in significant ways. @@ -63,13 +63,11 @@ It took over a year of discussion to work out the solution, particularly because Julia 1.5+ doesn't display deprecation warnings, unless you run julia with the `--depwarn=yes` flag. This was me in [this PR](https://github.com/JuliaLang/julia/pull/35362). -It's not something I am super happy about. -Though I think it makes sense. +It's not something I am super happy about, although I think it makes sense. Using deprecated methods is actually fine, as long as your dependencies follow SemVer, and you use `^` (i.e. default) bounding in your Project.toml's `[compat]`, which everyone does, because it's enforced by the auto-merge script in the General registry. Often it is even necessary if you want to keep compatibility for a while. -Solving deprecations is a thing you should actively chose to do. -Not casually, when trying to do something else. +Solving deprecations is a thing you should actively choose to do rather than casually when trying to do something else. In particular, when updating to support a new major release of one of your dependencies, you should follow a process. Something like: From ddabd87f0524908063f48df7e2fe03bdda961aa5 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 11:13:12 +0000 Subject: [PATCH 51/62] Apply suggestions from code review Co-authored-by: Nils --- ...-14-Julia-1.6-what-has-changed-since-1.0.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md index c2bd6f13..30349b0a 100644 --- a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md @@ -74,21 +74,21 @@ Something like: 1. Check the release notes. 2. Relax compat bounds 3. Run your integration tests, if everything passes you are done. - 4. Of failed revert change to compat bounds rerun your integration tests paying attention to deprecation warnings. + 4. If tests failed, revert the change to compat bounds, then rerun your integration tests paying attention to deprecation warnings. Updating your dependencies should be an active choice. Potentially one that is automated, but not one that you do while adding another feature (if you can help it). -The core of the reason we disabled them is because they was actually breaking things. +The core of the reason we disabled them is because they were actually breaking things. Irrelevant deprecation warning spam from dependencies of dependencies was causing JuMP and LightGraphs to become too slow to be used. -Further, since they were from dependencies of dependencies the maintainers of JuMP and LightGraphs couldn’t even fix them, let alone the end users. +Further, since they were from dependencies of dependencies, the maintainers of JuMP and LightGraphs (let alone the end users) couldn’t even fix them. -Deprecation warnings are still turned on by default in tests, which makes sense since the tests (unlike normal use), are being run by maintainers of the package itself, not its users. -Though it still doesn't make a huge amount of sense to me, since spam from deprecation warnings floods out your actual errors that you want to see during testing. -For this reason [Invenia](https://invenia.ca) (my employer) has disabled deprecation warnings in the CI tests for all our closed source packages, and added a new test job that just does deprecation checking (set to error on deprecation and with the job allowed to fail, just so we are informed). +Deprecation warnings are still turned on by default in tests, which makes sense since the tests (unlike normal use) are being run by maintainers of the package itself, not its users. +This however still doesn't make a huge amount of sense to me, since spam from deprecation warnings floods out the actual errors that you want to see during testing. +For this reason, [Invenia](https://invenia.ca) (my employer) has disabled deprecation warnings in the CI tests for all our closed source packages, and added a new test job that just does deprecation checking (set to error on deprecation and with the job allowed to fail, just so we are informed). Hopefully one day we can improve the tooling around deprecation warnings. -An ideal behavior would be to only show deprecation warning if directly caused by a function call made from within the package module of your current active environment. +An ideal behavior would be to only show deprecation warnings if directly caused by a function call made from within the package module of your current active environment. I kind of know what we need to do to the logger to make that possible, but it is not yet something I have had time to do. ### Code highlighting for `code_llvm` and `code_native` @@ -105,13 +105,13 @@ That package does still work on Julia 1.0. ![Julia 1.6 code_llvm]({{site.url}}/posts_assets/Julia-1.0-1.6-changes/julia-1.6-code-llvm.png) The REPL itself still doesn't have syntax highlighting for Julia code though. -The [OhMyRepl](https://github.com/KristofferC/OhMyREPL.jl) package does provide that, and works all versions of Julia. +The [OhMyRepl](https://github.com/KristofferC/OhMyREPL.jl) package does provide that, and works for all versions of Julia. It is a lot more than a series of regexes though, so I don't think we are going to see it built into Julia too soon. Probably one day, though, as its big dependencies are also required if the parser wants to move to be written in Julia (though I also don't expect that any time soon). ### Clearer Stacktraces -Just like colored `code_llvm` colored stack-traces also originated in a package that was doing some nasty monkey-patching: [ClearStacktrace.jl](https://github.com/jkrumbiegel/ClearStacktrace.jl). +Just like colored `code_llvm`, colored stack-traces also originated in a package that was doing some nasty monkey-patching: [ClearStacktrace.jl](https://github.com/jkrumbiegel/ClearStacktrace.jl). This was added into Base itself in [#36134](https://github.com/JuliaLang/julia/pull/36134). **Julia 1.0:** From 31fd848088d0ff972986761db7a5654f2f1798dd Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 11:14:12 +0000 Subject: [PATCH 52/62] Apply suggestions from code review Co-authored-by: Nils --- ...-14-Julia-1.6-what-has-changed-since-1.0.md | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md index 30349b0a..c09b61a7 100644 --- a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md @@ -131,12 +131,11 @@ The whole thing just looks more modern and polished. [Julia 1.5 added built-in support](https://julialang.org/blog/2020/05/rr/) for [`rr`](https://rr-project.org/) (Linux only for now). This is not useful so much for debugging Julia code, but for debugging Julia itself. -If you get into one of the very rare situations where the language is doing something truly nonsense; you can send a bug report recorded via `rr`, and someone can debug *exactly* what is happening. -Event to the extent of [diagnosing faulty RAM](https://julialang.org/blog/2020/09/rr-memory-magic/). +If you get into one of the very rare situations where the language is doing something truly nonsensical, you can send a bug report recorded via `rr` and someone can debug *exactly* what is happening - even to the extent of [diagnosing faulty RAM](https://julialang.org/blog/2020/09/rr-memory-magic/). `rr` is an impressively cool piece of tech. It leverages the fact that linux's `fork` is incredibly cheap, because it takes advantage of [copy-on-write](https://en.wikipedia.org/wiki/Copy-on-write). `rr` basically `fork`s before