Over the last decade or so, AWS Lambda has become a core component of many IT infrastructures. Whether you're building event-driven applications, automating backend tasks, or handling real-time data processing, Lambda provides a powerful serverless computing foundation.
But despite its in-built fault tolerance, Lambda can run into issues that disrupt workflows and impact performance. These issues, especially those related to cold starts, timeouts, and permissions, can be hard to debug. Without proper monitoring and troubleshooting techniques, you risk prolonged downtime, increased operational costs, and a frustrating development experience.
This guide will help you systematically troubleshoot different kinds of AWS Lambda errors, including execution failures, performance bottlenecks, permission errors, and common misconfigurations. Let’s get started!
AWS Lambda overview
AWS Lambda is a serverless computing service that lets you run code without having to provision the backend infrastructure. This means that you don’t have to manage servers and operating systems, or worry about scaling. It automatically scales based on demand, executes code in response to triggers, and only charges for the compute time used.
Lambda’s architecture and features make it a top choice for:
Building event-driven microservices: Lambda functions can integrate with other AWS services to create decoupled and scalable microservices that respond to events.
Automating operational tasks: Scheduled Lambda functions can automate routine tasks like data backups, log processing, and infrastructure management.
Real-time data processing: Lambda can process streaming data from sources like Kinesis and DynamoDB Streams.
Creating serverless APIs: Lambda functions can be exposed as HTTP endpoints through Amazon’s API Gateway.
Extending SaaS applications: It can also be used to add custom logic and functionality to existing SaaS applications through event-driven integrations.
Lambda architecture and key components
To become a fast Lambda troubleshooter, you must develop a deep understanding of how it works. Let’s discuss key Lambda components and concepts below.
AWS Lambda functions
A Lambda function is the core unit of execution in AWS Lambda. It consists of:
Code: The actual logic written in supported languages like Python, Node.js, Java, Go, or .NET.
Runtime: The environment that executes the function (e.g., Python 3.9, Node.js 18).
Execution role: The IAM role that grants permissions to access AWS resources.
Timeout and memory settings: Defines the maximum execution duration and allocated memory (128 MB to 10 GB).
Environment variables: Key-value pairs used to tweak the behavior of the function.
Event sources (triggers)
Lambda functions execute in response to events. These events can originate from AWS services or external applications. Here are some examples of function sources:
API Gateway: HTTP requests trigger Lambda functions to process API calls.
S3 Bucket events: Functions execute when files are created, updated, or deleted in S3.
DynamoDB streams: Triggers functions when table items are inserted, modified, or deleted.
SNS (Simple Notification Service) and SQS (Simple Queue Service): Functions consume messages from SNS topics and SQS queues.
CloudWatch events: Schedules automated Lambda executions based on time or event patterns.
Invocation models
Lambda supports different execution models based on how it is triggered:
Synchronous invocation: The caller waits for the function to return a response (e.g., API Gateway, Application Load Balancer).
Asynchronous invocation: Events are queued, and Lambda processes them in the background (e.g., S3, SNS).
Poll-based invocation: Lambda polls event sources like SQS, DynamoDB Streams, and Kafka.
AWS Lambda execution environment
When a function runs, AWS Lambda creates an execution environment, which consists of:
Sandboxed runtime: Isolated runtime environment with allocated memory and CPU.
Cold and warm starts: Cold starts happen when a new execution environment is created, and can cause latency. Warm starts reuse an existing environment to speed up execution.
Temporary storage (/tmp): Lambda provides 512 MB to 10,240 MB of ephemeral /tmp storage per execution environment, usable as a transient cache across warm invocations.
Networking: Functions can run in a VPC to access private resources like RDS databases.
Permissions and roles
Lambda functions use IAM roles to interact with other AWS services in a secure manner. These roles define the permissions required for your function to access resources like S3 buckets, DynamoDB tables, or SQS queues.
For example, if your Lambda function needs to read data from an S3 bucket, you would assign an IAM role that includes the s3:GetObject permission for that specific bucket.
Tools for Lambda troubleshooting
When debugging Lambda issues, having the right tools can make all the difference. Let’s discuss some you must leverage:
Amazon CloudWatch
Amazon CloudWatch is the primary monitoring and logging service for AWS Lambda. Here’s how it can come in handy while troubleshooting:
It stores the logs generated by your Lambda functions, including execution results, errors, and debug messages.
It provides real-time performance metrics like invocation count, duration, memory usage, and error rates.
You can set thresholds for metrics and trigger notifications when anomalies occur.
You can analyze log data using queries to filter and troubleshoot specific issues.
For example, if a function is repeatedly failing, you can review the logs in CloudWatch to identify the root cause. Similarly, if a function is timing out, you can check the Duration and Timeout metrics to see if it needs more time or if there’s a bottleneck.
AWS X-Ray
AWS X-Ray is a distributed tracing tool that helps analyze and debug Lambda function executions by mapping the request flow across different AWS services. Here are some of its key features:
Tracks requests from start to finish, which can help determine the location of the bottleneck.
