Avoid Common AWS Lambda Pitfalls
7 best practices that work in production
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AWS Lambda is one of the most popular services offered by AWS. It is also one of the widely used services in event driven architectures.
AWS Lambda is very cost-effective and provides great performance if used effectively. Else, it can turn into a nightmare if not configured and optimized correctly.
I have been using AWS Lambda in production environments for more than 3 years now.
Keeping the AWS Lambda functions stable and cost-efficient in production is a challenge.
Cold starts, lambda timeout issues due to misconfiguration, and unpredictable costs due to long processing times can turn your serverless architecture success story into a budgetary headaches, with performance hiccups that frustrate your end-users.
In today’s newsletter, I will be sharing the 7 best practices that have helped me avoid major performance and cost issues while using Lambda functions in production.
Let’s dive in!
7 Best Practices of Using Lambda Functions in Production
#1: Optimize Function Performance
Cold Starts
Keep your deployment package to the minimum size necessary.
For instance, if you’re using Node.js, don’t include the entire node_modules directory—only the packages you use.
Overlooking this can lead to increased latency due to longer cold starts.
Memory Configuration
For example, a function that processes images will require more memory than one dealing with plain text.
A common mistake is not regularly profiling your Lambda to adjust its memory setting, which can lead to either underutilized resources or performance bottlenecks.
#2: Implement Proper Error Handling
Graceful error handling could involve setting up a retry mechanism with exponential backoff for transient issues. Not having proper error handling can result in repeated processing of bad events or data loss.
Use Dead Letter Queues (DLQs) to capture and analyze invocation failures. Without DLQs, you might miss failed executions, which could be critical in transactional systems.
#3: Use Environment Variables for Configuration
For example, use environment variables to store API keys or database URIs.
A common mistake is embedding such configurations in the code, which not only creates security risks but also makes it harder to change configurations across different environments.
#4: Secure Your Lambda Functions
Practice the principle of least privilege by creating an IAM role with the minimum set of permissions needed.
For example, if a Lambda function only needs to read from an S3 bucket, don’t give it write permissions. Neglecting this can lead to security vulnerabilities.
Encrypt sensitive information using AWS KMS.
A common mistake is to leave sensitive data in plain text, which can lead to security breaches.
#5: Monitor and Optimize Costs
Set CloudWatch Alarms to monitor not just the performance metrics but also the cost metrics.
For instance, an unexpectedly high number of Lambda invocations can result in a significant cost spike.
A typical mistake is not monitoring costs, which can lead to budget overruns.
Regularly reviewing Lambda metrics can help you identify inefficiencies.
Not doing so might cause you to miss opportunities to reduce cost, such as optimizing the execution time of your functions.
#6: Implement CI/CD for Lambda Functions
Automate deployments using AWS CodePipeline, and incorporate practices like blue-green deployments to minimize downtime and risks associated with updates.
Failing to implement a robust CI/CD pipeline can lead to manual errors and downtime during deployments.
#7: Manage Dependencies Efficiently
Use tools like Webpack or Rollup to bundle your Lambda functions with only the necessary pieces of code from your dependencies.
Including entire libraries can lead to large deployment packages that are slow to upload and can increase cold start times.
Lambda Layers can be used to manage and share dependencies across multiple Lambda functions.
A common mistake is to include the same dependencies in every function package, which increases the deployment size unnecessarily and makes updates cumbersome.
Adhering to these expanded best practices helps maintain an efficient, secure, and cost-effective production environment for your AWS Lambda functions, avoiding the common pitfalls that can hamper performance and reliability.
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