Mastering AWS Lambda: 5 tips from my own experience

Greetings, cloud enthusiasts!

With over five years on the AWS battlefield and an AWS Developer Associate certification in my toolkit, I've come to appreciate the finesse of serverless architecture with AWS Lambda. It's a bit like being a maestro in an orchestra of functions - every tweak can turn a cacophony into a symphony.

As we unfold the pages of Lambda lore together, I'll share the nuances that make all the difference. Because in the end, serverless success isn't just about understanding the cloud - it's about making it work for you, not the other way around. In this article, I'll talk about five little-known but invaluable tips that I consider important when working with AWS Lambda.

Let’s get to the good stuff.

The Art of Execution Environment Reuse

Imagine a bustling city where every building is constructed and deconstructed for each person's visit. Chaos, right? In the AWS cloud, Lambda functions can be likened to the buildings in such a city; effective reuse of these structures is the cornerstone of a thriving serverless environment.

When we look at a Python function for user authentication using AWS Cognito, placing the creation of the Cognito client within the lambda_handler is akin to erecting a new building for every new visitor - a needless and resource-intensive endeavor:

import boto3

def lambda_handler(event, context):
    # Inside the handler, a new Cognito client is created on each invocation
    cognito_client = boto3.client("cognito-idp")
    # Code to authenticate with Cognito using the email and password
    # ...        

A more efficient approach builds our Cognito client outside the lambda_handler, allowing it to be reused by subsequent visitors without the need for reconstruction, saving both time and resources:

import boto3

# Cognito client created outside the lambda_handler, ready for reuse
cognito_client = boto3.client("cognito-idp")

def lambda_handler(event, context):
    # Reuse the existing 'building'
    # Code to authenticate with Cognito using the email and password
    # ...        

This pattern of reuse is not just about speed; it reflects a deeper understanding of the Lambda execution model. However, it's important to note that this isn't limited to Cognito clients. The initialization of any resource clients, such as those for RDS / DynamoDB or external service calls that can be cached, should be approached with the same strategy to optimize performance.

INFO: For instance, creating a Cognito client might appear to be a trivial operation, but in reality, it initiates an HTTP request to retrieve JWKs (JSON Web Keys) from Cognito for each function invocation, using them to verify the signature of JWT tokens - a process best not repeated more than necessary.        

Understanding AWS Lambda's execution environment reuse is clearly outlined in AWS's documentation, particularly under "Take advantage of execution environment reuse to improve the performance of your function". For a skilled engineer, it’s essential not just to use these features, but to comprehend their workings. This deeper insight enables more effective and efficient utilization of Lambda's capabilities.

However, this understanding must be coupled with caution, especially regarding global variables. Consider this Python example:

# A potentially problematic global variable
user_sessions = {}

def lambda_handler(event, context):
    user_id = event['user_id']
    if user_id not in user_sessions:
        # Initialize a new session for the user
        user_sessions[user_id] = initialize_session()
    # Proceed with the existing or new session
    # ...        

In this snippet, user_sessions is intended to track user sessions. However, being global, it retains data across different invocations, potentially leading to mixed-up sessions as different users trigger the function. This unintended persistence is a classic example of how global variables can behave unexpectedly if not handled with care.

Similarly, the use of the /tmp directory in Lambda functions requires careful management. While /tmp provides a temporary storage space that persists across invocations, it has a limited capacity. Excessive or careless use of this space, without proper cleanup, can lead to storage overflow issues.

In summary, effective Lambda function development is about more than just following best practices; it's about understanding the why and how, ensuring that every function and global variable serves its purpose correctly and efficiently.

Leveraging Environment Variables for Operational Efficiency

Hardcoding values in your code is akin to leaving your house keys under the doormat - obvious and risky. This is where environment variables come into play, acting as secure placeholders for operational parameters. But beyond security, let's talk about their performance benefits.

The Performance Edge

Fetching configuration data or sensitive information on every invocation of your Lambda function can be resource-intensive if you're relying on services like AWS Systems Manager Parameter Store or AWS Secrets Manager. Each retrieval requires a separate request to these services, which, while secure, adds latency to your function execution. In contrast, accessing these values through environment variables is much quicker. The values are loaded when the Lambda function is initiated, eliminating the need to fetch them for every invocation.

Here's an example of how you can set environment variables using AWS CloudFormation, particularly for retrieving a value from AWS Secrets Manager:

MyLambdaFunction:
  Type: AWS::Lambda::Function
  Properties:
    Environment:
      Variables:
        API_SETTINGS: !Sub '{{resolve:ssm:/${Env}/some-api/settings}}'
        API_KEY: !Sub '{{resolve:secretsmanager:${Env}/some-api:SecretString:api-key}}'        

In this setup, API_SETTINGS is sourced from the Parameter Store, and API_KEY is fetched from Secrets Manager. This method offers the dual benefits of improved performance and maintaining the security of sensitive data.

