Best practices for Amazon Redshift Lambda User-Defined Functions

This article provides best practices for optimizing Amazon Redshift Lambda User-Defined Functions (UDFs) to improve performance and reduce costs.

• Choose efficient programming languages; Golang can be 100x faster than Python
• Use existing libraries like Pandas for better performance and resource efficiency
• Minimize payload sizes to reduce Lambda communication overhead and batching
• Set MAX_BATCH_SIZE to avoid retries when return data exceeds Lambda limits
• Leverage batch processing with memoization to cache results and reduce execution time
• Request account-level Lambda concurrency quota increases beyond default 1,000 limit
• Use reserved concurrency to isolate specific Lambda functions from account-wide limits
• Integrate external services like OPA and Protegrity instead of reimplementing functionality
• Use MAX_BATCH_ROWS setting for services with limited batch size constraints

In summary, optimizing Lambda UDFs requires careful language selection, efficient payload management, strategic batching, proper concurrency configuration, and leveraging external services for better performance and cost efficiency.