Step-by-Step Guide: Installing Python Dependency on AWS Lambda

Table of Contents

1. Introduction
2. Background on Lambda functions
3. The challenge of using dependencies in Lambda functions
4. The easiest way to add dependencies to a Python Lambda function
5. Creating a new Lambda function
6. Installing the necessary packages
7. Compressing and uploading the zip file
8. Working with the Lambda function
9. Making changes to the code
10. Updating the Handler section
11. Testing and deploying the Lambda function
12. Conclusion

How to Create Lambda Functions with Dependencies

Lambda functions are a powerful tool provided by AWS for serverless computing. However, one of the challenges developers often face when using Lambda functions is how to include external dependencies that are not included by default in the Python runtime.

In this article, we will explore the easiest way to add dependencies to a Python Lambda function, allowing You to leverage popular packages like pandas and numpy. We will walk through the step-by-step process of creating a new Lambda function, installing the necessary packages, compressing and uploading the zip file, and working with the Lambda function.

But first, let's provide some background on Lambda functions and the challenge of using dependencies in them.

Background on Lambda functions

Lambda functions allow you to run your code without provisioning or managing servers. You can write your Lambda function code in supported languages, such as Python, and AWS takes care of the rest, handling automatic scaling, high availability, and fault tolerance. This makes Lambda functions a convenient choice for a wide range of applications, from simple scripts to complex microservices.

However, by default, Lambda functions come with a limited set of Python modules available in the runtime environment. This means that if you want to use external libraries like pandas or numpy, you need to find a way to include them in your Lambda function.

The challenge of using dependencies in Lambda functions

Including external dependencies in a Lambda function can be challenging. AWS provides a feature called Lambda Layers, which allows you to attach additional code and content to your Lambda function. However, using Lambda Layers requires additional setup and can sometimes be cumbersome, especially for developers who are new to AWS.

In this article, we will explore a simpler approach to adding dependencies to a Python Lambda function. This method involves creating a zip file that contains both your Lambda function code and the required dependencies. By compressing and uploading this zip file to AWS, you can include your dependencies without the need for Lambda Layers.

The easiest way to add dependencies to a Python Lambda function

Now that we understand the challenge of using dependencies in Lambda functions, let's dive into the easiest way to add them to a Python Lambda function.

Step 1: Creating a new Lambda function

The first step is to Create a new Lambda function on the AWS console. Give your function a Meaningful name that reflects its purpose. For this example, we will call our function "my-example-yt".

Step 2: Installing the necessary packages

To install the required packages, navigate to your desktop and create a new folder. In this folder, write your Python code and save it with a descriptive name. For this example, we will call our Python file "example.py".

Next, open a command prompt or terminal and navigate to the folder where you saved your Python file. Use the pip install command to install the required packages. For example, if you need to install the "OpenAI" Package, Type pip install openai.

Step 3: Compressing and uploading the zip file

After installing the required packages, you need to compress the folder into a zip file. This zip file should contain both your Python file and the installed packages.

Once you have the zip file, go back to the AWS Lambda console and click "Upload" in the "Function code" section. Select your zip file and click "Save".

Please note that uploading a zip file will overwrite any existing code or files in your Lambda function.

Step 4: Working with the Lambda function

Now that you have uploaded the zip file, you can start working with your Lambda function. Open your Lambda function in the AWS console and make any necessary changes to the code.

In your Python code, you can import and use the installed packages. For example, if you installed the "openai" package, you can use import openai to import it into your code.

Step 5: Updating the Handler section

In the Lambda function configuration, there is a section called "Handler". This section specifies the entry point for your Lambda function.

Update the "Handler" section to match the name of your Python file and the name of the function within that file. For example, if your Python file is named "example.py" and your function is named "lambda_ai", the "Handler" section should be "example.lambda_ai".

Click "Save" to save your changes.

Step 6: Testing and deploying the Lambda function

To test your Lambda function, click "Test" in the AWS console and provide an event name. Click "Run" to execute the function and see the output.

Once you are satisfied with the results, click "Deploy" to save your changes and make the Lambda function available for use.

Conclusion

Adding dependencies to a Python Lambda function can be a challenging task, but with the right approach, it becomes much easier. In this article, we have explored the easiest way to add dependencies to a Python Lambda function, allowing you to use external libraries like pandas and numpy.

By following the step-by-step process of creating a new Lambda function, installing the necessary packages, compressing and uploading the zip file, and working with the Lambda function, you can leverage the full power of Python and AWS Lambda in your serverless applications.

So why wait? Start adding dependencies to your Lambda functions today and unlock a world of possibilities.

Highlights

• Lambda functions are a powerful tool for serverless computing provided by AWS.
• The default Python runtime in Lambda functions lacks several popular packages like pandas and numpy.
• The challenge of including dependencies in Lambda functions can be overcome by creating a zip file with the required packages.
• By following a simple process of creating a new Lambda function, installing packages, compressing and uploading the zip file, and making necessary code changes, you can use external libraries in your Lambda functions.
• This method avoids the complexity of using Lambda Layers and allows you to quickly add dependencies to your functions.

Frequently Asked Questions

Q: Can I use any external package in a Lambda function?

A: As long as the package is compatible with the Python version used by AWS Lambda, you can include any external package in your function.

Q: Are there any restrictions on the size of the zip file?

A: Yes, AWS imposes a maximum size limit for Lambda function deployment packages, which includes both the code and any included dependencies. Make sure to take this limit into account when using this approach.

Q: Can I update the dependencies in a deployed Lambda function?

A: Yes, you can update the dependencies by creating a new zip file and uploading it to your Lambda function. However, keep in mind that this will overwrite any existing code or files in the function.