Top 10 AWS Lambda Best Practices

AWS Lambda is a serverless computing service provided by Amazon Web Services that lets you run code without provisioning or managing servers. Lambda automatically handles the infrastructure, scaling, and administration, allowing developers to focus on writing code.

When using AWS Lambda, you create functions, which are self-contained units of code that perform specific tasks. These functions can be triggered by various events, such as HTTP requests via Amazon API Gateway, changes to data in Amazon S3 or DynamoDB, or messages from Amazon SNS. 

It’s important to follow best practices in serverless computing platforms like AWS Lambda, to ensure efficiency and performance. These practices help improve resource management and lower operational costs. Following these best practices also ensures scalability and reliability while handling varying workloads, important when applications grow and evolve, demanding more from the underlying infrastructure.

In this article we cover the following best practices:

1. Choose an Efficient Runtime

Selecting the appropriate runtime for AWS Lambda functions impacts their performance, cost, and development experience. AWS Lambda supports multiple runtimes, including Node.js, Python, Java, Go, and .NET. Each runtime has distinct strengths and trade-offs that need to be considered based on application requirements.

For example, Node.js and Python are popular for their quick startup times and efficient handling of asynchronous operations, making them well-suited for most general-purpose serverless applications. These languages also have extensive libraries, which can accelerate development. 

Java and .NET can provide better performance for compute-intensive tasks due to their concurrency models and optimizations, but they tend to suffer from longer cold start times, which can impact user-facing applications.

To choose the most efficient runtime, evaluate the application’s needs, such as execution time, resource consumption, and the development team’s familiarity with the language. Additionally, conduct benchmarking tests to compare runtimes under realistic workloads. 

2. Configure Optimal Memory

AWS Lambda allows you to allocate memory for functions, ranging from 128 MB to 10 GB. The memory allocation is proportional to the CPU power allocated to the function, meaning more memory results in more CPU power. Configuring the optimal memory allocation aids in balancing performance and cost:

1. Benchmarking: Conduct performance tests with different memory settings to identify the optimal configuration. Start with a lower memory setting and gradually increase it while monitoring the execution time and resource utilization.
2. CloudWatch monitoring: Use AWS CloudWatch to gather metrics on your function’s execution time, memory usage, and cost. Analyze these metrics to determine if your function is over-provisioned or under-provisioned and adjust the memory allocation accordingly.
3. Cost-performance trade-off: Consider the cost implications of increasing memory. While more memory can lead to faster execution times, it also increases the cost per invocation. Aim to find a balance where the function performs efficiently without incurring unnecessary expenses.

3. Optimize the Function Package Size

The size of your Lambda function’s deployment package can impact its cold start time and overall performance. To optimize the function package size:

1. Manage dependencies: Include only the necessary libraries and dependencies in your deployment package. Review and remove any unused or redundant libraries. Consider using tools like Webpack or Parcel to bundle and tree-shake your dependencies, eliminating unused code.
2. Use AWS Lambda layers: These layers separate common dependencies that can be shared across multiple functions. This reduces the size of individual deployment packages and promotes code reuse and maintainability.
3. Minify the code: Minify and compress your code and dependencies. Use tools like UglifyJS for JavaScript or Pyminifier for Python to reduce the size of the source files.
4. Exclude development dependencies: Ensure that only production dependencies are included in the deployment package. Use tools like npm or pip with the appropriate flags to exclude development dependencies.

4. Minimize the Deployment Artifact Size

Deployment artifacts include the code and resources required for a Lambda function. Minimizing their size aids in reducing deployment times and cold start durations. To minimize the size of your deployment artifacts:

1. Ensure compression: Always compress your deployment package using zip or other compression tools. This reduces the size of the package and speeds up the deployment process.
2. Exclude unnecessary files: Use configuration files like .npmignore or .dockerignore to exclude unnecessary files from the deployment package, such as documentation, tests, and local configuration files.
3. Optimize assets: If your function relies on images, fonts, or other assets, optimize them for size. Use tools like ImageMagick or TinyPNG to compress images without compromising quality.
4. Use Lambda container images: Consider using Lambda container images for larger deployment packages. These images can be up to 10 GB in size and provide more flexibility in terms of dependencies and runtime environment.

5. Mitigate Cold Starts

Cold starts occur when a Lambda function is invoked after being idle for some time, leading to increased latency as the execution environment initializes. This can be particularly problematic for latency-sensitive applications. To mitigate cold starts, you can use several strategies:

1. Provisioned concurrency: AWS offers provisioned concurrency, which keeps a specified number of instances of your function warm and ready to handle incoming requests. This ensures consistent performance but comes at an additional cost.
2. Optimize initialization code: Reduce the amount of code and dependencies that need to be loaded during initialization. Keep your functions lightweight by minimizing external libraries and reducing the complexity of the initialization process.
3. Warm-up strategies: Schedule CloudWatch Events or use other periodic triggers to invoke the Lambda functions at regular intervals, preventing them from going idle. This keeps the execution environment warm and reduces the likelihood of cold starts.
4. Container reuse: Design your functions to take advantage of container reuse. Ensure that your function logic can benefit from warmed-up containers, reducing the impact of cold starts.

