AWS Lambda provides basic logging functionality through CloudWatch Logs, allowing you to capture and analyze logs generated by your Lambda functions. However, with the recently introduced "advanced logging" functionality you can capture Lambda function logs in JSON structured format without having to use your own logging libraries. JSON structured logs make it easier to search, filter, and analyze large volumes of log entries.
You can control the log level granularity of Lambda function logs without making any code changes, enabling more effective debugging and troubleshooting.
Aslo, you can also set which Amazon CloudWatch log group Lambda sends logs to, making it easier to aggregate and manage logs at scale.
Here are some practices you can follow for more advanced logging with AWS Lambda:
Custom Logging:
Integrate custom logging libraries into your Lambda function code to provide more detailed and structured logs. Popular libraries include Winston or Bunyan for Node.js, or Python's logging module.
CloudWatch Logs Retention and Exports:
Configure retention settings for your CloudWatch Logs to control how long log data is retained. You can also export logs to Amazon S3 for long-term storage or analysis with other tools.
Log Formatting:
Format your logs in a way that facilitates easy analysis. Include relevant information such as timestamps, request/response details, and any custom context that helps in debugging.
CloudWatch Metrics:
Create custom CloudWatch Metrics based on log data. This can be achieved using CloudWatch Alarms and custom metric filters, allowing you to set up alerts based on specific log events.
X-Ray Integration:
If you are using AWS X-Ray for distributed tracing, integrate it with your Lambda functions. This provides additional insights into the performance of your serverless applications.
Structured Logging:
Use structured logging to make it easier to search and analyze logs. Tools like AWS CloudWatch Logs Insights support querying and analyzing structured log data.
Under Monitoring and operations tools -> Logging configurations -> click Edit button and change the log format structure from TEXT (default) to JSON, set the log level (INFO/DEBUG/ERROR) and then set the log group where you want the logs to be aggregated to, save the changes then you all good to use the advanced logging functionality.
Environment Variables:
Utilize environment variables to configure logging behavior dynamically. This can be useful for adjusting log levels or enabling/disabling certain types of logs based on the environment (e.g., development, production).
Lambda Layers:
Consider using Lambda Layers to manage and share common logging configurations or libraries across multiple Lambda functions.
Error Handling and Stack Traces:
Ensure that your logs include sufficient information for error handling, including stack traces. This information is invaluable for diagnosing issues.
Third-Party Logging Solutions:
Explore third-party logging solutions that integrate well with AWS Lambda, such as Datadog, Loggly, or Splunk.
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