{"Entry":{"collection":"plan","key":"merge-join","name":"Merge Join","aliases":["Merge","MergeJoin","T_MergeJoin"],"metadata":{"aliases":["Merge","MergeJoin","T_MergeJoin"],"category":"Join","content_hash":"fc2274bf13780cd868a3c75ea485339a90f50f739c2df501be1d6ee5c65b27c8","derived_content_hash":"992ff8a36994ad73ddf6786bdae3d518bbc47755773f3f001b8fd97cde809650","imported_at":"2026-09-30T00:40:44.13693+08:00","name":"Merge Join","name_zh":"MergeJoin","slug":"merge-join","summary":"Joins ordered input streams using merge clauses."}},"Definition":{"Collection":"plan","Key":"merge-join","SourceDatabase":"center","Version":"18","SourceTable":"plan_node","SourceKey":"merge-join","SourceRevision":"555610c24d53e4316da5b7d3fc25c279d96856d5e0e23ee308c328c5fa881d9f","Facts":{"comparison_data":{"initializer":"ExecInitMergeJoin","memory_mechanism":"unclassified","node_tag":"T_MergeJoin","parallel_callbacks":[],"partial_modes":[],"strategies":[],"text_names":["Merge"]},"comparison_hash":"b17771ee4d48585afbaba91552e5eb00f05a4b6784aa491387217937a4a30511","description":["Joins ordered input streams using merge clauses."],"description_zh":"利用归并条件连接有序输入流。","description_zh_html":"\u003cp\u003e利用归并条件连接有序输入流。\u003c/p\u003e\u003cp\u003e核心节点标识：\u003ccode\u003eT_MergeJoin\u003c/code\u003e；EXPLAIN 名称：\u003ccode\u003eMerge\u003c/code\u003e。\u003c/p\u003e\u003cp\u003e本版本源码中的并行执行或观测回调：该节点实现中未提取到此类回调。回调的存在不表示该节点必然支持共享并行扫描；在并行工作进程中执行也不等于具有并行感知能力。\u003c/p\u003e\u003ch3\u003e同版本中文手册节选\u003c/h3\u003e\u003cp\u003e\n    查询计划的结构是一棵由\u003cem class=\"firstterm\"\u003e计划节点\u003c/em\u003e组成的树。树的最底层是扫描节点，它们从表中返回原始行。不同的表访问方法对应不同类型的扫描节点，例如顺序扫描、索引扫描和位图索引扫描。也有一些并非表的行来源，例如 \u003ccode class=\"literal\"\u003eVALUES\u003c/code\u003e 子句和 \u003ccode class=\"literal\"\u003eFROM\u003c/code\u003e 中的返回集合函数，它们也各自有对应的扫描节点类型。如果查询需要对原始行执行连接、聚合、排序或其他操作，那么扫描节点之上还会出现额外的节点来完成这些操作。同样，这些操作通常不止一种实现方式，因此这里也会出现不同的节点类型。\u003ccode class=\"command\"\u003eEXPLAIN\u003c/code\u003e 会为计划树中的每个节点输出一行，显示基本节点类型以及规划器对该计划节点执行代价的估计值。还可能出现相对节点摘要行缩进的附加行，用来显示该节点的更多属性。第一行，也就是最顶层节点的摘要行，给出了整个计划的估计总执行代价；规划器力图最小化的正是这个数字。\n   \u003c/p\u003e\u003cp\u003e\n    下面是一个简单的示例，仅用于展示输出的样子：\n\n\u003c/p\u003e\u003cp\u003e\n    由于这个查询没有 \u003ccode class=\"literal\"\u003eWHERE\u003c/code\u003e 子句，它必须扫描表中的所有行，因此规划器选择了一个简单的顺序扫描计划。圆括号中的数字从左到右依次表示：\n\n    \u003c/p\u003e\u003cp\u003e\n    这些代价使用由规划器代价参数决定的任意单位来衡量（见\u003ca class=\"xref\" href=\"/zh/docs/18/runtime-config-query.html#RUNTIME-CONFIG-QUERY-CONSTANTS\"\u003e第 19.7.2 节\u003c/a\u003e）。传统上通常以磁盘页读取作为代价单位；也就是说，惯例上将 \u003ca class=\"xref\" href=\"/zh/docs/18/runtime-config-query.html#GUC-SEQ-PAGE-COST\"\u003eseq_page_cost\u003c/a\u003e 设为 \u003ccode class=\"literal\"\u003e1.0\u003c/code\u003e，其他代价参数都相对它来设定。本节中的示例都使用默认代价参数。\n   \u003c/p\u003e\u003cp\u003e\u003ca href=\"/zh/docs/18/using-explain.html#USING-EXPLAIN-BASICS\"\u003e查看完整中文手册与上下文\u003c/a\u003e。\u003c/p\u003e\u003cp\u003e本页开头的中文说明为 PG.CENTER 对编辑说明的翻译；手册节选保留对应版本中文文档原文。源码信息及手册示例不代表重新进行过运行测试。\u003c/p\u003e","evidence_kind":"source and documentation","explain_names":["Merge"],"explain_prefixes":["Parallel","Async"],"facts":[{"label":"Core node tag","value":"T_MergeJoin"},{"label":"Structured EXPLAIN Node Type","value":"Merge Join"},{"label":"Inputs","value":"Ordered outer and inner child plans"},{"label":"Output","value":"Joined tuples according to the selected join type"},{"label":"Executor initializer","value":"ExecInitMergeJoin"},{"label":"Memory mechanism","value":"unclassified"}],"facts_zh":[{"label":"核心节点标签","value":"T_MergeJoin"},{"label":"结构化 EXPLAIN 节点类型","value":"Merge