lambda-decorators 0.1b1

A collection of useful decorators for making AWS Lambda handlers

A collection of useful decorators for making AWS Lambda handlers

NOTE: this is in very early stages of development.

lambda_decorators is a collection of useful decorators for writing Python handlers for AWS Lambda. They allow you to avoid boiler plate for common things such as CORS headers, JSON serialization, etc.

Quick example

# handler.py

from lambda_decorators import cors_headers, json_http_resp, load_json_body

@cors_headers
@json_http_resp
@load_json_body
def handler(event, context):
    return {'hello': event['body']['name']}

When deployed to Lambda behind API Gateway and cURL’d:

$ curl -d '{"name": "world"}' https://example.execute-api.us-east-1.amazonaws.com/dev/hello
{"hello": "world"}

Install

If you are using the serverless framework I recommend using serverless-python-requirements

sls plugin install -n serverless-python-requirements
echo lambda-decorators >> requirements.txt

Or if using some other deployment method to AWS Lambda you can just download the entire module because it’s only one file.

curl -O https://raw.githubusercontent.com/dschep/lambda-decorators/master/lambda_decorators.py

Included Decorators:

lambda_decorators includes the following decorators to avoid boilerplate for common usecases when using AWS Lambda with Python.

• async_handler - support for async handlers

• cors_headers - automatic injection of CORS headers

• dump_json_body - auto-serialization of http body to JSON

• json_http_resp - automatic serialization of python object to HTTP JSON response

• load_json_body - auto-deserialize of http body from JSON

• no_retry_on_failure - detect and stop retry attempts for scheduled lambdas

See each individual decorators for specific usage details and the example for some more use cases.

Writing your own

lambda_decorators includes utilities to make building your own decorators easier. The before, after, and on_exception decorators can be applied to your own functions to turn them into decorators for your handlers. For example:

import logging
from lambda_decorators import before

@before
def log_event(event, context):
    logging.debug(event)
    return event, context

@log_event
def handler(event, context):
    return {}

And if you want to make a decorator that provides two or more of before/after/on_exception functionality, you can use LambdaDecorator:

import logging
from lambda_decorators import LambdaDecorator

class log_everything(LambdaDecorator):
    def before(event, context):
        logging.debug(event, context)
        return event, context
    def after(retval):
        logging.debug(retval)
        return retval
    def on_exception(exception):
        logging.debug(exception)
        return {'statusCode': 500}

@log_everything
def handler(event, context):
    return {}

Why

Initially, I was inspired by middy which I’ve tried out in JavaScript and was happy with it. So naturally, I thought I’d like to have something similar in Python too. But then as I thought about it more, it seemed that when thinking of functions as the compute unit, when using python, decorators pretty much are middleware! So instead of building a middleware engine and a few middlewares, I just built a few useful decorators and utilities to build them.

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Full API Documentation