[[feature collection]] requested Functions and query operators

[beckettsean]

This issue contains a list of related feature requests that are not on the near-term roadmap. The feature requests in this issue are all new functions that have been requested. If you want to request a function not already listed please make a comment on this issue, and we will add it to the checklist.

Aggregations

• moving averages function: Should support moving averages aggregate function #77
• histogram Wire up HISTOGRAM transformation #3674
• cumulative counts Add support for cumulative counts #813
• HDR histogram integrate hdrhistogram #739
• histogram with log-scaled buckets Histogram with logarithmic scaled buckets (or self-defined) #984
• time weighted aggregations Time weighted average/mean #7445
• logarithmic mean Logarithmic mean #2482
• integrals Aggregate Integral Function #1400
• cumulative distribution function: Cumulative Distribution Functions #7261

Selectors

• faster, slightly less accurate percentages calculation What about less accurate but more efficient percentile calculation? #513
• lag variables Support lag variables #142
• lead variables Support lead variables #143
• upper bound, lower bound Support more math (power, logarithm, trigonometric functions) #659
• average "gauges" with awareness of time spent in state Average "gauges" #1115
• mode Wire up MODE aggregate #1823

Transformations

• trigonometric functions (sine, cosine, etc.) Support more math (power, logarithm, trigonometric functions) #659
• hyperloglog Add hyperloglog functions #717
• dot-product [feature request] DOT aggregate functionality or arithmetic inside of aggregates #5095

Operators

• difference Wire up DIFFERENCE aggregate #1825
• elapsed time between events Time between events #1856
• exponents and logarithms Support more math (power, logarithm, trigonometric functions) #659
• ceiling and floor functions [feature request] Should support ceil and floor functions #3691
• trend operators: linear and least square New operators to do trending #5358
• DIFFERENCE of two fields Enhancing DIFFERENCE() to support 2 columns #761

InfluxQL enhancements

• general support for nested functions Enhancement request: Allow nested select functions, ex: select sum(derivate(value))...; #834
  • percentile + derivative [0.9.6.1] Nth percentile for derivative series #5150
  • percentage of values above/below given value (inverse PERCENTILE)
  • Top accepts nested functions Top N of an Avg Value #2467, [Feature Request] Allow for Nested aggregate in TOP #5345
• CAST into different data types [feature request] Add CAST function #2911
• fill(previous) without GROUP BY [feature request] keep last value or fill(previous) without a group by #3633
• User defined functions Support for user defined functions #6891
• Boolean type cast Feature-Request: Allow type casts from boolean to int/float (0/1) #7562

[tbowmo]

Is there any timeline for these functions? (I'm very interested in the aggregate integral functions, to calculate kWh from watts). Seems that a feature request was opened a year ago.

[beckettsean]

There is no timeline for each specific function/feature. All work on new functions was on hold while the query engine was refactored, and that refactoring was merged into InfluxDB 0.11. We plan to introduce a few functions with each release from now on.

[timgriffiths]

Would also be great to get #3633 Added to your list this feature would be very useful on the client side when graphing data.

[sidgod]

+1 for histogram()

[charlottetucker]

+1 for CAST function. Seriously, why isn't this a thing?

[AnnapoorniS]

Is user defined function feature available in influx db? It will of great help if somebody could help me with sample in influxdb, which are similar to stored procedures. Any suggestions? Thanks in advance.

[timhallinflux]

@AnnapoorniS I'd suggest looking at Flux https://github.com/influxdata/platform/tree/master/query

[henriklb]

Assuming that because #834 is checked it is seen as completed.

These functions does still not work (v.1.5.1) and would very be useful for me:
sum(non_negative_derivative(value))
sum(derivative(value))

Thanks

[balakrishnabk]

aggregation with respect to weekday and weeknumber based on the timestamp

[matthenning]

Does #3552 fit in here?

[MarcoPignati]

+1 for histogram #3674

[mrungue]

I'd like to request for a new summary function specifically made for time series visualization: Largest-Triangle-Three-Buckets.

I've been using mean to downsample data, but averaging time series data results in a smooth line which does not accurately represents the underlying data.

Sveinn Steinarsson wrote a thesis titled "Downsampling Time Series for
Visual Representation" that describes and tests algorithms for that purpose. The d3fc library makes use of "Largest-Triangle-Three-Buckets" in the front end, but it would be best if InfluxDB could downsample the data to transmit and process as few bytes as possible.

+1

[n1nj4888]

👍 +1 for timeshift functions - Seems like this has been requested multiple times by a number of users

[sfitts]

The checklist implies that both "percentile + derivative" (#5150) and "Top accepts nested functions" (#2467, #5345) are addressed. However, the documentation says that neither percentile nor top supports nested functions (as of 1.7). So... is the checklist wrong? Can you do these without nested function support? Is the doc wrong?