The Balance of Security and Efficiency

While environment variables enhance performance, their use must be balanced with security considerations. Directly storing sensitive data like database passwords in environment variables can be risky if not managed properly (instead, it is recommended to store the ARN to your secret in the SecretsManager). For such cases, integrating Secrets Manager or Parameter Store, combined with caching within your Lambda code, provides a secure yet efficient solution.

By the way, AWS Lambda Powertools for Python is a great tool that helps you develop with best practices, in particular, it provides high-level functions for retrieving parameters and caching them. I will talk about this tool in more detail in the next article.

Optimizing Deployment Packages with Lambda Layers

Minimizing the size of your Lambda deployment package is crucial for performance. A common pitfall is bundling all dependencies into a single, monolithic Lambda Layer, used across various functions. This 'one-size-fits-all' approach can lead to bloated functions, carrying unnecessary weight that slows down deployment and execution.

The Power of Multiple Layers

AWS Lambda allows up to 5 Layers per function, a feature that can be strategically utilized to optimize performance. Think of a large application or service with numerous Lambda functions, each with its unique dependency requirements. By segregating these dependencies into different Layers, you effectively create a modular system, where each function loads only what it needs.

For example, imagine splitting your dependencies into five distinct Layers, each varying in size (5MB, 8MB, 21MB, 3MB, 15MB). Now, if a particular Lambda function requires only two of these Layers (say, 5MB + 8MB), its additional load is just 13MB, a significant reduction from the hypothetical 52MB of a single combined Layer. This selective inclusion drastically reduces the deployment time and enhances overall performance.

My Layering Strategy: A House Analogy

Personally, on one of my projects, I divided Layers using the following abstraction:

• The Foundation: For essential utilities. In my case, this included AWS Powertools, providing a base for enhanced functionality and efficiency.
• The Walls: Dedicated to authentication and authorization. These are the protective barriers, ensuring secure access and interactions.
• The Kitchen: basic things needed for application functionality (common dependencies for all Lambda functions).
• The Entryways: Layers for interfacing with external APIs and services, much like how windows and doors connect a house to the outside world.
• The Roof: Housing dependencies that are less frequently used, similar to storing seldom-used items in an attic.

This metaphorical approach to organizing Lambda Layers ensures each function in your serverless architecture is equipped with precisely what it needs, and nothing more. It's about creating an efficient, streamlined environment where every component serves its purpose, contributing to a lean, fast, and effective serverless application.

Enhancing Security in AWS Lambda with DynamoDB and S3 through VPC Endpoints

When integrating AWS Lambda with services like DynamoDB or S3, it's common to operate without a Virtual Private Cloud (VPC), as these services can communicate directly over the internet. However, for enhanced security, it's advisable to route this traffic through a VPC using VPC Endpoints. This setup ensures that the data exchanged between your Lambda functions and these services remains within the AWS network, mitigating the risks associated with internet-facing access.

Setting Up VPC Endpoints for Enhanced Security

Configuring a VPC for your Lambda function typically involves specifying security groups and subnets. The process is straightforward if you're already familiar with setting up a VPC for Lambda. Here's a basic CloudFormation template to define a VPC configuration for a Lambda function:

VpcConfig:
  SecurityGroupIds: !Ref SecurityGroups
  SubnetIds: !Ref Subnets        

The next step is to establish a VPC Endpoint, which allows your Lambda functions to privately connect to other AWS services like DynamoDB or S3. The VPC Endpoint configuration requires specifying the VPC ID and the RouteTableIds associated with your Lambda function's subnets. Here's an example of setting up a VPC Endpoint for DynamoDB in a CloudFormation template:

DynamoDBEndpoint:
  Type: "AWS::EC2::VPCEndpoint"
  Properties:
    ServiceName: !Sub "com.amazonaws.${AWS::Region}.dynamodb"
    VpcId: !Ref VpcId
    VpcEndpointType: "Gateway"
    RouteTableIds: !Ref RouteTableIds        

With this setup, all interactions between your Lambda function and DynamoDB occur within the confines of your VPC. This internal network routing not only enhances security but also can improve performance by reducing latency.

The Importance of VPC Endpoints

Using VPC Endpoints is crucial for maintaining a secure architecture, especially when handling sensitive data or operating in regulated environments. It ensures that your data doesn't traverse the public internet, reducing exposure to potential threats. This approach is part of a broader best practice in cloud security - minimizing the attack surface by keeping as much of your data and communication internal to your cloud environment as possible.

How to save money while using Provisioned Concurrency

Provisioned Concurrency in AWS Lambda is a powerful feature designed to optimize performance and manage costs. Let's break down what it is and how it works before delving into a practical use case.

Understanding Provisioned Concurrency

Essentially, Provisioned Concurrency keeps a specified number of Lambda instances pre-initialized and ready to respond instantly to invocations. This is particularly beneficial for functions that experience variable traffic, ensuring consistent performance without the latency typically associated with "cold starts".

