postgresql/src/backend/executor/execProcnode.c

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/*-------------------------------------------------------------------------
*
* execProcnode.c
* contains dispatch functions which call the appropriate "initialize",
* "get a tuple", and "cleanup" routines for the given node type.
* If the node has children, then it will presumably call ExecInitNode,
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* ExecProcNode, or ExecEndNode on its subnodes and do the appropriate
* processing.
*
* Portions Copyright (c) 1996-2020, PostgreSQL Global Development Group
* Portions Copyright (c) 1994, Regents of the University of California
*
*
* IDENTIFICATION
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* src/backend/executor/execProcnode.c
*
*-------------------------------------------------------------------------
*/
/*
* NOTES
* This used to be three files. It is now all combined into
* one file so that it is easier to keep the dispatch routines
* in sync when new nodes are added.
*
* EXAMPLE
* Suppose we want the age of the manager of the shoe department and
* the number of employees in that department. So we have the query:
*
* select DEPT.no_emps, EMP.age
* from DEPT, EMP
* where EMP.name = DEPT.mgr and
* DEPT.name = "shoe"
*
* Suppose the planner gives us the following plan:
*
* Nest Loop (DEPT.mgr = EMP.name)
* / \
* / \
* Seq Scan Seq Scan
* DEPT EMP
* (name = "shoe")
*
* ExecutorStart() is called first.
* It calls InitPlan() which calls ExecInitNode() on
* the root of the plan -- the nest loop node.
*
* * ExecInitNode() notices that it is looking at a nest loop and
* as the code below demonstrates, it calls ExecInitNestLoop().
* Eventually this calls ExecInitNode() on the right and left subplans
* and so forth until the entire plan is initialized. The result
* of ExecInitNode() is a plan state tree built with the same structure
* as the underlying plan tree.
*
* * Then when ExecutorRun() is called, it calls ExecutePlan() which calls
* ExecProcNode() repeatedly on the top node of the plan state tree.
* Each time this happens, ExecProcNode() will end up calling
* ExecNestLoop(), which calls ExecProcNode() on its subplans.
* Each of these subplans is a sequential scan so ExecSeqScan() is
* called. The slots returned by ExecSeqScan() may contain
* tuples which contain the attributes ExecNestLoop() uses to
* form the tuples it returns.
*
* * Eventually ExecSeqScan() stops returning tuples and the nest
* loop join ends. Lastly, ExecutorEnd() calls ExecEndNode() which
* calls ExecEndNestLoop() which in turn calls ExecEndNode() on
* its subplans which result in ExecEndSeqScan().
*
* This should show how the executor works by having
* ExecInitNode(), ExecProcNode() and ExecEndNode() dispatch
* their work to the appropriate node support routines which may
* in turn call these routines themselves on their subplans.