[timhallinflux]

Hey @sfitts! (from the Forte days?!?) ... I believe you can do these now with subqueries. So, while it's not a direct nesting, it can be done that way. The primary focus going forward in terms of extending the query surface area is going to be done via Flux. https://docs.influxdata.com/flux/v0.7/

InfluxQL will, of course, continue to be supported. But there are challenges that we are going to address at the query engine layer and then open up the ability to address so many of these requests via Flux. Have a look, let us know!

[sfitts]

@timhallinflux just after I wrote this it dawned on me that subqueries were probably the answer -- thanks for the confirmation. Also hadn't picked up on the fact that a query language replacement was in the works, so I'll definitely check that out.

(and yep -- I date back to Forte 👍)

[timhallinflux]

Good to reconnect! I want to be super clear.... we are not going to "replace" InfluxQL. As we continue forward, InfluxQL will continue to be the primary on-ramp and supported. But, in terms of working with time series data -- we determined that a functional language can be a powerful way to manipulate the functions, results, and simplify developer code (in the end). So many of these requests were part of our design center for Flux itself and ensuring that we can deliver on them. We have been listening, observing, and attempting to address many of these for multiple years now. We started and failed at least twice...a couple of attempts that never saw the light of day and weren't "ship worthy".

With Flux, we are on the brink of breaking through and delivering on this list (and more!) while continuing to support InfluxQL. i.e. Histogram...already in. So, we maintain the easy on-ramp via InfluxQ. If that is all you need...great! But, if you need more power...and there are a number of time series use cases which certainly do -- particularly given this list, Flux will be there. In 1.7 InfluxDB, there are two query engines that run in parallel. In 2.0, the Flux engine will be the primary engine and InfluxQL will run in a compatibility mode on top of that engine. Hope that helps clarify!

[sfitts]

Makes sense (and thanks for the clarification).

[tecmatia-dp]

3 years and Boolean cast to Integer (1/0) is not yet implemented...

[uski]

Thank you for the hard work. I just came here to express that more functions would definitely be very useful.
I am currently held back by the lack of logarithmic mean aggregator and I believe the query language is not flexible enough to allow me to do it from the query itself.

So, +1 for log mean please !

[andrewthad]

Boolean type cast would be really useful in my work place.

[gpratt3151]

+1 for Histogram

[MartinMuzatko]

I'd like to have CAST from string to boolean to integer
"true" => true => 1
"false" => false => 0

[anthosz]

+1 to CAST from boolean to integer

[timhallinflux]

Casting boolean -> int has been implemented in Flux. This is available as a technical preview in 1.7 and we are just about to release 1.8 which includes some additional, significant updates.

https://docs.influxdata.com/flux/v0.50/introduction/flux-vs-influxql/

[garcipat]

I'd like to request for a new summary function specifically made for time series visualization: Largest-Triangle-Three-Buckets.

I've been using mean to downsample data, but averaging time series data results in a smooth line which does not accurately represents the underlying data.

Sveinn Steinarsson wrote a thesis titled "Downsampling Time Series for Visual Representation" that describes and tests algorithms for that purpose. The d3fc library makes use of "Largest-Triangle-Three-Buckets" in the front end, but it would be best if InfluxDB could downsample the data to transmit and process as few bytes as possible.

Has anybody worked in that and worked out at least an influx aggregation function for that? I would love to have this otherwise I will build it myself or will have to go to a less accurate algorithm

[garcipat]

To add something usefull here regarding filtering of peaks, I implemented a simpler algorithm for aggregation by comparing values to the average of a time window, maybe that helps someone else as well:

`
import "math"
import "join"

fields = ["test", "test2"]

data = from(bucket: "MyBucket")
  |> range(start: 2024-03-11T13:00:00Z, stop: 2024-03-11T13:05:00Z)
  |> filter(fn: (r) => r._measurement == "measurement")
  |> filter(fn: (r) => contains(value: r._field, set: fields))
  |> window(every: 1m)

mean = data
  |> mean()
  |> duplicate(column: "_start", as: "_time")

diff = join.full(left: data, right: mean,
  on: (l,r) =>  l._time == r._time and l._field == r._field and l._measurement == r._measurement,
  as: (l,r) => {
    time = if exists l._time then l._time else r._time
    return {l with _time: time, _value: l._value, _mean: r._value}
  })
  |> fill(column: "_mean", usePrevious: true)
  |> filter(fn: (r) => exists r._value)
  |> map(fn: (r) => ({r with _diff: math.abs(x: (r._value - r._mean))}))
  |> reduce(fn: (r, accumulator) => ({
        _value: if (exists r._value and r._diff > accumulator._maxDiff) then
            r._value
            else accumulator._value,
        _maxDiff: if r._diff > accumulator._maxDiff then
            r._diff
            else accumulator._maxDiff
    }), identity: { _value: 0.0, _maxDiff: 0.0 })
  |> duplicate(column: "_stop", as: "_time")
  |> window(every: inf)
  |> yield(name: "diff")
`

You can change the aggregation window (in the example 1m), the range and the filtered fields.