Welcome hacker!
This document will make your life easier by helping you setup a development environment, IDEs, tests, coding practices, or anything that will help you be more productive. If you found something is missing or inaccurate, update this guide and send a Pull Request.
For setting up a local development environment, we recommend using Gitpod - a service that allows you to spin up an in-browser Visual Studio Code-compatible editor, with everything set up and ready to go for development on this project. Just click the button below to create your private workspace:
This will start a new Gitpod workspace, and immediately kick off a build of the code. Once it's done, you can start working.
Gitpod is free for 50 hours per month - make sure to stop your workspace when you're done (you can always resume it later, and it won't need to run the build again).
AWS SAM CLI is mainly written in Python 3 and we support Python 3.6, 3.7 and 3.8. So having a Python environment with aforementioned versions is required.
Having a dedicated Python virtual environment ensures it won't "pollute" or get "polluted"
by other python packages. Here we introduce two ways of setting up a Python virtual environment:
(1) Python's built in venv
and (2) pyenv
.
Note: pyenv
currently only supports macOS and Linux. If you are a
Windows users, consider using pyenv-win.
venv |
pyenv |
|
---|---|---|
Pick if you want ... | Easy setup | You want to develop and test SAM CLI in different Python versions |
python3 -m venv .venv # one time setup: create a virtual environment to directory .venv
source .venv/bin/activate # activate the virtual environment
Install pyenv
and pyenv-virtualenv
plugin
On macOS with Homebrew
brew install pyenv
brew install pyenv-virtualenv
or using pyenv-installer and git
curl https://pyenv.run | bash # https://github.com/pyenv/pyenv-installer
exec $SHELL # restart your shell so the path changes take effect
git clone https://github.com/pyenv/pyenv-virtualenv.git $(pyenv root)/plugins/pyenv-virtualenv
exec $SHELL # restart your shell to enable pyenv-virtualenv
Next, setup a virtual environment and activate it:
# Assuming you want to develop AWS SAM CLI in Python 3.8.9
pyenv install 3.8.9 # install Python 3.8.9 using pyenv
pyenv virtualenv 3.8.9 samcli38 # create a virtual environment using 3.8.9 named "samcli38"
pyenv activate samcli38 # activate the virtual environment
Clone the AWS SAM CLI repository to your local machine if you haven't done that yet.
# Using SSH
git clone git@github.com:aws/aws-sam-cli.git
or
# Using HTTPS
git clone https://github.com/aws/aws-sam-cli.git
(make sure you have virtual environment activated)
cd aws-sam-cli
make init # this will put a file `samdev` available in $PATH
Windows users can use PowerShell and Make.ps1
script which performs the tasks from Makefile
without *nix make
tool.
cd aws-sam-cli
./Make -Init
Now you can verify whether the dev AWS SAM CLI is available:
samdev --version # this will print something like "SAM CLI, version x.xx.x"
# Change the AWS SAM CLI version to 123.456.789
echo '__version__ = "123.456.789"' >> samcli/__init__.py
samdev --version # this will print "SAM CLI, version 123.456.789"
If you want to run the latest version of SAM Transformer or work on it at the same time, you can clone it locally and install it in your virtual environment. This is useful if you want to validate your templates against any new, unreleased SAM features ahead of time.
# Make sure it is not in AWS SAM CLI repository
# clone the AWS SAM repo
git clone git@github.com:aws/serverless-application-model.git
# or using HTTPS: git clone https://github.com/aws/serverless-application-model.git
cd serverless-application-model
Make sure you are in the same virtual environment as the one you are using with SAM CLI.
source <sam-cli-directory-path>/.venv/bin/activate # if you chose to use venv to setup the virtual environment
# or
pyenv activate samcli38 # if you chose to use pyenv to setup the virtual environment
Install the SAM Transformer in editable mode so that all changes you make to the SAM Transformer locally are immediately picked up for SAM CLI.
pip install -e .
Move back to your SAM CLI directory and re-run init, If necessary: open requirements/base.txt and replace the version number of aws-sam-translator with the version number
specified in your local version of serverless-application-model/samtranslator/__init__.py
# Make sure you are back to your SAM CLI directory
make init
Or on Windows
./Make -Init
Above demonstrates how to setup the environment, which is enough to play with the AWS SAM CLI source code. However, if you want to contribute to the repository, there are a few more things to consider.
We support 3.6, 3.7 and 3.8 versions. Our CI/CD pipeline is setup to run unit tests against all Python versions. So make sure you test it with all versions before sending a Pull Request. See Unit testing with multiple Python versions.
If you chose to use pyenv
in the previous session, setting up a
different Python version should be easy:
(assuming you are in virtual environment samcli38
)
# Your shell now should looks like "(samcli38) $"
pyenv deactivate samcli38 # "(samcli38)" will disappear
pyenv install 3.7.10 # one time setup
pyenv virtualenv 3.7.10 samcli37 # one time setup
pyenv activate samcli37
# Your shell now should looks like "(samcli37) $"
# You can verify the version of Python
python --version # Python 3.7.10
make init # one time setup, this will put a file `samdev` available in $PATH
For Windows, use your favorite tool for managing different python versions and environments and call ./Make -Init
to initialize each of the environments.