Join"},{"label":"输入","value":"有序的外侧和内侧子计划"},{"label":"输出","value":"按所选连接类型生成的连接元组"},{"label":"执行器初始化函数","value":"ExecInitMergeJoin"},{"label":"内存机制","value":"unclassified"}],"memory":{"description":"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.","evidence":[{"archive_sha256":"555610c24d53e4316da5b7d3fc25c279d96856d5e0e23ee308c328c5fa881d9f","label":"src/backend/executor/nodeMergejoin.c","path":"src/backend/executor/nodeMergejoin.c","sha256":"df6726b291493e06be7c43e5664bfb4251267eff66481eaf8e04969c39612b25","url":"https://ftp.postgresql.org/pub/source/v18.6/postgresql-18.6.tar.bz2"}],"mechanism":"unclassified","source_notes":[]},"node_tag":"T_MergeJoin","parallel_callbacks":[],"partial_modes":[],"related":[{"label":"EXPLAIN","url":"/wiki/sql/explain/?v=18"},{"label":"Using EXPLAIN","url":"/docs/18/using-explain.html"},{"label":"Parallel plans","url":"/docs/18/parallel-plans.html"},{"label":"enable_mergejoin","url":"/wiki/guc/enable_mergejoin/?v=18"}],"release":{"channel":"stable","label":"18.6","major":"18","ref":"PostgreSQL 18.6 source archive","revision":"555610c24d53e4316da5b7d3fc25c279d96856d5e0e23ee308c328c5fa881d9f","source_snapshot_utc":"","source_url":"https://ftp.postgresql.org/pub/source/v18.6/postgresql-18.6.tar.bz2"},"runtime_verified":false,"sections":[{"paragraphs":["Structured formats use the Node Type above. Text-format spellings can also include operation, strategy, join type, scan direction or aggregation-stage attributes.","Text names recorded by this source: Merge.","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":["Merge join requires its input data to be sorted on the join keys. In this example each input is sorted by using an index scan to visit the rows in the correct order; but a sequential scan and sort could also be used. (Sequential-scan-and-sort frequently beats an index scan for sorting many rows, because of the nonsequential disk access required by the index scan.)","One way to look at variant plans is to force the planner to disregard whatever strategy it thought was the cheapest, using the enable/disable flags described in Section 19.7.1 . (This is a crude tool, but useful. See also Section 14.3 .) For example, if we're unconvinced that merge join is the best join type for the previous example, we could try","which shows that the planner thinks that hash join would be nearly 50% more expensive than merge join for this case. Of course, the next question is whether it's right about that. We can investigate that using EXPLAIN ANALYZE , as discussed below .","As seen in this example, when the query is an INSERT , UPDATE , DELETE , or MERGE command, the actual work of applying the table changes is done by a top-level Insert, Update, Delete, or Merge plan node. The plan nodes underneath this node perform the work of locating the old rows and/or computing the new data. So above, we see the same sort of bitmap table scan we've seen already, and its output is fed to an Update node that stores the updated rows. It's worth noting that although the data-modifying node can take a considerable amount of run time (here, it's consuming the lion's share of the time), the planner does not currently add anything to the cost estimates to account for that work. That's because the work to be done is the same for every correct query plan, so it doesn't affect planning decisions.","When an UPDATE , DELETE , or MERGE command affects a partitioned table or inheritance hierarchy, the output might look like this:","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 *\nFROM tenk1 t1, onek t2\nWHERE t1.unique1 \u003c 100 AND t1.unique2 = t2.unique2;\n\n                                        QUERY PLAN\n------------------------------------------------------------------------------------------\n Merge Join  (cost=0.56..233.49 rows=10 width=488)\n   Merge Cond: (t1.unique2 = t2.unique2)\n   -\u003e  Index Scan using tenk1_unique2 on tenk1 t1  (cost=0.29..643.28 rows=100 width=244)\n         Filter: (unique1 \u003c 100)\n   -\u003e  Index Scan using onek_unique2 on onek t2  (cost=0.28..166.28 rows=1000 width=244)","paragraphs":["Example copied from the PostgreSQL 18.6 manual; it was not executed for this collection.","Another possible type of join is a merge join, illustrated here:"],"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":["Merge-join is done by joining the inner and outer tuples satisfying join clauses of the form ((= outerKey innerKey) ...). The join clause list is provided by the query planner and may contain more than one (= outerKey innerKey) clause (for composite sort key).","However, the query executor needs to know whether an outer tuple is \"greater/smaller\" than an inner tuple so that it can \"synchronize\" the two relations. For example, consider the following relations:","outer: (0 ^1 1 2 5 5 5 6 6 7) current tuple: 1 inner: (1 ^3 5 5 5 5 6) current tuple: 3","To continue the merge-join, the executor needs to scan both inner and outer relations till the matching tuples 5. It needs to know that currently inner tuple 3 is \"greater\" than outer tuple 1 and therefore it should scan the outer relation first to find a matching tuple and so on.","Therefore, rather than directly executing the merge join clauses, we evaluate the left and right key expressions separately and then compare the columns one at a time (see MJCompare). The planner passes us enough information about the sort ordering of the inputs to allow us to determine how to make the comparison. We may use the appropriate btree comparison function, since Postgres' only notion of ordering is specified by btree opfamilies."],"title":"Executor implementation notes"},{"code":"case T_MergeJoin:\n\t\t\tpname = \"Merge\";\t/* \"Join\" gets added by jointype switch */\n\t\t\tsname = \"Merge Join\";\n\t\t\tbreak;","title":"EXPLAIN identity in core source"}],"sections_zh":[{"paragraphs":["结构化格式使用上述 Node Type。文本格式名称还可能包含操作、策略、连接类型、扫描方向或聚合阶段属性。","此源码记录的文本名称：Merge.","并行感知与并行安全是不同的计划属性。在并行工作进程内运行的节点不一定是并行感知节点。"],"title":"EXPLAIN 名称与属性"},{"paragraphs":["本次抽取不为此节点设定统一的内存上限或落盘策略。请查看同一构建的实现、相关表达式或提供方。"],"title":"内存与临时存储"},{"paragraphs":["以下源码回调可以协调执行或收集工作进程的测量数据。回调存在不代表该节点普遍支持共享并行扫描或共享状态。","此构建的回调：none extracted from this node implementation."],"title":"并行执行与运行信息采集"},{"paragraphs":["归并连接要求输入数据按连接键排序。在这个例子中，两个输入都通过索引扫描按正确顺序访问行而完成排序；不过也可以采用顺序扫描再排序的方式。（对于需要排序很多行的情况，顺序扫描加排序往往会胜过索引扫描，因为索引扫描需要非顺序磁盘访问。）","观察备选计划的一种方法，是利用 第 19.7.1 节 中描述的启用/禁用标志，强迫规划器忽略它认为最便宜的策略。（这是个粗糙但有用的工具。另见 第 14.3 节 。）例如，如果我们并不确信前一个示例中归并连接真的是最佳连接类型，可以试试：","这表明，规划器认为在这个场景下，哈希连接的代价几乎比归并连接高出 50%。当然，接下来的问题就是它是否判断正确。我们可以像 下文 所述，使用 EXPLAIN ANALYZE 来研究。","如本例所示，查询为 INSERT、UPDATE、DELETE 或 MERGE 命令时，实际应用表变更的工作由顶层 Insert、Update、Delete 或 Merge 计划节点完成。其下的计划节点负责定位旧行和／或计算新数据。因此，上例仍采用前面见过的位图表扫描，其输出传给 Update 节点，由后者存储更新后的行。虽然数据修改节点可能消耗相当多的运行时间（本例中占了绝大部分），但规划器目前并未将这部分工作计入代价估算。这是因为任何正确查询计划需要完成的这部分工作都相同，因此不会影响规划决策。","当 UPDATE 、 DELETE 或 MERGE 命令影响分区表或继承层次时，输出可能像这样：","归并连接的测量结果也可能产生误导。若一侧输入已耗尽，另一侧的下一个键值又大于已耗尽输入的最后一个键值，就不会再有匹配，因此会停止读取另一侧。这样便未读取该子节点的全部行，类似 LIMIT 的情况。另外，外侧（第一个）子节点有重复键值时，内侧（第二个）子节点会回退并重新扫描匹配该键值的行。EXPLAIN ANALYZE 会将内侧重复输出的行计作额外的真实行。