every system call, and serializes what is going on; so you have a full track of everything that happened. -The `rr` integration is really nice and easy to use; I had someone who was part of our architecture and operations team (i.e. who writes way more CloudFormation than Julia) run it to submit a support request; and they had no real troubles. +The `rr` integration is really nice and easy to use; I had someone who was part of our architecture and operations team (i.e. who writes way more CloudFormation than Julia) run it to submit a support request and they had no real troubles. ## Syntax @@ -148,16 +147,15 @@ This will be familiar to Python folks who love to write things like: `import num I hope we never see it used that ubiquitously in julia. I am quite happy with `using Foo` which imports into scope everything the author of `Foo` exported. Python people find that super weird and scary; but it's fine. -Note that in Julia, many (most?) things you use don't belong to the scope of module that defined them anyways; they are overloaded functions e.g. from `Base`. +Note that in Julia, many (most?) things you use don't belong to the scope of the module that defined them anyways; they are overloaded functions e.g. from `Base`. So what you import doesn't matter as much as you might think. I recently learned that Haskell is like Julia with a default of importing all exports. -It has been fine for julia, and find for Haskell for ages. -Though in neither is it required. +It has been fine for julia, and fine for Haskell for ages, though in neither is it required. The real value of `import FooBar as fb` is not to have a short abbreviation so you can do `fb.quux`. That was already possible via `const fb = FooBar`. It is to handle cases where the package name itself conflicts with an identifier. -For example (as has often occurred) if one has uses a `Pipe` in the REPL, and then later wants to load [Pipe.jl](https://github.com/oxinabox/Pipe.jl/) via `import Pipe` then one gets a name clash before it can be even be loaded. +For example (as has often occurred) if one uses a `Pipe` in the REPL, and then later wants to load [Pipe.jl](https://github.com/oxinabox/Pipe.jl/) via `import Pipe` then one gets a name clash before it can be even be loaded. Now one can do `import Pipe as PipingPipe`. ### NamedTuple/keyword arguments automatic naming @@ -179,7 +177,7 @@ foo(x1, x2; a=10, b=20, c=30, op=+) = foo(Bar(op(x1, x2)); a, b, c) This does come with the requirement to separate keyword arguments from positional arguments by `;`, but I have always done this, and the [BlueStyle guide requires it](https://github.com/invenia/BlueStyle#keyword-arguments). It feels like we are fully leveraging the distinction of keyword from positional arguments by allowing this. -Where as the previous motivation for this distinction (vs e.g. C# and Python that allow any positional argument to be passed by name), was to make the name of positional arguments not part of the public API, thus avoiding changing it being a breaking change. +Whereas the previous motivation for this distinction (vs e.g. C# and Python that allow any positional argument to be passed by name), was to make the name of positional arguments not part of the public API, thus avoiding changing it being a breaking change. This new feature feels more ... featureful. The same syntax can be used to create `NamedTuple`s. @@ -191,7 +189,7 @@ julia> price = [2, 4, 1]; julia> (;product, price) (product = ["fries", "burger", "drink"], price = [2, 4, 1]) ``` -This is particularly cool for constructing `NamedTuple`s of `Vector`s, which is a valid [Tables.jl Table](https://tables.juliadata.org/stable/#Tables.columntable). +This is particularly cool for constructing a `NamedTuple` of `Vector`s, which is a valid [Tables.jl Table](https://tables.juliadata.org/stable/#Tables.columntable). It is interesting to note that for the logging macros introduced in Julia 1.0, this is how they have always worked. E.g. `@info "message" foo bar` and `@info "message" foo=foo bar=bar` display the same, which has always felt natural. @@ -211,7 +209,7 @@ This was promised in 2016 as a feature for 1.0 (released 2018), but we actually In short, the process of allocating memory from the heap is fairly slow\*, whereas allocating memory on the stack is basically a no-op. Indeed, Julia benchmarking tools don't count allocations on the stack as allocations at all. One can find extensive write ups of heap vs. stack allocations and how it works in general (though some mix the C specific factors with the CPU details). -In Julia all mutable objects live are allocated on the heap. +In Julia, all mutable objects live on the heap. Until recently, immutable objects that contained references to heap allocated objects also had to live on the heap, i.e. only immutable objects with immutable fields (with immutable fields with...) could live on the stack. But with this change, all immutable objects can live on the stack, even if some of their fields live on the heap. An important consequence of this is that wrapper types, such as the `SubArray` returned from `@view x[1:2]`, now have no overhead to create. From 655995c14bb28afc83b71bc74c221809b98b5278 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 11:16:31 +0000 Subject: [PATCH 53/62] Apply suggestions from code review Co-authored-by: Nils --- ...14-Julia-1.6-what-has-changed-since-1.0.md | 23 +++++++++---------- 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md index c09b61a7..87c05d52 100644 --- a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md @@ -54,7 +54,7 @@ Now in the REPL, assigning to a global variable within a for-loop actually assig The same behavior outside the REPL now gives a warning. Personally, this change never affected me because I never write for-loops that assign variables at global scope. -Indeed basically all I code I write is inside functions. +Indeed basically all code I write is inside functions. But I do see how this causes problems for some interactive workflows, e.g. when demonstrating something. See the [main GitHub issue](https://github.com/JuliaLang/julia/issues/28789) and the longest [Discourse thread](https://discourse.julialang.org/t/another-possible-solution-to-the-global-scope-debacle/15894), though there were many others. It took over a year of discussion to work out the solution, particularly because many of the more obvious solutions would be breaking in significant ways. @@ -220,7 +220,7 @@ I find that in practice this often adds up to a 10-30% speed-up in real world co ## Pkg stdlib and the General Registry Some of the biggest changes have been in maturing out Pkg3, and its surroundings. -Writing this section is a bit hard as there is no `NEWS.md` nor `HISTORY.md` for the Pkg stdlib; and the General registry is more policy than software. +Writing this section is a bit hard as there is no `NEWS.md` nor `HISTORY.md` for the Pkg stdlib, and the General registry is more policy than software. ### Resolver willing to downgrade packages to install new ones (Tiered Resolution) @@ -238,23 +238,23 @@ For example, consider the following set of packages with different versions and - v1: compatible only with Foo v1 If you did `pkg"add Foo Bar Qux"` you would end up with **Foo** v1, **Bar** v1, and **Qux** v1. -But if you did first: `pkg"add Foo Bar"` (which would install **Foo** v2, and **Bar** v2), +But if you first did: `pkg"add Foo Bar"` (which would install **Foo** v2, and **Bar** v2), and then did `pkg"add Qux"`, then on Julia 1.0 you would get an error, as it would refuse to downgrade **Foo** and **Bar** to v1, as is required to allow **Qux** to be installed. -This meant that the package manager is effectively stateful, which turns out to be really counter intuitive. +This meant that the package manager is effectively stateful, which turns out to be really counterintuitive. The way to resolve this in practice was to delete the `Manifest.toml`, which is where the state is stored, and do it again as a single action. This was a significant problem for [test-time dependencies](https://github.com/JuliaLang/Pkg.jl/issues/1352), where if you had a test-time dependency with an indirect dependency shared with a main dependency of the package, but that was only compatible with an older version of the indirect dependency, you would be unable to run tests as resolving the test-time dependency would fail. -With the new tiered resolved, `pkg"add ..."