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Text-format spellings can also include operation, strategy, join type, scan direction or aggregation-stage attributes.","Text names recorded by this source: Limit.","Parallel-aware and parallel-safe are different plan properties. A node running inside a parallel worker is not necessarily a parallel-aware node."],"title":"EXPLAIN names and attributes"},{"paragraphs":["This extraction does not assign a universal memory limit or spill policy to this node. Inspect the same-build implementation and its expressions or provider."],"title":"Memory and temporary storage"},{"paragraphs":["The source callbacks below can coordinate execution or collect worker instrumentation. Their presence is not a blanket claim that this node supports a shared parallel scan or shared state.","Callbacks in this build: none extracted from this node implementation."],"title":"Parallel execution and instrumentation"},{"paragraphs":["Estimated total cost. This is stated on the assumption that the plan node is run to completion, i.e., all available rows are retrieved. In practice a node's parent node might stop short of reading all available rows (see the LIMIT example below).","Compared to regular sorts, sorting incrementally allows returning tuples before the entire result set has been sorted, which particularly enables optimizations with LIMIT queries. It may also reduce memory usage and the likelihood of spilling sorts to disk, but it comes at the cost of the increased overhead of splitting the result set into multiple sorting batches.","This is the same query as above, but we added a LIMIT so that not all the rows need be retrieved, and the planner changed its mind about what to do. Notice that the total cost and row count of the Index Scan node are shown as if it were run to completion. However, the Limit node is expected to stop after retrieving only a fifth of those rows, so its total cost is only a fifth as much, and that's the actual estimated cost of the query. This plan is preferred over adding a Limit node to the previous plan because the Limit could not avoid paying the startup cost of the bitmap scan, so the total cost would be something over 25 units with that approach.","There are cases in which the actual and estimated values won't match up well, but nothing is really wrong. One such case occurs when plan node execution is stopped short by a LIMIT or similar effect. For example, in the LIMIT query we used before,","the estimated cost and row count for the Index Scan node are shown as though it were run to completion. But in reality the Limit node stopped requesting rows after it got two, so the actual row count is only 2 and the run time is less than the cost estimate would suggest. This is not an estimation error, only a discrepancy in the way the estimates and true values are displayed.","Merge joins also have measurement artifacts that can confuse the unwary. A merge join will stop reading one input if it's exhausted the other input and the next key value in the one input is greater than the last key value of the other input; in such a case there can be no more matches and so no need to scan the rest of the first input. This results in not reading all of one child, with results like those mentioned for LIMIT . Also, if the outer (first) child contains rows with duplicate key values, the inner (second) child is backed up and rescanned for the portion of its rows matching that key value. EXPLAIN ANALYZE counts these repeated emissions of the same inner rows as if they were real additional rows. When there are many outer duplicates, the reported actual row count for the inner child plan node can be significantly larger than the number of rows that are actually in the inner relation."],"title":"Same-version manual discussion"},{"blocks":[{"code":"EXPLAIN SELECT * FROM tenk1 ORDER BY hundred, ten LIMIT 100;\n\n                                              QUERY PLAN\n------------------------------------------------------------------------------------------------\n Limit  (cost=19.35..39.49 rows=100 width=244)\n   -\u003e  Incremental Sort  (cost=19.35..2033.39 rows=10000 width=244)\n         Sort Key: hundred, ten\n         Presorted Key: hundred\n         -\u003e  Index Scan using tenk1_hundred on tenk1  (cost=0.29..1574.20 rows=10000 width=244)","paragraphs":["Example copied from the PostgreSQL 18.6 manual; it was not executed for