因此外侧重复值很多时，内侧计划节点报告的实际行数可能显著超过内侧关系中的实际行数。"],"title":"同版本手册说明"},{"blocks":[{"code":"EXPLAIN SELECT *\nFROM tenk1 t1, onek t2\nWHERE t1.unique1 \u003c 100 AND t1.unique2 = t2.unique2;\n\n                                        QUERY PLAN\n------------------------------------------------------------------------------------------\n Merge Join  (cost=0.56..233.49 rows=10 width=488)\n   Merge Cond: (t1.unique2 = t2.unique2)\n   -\u003e  Index Scan using tenk1_unique2 on tenk1 t1  (cost=0.29..643.28 rows=100 width=244)\n         Filter: (unique1 \u003c 100)\n   -\u003e  Index Scan using onek_unique2 on onek t2  (cost=0.28..166.28 rows=1000 width=244)","paragraphs":["示例摘自 PostgreSQL 18.6 手册；本百科未实际执行此示例。","另一种可能的连接方式是归并连接，如下所示："],"source":{"label":"PostgreSQL 18.6 · using-explain","path":"using-explain.html","sha256":"60040c30180093418a0affe56dd27dff9df2504b705b38039589e458bf5c31ed","url":"/docs/18/using-explain.html#USING-EXPLAIN-BASICS"}}],"title":"本版手册中的示例"},{"paragraphs":["归并连接对满足 ((= outerKey innerKey) ...) 形式连接条件的内外侧元组执行连接。连接条件列表由查询规划器提供，可以包含多个 (= outerKey innerKey) 条件，以支持复合排序键。","不过，查询执行器需要判断外侧元组是否“大于／小于”内侧元组，以便“同步”两个关系。例如，考虑以下关系：","outer: (0 ^1 1 2 5 5 5 6 6 7) current tuple: 1 inner: (1 ^3 5 5 5 5 6) current tuple: 3","为继续归并连接，执行器需要扫描内外侧关系，直到找到匹配的元组 5。它需要知道当前内侧元组 3“大于”外侧元组 1，因此应先扫描外侧关系以寻找匹配，依此类推。","因此，不直接执行归并连接条件，而是分别求值左右键表达式，再逐列比较（参见 MJCompare）。规划器提供足够的输入排序信息，让执行器确定比较方法。可以使用相应的 B-tree 比较函数，因为 Postgres 的排序语义仅由 B-tree 操作符家族定义。"],"title":"执行器实现说明"},{"code":"case T_MergeJoin:\n\t\t\tpname = \"Merge\";\t/* \"Join\" gets added by jointype switch */\n\t\t\tsname = \"Merge Join\";\n\t\t\tbreak;","title":"核心源码中的 EXPLAIN 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clauses.\u003c/p\u003e","SourceRevision":"555610c24d53e4316da5b7d3fc25c279d96856d5e0e23ee308c328c5fa881d9f","ContentHash":"07dac96c646bfbf676779d37f520b10fea19453f10eb054be7a317925ef4d752","Payload":{"description":["Joins ordered input streams using merge clauses."],"related":[{"label":"EXPLAIN","url":"/wiki/sql/explain/?v=18"},{"label":"Using EXPLAIN","url":"/docs/18/using-explain.html"},{"label":"Parallel plans","url":"/docs/18/parallel-plans.html"},{"label":"enable_mergejoin","url":"/wiki/guc/enable_mergejoin/?v=18"}],"sections":[{"paragraphs":["Structured formats use the Node Type above. Text-format spellings can also include operation, strategy, join type, scan direction or aggregation-stage attributes.","Text names recorded by this source: Merge.","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":["Merge join requires its input data to be sorted on the join keys. In this example each input is sorted by using an index scan to visit the rows in the correct order; but a sequential scan and sort could also be used. (Sequential-scan-and-sort frequently beats an index scan for sorting many rows, because of the nonsequential disk access required by the index scan.)","One way to look at variant plans is to force the planner to disregard whatever strategy it thought was the cheapest, using the enable/disable flags described in Section 19.7.1 . (This is a crude tool, but useful. See also Section 14.3 .) For example, if we're unconvinced that merge join is the best join type for the previous example, we could try","which shows that the planner thinks that hash join would be nearly 50% more expensive than merge join for this case. Of course, the next question is whether it's right about that. We can investigate that using EXPLAIN ANALYZE , as discussed below .","As seen in this example, when the query is an