We format our code using Black and verify the source code is
black compliant in AppVeyor during PRs. Black will be installed automatically with make init
or ./Make -Init
on Windows.
There are generally 3 options to make sure your change is compliant with our formatting standard:
make black
On Windows:
./Make -Black
Since black is installed in virtualenv, when you follow this instruction, which black
might give you this
/Users/<username>/.pyenv/shims/black
However, IDEs such PyChaim (using FileWatcher) will have a hard time
invoking /Users/<username>/.pyenv/shims/black
and this will happen:
pyenv: black: command not found
The `black' command exists in these Python versions:
3.8.9/envs/samcli38
samcli38
A simple workaround is to use /Users/<username>/.pyenv/versions/samcli37/bin/black
instead of /Users/<username>/.pyenv/shims/black
.
We have integrated black into git hooks through pre-commit.
After installing pre-commit, run pre-commit install
in the root of the project. This will install black for you and run the black formatting on commit.
This commands will run the AWS SAM CLI code through various checks including lint, formatter, unit tests, function tests, and so on.
make pr
Use Make.ps1
script on Windows instead:
./Make -pr
We also suggest to run make pr
or ./Make -pr
in all Python versions.
Currently, SAM CLI only supports Python3 versions (see setup.py for exact versions). For the most part, code that works in Python3.6 will work in Python3.7. You only run into problems if you are trying to use features released in a higher version (for example features introduced into Python3.7 will not work in Python3.6). If you want to test in many versions, you can create a virtualenv for each version and flip between them (sourcing the activate script). Typically, we run all tests in one python version locally and then have our ci (appveyor) run all supported versions.
make integ-test
- To run integration test against global SAM CLI
installation. It looks for a command named sam
in your shell.
SAM_CLI_DEV=1 make integ-test
- To run integration tests against
development version of SAM CLI. This is useful if you are making changes
to the CLI and want to verify that it works. It is a good practice to
run integration tests before submitting a pull request.
On Windows, the behaviour is slightly different. ./Make -IntegTest
runs integration tests only against development version of SAM CLI.
Make.ps1
script always sets environment to dev
before running any command and resets SAM_CLI_DEV
when done, even if a command fails.
$env:SAM_CLI_DEV = 1
try {
# execute commands here
...
}
finally {
$env:SAM_CLI_DEV = ''
}
Please follow these code conventions when making your changes. This will align your code to the same conventions used in rest of the package and make it easier for others to read/understand your code. Some of these conventions are best practices that we have learnt over time.
- Use numpy docstring format for docstrings. Some parts of the code still use an older, unsupported format. If you happened to be modifying these methods, please change the docstring format as well.
- Don't write any code in
__init__.py
file - Module-level logger variable must be named as
LOG
- If your method wants to report a failure, it must raise a custom
exception. Built-in Python exceptions like
TypeError
,KeyError
are raised by Python interpreter and usually signify a bug in your code. Your method must not explicitly raise these exceptions because the caller has no way of knowing whether it came from a bug or not. Custom exceptions convey are must better at conveying the intent and can be handled appropriately by the caller. In HTTP lingo, custom exceptions are equivalent to 4xx (user's fault) and built-in exceptions are equivalent to 5xx (Service Fault) - CLI commands must always raise a subclass of
click.ClickException
to signify an error. Error code and message must be set as a part of this exception and not explicitly returned by the CLI command. - Don't use
*args
or**kwargs
unless there is a really strong reason to do so. You must explain the reason in great detail in docstrings if you were to use them. - Library classes, ie. the ones under
lib
folder, must not use Click. Usage of Click must be restricted to thecommands
package. In the library package, your classes must expose interfaces that are independent of the user interface, be it a CLI thru Click, or CLI thru argparse, or HTTP API, or a GUI. - Do not catch the broader
Exception
, unless you have a really strong reason to do. You must explain the reason in great detail in comments.
We need thorough test coverage to ensure the code change works today, and continues to work in future. When you make a code change, use the following framework to decide the kinds of tests to write:
-
When you adds/removed/modifies code paths (aka branches/arcs), write unit tests with goal of making sure the flow works. Focus on verifying the flow and use mocks to isolate from as many external dependencies as you can. "External dependencies" includes system calls, libraries, other classes/methods you wrote but logically outside of the system-under-test.
Aim to test with complete isolation
-
When your code uses external dependencies, write functional tests to verify some flows by including as many external dependencies as possible. Focus on verifying the flows that directly use the dependencies.
Aim to test one or more logically related components. Includes docker, file system, API server, but might still mock some things like AWS API calls.
-
When your code adds/removes/modifies a customer facing behavior, write integration tests. Focus on verifying the customer experience works as expected.
Aim to test how a customer will use the feature/command. Includes calling AWS APIs, spinning up Docker containers, mutating files etc.
A design document is a written description of the feature/capability you are building. We have a design document template to help you quickly fill in the blanks and get you working quickly. We encourage you to write a design document for any feature you write, but for some types of features we definitely require a design document to proceed with implementation.
When do you need a design document?
- Adding a new command
- Making a breaking change to CLI interface
- Refactoring code that alters the design of certain components
- Experimental features