this collection.","If a part of the plan guarantees an ordering on a prefix of the required sort keys, then the planner may instead decide to use an Incremental Sort step:"],"source":{"label":"PostgreSQL 18.6 · using-explain","path":"using-explain.html","sha256":"60040c30180093418a0affe56dd27dff9df2504b705b38039589e458bf5c31ed","url":"/docs/18/using-explain.html#USING-EXPLAIN-BASICS"}},{"code":"EXPLAIN SELECT * FROM tenk1 WHERE unique1 \u003c 100 AND unique2 \u003e 9000 LIMIT 2;\n\n                                     QUERY PLAN\n-------------------------------------------------------------------------------------\n Limit  (cost=0.29..14.28 rows=2 width=244)\n   -\u003e  Index Scan using tenk1_unique2 on tenk1  (cost=0.29..70.27 rows=10 width=244)\n         Index Cond: (unique2 \u003e 9000)\n         Filter: (unique1 \u003c 100)","paragraphs":["Example copied from the PostgreSQL 18.6 manual; it was not executed for this collection.","Here is an example showing the effects of LIMIT :"],"source":{"label":"PostgreSQL 18.6 · using-explain","path":"using-explain.html","sha256":"60040c30180093418a0affe56dd27dff9df2504b705b38039589e458bf5c31ed","url":"/docs/18/using-explain.html#USING-EXPLAIN-BASICS"}}],"title":"Examples from this manual build"},{"paragraphs":["nodeLimit.c Routines to handle limiting of query results where appropriate","This is a very simple node which just performs LIMIT/OFFSET filtering on the stream of tuples returned by a subplan.","First call for this node, so compute limit/offset. (We can't do this any earlier, because parameters from upper nodes will not be set during ExecInitLimit.) This also sets position = 0 and changes the state to LIMIT_RESCAN.","The subplan returns too few tuples for us to produce any output at all.","Tuple at limit is needed for comparison in subsequent execution to detect ties."],"title":"Executor implementation notes"},{"code":"case T_Limit:\n\t\t\tpname = sname = \"Limit\";\n\t\t\tbreak;","title":"EXPLAIN identity in core 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Text-format spellings can also include operation, strategy, join type, scan direction or aggregation-stage attributes.","Text names recorded by this source: Limit.","Parallel-aware and parallel-safe are different plan properties. A node running inside a parallel worker is not necessarily a parallel-aware node."],"title":"EXPLAIN names and attributes"},{"paragraphs":["This extraction does not assign a universal memory limit or spill policy to this node. Inspect the same-build implementation and its expressions or provider."],"title":"Memory and temporary storage"},{"paragraphs":["The source callbacks below can coordinate execution or collect worker instrumentation. Their presence is not a blanket claim that this node supports a shared parallel scan or shared state.","Callbacks in this build: none extracted from this node implementation."],"title":"Parallel execution and instrumentation"},{"paragraphs":["Estimated total cost. This is stated on the assumption that the plan node is run to completion, i.e., all available rows are retrieved. In practice a node's parent node might stop short of reading all available rows (see the LIMIT example below).","Compared to regular sorts, sorting incrementally allows returning tuples before the entire result set has been sorted, which particularly enables optimizations with LIMIT queries. It may also reduce memory usage and the likelihood of spilling sorts to disk, but it comes at the cost of the increased overhead of splitting the result set into multiple sorting batches.","This is the same query as above, but we added a LIMIT so that not all the rows need be retrieved, and the planner changed its mind about what to do. Notice that the total cost and row count of the Index Scan node are shown as if it were run to completion. However, the Limit node is expected to stop after retrieving only a fifth of those rows, so its total cost is only a fifth as much, and that's the actual estimated cost of the query. This plan is preferred over adding a Limit node to the previous plan because the Limit could not avoid paying the startup cost of the bitmap scan, so the total cost would be something over 25 units with that approach.","There are cases in which the actual and estimated values won't match up well, but nothing is really wrong. One such case occurs when plan node execution is stopped short by a LIMIT or similar effect. For