Recently I have been doing a lot of lambda coding in AWS. After doing a lot of web services, I was really missing the ability to debug my code line by line in my local environment. I use PyCharm CE a lot, and to have PyCharm debug my lambda code, was to me, an awesome idea. Regardless, using the following way, we can set up the code in VS Code also.

I could not find the proper tutorial on the internet. All the topics were around using SAM CLI, which I didn’t want to use.

Prerequisites

We must have the AWS credentials available in the home 

~/.aws/credentials

Let’s start…

Here is my sample lambda function.

A sample lambda function with 2 triggers, one from S3-Put and another from SQS receive message.

This function also has some environment variables.

The function also has a layer. Here is the code for the layer

# dummy_layer.py
def call_dummy():
    return "Hello from dummy layer"

To properly set it up in PyCharm we will use this project structure.

Things to note

1. The events folder will have all the events which will invoke my lambda function locally.

2. The python folder and all the components inside it are the layer codes. We can add any number of layers here.

3.

.env

4.

lambda_function.py

5.

run.py

Setup

We will copy the lambda code from AWS, if available already, or will write the code, debug it and then deploy it. We will create the environment where the lambda function will run and use the real event to trigger the call.

AWS resources can also be referenced like shown in the code. The 

boto3

~/.aws/credentials

We will add any third-party layer code also.

1. Copy the lambda code from AWS console to the lambda_function.py file.

In our case, we have:

import json

import boto3

from dummy_layer import call_dummy

s3 = boto3.client("s3")


def lambda_handler(event, context):
    print(call_dummy())
    print(s3.list_buckets())
    return {
        'statusCode': 200,
        'body': json.dumps('Hello from Lambda!')
    }

2. Add the layer code as source set.

We need to add the lambda layer code as the python source set so that we can import them into the lambda function.

3. Where to get the events

We can get sample events from the test configuration in the lambda code console:

The cloudwatch logs, will be actual events invoking the function.

The AWS Toolkit plugin in PyCharm:

4. Code the run.py file

Let’s load the s3 put file to simulate a put event and invoke the function.

import json

from lambda_function import lambda_handler


class Context:
    def __init__(self, arn: str):
        self.invoked_function_arn = arn


if __name__ == "__main__":
    event = {}
    with open("events/s3-put.json", 'r') as f:
        event = json.load(f)
        lambda_handler(event, Context("Sample ARN"))

5. Configure .env file

Install the .env file support plugin from jetbrains marketplace.

Add all the environment configuration in the .env file for local debugging. This is simulating the environment variable configuration in the AWS lambda console.

6. Create run configuration.

Finally, create a python run configuration for the run.py file with the .env file setup. This is how we simulate the environment variable, just like in AWS console:

That’s it …

We are done.

Now just run the run configuration and debug away.

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