{"Entry":{"collection":"plan","key":"group","name":"Group","aliases":["Group","T_Group"],"metadata":{"aliases":["Group","T_Group"],"category":"Aggregation","content_hash":"416e3ab8f1dcba42ca83b9d6d8d13107ea763e26250afcd2b26631c28618622f","derived_content_hash":"9826c7c815aa16445970300bc9dd87aebcb82a10715d2cd325180b6d22d2405c","imported_at":"2026-09-30T00:40:44.001041+08:00","name":"Group","name_zh":"Group","slug":"group","summary":"Groups an ordered input stream by its grouping columns."}},"Definition":{"Collection":"plan","Key":"group","SourceDatabase":"center","Version":"18","SourceTable":"plan_node","SourceKey":"group","SourceRevision":"555610c24d53e4316da5b7d3fc25c279d96856d5e0e23ee308c328c5fa881d9f","Facts":{"comparison_data":{"initializer":"ExecInitGroup","memory_mechanism":"unclassified","node_tag":"T_Group","parallel_callbacks":[],"partial_modes":[],"strategies":[],"text_names":["Group"]},"comparison_hash":"c75b880801aecfa8f510650c6bb3c2e0aa820810550fefe9c62d04727c0c0048","description":["Groups an ordered input stream by its grouping columns."],"description_zh":"按分组列对有序输入分组。","description_zh_html":"\u003cp\u003e按分组列对有序输入分组。\u003c/p\u003e\u003cp\u003e核心节点标识：\u003ccode\u003eT_Group\u003c/code\u003e；EXPLAIN 名称：\u003ccode\u003eGroup\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\u003cspan class=\"productname\"\u003ePostgreSQL\u003c/span\u003e 通过分两个阶段进行聚合来支持并行聚合。首先，每个参与查询并行部分的进程执行一个聚合步骤，为该进程所见到的每个分组产生一个部分结果。这在计划中体现为一个 \u003ccode class=\"literal\"\u003ePartial Aggregate\u003c/code\u003e 节点。然后，部分结果通过 \u003ccode class=\"literal\"\u003eGather\u003c/code\u003e 或 \u003ccode class=\"literal\"\u003eGather Merge\u003c/code\u003e 传送给领导者。最后，领导者会把来自所有工作进程的结果再次聚合，以产生最终结果。这在计划中体现为一个 \u003ccode class=\"literal\"\u003eFinalize Aggregate\u003c/code\u003e 节点。\n  \u003c/p\u003e\u003cp\u003e\n    由于 \u003ccode class=\"literal\"\u003eFinalize Aggregate\u003c/code\u003e 节点运行在领导者进程上，因此对于那些相对于输入行数会产生较多分组的查询，查询规划器会认为它不太有利。例如，在最坏情况下，\u003ccode class=\"literal\"\u003eFinalize Aggregate\u003c/code\u003e 节点看到的分组数可能与所有工作进程在 \u003ccode class=\"literal\"\u003ePartial Aggregate\u003c/code\u003e 阶段看到的输入行数一样多。对于这种情况，使用并行聚合显然不会带来性能收益。查询规划器会在规划过程中考虑这一点，因此在这种场景下不太可能选择并行聚合。\n  \u003c/p\u003e\u003cp\u003e\n    并行聚合并非在所有情况下都受支持。每个聚合都必须是\u003ca class=\"link\" href=\"/zh/docs/18/parallel-safety.html\"\u003e并行安全\u003c/a\u003e的，并且必须具有合并函数。如果该聚合具有类型为 \u003ccode class=\"literal\"\u003einternal\u003c/code\u003e 的转移状态，那么它还必须具有序列化和反序列化函数。更多细节请参见 \u003ca class=\"xref\" href=\"/zh/docs/18/sql-createaggregate.html\"\u003e\u003cspan class=\"refentrytitle\"\u003eCREATE AGGREGATE\u003c/span\u003e\u003c/a\u003e。如果任何聚合函数调用包含 \u003ccode class=\"literal\"\u003eDISTINCT\u003c/code\u003e 或 \u003ccode class=\"literal\"\u003eORDER BY\u003c/code\u003e 子句，则不支持并行聚合。对于有序集聚合，或者当查询涉及 \u003ccode class=\"literal\"\u003eGROUPING SETS\u003c/code\u003e 时，也不支持并行聚合。只有当查询涉及的所有连接也都属于计划并行部分时，才能使用并行聚合。\n  \u003c/p\u003e\u003cp\u003e\u003ca href=\"/zh/docs/18/parallel-plans.html#PARALLEL-AGGREGATION\"\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":["Group"],"explain_prefixes":["Parallel","Async"],"facts":[{"label":"Core node tag","value":"T_Group"},{"label":"Structured EXPLAIN Node Type","value":"Group"},{"label":"Inputs","value":"One ordered child plan"},{"label":"Output","value":"One tuple per group"},{"label":"Executor initializer","value":"ExecInitGroup"},{"label":"Memory mechanism","value":"unclassified"}],"facts_zh":[{"label":"核心节点标签","value":"T_Group"},{"label":"结构化 EXPLAIN 节点类型","value":"Group"},{"label":"输入","value":"一个有序子计划"},{"label":"输出","value":"每组一个元组"},{"label":"执行器初始化函数","value":"ExecInitGroup"},{"label":"内存机制","value":"unclassified"}],"memory":{"description":"This extraction does not assign a universal memory limit or spill policy to this node. 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