Troubleshooting and Monitoring AWS Lambda Functions with Amazon CloudWatch
AWS Lambda automatically monitors Lambda functions on your behalf, reporting metrics through Amazon CloudWatch. To help you monitor your code as it executes, Lambda automatically tracks the number of requests, the latency per request, and the number of requests resulting in an error and publishes the associated CloudWatch metrics. You can leverage these metrics to set CloudWatch custom alarms. For more information about CloudWatch, see the Amazon CloudWatch User Guide.
You can view request rates and error rates for each of your Lambda functions by using the AWS Lambda console, the CloudWatch console, and other Amazon Web Services (AWS) resources. The following topics describe Lambda CloudWatch metrics and how to access them.
You can insert logging statements into your code to help you validate that your code is
working as expected. Lambda automatically integrates with Amazon CloudWatch Logs and pushes all logs from
your code to a CloudWatch Logs group associated with a Lambda function (/aws/lambda/
To learn more about log groups and accessing them through the CloudWatch console, see the Monitoring System, Application, and Custom
Log Files in the Amazon CloudWatch User Guide. For information about how to access CloudWatch log entries,
see Accessing Amazon CloudWatch Logs for AWS Lambda.
If your Lambda function code is executing, but you don't see any log data being generated after several minutes, this could mean your execution role for the Lambda function did not grant permissions to write log data to CloudWatch Logs. For information about how to make sure that you have set up the execution role correctly to grant these permissions, see Manage Permissions: Using an IAM Role (Execution Role).
AWS Lambda Troubleshooting Scenarios
This sections describes examples of how to monitor and troubleshoot your Lambda functions using the logging and monitoring capabilities of CloudWatch.
Troubleshooting Scenario 1: Lambda Function Not Working as Expected
In this scenario, you have just finished Tutorial: Using AWS Lambda with Amazon S3. However, the Lambda function you created to upload a thumbnail image to Amazon S3 when you create an S3 object is not working as expected. When you upload objects to Amazon S3, you see that the thumbnail images are not being uploaded. You can troubleshoot this issue in the following ways.
To determine why your Lambda function is not working as expected
Check your code and verify that it is working correctly. An increased error rate would indicate that it is not.
You can test your code locally as you would any other Node.js function, or you can test it within the Lambda console using the console's test invoke functionality, or you can use the AWS CLI
Invokecommand. Each time the code is executed in response to an event, it writes a log entry into the log group associated with a Lambda function, which is /aws/lambda/
Following are some examples of errors that might show up in the logs:
If you see a stack trace in your log, there is probably an error in your code. Review your code and debug the error that the stack trace refers to.
If you see a
permissions deniederror in the log, the IAM role you have provided as an execution role may not have the necessary permissions. Check the IAM role and verify that it has all of the necessary permissions to access any AWS resources that your code references. To ensure that you have correctly set up the execution role, see Manage Permissions: Using an IAM Role (Execution Role).
If you see a
timeout exceedederror in the log, your timeout setting exceeds the run time of your function code. This may be because the timeout is too low, or the code is taking too long to execute.
If you see a
memory exceedederror in the log, your memory setting is too low. Set it to a higher value. For information about memory size limits, see CreateFunction. When you change the memory setting, it can also change how you are charged for duration. For information about pricing, see the AWS Lambda product website.
Check your Lambda function and verify that it is receiving requests.
Even if your function code is working as expected and responding correctly to test invokes, the function may not be receiving requests from Amazon S3. If Amazon S3 is able to invoke the function, you should see an increase in your CloudWatch requests metrics. If you do not see an increase in your CloudWatch requests, check the access permissions policy associated with the function.
Troubleshooting Scenario 2: Increased Latency in Lambda Function Execution
In this scenario, you have just finished Tutorial: Using AWS Lambda with Amazon S3. However, the Lambda function you created to upload a thumbnail image to Amazon S3 when you create an S3 object is not working as expected. When you upload objects to Amazon S3, you can see that the thumbnail images are being uploaded, but your code is taking much longer to execute than expected. You can troubleshoot this issue in a couple of different ways. For example, you could monitor the latency CloudWatch metric for the Lambda function to see if the latency is increasing. Or you could see an increase in the CloudWatch errors metric for the Lambda function, which might be due to timeout errors.
To determine why there is increased latency in the execution of a Lambda function
Test your code with different memory settings.
If your code is taking too long to execute, it could be that it does not have enough compute resources to execute its logic. Try increasing the memory allocated to your function and testing the code again, using the Lambda console's test invoke functionality. You can see the memory used, code execution time, and memory allocated in the function log entries. Changing the memory setting can change how you are charged for duration. For information about pricing, see AWS Lambda.
Investigate the source of the execution bottleneck that is using logs.
You can test your code locally, as you would with any other Node.js function, or you can test it within Lambda using the test invoke capability on the Lambda console, or using the
asyncInvokecommand by using AWS CLI. Each time the code is executed in response to an event, it writes a log entry into the log group associated with a Lambda function, which is named aws/lambda/
<function name>. Add logging statements around various parts of your code, such as callouts to other services, to see how much time it takes to execute different parts of your code.