` will try a number of increasingly relaxed strategies to try and install the new package while making minimal changes to the versions of the existing packages that are already installed. +With the new tiered resolver, `pkg"add ..."` will try a number of increasingly relaxed strategies to try and install the new package while making minimal changes to the versions of the existing packages that are already installed. You can see the full description of the [`--preserve` options in the Pkg docs](https://julialang.github.io/Pkg.jl/v1/repl/#repl-add). - First it will try and avoid all changes. - Then it will try and avoid changing the version of direct dependencies, but allow changes to the version of indirect dependencies. - Then it will allow changes to direct dependencies, but will avoid changing the major (or minor pre-1.0) version, even if `Project.toml`’s `[compat]` says it is allowed to. - Then it will allow changes to everything (as long as permitted by `Project.toml`’s `[compat]` section) - - Finally it will give up and error if it still can’t find a compatible set of package that let it add the new one. + - Finally it will give up and error if it still can’t find a compatible set of packages that let it add the new one. -For a package with a fully filled in `[compat]` section in the `Project.toml`, then it is always fine to allow changes to everything -- which is what this will do if it has to. +For a package with a fully filled in `[compat]` section in the `Project.toml`, it is always fine to allow changes to everything -- which is what this will do if it has to. If one hasn't setup the `[compat]` section then this can be nice to minimize changes. (But also: please setup your `[compat]` bounds, for your own sake.) @@ -274,8 +274,7 @@ It's easy to forget this one, but it is actually one of the biggest usability en More dramatic, is the parallelism of precompilation added in 1.6. Precompiling a package requires precompiling all its dependencies first (and so forth). -This is now done in parallel, and (as mentioned) is automatically triggered when you complete Pkg operations. -In contrast to happening serially the first time a package is loaded. +This is now done in parallel, and (as mentioned) is automatically triggered when you complete Pkg operations, in contrast to happening serially the first time a package is loaded. No more waiting 5 minutes the first time you load a package with a lot of dependencies. Further, the spiffy animated output shows you what is precompiling at any given time, as well as a progress bar, which makes it feel (even) faster. @@ -330,7 +329,7 @@ I think the new method is much cleaner and easier to read. ### Temporary Environments Julia 1.5 added `pkg"activate --temp"` which will create and activate a temporary environment. -This environment is deleted when Julia exists. +This environment is deleted when Julia exits. This is incredibly handy for: - Reproducing an issue. @@ -348,10 +347,10 @@ Though there is a better way if you use `test/Project.toml`. ### Test Dependencies in their own Project.toml In Julia 1.0 and 1.1, [test specific dependencies are listed in the `[extras]` section](https://julialang.github.io/Pkg.jl/v1/creating-packages/#Test-specific-dependencies-in-Julia-1.0-and-1.1), and under `[targets] test=[...]`; with their compatibility listed in the main `[compat]`. It seemed like this might be extended for other things in the future, perhaps documentation. -But it was found for documentation a separate `Project.toml` worked well (as long as you `pkg"dev .."` the parent package directory into its `Manifest.toml` so it uses the right version of the package it is documenting). +But it was found that for documentation, a separate `Project.toml` worked well (as long as you `pkg"dev .."` the parent package directory into its `Manifest.toml` so it uses the right version of the package it is documenting). The new `test/Project.toml` extends that idea. -One part of that extension is to remove the need to `dev ..`, and other part is to make available all the main dependencies. +One part of that extension is to remove the need to `dev ..`, the other part is to make available all the main dependencies. It actually works on a different mechanism than the docs. It relies on stacked environments, which is a feature Julia has had since 1.0 via `LOAD_PATH`, but that is rarely used. From 2039656c0b0d9cb1bb78677560d7ebe376ee632e Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 11:19:20 +0000 Subject: [PATCH 54/62] Apply suggestions from code review Co-authored-by: Nils --- ...14-Julia-1.6-what-has-changed-since-1.0.md | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md index 87c05d52..37f54843 100644 --- a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md @@ -354,7 +354,7 @@ One part of that extension is to remove the need to `dev ..`, the other part is It actually works on a different mechanism than the docs. It relies on stacked environments, which is a feature Julia has had since 1.0 via `LOAD_PATH`, but that is rarely used. -One advantage of this is that you can activate that Project.toml, on top of the your existing environment by adding the test directory to your `LOAD_PATH`. +One advantage of this is that you can activate that Project.toml on top of your existing environment by adding the test directory to your `LOAD_PATH`. I wrote some more details on exactly how to do that on [Discourse]( https://discourse.julialang.org/t/activating-test-dependencies/48121/7?u=oxinabox). It feels kind of hacky, because it is. @@ -365,12 +365,12 @@ At some point there might be a nicer user interface for stacked environments lik This story was beginning to be told around Julia 1.0 time, but it wasn't really complete nor built into Pkg until Julia 1.3. -You can read the documentation on [Artifact](https://julialang.github.io/Pkg.jl/v1/artifacts/), [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl), and [Yggdasil](https://github.com/JuliaPackaging/Yggdrasil) for full details. +You can read the documentation on [Artifacts](https://julialang.github.io/Pkg.jl/v1/artifacts/), [BinaryBuilder](https://github.com/JuliaPackaging/BinaryBuilder.jl), and [Yggdrasil](https://github.com/JuliaPackaging/Yggdrasil) for full details. This is the story about how Julia works with binary dependencies. As of now, it works