A Practical Example: Authentication Endpoints

Consider an application with /login and /refresh endpoints. These endpoints are critical for user authentication, issuing JWTs during login and refreshing them as needed. Given their frequent use, these functions are ideal candidates for Provisioned Concurrency. This setup ensures that user authentication requests are handled swiftly, enhancing user experience.

Instead of creating separate Lambda functions for each endpoint, we can consolidate them into a single function and apply Provisioned Concurrency to it. Here’s an example using AWS CloudFormation:

Events:
  LogInEvent:
    Type: Api
    Properties:
      RestApiId: !Ref CustomerApi
      Path: /customer/api/v1/login
      Method: POST
      Auth:
        ApiKeyRequired: true
  RefreshEvent:
    Type: Api
    Properties:
      RestApiId: !Ref CustomerApi
      Path: /customer/api/v1/refresh
      Method: POST
      Auth:
        ApiKeyRequired: true        

In this configuration, both LogInEvent and RefreshEvent are handled by the same Lambda function, making efficient use of Provisioned Concurrency.

Tailoring Provisioned Concurrency for Environments

It’s important to tailor Provisioned Concurrency to the specific needs of each environment. For instance, a production environment might require more concurrency than a development or testing environment. Here's how you can configure different concurrency levels using CloudFormation:

Conditions:
  IsProductionEnv: !Equals [ !Ref Environment, "prod" ]

MyLambdaFunction:
  Type: AWS::Serverless::Function
  Properties:
    FunctionName: !Sub "my-function-${Environment}"
    # ... other properties ...
    AutoPublishAlias: live
    ProvisionedConcurrencyConfig:
      ProvisionedConcurrentExecutions: !If [ IsProductionEnv, 3, 1 ]        

In this example, the Lambda function in the production environment (prod) is configured with a higher level of Provisioned Concurrency (3) compared to other environments (1). This approach ensures that the production environment has sufficient capacity to handle higher traffic loads efficiently.

Audience Query: Does anyone know if it's possible to specify Provisioned Concurrency only for the production environment, using a single template for all environments? Note that using !Ref AWS::NoValue doesn’t work in this case.

By strategically implementing Provisioned Concurrency, we can optimize the performance of critical Lambda functions and manage costs effectively, ensuring resources are used where they're needed most. This practice not only saves costs but also contributes to a seamless and responsive user experience.

Bonus: Setting Up Log Retention Policies for AWS Lambda

In the realm of AWS Lambda, logs play a crucial role in monitoring and troubleshooting. However, managing these logs, especially their retention and storage, is often overlooked. Here's where setting up a retention policy for your log groups becomes vital, particularly when using AWS CloudFormation or the Serverless Application Model (SAM).

Why Log Retention Matters

Logs can grow exponentially, especially when you have multiple functions or a high-traffic application. Without a retention policy, you might end up storing logs indefinitely, leading to unnecessary storage costs and clutter. Setting a retention policy helps in maintaining only the necessary logs, reducing costs and aiding in efficient log management.

Implementing Retention Policy with CloudFormation

Here’s how you can define log retention policies for different environments using CloudFormation:

Mappings:
  EnvironmentSettings:
    prod:
      LogRetentionInDays: 90  # Retain logs for 3 months in production
    test:
      LogRetentionInDays: 30  # Retain logs for 1 month in test
    dev:
      LogRetentionInDays: 14  # Retain logs for 2 weeks in development

SomeFunction:
  Type: AWS::Serverless::Function
  Properties:
    FunctionName: !Sub "some-function-${Env}"
    # ... other properties ...

SomeFunctionLogGroup:
  Type: AWS::Logs::LogGroup
  Properties:
    LogGroupName: !Sub "/aws/lambda/some-function-${Env}"
    RetentionInDays: !FindInMap [EnvironmentSettings, !Ref Env, LogRetentionInDays]        

In this configuration, we use a Mappings section to define different log retention periods for production, test, and development environments. The SomeFunctionLogGroup is then set up with a RetentionInDays property that varies based on the environment, ensuring that logs are kept for an appropriate duration depending on the environment's needs.

The Benefits

Implementing such a log retention policy ensures that you're not keeping logs longer than necessary, thereby optimizing storage costs and management overhead. It also contributes to better compliance, especially in environments with strict data retention policies.

I hope you've found these tips insightful and applicable to your work with AWS Lambda. The journey through serverless architecture is one of continuous learning and adaptation. By embracing best practices like execution environment reuse, efficient use of environment variables, strategic deployment package optimization, security enhancements with VPC Endpoints, cost-effective use of Provisioned Concurrency, and prudent log retention policies, you're not just coding; you're crafting resilient, efficient, and secure cloud-native applications.

May these practices serve you well in your AWS Lambda adventures, helping you build more robust, efficient, and cost-effective solutions. Remember, the beauty of serverless is in its simplicity and scalability, and with these tips in hand, you're well-equipped to harness its full potential.

Happy coding!