Indexing in PostgreSQL: Performance Evaluation and Use Cases

Efficient indexing remains a central factor in achieving predictable performance in modern relational database systems. PostgreSQL provides six native index types—B-Tree, Hash, GiST, SP-GiST, GIN, and BRIN—yet their relative behaviour under different workloads has been characterized in a variety of empirical studies, technical reports and official documentation rather than within a single unified benchmark. This paper presents a comparative, literaturebased analysis of these index types across transactional (OLTP), analytical (OLAP), full-text, JSONB, spatial and time-series workloads. Drawing from existing benchmarks and evaluations, the study synthesizes reported insights on index build time, query latency, storage footprint, maintenance overhead, and index bloat across PostgreSQL deployments. Prior work consistently finds that B-Tree remains the most robust default choice for OLTP equality and range workloads; GIN provides the lowest latency for fulltext and JSONB containment queries at the cost of substantial maintenance overhead and index bloat; GiST and SP-GiST dominate spatial workloads; and BRIN offers the best scalability for append-only analytical and time-series tables due to compact block-range summarization. The analysis highlights that index selection in PostgreSQL must be guided by workload semantics, update intensity, and storage constraints rather than by generic heuristics. Based on the synthesized findings, the paper proposes a practical recommendation matrix that maps workloads to suitable index types, providing actionable guidelines for database practitioners. By consolidating previously fragmented benchmark evidence into a single coherent review, the study clarifies when each native PostgreSQL index type is likely to be the most effective choice in practice.