really well. Sometimes people misunderstand the claims about Julia solving the two language problem as saying everything should be rewritten in Julia. -Which is far from the truth, ain't noone got time for that. +Which is far from the truth, ain't nobody got time for that. Julia has always had great foreign function interfacing (FFI); like [`ccall`](https://docs.julialang.org/en/v1/manual/calling-c-and-fortran-code/) (as well as [PyCall](https://github.com/JuliaPy/PyCall.jl/), [RCall](https://github.com/JuliaInterop/RCall.jl), [JavaCall](https://github.com/JuliaInterop/JavaCall.jl), and a bunch of others, for non-C-style binaries). While FFI makes it easy to call binaries, how about actually getting them onto your machine? @@ -383,27 +383,27 @@ No more running arbitrary code during installs. The integration of installing the binaries into Pkg for Julia 1.3 is a major reason why many packages have dropped Julia 1.0 support. While you can still use BinaryProvider.jl for the download, it is much cleaner and easier to just use Julia 1.3+ and have the Artifact system take care of it. -I feel it is worth really mentioning [BinaryBuilder](https://binarybuilder.org/) here. +I feel it is worth mentioning [BinaryBuilder](https://binarybuilder.org/) here. While it has existed since before 1.0, it's really grown. -It's a super smooth cross-compilation environment, that can used to build binaries for every platform Julia runs on. +It's a super smooth cross-compilation environment, that can be used to build binaries for every platform Julia runs on. This is an amazing tool, built on [containerization](https://en.wikipedia.org/wiki/OS-level_virtualization). -It's a kind of build tool one dreamed of having 10 years ago. -It's far more general than Julia, I know people have at least experimented with using it with Nim. +It's the kind of build tool one dreamed of having 10 years ago. +It's far more general than Julia, and I know people have at least experimented with using it with Nim. I hope more things start using it. A novel use of this infrastructure is [`ygg`](https://discourse.julialang.org/t/announcing-ygg/54687). -`ygg` is effectively an installer for Yggdasil hosted binaries, that runs outside of julia. +`ygg` is effectively an installer for Yggdrasil hosted binaries that runs outside of julia. This pretty similar to a simple OS independent package manager (some-what like Conda or HomeBrew). -This is really neat, not simply because it is making use of all that work into cross-compiling the universe, but because its actually really useful. +This is really neat, not simply because it is making use of all the work that went into cross-compiling the universe, but because it's actually really useful. `ygg` installs everything to the normal julia artifact storage location, and doesn't require any kind of admin privileges. This is huge for working on systems with restricted privileges, like university PCs. -I used to use [junest](https://github.com/fsquillace/junest) for this, but it was a bit flakey because it used scary `proot` hacks. +I used to use [junest](https://github.com/fsquillace/junest) for this, but it was a bit flaky because it used scary `proot` hacks. When it broke, I often resorted to compiling things from source, so I could install them to my home directory. I have had to bootstrap a lot of things following [Linux From Scratch](http://www.linuxfromscratch.org/blfs/). `ygg` leverages the fact that the BinaryBuilder cross-compilation setup has already sorted out how to make portable binaries. ### Full Transition off METADATA.jl and REQUIRE files, and onto the General Registry -Even though Julia 1.0 had Pkg3 and was supposed to use registries, for a long time the old Pkg2 [METADATA.jl](https://github.com/JuliaLang/METADATA.jl) pseudo-registry was used as the the canonical source of truth. +Even though Julia 1.0 had Pkg3 and was supposed to use registries, for a long time the old Pkg2 [METADATA.jl](https://github.com/JuliaLang/METADATA.jl) pseudo-registry was used as the canonical source of truth. Registering new releases was made against that, and then a script synchronized the [General](https://github.com/JuliaRegistries/General) registry to match it. This was to allow time for packages to change over to supporting Julia 1.0, while still also making releases that supported Julia 0.6. It wasn't until about a year later that General registry became the one source of truth, and METADATA.jl was retired. From 8e07947690a87533fc14ca363afbfaabb1eae36d Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 11:22:57 +0000 Subject: [PATCH 55/62] Apply suggestions from code review Co-authored-by: Nils --- ...03-14-Julia-1.6-what-has-changed-since-1.0.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md index 37f54843..c2a7ce4a 100644 --- a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md @@ -409,10 +409,10 @@ This was to allow time for packages to change over to supporting Julia 1.0, whil It wasn't until about a year later that General registry became the one source of truth, and METADATA.jl was retired. This was kind of sucky, because a lot of the power of Pkg3 was blocked until then. -In particular, since the registries compat was generated from the REQUIRE file, and the `REQUIRE` files had kind of [gross requirement specification](https://docs.julialang.org/en/v0.6/manual/packages/#Requirements-Specification-1), and everyone basically just lower-bounded things, or didn't bound compat at all. +In particular, since the registries compat was generated from the REQUIRE file, and the `REQUIRE` files had a kind of [gross requirement specification](https://docs.julialang.org/en/v0.6/manual/packages/#Requirements-Specification-1), everyone basically just lower-bounded things, or didn't bound compat at all. Which made sense because with the single global environment that Pkg2 had, you basically needed the whole ecosystem to be compatible, so restricting was bad. But `Project.toml` has the much better default [`^`-bounds](https://julialang.github.io/Pkg.jl/v1/compatibility/#Version-specifier-format-1) to accept only non-breaking changes by SemVer. -And per project environments, things don't all have to be compatible -- just things used in a particular project (rather than every project you might ever do). +And with per project environments, things don't all have to be compatible -- just things used in a particular project (rather than every project you might ever do). #### Automatic Merging Initially after the transition, all registry PRs to make a new release needed manual review by one of the very small number of General registry maintainers. @@ -420,7 +420,7 @@ Initially after the transition, all registry PRs to make a new release needed ma I am a big fan of [releasing after every non-breaking PR](https://www.oxinabox.net/2019/09/28/Continuous-Delivery-For-Julia-Packages.html). Why would you not want those bug-fixes and new features released? It is especially rude to contributors who will make a fix or a feature, but then you don't let them use it because you didn't tag a release. -Plus it makes tracking down bugs easier, if it occurs on a precise version you know the PR that caused it. +Plus it makes tracking down bugs easier: if it occurs on a precise version, you know the PR that caused it. But with manual merging it feels bad to tag 5 releases in a day. Now we have automatic merging, so it is fine. At time of writing, [ChainRules.jl](https://github.com/JuliaDiff/ChainRules.jl/) was up to 78 releases. @@ -435,9 +435,9 @@ This was painful when it was done, since it rewrote compat in the registry, whic But now that it is done, it is good. #### Requirement to have a `Project.toml` -Finally the legacy ability to `pkg"dev ..."