Visualizes interactions between Lambda and other AWS services like DynamoDB, S3, and API Gateway.
Displays how long each part of a function takes, helping you identify optimization avenues.
Encrypts trace (and related) data at rest and in transit.
For example, if a Lambda function that retrieves data from DynamoDB runs slowly, AWS X-Ray can be used to determine whether the bottleneck is a database query or the function itself. Similarly, if a function that integrates with API Gateway keeps timing out, X-Ray can show whether the delay is caused by a slow downstream API or a network latency between the two services.
Site24x7’s AWS Lambda monitoring tool
Site24x7 is a purpose-built monitoring platform that provides advanced observability into AWS Lambda functions. Here are some of its key features:
Provides end-to-end visibility in your serverless environment by tracking function executions, latencies, and errors with detailed reports.
Offers a single, centralized, easy-to-use interface for visualizing Lambda performance.
Can be set up to monitor Lambda alongside other AWS resources like EC2, RDS, and S3.
Enables customizable alerts based on performance metrics and error thresholds.
For example, if a function is cold starting frequently, you can use the Site24x7 dashboard to track the exact frequency and impact on response times. Similarly, if a function starts to fail intermittently, Site24x7 can raise an alert to notify your team.
AWS Lambda issue troubleshooting guide
This section covers common Lambda problems across different categories, along with their symptoms and troubleshooting steps.
Invocation errors
Lambda fails to start or execute the function when triggered.
Symptoms:
CloudWatch logs show "Task timed out after X seconds" or "Unhandled invocation error".
API Gateway returns a 502 Bad Gateway or 504 Gateway Timeout error.
Troubleshooting:
Verify that the function is deployed correctly and not in a deleted or disabled state.
If using an event source like API Gateway or S3, ensure that the event is triggering the function in the expected manner.
Review the timeout setting in Lambda and increase it if necessary.
Confirm that the function has the required IAM permissions to execute.
Test the trigger (e.g., API Gateway, S3, SNS) to confirm that events are reaching Lambda.
Runtime errors
The function executes but encounters an error during runtime.
Symptoms:
The application doesn’t run as expected.
The function returns an HTTP 500 error or fails with an unhandled exception.
Troubleshooting:
Check CloudWatch Logs for error messages and stack traces.
Ensure that all required environment variables are set correctly.
Confirm that all necessary dependencies are included in the deployment package.
If using Python or Node.js, verify that the runtime version matches the expected library versions.
Wrap function logic in try-catch blocks to log and handle errors gracefully.
If not done already, enable AWS X-Ray to trace execution and identify bottlenecks.
Permission errors
Lambda lacks the required permissions to access resources like S3, DynamoDB, or other AWS services.
Symptoms:
CloudWatch logs show "AccessDeniedException", "UnauthorizedOperation", or similar errors.
The function is unable to read from or write to other AWS services.
Troubleshooting:
Ensure that the IAM role attached to Lambda has the correct policies.
If using VPC access, verify that the relevant security group and subnet settings allow communication.
Check the resource-based policies for services like S3 and DynamoDB to ensure that they allow Lambda to access them.
Test with AWS STS (Security Token Service) to confirm role permissions.
Event source mapping errors
Lambda is unable to process events from a trigger like SQS, DynamoDB Streams, or Kinesis.
Symptoms:
CloudWatch logs show "Event source mapping disabled" or "Records were not processed".
The event source service shows unprocessed records or retry attempts.
Troubleshooting:
Ensure that the event source is correctly configured to invoke the function.
Check IAM permissions to confirm Lambda can access the event source.
If using SQS or Kinesis, verify the batch size and concurrency settings.
Look for dead-letter queue (DLQ) messages to diagnose failed events.
Cold start delays
Lambda execution times increase during scale-up due to initialization overhead.
Symptoms:
Increased latency on first invocation after a period of inactivity.
Requests occasionally take longer than expected.
Troubleshooting:
Use provisioned concurrency to keep instances warm.
Increase memory allocation, as more memory means more CPU power.
Avoid unnecessary external dependencies to reduce initialization time.
Keep global variables outside the handler function to persist across invocations.
Integration errors
Lambda fails when trying to interact with other AWS services like API Gateway, DynamoDB, or S3.
Symptoms:
Integration failures start appearing in the logs of the integrated service.
Data does not get written to or read from DynamoDB, S3, or other services.
Troubleshooting:
Verify that the service endpoint URL is correct (e.g., for API Gateway).
Check IAM permissions to confirm Lambda has read/write access to the service.
Inspect CloudWatch logs for request details and error messages.
Test the service separately using AWS CLI to isolate the issue.
Timeout issues
Lambda runs longer than its configured timeout period and gets forcefully terminated.
Symptoms:
CloudWatch logs show "Task timed out after X seconds".
API Gateway returns 504 Gateway Timeout.