6. Set Conservative Timeouts

Setting appropriate timeouts for Lambda functions helps avoid unnecessary charges and manage function behavior. AWS Lambda allows you to set timeouts ranging from 1 second to 15 minutes. To determine the right timeout duration:

1. Analyze execution times: Review historical execution times using AWS CloudWatch metrics. Set the timeout slightly above the average execution duration to accommodate variations while avoiding excessively long timeouts.
2. Error handling and retries: Implement error handling and retry logic within your functions to manage timeouts. Ensure that the functions can gracefully handle partial failures and retry operations without causing adverse effects.
3. Adaptive timeout settings: Continuously monitor the execution durations and adjust the timeout settings as needed. For dynamic workloads, consider implementing adaptive timeout strategies that adjust based on real-time performance data.

7. Implement Asynchronous Invocations

Asynchronous invocations allow Lambda functions to handle events without waiting for a response, which is useful for decoupling and scaling your architecture. Use the following mechanisms to implement asynchronous invocations:

1. Event-driven architectures: Use AWS services like SNS (Simple Notification Service), SQS (Simple Queue Service), or EventBridge to trigger Lambda functions asynchronously. These services enable you to build event-driven architectures that are scalable and resilient.
2. Dead Letter Queues (DLQs): Configure DLQs to capture failed invocations. This allows you to analyze and address issues without losing critical events. DLQs can be set up in SQS or SNS to store messages that couldn’t be processed successfully.
3. Monitoring and alerts: Use AWS CloudWatch to monitor the success and failure rates of asynchronous invocations. Set up alerts to notify you of any significant changes in invocation patterns or failure rates.
4. Retry mechanisms: Implement retry mechanisms within the functions to handle transient errors gracefully. AWS Lambda automatically retries asynchronous invocations twice, but you can add additional logic to manage retries more effectively.

8. Implement Batch Processing

Batch processing allows you to handle multiple records in a single Lambda invocation, improving resource utilization and reducing costs. Use the following mechanisms to implement batch processing:

1. Amazon SQS and Kinesis: Integrate your Lambda functions with Amazon SQS and Kinesis to process messages and records in batches. These services provide built-in support for batch processing and can trigger Lambda functions with a batch of records.
2. Configure batch size: Set appropriate batch sizes based on your workload and function’s memory and timeout settings. The batch size should be large enough to optimize processing but not so large that it exceeds the function’s resource limits.
3. Error handling: Enable error handling to manage partial failures within batches. Ensure that the function can process individual records if some records in the batch fail. Use DLQs to capture and retry failed records.
4. Parallel processing: Consider using parallel processing techniques within the Lambda function to better handle batches. This can be achieved using multi-threading or asynchronous processing within the function code.

9. Implement Caching Strategies

Caching is crucial for reducing latency and improving the performance of Lambda functions. A successful caching strategy can minimize redundant data fetching and enhance function responsiveness. Use these mechanisms to implement caching strategies:

1. Lambda extensions: Use Lambda extensions to cache data locally within the function’s execution environment. Extensions allow you to maintain state between invocations and reduce the need for repeated data fetching.
2. External caching services: Use external caching services like Amazon ElastiCache or DynamoDB Accelerator (DAX) to store frequently accessed data. These services provide high-performance, in-memory caching that can reduce data retrieval times.
3. In-memory caching: Implement in-memory caching within your function’s code using data structures like dictionaries or arrays to store frequently accessed data during the function’s execution. This is particularly useful for short-lived, repeated data access.
4. Cache invalidation: Establish an invalidation strategy to ensure that cached data remains fresh and accurate. This may involve time-based expiration, event-driven invalidation, or manual cache purging.

10. Prefer a Stateless and Ephemeral Design

Designing Lambda functions to be stateless and ephemeral ensures they can scale efficiently and handle failures gracefully. Stateless functions do not retain any state between invocations, and ephemeral functions do not rely on the local file system or in-memory state. To achieve this:

1. Avoid local state: Ensure that your functions do not rely on the local file system or in-memory state between invocations. Instead, store stateful data in external services like Amazon S3, DynamoDB, or RDS. This makes your functions more resilient and easier to scale.
2. Enable external state management: Use external state management solutions to persist data across function invocations. Services like DynamoDB and S3 provide durable storage that can be accessed by multiple instances of a function, ensuring data consistency.
3. Ensure idempotent operations: Design the functions to perform idempotent operations, meaning they produce the same result regardless of how many times they are executed. This prevents unintended side effects of retries or duplicate invocations.
4. Support ephemeral data: Use temporary storage solutions, such as AWS Lambda’s /tmp directory, for ephemeral data that does not need to persist between invocations. This can be useful for intermediate processing and temporary caching.

Related content: Read our guide to lambda architecture

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