` packages that had only a `REQUIRE` file and no `Project.toml` was removed in Julia 1.4. +Finally, the legacy ability to `pkg"dev ..."` packages that had only a `REQUIRE` file and no `Project.toml` was removed in Julia 1.4. You can still `pkg"add ..."` them if they were registered during the transition period. -But to edit the projects they must a `Project.toml` file, which is fine since all the tools to register releases also require you to have `Project.toml` now. +But to edit the projects, they must have a `Project.toml` file, which is fine since all the tools to register releases also require you to have `Project.toml` now. ### PkgServer @@ -448,7 +448,7 @@ We were like _"Still slow, what are you talking about?"_. Turns out that GitHub's connection to Australia (at least for that API), is slow. Australian Internet has infamously high latency, downloading dozens of packages is often slow. GitHub wasn't optimized for this kind of use, it's not a content distribution network. -Its optimized for occationally cloning things you want to develop, not rapidly downloading dozens of things that you want to use. +Its optimized for occasionally cloning things you want to develop, not rapidly downloading dozens of things that you want to use. The new PkgServer solves this. It *is* optimized for distributing packages. @@ -460,8 +460,8 @@ Not only does this solve the latency problems of connecting to some GitHub serve Its always been possible to host Julia packages on anything supporting git. There are [over a dozen packages in General Registry hosted on GitLab](https://github.com/JuliaRegistries/General/search?q=gitlab) for example. My employer, [Invenia](https://www.invenia.ca/), has all our internal packages hosted on GitLab, and registered on our internal registry. -But in effect, prior to PkgServer, GitHub was 1st class in Pkg, due to its download as a tarball feature. -It makes sense, GitLab and BitBucket don't even provide an API for downloading a repo via content (tree) hash (only commit hash), so it can't be implemented inside Pkg itself. +But in effect, prior to PkgServer, GitHub was 1st class in Pkg, due to its download-as-a-tarball feature. +It makes sense - GitLab and BitBucket don't even provide an API for downloading a repo via content (tree) hash (only commit hash), so it can't be implemented inside Pkg itself. But by going via PkgServer, everything can be consistently served. PkgServer has not been without struggles. From 448335277ba603aa56307f306108b6c602503668 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 11:24:26 +0000 Subject: [PATCH 56/62] Apply suggestions from code review Co-authored-by: Nils --- ...14-Julia-1.6-what-has-changed-since-1.0.md | 22 +++++++++---------- 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md index c2a7ce4a..ec1f0ad5 100644 --- a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md @@ -464,8 +464,7 @@ But in effect, prior to PkgServer, GitHub was 1st class in Pkg, due to its downl It makes sense - GitLab and BitBucket don't even provide an API for downloading a repo via content (tree) hash (only commit hash), so it can't be implemented inside Pkg itself. But by going via PkgServer, everything can be consistently served. -PkgServer has not been without struggles. -Both social and technical. +PkgServer has not been without struggles, both social and technical. Early versions of the system stored some metrics, so that package authors could be informed of how many users their package had, what operating systems they used etc. After much debate, it was concluded that this might be too invasive, and it was removed before the Julia 1.5 release. On the technical side, it initially had some struggles with keeping up with the load, with some servers becoming overloaded. @@ -486,7 +485,7 @@ It is many, many, small methods, most of which are only called once. And unlike most Julia code, it doesn't actually benefit all that much from Julia's JIT. Julia's JIT is normally specializing code, and running a ton of optimizations. But plotting itself isn't in the hot-loop -- optimizing the code takes longer than running it the few dozen times it might be used unoptimized. -To make a long-story short, plotting is the postcard example for Julia needing to compile things before it can run them. +To make a long-story short, plotting is the poster child example for Julia needing to compile things before it can run them. **Julia 1.0:** @@ -500,7 +499,7 @@ julia> @time (using Plots; display(plot(1:0.1:10, sin.(1:0.1:10)))) 9.694037 seconds (18.29 M allocations: 1.164 GiB, 4.17% gc time, 0.40% compilation time) ``` -Sure it is still not instant, but it's a lot faster than it was. +Sure it is still not instantaneous, but it's a lot faster than it was. Note also that in the above timing I had already run precompilation, which seems fairest. Though as discussed earlier, precompilation caches often would be deleted in 1.0; and conversely are created much faster in 1.6 due to parallelization. @@ -509,15 +508,15 @@ If you were to count the precompilation time I regularly saw in 1.0, TTFP could ### Invalidations Consider a function `foo` with a method `foo(::Number)`. -If some other function calls it, for example `bar(x::Int) = 2*foo(x)`, the JIT will compile in an instruction for exactly the method instance to call -- a fast static dispatch, possibly even inlined. -If the user then defines a new, more specific method `foo(::Int)`, then the compiled code for `Bar` need to be invalidated so it will call the new one. +If some other function calls it, for example `bar(x::Int) = 2*foo(x)`, the JIT will compile instruction for exactly the method instance to call -- a fast static dispatch, possibly even inlined. +If the user then defines a new, more specific method `foo(::Int)`, the compiled code for `Bar` needs to be invalidated so it will call the new one. It needs to be recompiled -- which means anything that statically dispatches to it needs to be recompiled, and so forth. This is an invalidation. It's an important feature of the language. It is key to extensibility. It doesn't normally cause too many problems, since generally, basically everything is defined before anything is called, and thus before anything is compiled. -A notable exception to this is Base an the other standard libraries. +A notable exception to this is Base and the other standard libraries. These are compiled into the so-called system image. Furthermore, methods in these standard libraries are some of the most overloaded, thus most likely to be invalidated. @@ -525,8 +524,7 @@ A bunch of work has gone into dealing with invalidations better. Not just point-fixes to remove calls that were likely to be invalidated, but several changes to the compiler. One particular change was not triggering cascading invalidations for methods that couldn't actually be called due the being ambiguous. As a result, a lot of user code that triggered invalidations on 1.5 no longer does so on 1.6. -The end result of this is faster compilation after loading packages, since it