How to Tell Whether Your Slow Query Is a Planning or an Execution Problem

PostgreSQL’s EXPLAIN (ANALYZE, BUFFERS) separates Planning Time, spent selecting a query plan, from Execution Time, spent performing the selected operations, and the distinction determines the appropriate optimization strategy. A large partitioned table can incur substantial planning overhead because PostgreSQL must inspect and price many partitions even when pruning ultimately leaves only one to scan, while adding indexes may worsen this cost by creating more paths to evaluate and increasing write overhead. By contrast, execution-bound queries are indicated by long node runtimes, disk reads, temporary-file spills from undersized work memory, or scans that discard many rows; these may benefit from memory adjustments, indexing, or improved storage layout. Incorrect cardinality estimates can also cause poor plans, particularly for correlated columns, and may be improved through extended statistics, higher statistics targets, and ANALYZE, although dependency statistics are limited for range predicates. For planning-heavy repeated queries, prepared statements and generic plan caching can sharply reduce recurring planning time, while reducing excessive partition counts through coarser partitions addresses the underlying structural cost. The text also presents Tiger Data hypertables and continuous aggregates as architectural approaches intended to reduce planning overhead and avoid repeatedly scanning large raw datasets.

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