INSERT , UPDATE , DELETE , or MERGE command, the actual work of applying the table changes is done by a top-level Insert, Update, Delete, or Merge plan node. The plan nodes underneath this node perform the work of locating the old rows and/or computing the new data. So above, we see the same sort of bitmap table scan we've seen already, and its output is fed to an Update node that stores the updated rows. It's worth noting that although the data-modifying node can take a considerable amount of run time (here, it's consuming the lion's share of the time), the planner does not currently add anything to the cost estimates to account for that work. That's because the work to be done is the same for every correct query plan, so it doesn't affect planning decisions.","When an UPDATE , DELETE , or MERGE command affects a partitioned table or inheritance hierarchy, the output might look like this:","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 *\nFROM tenk1 t1, onek t2\nWHERE t1.unique1 \u003c 100 AND t1.unique2 = t2.unique2;\n\n                                        QUERY PLAN\n------------------------------------------------------------------------------------------\n Merge Join  (cost=0.56..233.49 rows=10 width=488)\n   Merge Cond: (t1.unique2 = t2.unique2)\n   -\u003e  Index Scan using tenk1_unique2 on tenk1 t1  (cost=0.29..643.28 rows=100 width=244)\n         Filter: (unique1 \u003c 100)\n   -\u003e  Index Scan using onek_unique2 on onek t2  (cost=0.28..166.28 rows=1000 width=244)","paragraphs":["Example copied from the PostgreSQL 18.6 manual; it was not executed for this collection.","Another possible type of join is a merge join, illustrated here:"],"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":["Merge-join is done by joining the inner and outer tuples satisfying join clauses of the form ((= outerKey innerKey) ...). The join clause list is provided by the query planner and may contain more than one (= outerKey innerKey) clause (for composite sort key).","However, the query executor needs to know whether an outer tuple is \"greater/smaller\" than an inner tuple so that it can \"synchronize\" the two relations. For example, consider the following relations:","outer: (0 ^1 1 2 5 5 5 6 6 7) current tuple: 1 inner: (1 ^3 5 5 5 5 6) current tuple: 3","To continue the merge-join, the executor needs to scan both inner and outer relations till the matching tuples 5. It needs to know that currently inner tuple 3 is \"greater\" than outer tuple 1 and therefore it should scan the outer relation first to find a matching tuple and so on.","Therefore, rather than directly executing the merge join clauses, we evaluate the left and right key expressions separately and then compare the columns one at a time (see MJCompare). The planner passes us enough information about the sort ordering of the inputs to allow us to determine how to make the comparison. We may use the appropriate btree comparison function, since Postgres' only notion of ordering is specified by btree opfamilies."],"title":"Executor implementation notes"},{"code":"case T_MergeJoin:\n\t\t\tpname = \"Merge\";\t/* \"Join\" gets added by jointype switch */\n\t\t\tsname = \"Merge Join\";\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":"Merge Join","label":"Merge"}],"title":"EXPLAIN labels in this source build"}]}},"RequestedLocale":"en","Fallback":false,"Versions":["10","11","12","13","14","15","16","17","18","19","20"],"Locales":["en","zh-Hans"],"Signatures":null,"Spellings":null,"SQLState":null,"Evidence":null}