doesn't have to recompile a ton of invalidated method instances. -i.e. decreased time to first plot. +The end result of this is faster compilation after loading packages, since it doesn't have to recompile a ton of invalidated method instances - i.e. a decreased time to first plot. This has had a huge effect on [Revise.jl](https://github.com/timholy/Revise.jl) which started to take several seconds to load when it gained the dependency on [JuliaInterpreter.jl](https://github.com/JuliaDebug/JuliaInterpreter.jl); which isn't much, but when you do it every time you start Julia it is an annoying lack of snappiness. @@ -563,7 +561,7 @@ For example, in [Zygote](https://github.com/FluxML/Zygote.jl), generating the gr So you want to be able to trigger the regeneration of this code when that original method changes. -Basically, this allows code that uses [Cassette.jl](https://github.com/JuliaLabs/Cassette.jl), and [IRTools.jl](https://github.com/MikeInnes/IRTools.jl) and similar to not suffer from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. +Basically, this allows code that uses [Cassette.jl](https://github.com/JuliaLabs/Cassette.jl), [IRTools.jl](https://github.com/MikeInnes/IRTools.jl) and similar approaches to not suffer from [#265](https://github.com/JuliaLang/julia/issues/265)-like problems. A particular case of this is for [Zygote](https://github.com/FluxML/Zygote.jl), where redefining a function called by the code that was being differentiated did not result in an updated gradient (unless `Zygote.refresh()`) was run. This was annoying for working in the REPL, where you might e.g. change your neural nets loss function without redefining the network. In that case, if you forgot to run `Zyote.refresh()` it would just train wrong. @@ -597,8 +595,8 @@ No more having to remember to set the `JULIA_NUM_THREADS` environment variable b ### 5-arg `mul!`: in-place generalized multiplication and addition The 5 arg `mul!(C, A, B, α, β)` performs the operation equivalent to: `C .= A*B*α + C*β`, where `α`, `β` are scalars and `A`, `B` and `C` are compatible mixes of scalars, matrices and vectors. I am still of the opinion that it should have been called `muladd!`. -This is the human-friendly version of `BLAS.gemm!` (i.e. GEneralized Matrix Multiplication) and it's ilk, thus the name `mul!`. -It promises to alway compute the in-place mul-add in the most efficient, correct way for any `AbstractArray` subtype. +This is the human-friendly version of `BLAS.gemm!` (i.e. GEneralized Matrix Multiplication) and its ilk, thus the name `mul!`. +It promises to always compute the in-place mul-add in the most efficient, correct way for any `AbstractArray` subtype. In contrast, `BLAS.gemm!` computes the same thing, but with a bunch of conditions. It must be a strided array containing only BLAS scalars, and if one of the inputs is conjugated/transposed you need to input it in non-conjugated/transposed form, and then tell `BLAS.gemm!` via passing in `'C'` or `T` rather than `N`. 5-arg `mul!` takes care of all that for you dispatching to `BLAS.gemm!` or other suitable methods once it has that all sorted. From f2246b9d00741ed8a3f0a30f2909286a74009e5c Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 11:43:52 +0000 Subject: [PATCH 57/62] Apply suggestions from code review Co-authored-by: Nils --- ...14-Julia-1.6-what-has-changed-since-1.0.md | 21 ++++++++++--------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md index ec1f0ad5..2319c388 100644 --- a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md @@ -598,28 +598,27 @@ I am still of the opinion that it should have been called `muladd!`. This is the human-friendly version of `BLAS.gemm!` (i.e. GEneralized Matrix Multiplication) and its ilk, thus the name `mul!`. It promises to always compute the in-place mul-add in the most efficient, correct way for any `AbstractArray` subtype. In contrast, `BLAS.gemm!` computes the same thing, but with a bunch of conditions. -It must be a strided array containing only BLAS scalars, and if one of the inputs is conjugated/transposed you need to input it in non-conjugated/transposed form, and then tell `BLAS.gemm!` via passing in `'C'` or `T` rather than `N`. +It must be a strided array containing only BLAS scalars, and if one of the inputs is conjugated/transposed you need to input it in non-conjugated/transposed form, and then tell `BLAS.gemm!` via passing in `C` or `T` rather than `N`. 5-arg `mul!` takes care of all that for you dispatching to `BLAS.gemm!` or other suitable methods once it has that all sorted. Further, the existing 3-arg `mul!(C, A, B)` is a special case of it: -`mul!(C, A, B) = mul!(C, A, B, false, true)` (`true` and `false` being 1, and strong 0). +`mul!(C, A, B) = mul!(C, A, B, true, false)` (`true` and `false` being 1, and strong 0). So you can just implement the 5-arg form and be done with it. I personally didn't care about 5-arg `mul!` at all for a long time. It was yet another in-place function in `LinearAlgebra` that I would never use often enough to remember what it did, and thus wouldn't use. But I realized that it is a crucial function for my own area: automatic differentiation. -`mul(C, A, B, true, true)` is bthe in-place accumulation rule for the reverse mode equivalent of the product rule. +`mul(C, A, B, true, true)` is the in-place accumulation rule for the reverse mode equivalent of the product rule. ### You can now print and interpolate `nothing` into strings. This is one of mine [#32148](https://github.com/JuliaLang/julia/pull/32148), and I find it is such a usability enhancement. -So many small frustrations in Julia 1.0 related to interpolating a variable containing `nothing` into a string. -Often occurring when you are adding a quick `println` to debug something not being the value you expected; or when building a string for some other error message. +So many small frustrations in Julia 1.0 related to interpolating a variable containing `nothing` into a string, often occurring when you are adding a quick `println` to debug something not being the value you expected, or when building a string for some other error message. Arguably both of those are better done via other means (`@show`, and values stored in fields in the error type); but we don't always do what is best. Sometimes it is expedient to just interpolate things into strings without worrying about what type they are. ### `Base.download` now using libcurl -For a very long time the `download` function which retrieves things over HTTP, was implemented with an amazing hack. +For a very long time, the `download` function which retrieves things over HTTP was implemented with an amazing hack: It conditionally shelled out to different programs. On Windows it ran a mildly scary PowerShell script. On Unixen it first tried to use `curl`, then if that wasn't installed it tried to use `wget`, and then if that wasn't installed it tried to use `fetch`. @@ -645,7 +644,7 @@ But it doesn't have the problem of really confounding them if you are wrong, sin Right now this is not used anywhere in the standard libraries. At first, I thought that made sense since they can just edit the original `show_error`. But now I think since it can be used for a subset of a particular type of exception it could well be useful for some of [`Base`'s interfaces](https://docs.julialang.org/en/v1/manual/interfaces/) exactly as described above. -According