Adaptive Hash Join Performance Sensitivity Under Memory Pressure in Oracle DB

Abstract

This article investigates the performance sensitivity of adaptive hash joins in Oracle Database
environments operating under varying memory pressure conditions. A structured experimental
framework was used to analyze how join execution behavior transitions when available memory is
insufficient to maintain full in-memory hash structures. The results indicate that performance
degradation occurs sharply once memory thresholds are crossed, due to recursive partitioning and
spill-to-disk phases that substantially increase latency. Concurrency tests further demonstrated that
multiple simultaneous queries amplify memory contention, causing unpredictable transitions into
multi-pass join modes. Data distribution patterns were also found to influence spill severity, as
skewed key distributions concentrated memory demand into specific hash buckets. The study
highlights that stable performance depends not only on memory sizing but also on workload shaping
and execution planning strategies, especially in Oracle APEX-based analytical environments where
query patterns are dynamic. The findings contribute to improved understanding of memory-driven
execution variability and provide guidance for tuning hash join performance in both cloud and on
premise Oracle systems.