Aggregation job exceeds executor memory limits dropping records

Large aggregation jobs drop records after hitting executor memory limits, causing data loss and incomplete analytical results.

Increase executor memory allocation in Spark configuration (spark.executor.memory). Partition large jobs into smaller chunks using date ranges or key-based partitioning. Enable spill-to-disk for memory-intensive operations. Monitor executor memory usage metrics. Use incremental aggregation or pre-aggregation strategies. Tune spark.memory.fraction and spark.memory.storageFraction.