to [JuliaHub search](https://juliahub.com/ui/CodeSearch?q=register_error_hint&u=all&t=all), right now there is just one package using it. +According to [JuliaHub search](https://juliahub.com/ui/CodeSearch?q=register_error_hint&u=all&t=all), right now there is just one package using it: ColorTypes.jl is [using it to explain some _consensual type-piracy_](https://github.com/JuliaGraphics/ColorTypes.jl/blob/bd31741d162361ebd44ed05ae532266998d9ce9f/src/error_hints.jl), where for some functions on its types you need to load another package. This is an experimental features, so it's not covered by the SemVer guarantees of the rest of the language. @@ -655,10 +654,9 @@ Which they will probably do as people start dropping support for older Julia ver ### A bunch of curried functions -Julia has have ended up with a convention of providing curried methods of functions if that would be useful as the first argument for `filter`. +Julia has ended up with a convention of providing curried methods of functions if that would be useful as the first argument for `filter`. For example, `filter(isequal(2), [1,2,3,2,1])` is the same is `filter(x->isequal(x, 2), [1,2,3,2,1])`. -In particular these are boolean comparison-like functions: -with two arguments, where the thing being compared against is the second. +In particular these are boolean comparison-like functions with two arguments, where the thing being compared against is the second. Julia 1.0 had `isequal`, `==` and `in`. Since then we have added: `isapprox`, `<`,`<=`,`>`, `>=`, `!=`, `startswith`, `endswith`, and `contains`. @@ -683,6 +681,9 @@ Which I guess is its theoretical main point, but I think preventing people from The experimental [`Base.@locals`](https://github.com/JuliaLang/julia/issues/29733) was added, which returns a dictionary of local variables. That one surprised me; I though being able to access a dictionary of local variables would get in the way of the optimizer, since it would prevent it from being able to optimize variables that are used for intermediate values away entirely. +However, the way it functions is to generate code with creates and fills dictionary of with references to variables where it is used. +The dictionary is not maintained at all times. +So this shouldn't block optimizations in functions that don't use it. But the compiler folks know better than I do. [`splitpath`](https://github.com/JuliaLang/julia/issues/28156) is added, it's the opposite of `joinpath`. From 159f58a7ed44bb5beda6566038918fc6a3e6e91a Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 11:46:53 +0000 Subject: [PATCH 58/62] Apply suggestions from code review --- _posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md index 2319c388..3b9ae098 100644 --- a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md @@ -689,7 +689,7 @@ But the compiler folks know better than I do. [`splitpath`](https://github.com/JuliaLang/julia/issues/28156) is added, it's the opposite of `joinpath`. Kind of silly we didn't have that, and had been bugging me at least since 0.6. -On things that had been bugging me, I had wanted [`eachslice` and its special cases: `eachrow` & `eachcol`](https://github.com/JuliaLang/julia/issues/29749) since Julia 0.3 when I first started using it. +Similarly, I have wanted [`eachslice` and its special cases: `eachrow` & `eachcol`](https://github.com/JuliaLang/julia/issues/29749) since Julia 0.3 when I first started using the language. These are super handy, for example when you want to iterate through vectors of the rows of a matrix. `redirect_stderr` and `redirect_stdout` now work with `devnull`. @@ -709,7 +709,7 @@ I think for Julia 2.0 we should consider making it the default. Also added was a `sort` argument, which I don't see the point of so much, since `sort(readdir(x))` seems cleaner than `readdir(x; sort=true)`; and because I rarely rely on processing files in order. -`ccall` is now available as a macro `@ccall` which lets you specify +`ccall` is now available as a macro `@ccall` which lets you specify the types sin a similar way to Julia's normal type-assertions. There was a [short juliacon talk about this](https://www.youtube.com/watch?v=wofq1DdXM3s) So now one can do `@ccall(sqrt(4.0::Cdouble)::Cdouble)` rather than `ccall(:sqrt, Cdouble, (Cdouble,), 4.0,)` From 3673669c3036760951d62f4fde2ed69e94d35521 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 11:58:40 +0000 Subject: [PATCH 59/62] more --- ...1-03-14-Julia-1.6-what-has-changed-since-1.0.md | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md index 3b9ae098..ea2309f3 100644 --- a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md @@ -175,7 +175,7 @@ This new feature allows one to avoid writing `(...; a=a, b=b, c=c)`, and instead foo(x1, x2; a=10, b=20, c=30, op=+) = foo(Bar(op(x1, x2)); a, b, c) ``` -This does come with the requirement to separate keyword arguments from positional arguments by `;`, but I have always done this, and the [BlueStyle guide requires it](https://github.com/invenia/BlueStyle#keyword-arguments). +This does come with the requirement to separate keyword arguments from positional arguments by `;`, but I have always done this, and the two most popular style guides for Julia both require it ([BlueStyle](https://github.com/invenia/BlueStyle#keyword-arguments), [YASGuide](https://github.com/jrevels/YASGuide/tree/bb636744bb117a6e1f115789a1e0f5034b7360ae#other-syntax-guidelines)). It feels like we are fully leveraging the distinction of keyword from positional arguments by allowing this. Whereas the previous motivation for this distinction (vs e.g. C# and Python that allow any positional argument to be passed by name), was to make the name of positional arguments not part of the public API, thus avoiding changing it being a breaking change. This new feature feels more ... featureful. @@ -716,6 +716,10 @@ rather than `ccall(:sqrt, Cdouble, (Cdouble,), 4.0,)` This is what `ccall` always should have been. In Julia 1.7, [`invoke` is getting the same treatment.](https://github.com/JuliaLang/julia/pull/38438). +In 1.5 NamedTuples got a similarly styled macro to help construct the types. +This was added in [#34548](https://github.com/JuliaLang/julia/pull/34548). +Generally when working with NamedTuples you don't need to mention the type but when you do it was quite verbose and ugly. +Now you write: `@NamedTuple{str::String, x::Float64}` rather than `NamedTuple{(:str, :x), Tuple{String, Float64}`. ### More "Why didn't it always work that way" than I can count Since 1.0's release, there have been so many small improvements to functions that I didn't even know happened, because I assumed they always worked that way. @@ -728,4 +732,10 @@ Julia 1.6 is one hell of a more polished language. --- Thanks to all the people who contributed to editing the post. -Especially: [Mosè Giordano](https://giordano.github.io/), and [Chris de Graaf](https://cdg.dev/). + +Especially: +[Chris de Graaf](https://cdg.dev/), +[Nils Gudat](https://github.com/nilshg), +[Miguel Raz Guzmán Macedo](https://github.com/miguelraz), +[Mosè Giordano](https://giordano.github.io/), +and [Eric Hanson](https://ericphanson.com/). From a68a5a5fbd97414213aadf434a1efa3e2045ed51 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 13:13:24 +0000 Subject: [PATCH 60/62] fix rr references --- .../2021-03-14-Julia-1.6-what-has-changed-since-1.0.md | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md