Troubleshooting:
Optimize function logic to reduce execution time.
If calling external APIs, implement timeouts and retries in the request.
Use asynchronous execution for tasks that don’t need an immediate response.
Increase the Lambda function's timeout setting if the current limit is insufficient for the task's normal execution time. However, be mindful of cost implications.
Check for resource contention, such as CPU or memory limitations, which can slow down execution.
Out of memory errors
The function exceeds its allocated memory, leading to execution failures.
Symptoms:
You see “out of memory” or similar errors in the CloudWatch logs.
Monitoring dashboards report high memory usage before function crashes.
Troubleshooting:
Optimize code to reduce unnecessary object creation.
Stream large files instead of loading them all into memory.
Increase the allocated memory for the Lambda function. Monitor memory usage in CloudWatch or the Site24x7 dashboard to find the optimal memory allocation.
Use more efficient data structures and algorithms to minimize memory footprint.
Reduce the size of the deployment package by removing unnecessary dependencies and files.
Consider using a different runtime environment if the current one is known to have memory management issues.
VPC connectivity issues
Lambda cannot connect to resources inside a VPC.
Symptoms:
"Network connection timed out" error appears in logs.
The function fails to reach databases or other private services.
Troubleshooting:
Ensure that the function is attached to the correct VPC and subnets.
Verify that the security group rules allow inbound and outbound traffic on the necessary ports for the services your Lambda function needs to access.
Ensure that the DNS settings within your VPC are correctly configured, especially if your Lambda function is trying to resolve internal hostnames.
If accessing the internet, check that the function has a NAT Gateway or VPC endpoints.
Confirm that VPC CIDR blocks allow internal communication.
Use AWS VPC Flow Logs to debug network traffic issues.
Throttling issues
Lambda is exceeding its concurrency limit and requests are being throttled.
Symptoms:
CloudWatch logs show "Rate exceeded", "ThrottlingException", or similar errors.
Some invocations fail while others succeed.
Troubleshooting:
Optimize function efficiency to reduce execution time and free up capacity.
Use dead-letter queues (DLQs) to handle failed requests.
Use a monitoring tool like Site24x7 to track throttling events and other relevant metrics.
If you consistently experience throttling, request an increase in your account's concurrent execution limit from AWS support.
Implement retry logic with exponential backoff in your client application to handle throttled requests gracefully.
Throttle incoming requests at the API Gateway level to prevent overloading your Lambda function.
How to prevent AWS Lambda issues (best practices)
Let’s finish off this troubleshooting guide with some best practices that will help you prevent several of the aforementioned issues.
Optimize function performance
Keep functions lightweight by reducing dependencies and using only necessary libraries.
Use provisioned concurrency to reduce cold start delays for critical functions.
Increase memory allocation strategically, as it also improves CPU power.
Optimize logic and external calls to minimize function execution time.
Use proper error handling and retries
Implement structured error handling with try/catch or try/except blocks.
Configure AWS Lambda Destinations to capture failed executions for debugging.
Use AWS Step Functions for handling retries and fallback mechanisms in workflows.
Ensure that retry logic is enabled in event sources like SQS, SNS, and EventBridge.
Set up detailed logging and monitoring
Enable AWS CloudWatch logs for tracking function execution and failures.
Use AWS X-Ray for distributed tracing and performance bottleneck analysis.
Set up a monitoring tool like Site24x7 for real-time insights and alerts.
Ensure correct permissions and IAM policies
Follow the principle of least privilege when assigning IAM roles to Lambda.
Use AWS IAM Access Analyzer to validate permissions and spot misconfigurations.
Check resource-based policies for services like S3, DynamoDB, and API Gateway to ensure that Lambda has the required access.
Optimize event source configurations
Set appropriate batch sizes and concurrency settings for event-driven triggers (SQS, Kinesis, DynamoDB Streams).
Use dead-letter queues (DLQs) to capture and analyze failed events.
Ensure that the configurations of the API Gateway, S3, and EventBridge match Lambda’s expectations.
Manage dependencies efficiently
Package dependencies in layers to reduce deployment size and improve cold starts.
Use minimal Docker container images when deploying Lambda as a container.
Keep dependencies updated and remove unused libraries to reduce security risks.
Design for scalability
Avoid storing states in-memory; use DynamoDB, S3, or ElastiCache for state management.
Use asynchronous processing where possible to handle large workloads in an efficient manner.
Implement caching (Redis, Memcached) to reduce repeated calls to external services.
Regularly test and update functions
Use AWS’s SAM (Serverless Application Model) or Serverless Framework to test locally before deployment.
Continuously update runtime versions to benefit from performance and security improvements.
Perform load testing to ensure that functions handle expected traffic without throttling.
Conclusion
AWS Lambda is a fundamental component inside many distributed systems. Despite its inherent resilience, it can run into issues that affect performance, availability, and reliability. We hope that this guide has equipped you with the right tools and techniques to troubleshoot these issues effectively when they arise.