How to log AWS Lambda (Out of Memory) errors directly into Snowflake?

To capture AWS Lambda Out of Memory (OOM) errors and route them to Snowflake, you'll need to leverage Lambda's logging capabilities since these errors occur at the platform level when your function process is terminated.

Since your Python Lambda crashes instantly with OOM errors, you won't be able to catch these errors within your function code. Instead, you should focus on capturing these errors from Lambda's platform logs that are automatically sent to CloudWatch.

Here's an approach to route these errors to Snowflake:

1. Identify the error pattern: Lambda OOM errors typically appear in CloudWatch logs with messages like "Runtime exited with error: exit status 255" or similar platform-level error messages.

2. Set up CloudWatch Logs subscription:

• Create a CloudWatch Logs subscription filter that matches the OOM error pattern
• Route these filtered logs to a processing Lambda or Amazon Kinesis Data Firehose

3. Process and transform the logs:

• If using a processing Lambda, parse the log events to extract the error details and request payload
• Structure the data in a format suitable for Snowflake

4. Load data into Snowflake:

• Use Snowflake's data ingestion capabilities like Snowpipe
• Or use AWS services like Kinesis Data Firehose with Snowflake integration
• Alternatively, use a Lambda function with a Snowflake connector to insert the records

For more detailed error information, you can configure your Lambda function to use structured logging. While this won't help with capturing the OOM errors themselves (since the process dies), it will provide better context for other errors and make log parsing more reliable.

You can also adjust the logging level using environment variables to ensure you're capturing all relevant information before the OOM error occurs.

Remember that Lambda platform logs contain valuable information about the execution environment and error conditions that can help diagnose the root cause of OOM errors, such as memory allocation issues or inefficient code patterns.
Sources
Using the Lambda Logs API - AWS Lambda
Logger - Powertools for AWS Lambda (TypeScript)
ErrorObject - AWS Lambda