index ea2309f3..077176d9 100644 --- a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md @@ -133,9 +133,12 @@ The whole thing just looks more modern and polished. This is not useful so much for debugging Julia code, but for debugging Julia itself. If you get into one of the very rare situations where the language is doing something truly nonsensical, you can send a bug report recorded via `rr` and someone can debug *exactly* what is happening - even to the extent of [diagnosing faulty RAM](https://julialang.org/blog/2020/09/rr-memory-magic/). `rr` is an impressively cool piece of tech. -It leverages the fact that linux's `fork` is incredibly cheap, because it takes advantage of [copy-on-write](https://en.wikipedia.org/wiki/Copy-on-write). -`rr` basically `fork`s before every system call, and serializes what is going on; so you have a full track of everything that happened. -The `rr` integration is really nice and easy to use; I had someone who was part of our architecture and operations team (i.e. who writes way more CloudFormation than Julia) run it to submit a support request and they had no real troubles. +It records execution of a program, intercepting places (namely syscalls) that could be nondeterministic and storing the result. +It can then replay the recording (even on another machine), and see exactly what happened. +Allowing on to go into great detail on the state, and step backwards and forwards through time. +The `rr` integration is really nice and easy to use. +On a project I was running, I ended up getting a member of our architecture and operations team run it to submit a support request and they had no real troubles +(And they are by no means a Julia pro, they write mostly Cloudformation). ## Syntax From cbe98ea6bdc617b970da261604bfc11ab13d5e35 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 14:03:09 +0000 Subject: [PATCH 61/62] Add Table of Contents --- ....0.md => 2021-02-13-Julia-1.6-what-has-changed-since-1.0.md} | 2 ++ 1 file changed, 2 insertions(+) rename _posts/{2021-03-14-Julia-1.6-what-has-changed-since-1.0.md => 2021-02-13-Julia-1.6-what-has-changed-since-1.0.md} (99%) diff --git a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-02-13-Julia-1.6-what-has-changed-since-1.0.md similarity index 99% rename from _posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md rename to _posts/2021-02-13-Julia-1.6-what-has-changed-since-1.0.md index 077176d9..d9c7fb78 100644 --- a/_posts/2021-03-14-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-02-13-Julia-1.6-what-has-changed-since-1.0.md @@ -12,6 +12,8 @@ but that is not to say no new features have been added to the language. + + Julia 1.6 is a huge release and it is coming out relatively soon. RC-1 was [released recently](https://discourse.julialang.org/t/julia-v1-6-0-rc1-is-now-available/54775). I suspect we have at least a few more weeks before the final release. From 274df6a8a22e06bbc97de1d7ada13bf671db8675 Mon Sep 17 00:00:00 2001 From: Lyndon White Date: Sun, 14 Feb 2021 14:18:53 +0000 Subject: [PATCH 62/62] Move heap into interals --- ...13-Julia-1.6-what-has-changed-since-1.0.md | 27 ++++++++++--------- 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/_posts/2021-02-13-Julia-1.6-what-has-changed-since-1.0.md b/_posts/2021-02-13-Julia-1.6-what-has-changed-since-1.0.md index d9c7fb78..a85d4fb5 100644 --- a/_posts/2021-02-13-Julia-1.6-what-has-changed-since-1.0.md +++ b/_posts/2021-02-13-Julia-1.6-what-has-changed-since-1.0.md @@ -209,18 +209,6 @@ People often think it is cool to overload this in automatic differentiation pack Furthermore, `'` can now have Unicode suffixes added to it, to define a new suffix operator. For example, the one in `Base` which makes `A'ᵀ` give `transpose(A)` (rather than the adjoint/hermitian transpose which is returned by `A'`). -## Performance: References to the Heap from the Stack -This was promised in 2016 as a feature for 1.0 (released 2018), but we actually didn't get it until 1.5 (released 2020) with [#33886](https://github.com/JuliaLang/julia/issues/33886). -In short, the process of allocating memory from the heap is fairly slow\*, whereas allocating memory on the stack is basically a no-op. -Indeed, Julia benchmarking tools don't count allocations on the stack as allocations at all. -One can find extensive write ups of heap vs. stack allocations and how it works in general (though some mix the C specific factors with the CPU details). -In Julia, all mutable objects live on the heap. -Until recently, immutable objects that contained references to heap allocated objects also had to live on the heap, i.e. only immutable objects with immutable fields (with immutable fields with...) could live on the stack. -But with this change, all immutable objects can live on the stack, even if some of their fields live on the heap. -An important consequence of this is that wrapper types, such as the `SubArray` returned from `@view x[1:2]`, now have no overhead to create. -I find that in practice this often adds up to a 10-30% speed-up in real world code. - -(\* It's actually pretty fast, but it is the kind of thing that rapidly adds up; and it is slow vs operations that can happen without dynamic allocations.) ## Pkg stdlib and the General Registry @@ -550,6 +538,21 @@ Turning off compilation and type inference on the other hand turns off a ton mor These flags mean that plotting libraries and other similar things that don't benefit from the optimizer can just not use the optimizer. ## Internals + +### References to the Heap from the Stack (Performance) +This was promised in 2016 as a feature for 1.0 (released 2018), but we actually didn't get it until 1.5 (released 2020) with [#33886](https://github.com/JuliaLang/julia/issues/33886). +In short, the process of allocating memory from the heap is fairly slow\*, whereas allocating memory on the stack is basically a no-op. +Indeed, Julia benchmarking tools don't count allocations on the stack as allocations at all. +One can find extensive write ups of heap vs. stack allocations and how it works in general (though some mix the C specific factors with the CPU details). +In Julia, all\*\* mutable objects live on the heap. +Until recently, immutable objects that contained references to heap allocated objects also had to live on the heap, i.e. only immutable objects with immutable fields (with immutable fields with...) could live on the stack. +But with this change, all immutable objects can live on the stack, even if some of their fields live on the heap. +An important consequence of this is that wrapper types, such as the `SubArray` returned from `@view x[1:2]`, now have no overhead to create. +I find that in practice this often adds up to a 10-30% speed-up in real world code. + +(\* It's actually pretty fast, but it is the kind of thing that rapidly adds up; and it is slow vs operations that can happen without dynamic allocations.)
        +(\*\* Technically not all mutable objects live on the heap, because some never live at all, as they are optimized away so are never allocated in the first place.) + ### Manually Created Back-edges for Lowered Code Generated Functions This is a very niche and not really at all user-facing feature. But it is important for some really cool things, like [Cassette](https://github.com/jrevels/Cassette.jl).

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