Namespace Amazon.CDK.AWS.Lambda.Python.Alpha

Amazon Lambda Python Library

--- cdk-constructs: Experimental 
The APIs of higher level constructs in this module are experimental and under active development.
They are subject to non-backward compatible changes or removal in any future version. These are
not subject to the <a href="https://semver.org/">Semantic Versioning</a> model and breaking changes will be
announced in the release notes. This means that while you may use them, you may need to update
your source code when upgrading to a newer version of this package.

This library provides constructs for Python Lambda functions.

To use this module, you will need to have Docker installed.

Python Function

Define a PythonFunction:

new PythonFunction(this, "MyFunction", new PythonFunctionProps {
    Entry = "/path/to/my/function",  // required
    Runtime = Runtime.PYTHON_3_8,  // required
    Index = "my_index.py",  // optional, defaults to 'index.py'
    Handler = "my_exported_func"
});

All other properties of lambda.Function are supported, see also the AWS Lambda construct library.

Python Layer

You may create a python-based lambda layer with PythonLayerVersion. If PythonLayerVersion detects a requirements.txt or Pipfile or poetry.lock with the associated pyproject.toml at the entry path, then PythonLayerVersion will include the dependencies inline with your code in the layer.

Define a PythonLayerVersion:

new PythonLayerVersion(this, "MyLayer", new PythonLayerVersionProps {
    Entry = "/path/to/my/layer"
});

A layer can also be used as a part of a PythonFunction:

new PythonFunction(this, "MyFunction", new PythonFunctionProps {
    Entry = "/path/to/my/function",
    Runtime = Runtime.PYTHON_3_8,
    Layers = new [] {
        new PythonLayerVersion(this, "MyLayer", new PythonLayerVersionProps {
            Entry = "/path/to/my/layer"
        }) }
});

Packaging

If requirements.txt, Pipfile or poetry.lock exists at the entry path, the construct will handle installing all required modules in a Lambda compatible Docker container according to the runtime and with the Docker platform based on the target architecture of the Lambda function.

Python bundles are only recreated and published when a file in a source directory has changed. Therefore (and as a general best-practice), it is highly recommended to commit a lockfile with a list of all transitive dependencies and their exact versions. This will ensure that when any dependency version is updated, the bundle asset is recreated and uploaded.

To that end, we recommend using [pipenv] or [poetry] which have lockfile support.

    Packaging is executed using the Packaging class, which:

      Lambda with a requirements.txt

      .
├── lambda_function.py # exports a function named 'handler'
├── requirements.txt # has to be present at the entry path

      Lambda with a Pipfile

      .
├── lambda_function.py # exports a function named 'handler'
├── Pipfile # has to be present at the entry path
├── Pipfile.lock # your lock file

      Lambda with a poetry.lock

      .
├── lambda_function.py # exports a function named 'handler'
├── pyproject.toml # your poetry project definition
├── poetry.lock # your poetry lock file has to be present at the entry path

      Excluding source files

      You can exclude files from being copied using the optional bundling string array parameter assetExcludes:

      new PythonFunction(this, "function", new PythonFunctionProps {
    Entry = "/path/to/poetry-function",
    Runtime = Runtime.PYTHON_3_8,
    Bundling = new BundlingOptions {
        // translates to `rsync --exclude='.venv'`
        AssetExcludes = new [] { ".venv" }
    }
});

      Including hashes

      You can include hashes in poetry using the optional boolean parameter poetryIncludeHashes:

      new PythonFunction(this, "function", new PythonFunctionProps {
    Entry = "/path/to/poetry-function",
    Runtime = Runtime.PYTHON_3_8,
    Bundling = new BundlingOptions {
        PoetryIncludeHashes = true
    }
});

      Excluding URLs

      You can exclude URLs in poetry using the optional boolean parameter poetryWithoutUrls:

      new PythonFunction(this, "function", new PythonFunctionProps {
    Entry = "/path/to/poetry-function",
    Runtime = Runtime.PYTHON_3_8,
    Bundling = new BundlingOptions {
        PoetryWithoutUrls = true
    }
});

      Custom Bundling

      Custom bundling can be performed by passing in additional build arguments that point to index URLs to private repos, or by using an entirely custom Docker images for bundling dependencies. The build args currently supported are:

        Additional build args for bundling that refer to PyPI indexes can be specified as:

        var entry = "/path/to/function";
var image = DockerImage.FromBuild(entry);

new PythonFunction(this, "function", new PythonFunctionProps {
    Entry = entry,
    Runtime = Runtime.PYTHON_3_8,
    Bundling = new BundlingOptions {
        BuildArgs = new Dictionary<string, string> { { "PIP_INDEX_URL", "https://your.index.url/simple/" }, { "PIP_EXTRA_INDEX_URL", "https://your.extra-index.url/simple/" } }
    }
});

        If using a custom Docker image for bundling, the dependencies are installed with pip, pipenv or poetry by using the Packaging class. A different bundling Docker image that is in the same directory as the function can be specified as:

        var entry = "/path/to/function";
var image = DockerImage.FromBuild(entry);

new PythonFunction(this, "function", new PythonFunctionProps {
    Entry = entry,
    Runtime = Runtime.PYTHON_3_8,
    Bundling = new BundlingOptions { Image = image }
});