example, in the LIMIT query we used before,","the estimated cost and row count for the Index Scan node are shown as though it were run to completion. But in reality the Limit node stopped requesting rows after it got two, so the actual row count is only 2 and the run time is less than the cost estimate would suggest. This is not an estimation error, only a discrepancy in the way the estimates and true values are displayed.","Merge joins also have measurement artifacts that can confuse the unwary. A merge join will stop reading one input if it's exhausted the other input and the next key value in the one input is greater than the last key value of the other input; in such a case there can be no more matches and so no need to scan the rest of the first input. This results in not reading all of one child, with results like those mentioned for LIMIT . Also, if the outer (first) child contains rows with duplicate key values, the inner (second) child is backed up and rescanned for the portion of its rows matching that key value. EXPLAIN ANALYZE counts these repeated emissions of the same inner rows as if they were real additional rows. When there are many outer duplicates, the reported actual row count for the inner child plan node can be significantly larger than the number of rows that are actually in the inner relation."],"title":"Same-version manual discussion"},{"blocks":[{"code":"EXPLAIN SELECT * FROM tenk1 ORDER BY hundred, ten LIMIT 100;\n\n                                              QUERY PLAN\n------------------------------------------------------------------------------------------------\n Limit  (cost=19.35..39.49 rows=100 width=244)\n   -\u003e  Incremental Sort  (cost=19.35..2033.39 rows=10000 width=244)\n         Sort Key: hundred, ten\n         Presorted Key: hundred\n         -\u003e  Index Scan using tenk1_hundred on tenk1  (cost=0.29..1574.20 rows=10000 width=244)","paragraphs":["Example copied from the PostgreSQL 18.6 manual; it was not executed for this collection.","If a part of the plan guarantees an ordering on a prefix of the required sort keys, then the planner may instead decide to use an Incremental Sort step:"],"source":{"label":"PostgreSQL 18.6 · using-explain","path":"using-explain.html","sha256":"60040c30180093418a0affe56dd27dff9df2504b705b38039589e458bf5c31ed","url":"/docs/18/using-explain.html#USING-EXPLAIN-BASICS"}},{"code":"EXPLAIN SELECT * FROM tenk1 WHERE unique1 \u003c 100 AND unique2 \u003e 9000 LIMIT 2;\n\n                                     QUERY PLAN\n-------------------------------------------------------------------------------------\n Limit  (cost=0.29..14.28 rows=2 width=244)\n   -\u003e  Index Scan using tenk1_unique2 on tenk1  (cost=0.29..70.27 rows=10 width=244)\n         Index Cond: (unique2 \u003e 9000)\n         Filter: (unique1 \u003c 100)","paragraphs":["Example copied from the PostgreSQL 18.6 manual; it was not executed for this collection.","Here is an example showing the effects of LIMIT :"],"source":{"label":"PostgreSQL 18.6 · using-explain","path":"using-explain.html","sha256":"60040c30180093418a0affe56dd27dff9df2504b705b38039589e458bf5c31ed","url":"/docs/18/using-explain.html#USING-EXPLAIN-BASICS"}}],"title":"Examples from this manual build"},{"paragraphs":["nodeLimit.c Routines to handle limiting of query results where appropriate","This is a very simple node which just performs LIMIT/OFFSET filtering on the stream of tuples returned by a subplan.","First call for this node, so compute limit/offset. (We can't do this any earlier, because parameters from upper nodes will not be set during ExecInitLimit.) This also sets position = 0 and changes the state to LIMIT_RESCAN.","The subplan returns too few tuples for us to produce any output at all.","Tuple at limit is needed for comparison in subsequent execution to detect ties."],"title":"Executor implementation notes"},{"code":"case T_Limit:\n\t\t\tpname = sname = \"Limit\";\n\t\t\tbreak;","title":"EXPLAIN identity in core source"}],"tables":[{"columns":[{"key":"label","label":"Text-format label"},{"key":"identity","label":"Structured node identity"}],"key":"explain-labels","rows":[{"identity":"Limit","label":"Limit"}],"title":"EXPLAIN labels in this source build"}]}},"RequestedLocale":"zh-Hans","Fallback":true,"Versions":["10","11","12","13","14","15","16","17","18","19","20"],"Locales":["en"],"Signatures":null,"Spellings":null,"SQLState":null,"Evidence":null}