Incremental Sort

memory

{"description":"The node passes work_mem to tuplesort. Sorting can use memory or temporary files; the actual method and space use depend on the input and plan.","evidence":[{"archive_sha256":"555610c24d53e4316da5b7d3fc25c279d96856d5e0e23ee308c328c5fa881d9f","label":"src/backend/executor/nodeIncrementalSort.c","path":"src/backend/executor/nodeIncrementalSort.c","sha256":"9fd15ce79cd3dd5c131c5bfb322039213cba9232fdfaa5b53e5f40e7d17195cf","url":"https://ftp.postgresql.org/pub/source/v18.6/postgresql-18.6.tar.bz2"},{"archive_sha256":"555610c24d53e4316da5b7d3fc25c279d96856d5e0e23ee308c328c5fa881d9f","label":"src/backend/utils/sort/tuplesort.c","path":"src/backend/utils/sort/tuplesort.c","sha256":"509d4cef598613bbf0725f697e1e1e33f3dfc8debed20169a6c4bf1ac9e5c2e3","url":"https://ftp.postgresql.org/pub/source/v18.6/postgresql-18.6.tar.bz2"}],"mechanism":"tuplesort","source_notes":["Incremental sort may be more efficient than plain sort, particularly on large datasets, as it reduces the amount of data to sort at once, making it more likely it fits into work_mem (eliminating the need to spill to disk). But the main advantage of incremental sort is that it can start producing rows early, before sorting the whole dataset, which is a significant benefit especially for queries with LIMIT.","Because incremental sort processes (potentially many) sort batches, we need to capture tuplesort stats each time we finalize a sort state. This summary data is later used for EXPLAIN ANALYZE output."]}

source inventory

{"executor":"src/backend/executor/execProcnode.c","explain":"src/backend/commands/explain.c","implementation":"src/backend/executor/nodeIncrementalSort.c"}