AWS Lambda: Building Serverless Functions with Python

Exciting news! You can now build serverless functions with Python using AWS Lambda!

But wait, what even is AWS Lambda? And why is it so exciting?

AWS Lambda is a serverless compute service that runs your code in response to events and automatically manages the compute resources for you, so you don't have to worry about provisioning or maintaining servers. You only pay for the compute time you consume, which makes it a cost-effective solution for building applications and services.

Now imagine being able to build serverless functions using the most widely-used programming language in the world - Python. This is a game-changer for developers and organizations that rely on Python for their codebase.

So, let's dive into how you can get started with building serverless functions using Python and AWS Lambda.

Getting Started with AWS Lambda and Python

First, you need an AWS account. If you don't have one already, you can sign up for free here.

Once you have an AWS account, you can create a new Lambda function using the AWS Management Console or the AWS CLI. For this article, we'll be using the Management Console.

Creating a New Lambda Function

1. Go to the Lambda console and click on "Create function".
2. Choose "Author from scratch" as the blueprint.
3. Give your function a name, select Python 3.8 as the runtime, and choose "Create function".

Adding Code to your Lambda Function

Now that you have created a new Lambda function, you need to add code to it. You can either upload a .zip file containing your Python code or use the inline code editor provided by AWS Lambda.

For this article, we'll be using the inline code editor to keep things simple.

1. Scroll down to the "Function code" section and click on "Edit code inline".
2. Delete the default code and paste the following code in its place:

import json

def lambda_handler(event, context):
    # TODO implement
    body = {
        "message": "Hello world"
    }

    response = {
        "statusCode": 200,
        "body": json.dumps(body)
    }

    return response

This is a simple Lambda function that returns a "Hello world" message in the response body.

3. Click on "Save" to save your code.

Testing Your Lambda Function

Let's test your Lambda function to make sure it is working correctly.

1. Click on "Test" in the top-right corner of the Lambda console.
2. Choose "Create new test event" as the test event template and give your test event a name.
3. Add the following code to the "Event body" section:

{
  "key1": "value1",
  "key2": "value2",
  "key3": "value3"
}

4. Click on "Create".
5. Click on "Test" to run your Lambda function with the test event.

If everything worked correctly, you should see a response body that says "Hello world"!

Building More Advanced Serverless Functions with Python

Now that you have a basic understanding of how to create and test a Lambda function using Python, let's dive into building more advanced serverless functions.

Working with External Libraries

One of the advantages of using Python with AWS Lambda is that you can easily work with external libraries by including them in your deployment package.

For example, let's say you want to use the requests library to make an HTTP request from your Lambda function. Here's how you can include it in your deployment package:

1. Create a new directory for your deployment package.

mkdir deployment-package

2. Install the requests library in the deployment package directory.

pip install requests -t deployment-package

3. Create a new .zip file containing your Lambda function code and the requests library.

cd deployment-package
zip -r ../deployment-package.zip *
cd ..

4. Upload the deployment package to your Lambda function.

aws lambda update-function-code --function-name your-function-name --zip-file fileb://deployment-package.zip

Now you can import and use the requests library in your Lambda function code:

import requests

def lambda_handler(event, context):
    r = requests.get("https://jsonplaceholder.typicode.com/todos/1")
    return {
        "statusCode": r.status_code,
        "body": r.json()
    }

Working with AWS Services

AWS Lambda integrates with many AWS services, making it easier to build serverless applications and services. Let's take a look at how you can work with some of these services using Python and AWS Lambda.

AWS S3

AWS S3 is a cloud storage service that is used to store and retrieve data. You can use AWS Lambda to interact with S3 in various ways such as uploading, downloading, and deleting objects.

Here's an example of a Lambda function that downloads a file from S3 and uploads it to another S3 bucket:

import boto3

s3 = boto3.client('s3')

def lambda_handler(event, context):
    source_bucket = event["source_bucket"]
    source_key = event["source_key"]
    dest_bucket = event["dest_bucket"]
    dest_key = event["dest_key"]

    # Download the file from S3
    response = s3.get_object(Bucket=source_bucket, Key=source_key)

    # Upload the file to another S3 bucket
    s3.put_object(Body=response['Body'].read(), Bucket=dest_bucket, Key=dest_key)

    return {
        "statusCode": 200,
        "body": "File uploaded successfully"
    }

AWS DynamoDB

AWS DynamoDB is a NoSQL database service that is used to store and retrieve data. You can use AWS Lambda to interact with DynamoDB in various ways such as querying, updating, and deleting data.

Here's an example of a Lambda function that adds a new item to a DynamoDB table:

import boto3

dynamodb = boto3.resource('dynamodb')

def lambda_handler(event, context):
    table_name = event["table_name"]
    item = event["item"]

    # Add the item to the DynamoDB table
    table = dynamodb.Table(table_name)
    table.put_item(Item=item)

    return {
        "statusCode": 200,
        "body": "Item added to DynamoDB table successfully"
    }

AWS Kinesis

AWS Kinesis is a real-time data streaming service that is used to process and analyze streaming data. You can use AWS Lambda to process data from Kinesis streams and perform various operations on the data.

Here's an example of a Lambda function that processes data from a Kinesis stream and writes the data to a DynamoDB table:

import boto3
import json

dynamodb = boto3.resource('dynamodb')

def lambda_handler(event, context):
    table_name = event["table_name"]
    records = event["Records"]

    # Process each record from the Kinesis stream
    for record in records:
        data = json.loads(record["Data"])

        # Write the data to the DynamoDB table
        table = dynamodb.Table(table_name)
        table.put_item(Item=data)

    return {
        "statusCode": 200,
        "body": "Data processed successfully"
    }

Summary

In this article, we explored how to build serverless functions using Python and AWS Lambda. We covered the basics of creating and testing a Lambda function and then moved on to building more advanced functions using external libraries and AWS services such as S3, DynamoDB, and Kinesis.

AWS Lambda is a powerful tool that enables developers to build serverless applications and services without worrying about the underlying infrastructure. And with the support for Python, it's now easier than ever to build serverless functions for your Python-based applications.

So go ahead and give it a try! Build your own serverless functions using Python and AWS Lambda and see how easy it is to scale and deploy your code. Happy coding!

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