aws-samples/performance-optimization-power
Performance optimization guidance for AWS Transform for mainframe refactor applications migrated from AS/400/iSeries or z/OS COBOL/DB2. Identify hotspots and apply SQLExecutorBuilder joins, DAO batch operations, and pre-load or lazy caching to eliminate database round-trips, then verify end to end.
Convert per-row DAO inserts and updates into batched database round-trips in the AWS Transform for mainframe runtime. Use when a loop performs multiple INSERTs or UPDATEs to the same table, when applying addRecordToBatch/batchWriteDirect/batchUpdateDirect, or when reasoning about DAO lock behavior.
Choose the right performance optimization technique for AWS Transform for mainframe refactor applications. Use when starting a performance investigation, deciding between SQLExecutorBuilder, DAO batching, or caching, or looking up JDBCType/SQLCODE references.
Identify performance bottlenecks in AWS Transform for mainframe refactor application code. Use when analyzing code for slow patterns, finding repeated DAO reads/writes in loops, spotting row-by-row processing that should be set-based, or reviewing code for optimization candidates.
Implement custom SQL, cursors, aggregations, and joins in the AWS Transform for mainframe runtime using SQLExecutorBuilder. Use when writing centralized prepared statements, setting up Sqlca, building cursor FETCH loops, mapping SQL parameters, changing cursor SQL, caching lookups in a HashMap (preloaded or lazy), or verifying an optimization.