Скачать или смотреть How to Force Parallel Lambda Execution Using AWS API Gateway Parameters

Discover techniques to optimize AWS Lambda executions, explore challenges with ephemeral storage, and learn how to leverage EFS for better performance without negative impacts.
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Managing AWS Lambda Executions: A Guide to Parallelism and Storage Optimization

In the world of cloud computing, AWS Lambda has become a popular choice for executing code in response to various events. However, there are instances where users face challenges with concurrent execution and storage limitations. One commonly encountered issue is the struggle to manage execution environments effectively, especially when dealing with large models in a serverless architecture. In this guide, we will explore the challenges of AWS Lambda execution and present potential solutions to optimize your environment for better performance.

The Challenge: AWS Lambda Execution Environments

When you invoke a Lambda function via an API Gateway, the underlying infrastructure handles the execution environments without user control. Here are the main points:

Single vs. Multiple Execution Environments: You cannot dictate whether a single Lambda function handles all incoming requests or if multiple instances run in parallel.

Storage Constraints: Each Lambda execution environment has a memory limit per request, including the ephemeral storage (the /tmp directory), which is capped at 512 MB. This can become problematic if your use case involves large models or data.

In many situations, particularly those involving large codebases (100-300 MB), invoking a second request with a different model can lead to running out of temporary storage because the Lambda function might be reusing an existing environment, which hasn’t yet cleared the previous invocation’s data.

The Solution: Leveraging EFS and Execution Controls

While it may not be possible to force AWS Lambda to create separate execution environments directly through parameter controls, there are effective strategies you can employ to manage this limitation.

1. Utilizing Amazon EFS (Elastic File System)

Amazon suggests using EFS to alleviate the restrictions associated with ephemeral storage. Here’s why it’s beneficial:

Expanded Storage Capacity: EFS provides virtually unlimited storage, which can help with larger models that exceed the 512 MB limit of the /tmp directory.

Persistent Data: Data stored in EFS persists beyond the lifespan of a Lambda execution, making it a suitable choice for applications that require ongoing access to large datasets.

Considerations:

While EFS may be slower than using the ephemeral /tmp directory, it can significantly reduce the risk of storage issues and help you maintain a cleaner application architecture.

2. Controlling Concurrency

Although AWS does not provide the ability to specifically manage which execution environment serves a request, you can set up concurrency limits to avoid capacity issues:

Set a Reserved Concurrency: By reserving concurrency for specific Lambda functions, you can ensure that instances are available to handle requests smoothly, thus mitigating the risk of hitting storage limits.

Implement Error Handling: Design your application to gracefully handle errors that may occur due to storage issues. This can include retry logic or fallback mechanisms to maintain service availability.

3. Experiment with Step Functions

Another potential approach is to implement AWS Step Functions, which help orchestrate multiple Lambda executions:

Orchestration: You can set up a state machine that dictates how and when Lambda functions are invoked.

Contextual Parameters: Provide conditional flows based on parameters, which can help in deciding which model to execute without overloading the existing storage.

Conclusion

While there is no straightforward way to control the execution environment of AWS Lambda, understanding how to leverage Ama