        You can set additional Docker options to configure the build environment:

        var entry = "/path/to/function";

new PythonFunction(this, "function", new PythonFunctionProps {
    Entry = entry,
    Runtime = Runtime.PYTHON_3_8,
    Bundling = new BundlingOptions {
        Network = "host",
        SecurityOpt = "no-new-privileges",
        User = "user:group",
        VolumesFrom = new [] { "777f7dc92da7" },
        Volumes = new [] { new DockerVolume { HostPath = "/host-path", ContainerPath = "/container-path" } }
    }
});

        Custom Bundling with Code Artifact

        To use a Code Artifact PyPI repo, the PIP_INDEX_URL for bundling the function can be customized (requires AWS CLI in the build environment):

        using Child.Process;


var entry = "/path/to/function";
var image = DockerImage.FromBuild(entry);

var domain = "my-domain";
var domainOwner = "111122223333";
var repoName = "my_repo";
var region = "us-east-1";
var codeArtifactAuthToken = ExecSync($"aws codeartifact get-authorization-token --domain {domain} --domain-owner {domainOwner} --query authorizationToken --output text").ToString().Trim();

var indexUrl = $"https://aws:{codeArtifactAuthToken}@{domain}-{domainOwner}.d.codeartifact.{region}.amazonaws.com/pypi/{repoName}/simple/";

new PythonFunction(this, "function", new PythonFunctionProps {
    Entry = entry,
    Runtime = Runtime.PYTHON_3_8,
    Bundling = new BundlingOptions {
        Environment = new Dictionary<string, string> { { "PIP_INDEX_URL", indexUrl } }
    }
});

        The index URL or the token are only used during bundling and thus not included in the final asset. Setting only environment variable for PIP_INDEX_URL or PIP_EXTRA_INDEX_URL should work for accessing private Python repositories with pip, pipenv and poetry based dependencies.

        If you also want to use the Code Artifact repo for building the base Docker image for bundling, use buildArgs. However, note that setting custom build args for bundling will force the base bundling image to be rebuilt every time (i.e. skip the Docker cache). Build args can be customized as:

        using Child.Process;


var entry = "/path/to/function";
var image = DockerImage.FromBuild(entry);

var domain = "my-domain";
var domainOwner = "111122223333";
var repoName = "my_repo";
var region = "us-east-1";
var codeArtifactAuthToken = ExecSync($"aws codeartifact get-authorization-token --domain {domain} --domain-owner {domainOwner} --query authorizationToken --output text").ToString().Trim();

var indexUrl = $"https://aws:{codeArtifactAuthToken}@{domain}-{domainOwner}.d.codeartifact.{region}.amazonaws.com/pypi/{repoName}/simple/";

new PythonFunction(this, "function", new PythonFunctionProps {
    Entry = entry,
    Runtime = Runtime.PYTHON_3_8,
    Bundling = new BundlingOptions {
        BuildArgs = new Dictionary<string, string> { { "PIP_INDEX_URL", indexUrl } }
    }
});

        Command hooks

        It is possible to run additional commands by specifying the commandHooks prop:

        var entry = "/path/to/function";
new PythonFunction(this, "function", new PythonFunctionProps {
    Entry = entry,
    Runtime = Runtime.PYTHON_3_8,
    Bundling = new BundlingOptions {
        CommandHooks = new Dictionary<string, object> {
            // run tests
            public string[] BeforeBundling(string inputDir)
            {
                return new [] { "pytest" };
            },
            public string[] AfterBundling(string inputDir)
            {
                return new [] { "pylint" };
            }
        }
    }
});

        The following hooks are available:

          They all receive the directory containing the dependencies file (inputDir) and the directory where the bundled asset will be output (outputDir). They must return an array of commands to run. Commands are chained with &&.

          The commands will run in the environment in which bundling occurs: inside the container for Docker bundling or on the host OS for local bundling.

          Docker based bundling in complex Docker configurations

          By default the input and output of Docker based bundling is handled via bind mounts. In situations where this does not work, like Docker-in-Docker setups or when using a remote Docker socket, you can configure an alternative, but slower, variant that also works in these situations.

          var entry = "/path/to/function";

new PythonFunction(this, "function", new PythonFunctionProps {
    Entry = entry,
    Runtime = Runtime.PYTHON_3_8,
    Bundling = new BundlingOptions {
        BundlingFileAccess = BundlingFileAccess.VOLUME_COPY
    }
});

          Troubleshooting

          Containerfile: no such file or directory

          If you are on a Mac, using Finch instead of Docker, and see an error like this:

          lstat /private/var/folders/zx/d5wln9n10sn0tcj1v9798f1c0000gr/T/jsii-kernel-9VYgrO/node_modules/@aws-cdk/aws-lambda-python-alpha/lib/Containerfile: no such file or directory

          That is a sign that your temporary directory has not been mapped into the Finch VM. Add the following to ~/.finch/finch.yaml:

          additional_directories:
  - path: /private/var/folders/
  - path: /var/folders/

          Then restart the Finch VM by running finch vm stop && finch vm start.

          Classes

          BundlingOptions

          (experimental) Options for bundling.
          PythonFunction

          (experimental) A Python Lambda function.
          PythonFunctionProps

          (experimental) Properties for a PythonFunction.
          PythonLayerVersion

          (experimental) A lambda layer version.
          PythonLayerVersionProps

          (experimental) Properties for PythonLayerVersion.

          Interfaces

          IBundlingOptions

          (experimental) Options for bundling.
          ICommandHooks

          (experimental) Command hooks.
          IPythonFunctionProps

          (experimental) Properties for a PythonFunction.
          IPythonLayerVersionProps

          (experimental) Properties for PythonLayerVersion.