*/
#include "postgres.h"
#include "executor/executor.h"
1999-07-16 07:00:38 +02:00
#include "executor/nodeAgg.h"
#include "executor/nodeAppend.h"
#include "executor/nodeBitmapAnd.h"
#include "executor/nodeBitmapHeapscan.h"
#include "executor/nodeBitmapIndexscan.h"
#include "executor/nodeBitmapOr.h"
#include "executor/nodeCtescan.h"
#include "executor/nodeCustom.h"
#include "executor/nodeForeignscan.h"
#include "executor/nodeFunctionscan.h"
Move targetlist SRF handling from expression evaluation to new executor node. Evaluation of set returning functions (SRFs_ in the targetlist (like SELECT generate_series(1,5)) so far was done in the expression evaluation (i.e. ExecEvalExpr()) and projection (i.e. ExecProject/ExecTargetList) code. This meant that most executor nodes performing projection, and most expression evaluation functions, had to deal with the possibility that an evaluated expression could return a set of return values. That's bad because it leads to repeated code in a lot of places. It also, and that's my (Andres's) motivation, made it a lot harder to implement a more efficient way of doing expression evaluation. To fix this, introduce a new executor node (ProjectSet) that can evaluate targetlists containing one or more SRFs. To avoid the complexity of the old way of handling nested expressions returning sets (e.g. having to pass up ExprDoneCond, and dealing with arguments to functions returning sets etc.), those SRFs can only be at the top level of the node's targetlist. The planner makes sure (via split_pathtarget_at_srfs()) that SRF evaluation is only necessary in ProjectSet nodes and that SRFs are only present at the top level of the node's targetlist. If there are nested SRFs the planner creates multiple stacked ProjectSet nodes. The ProjectSet nodes always get input from an underlying node. We also discussed and prototyped evaluating targetlist SRFs using ROWS FROM(), but that turned out to be more complicated than we'd hoped. While moving SRF evaluation to ProjectSet would allow to retain the old "least common multiple" behavior when multiple SRFs are present in one targetlist (i.e. continue returning rows until all SRFs are at the end of their input at the same time), we decided to instead only return rows till all SRFs are exhausted, returning NULL for already exhausted ones. We deemed the previous behavior to be too confusing, unexpected and actually not particularly useful. As a side effect, the previously prohibited case of multiple set returning arguments to a function, is now allowed. Not because it's particularly desirable, but because it ends up working and there seems to be no argument for adding code to prohibit it. Currently the behavior for COALESCE and CASE containing SRFs has changed, returning multiple rows from the expression, even when the SRF containing "arm" of the expression is not evaluated. That's because the SRFs are evaluated in a separate ProjectSet node. As that's quite confusing, we're likely to instead prohibit SRFs in those places. But that's still being discussed, and the code would reside in places not touched here, so that's a task for later. There's a lot of, now superfluous, code dealing with set return expressions around. But as the changes to get rid of those are verbose largely boring, it seems better for readability to keep the cleanup as a separate commit. Author: Tom Lane and Andres Freund Discussion: https://postgr.es/m/20160822214023.aaxz5l4igypowyri@alap3.anarazel.de
2017-01-18 21:46:50 +01:00
#include "executor/nodeGather.h"
#include "executor/nodeGatherMerge.h"
#include "executor/nodeGroup.h"
#include "executor/nodeHash.h"
#include "executor/nodeHashjoin.h"
Implement Incremental Sort Incremental Sort is an optimized variant of multikey sort for cases when the input is already sorted by a prefix of the requested sort keys. For example when the relation is already sorted by (key1, key2) and we need to sort it by (key1, key2, key3) we can simply split the input rows into groups having equal values in (key1, key2), and only sort/compare the remaining column key3. This has a number of benefits: - Reduced memory consumption, because only a single group (determined by values in the sorted prefix) needs to be kept in memory. This may also eliminate the need to spill to disk. - Lower startup cost, because Incremental Sort produce results after each prefix group, which is beneficial for plans where startup cost matters (like for example queries with LIMIT clause). We consider both Sort and Incremental Sort, and decide based on costing. The implemented algorithm operates in two different modes: - Fetching a minimum number of tuples without check of equality on the prefix keys, and sorting on all columns when safe. - Fetching all tuples for a single prefix group and then sorting by comparing only the remaining (non-prefix) keys. We always start in the first mode, and employ a heuristic to switch into the second mode if we believe it's beneficial - the goal is to minimize the number of unnecessary comparions while keeping memory consumption below work_mem. This is a very old patch series. The idea was originally proposed by Alexander Korotkov back in 2013, and then revived in 2017. In 2018 the patch was taken over by James Coleman, who wrote and rewrote most of the current code. There were many reviewers/contributors since 2013 - I've done my best to pick the most active ones, and listed them in this commit message. Author: James Coleman, Alexander Korotkov Reviewed-by: Tomas Vondra, Andreas Karlsson, Marti Raudsepp, Peter Geoghegan, Robert Haas, Thomas Munro, Antonin Houska, Andres Freund, Alexander Kuzmenkov Discussion: https://postgr.es/m/CAPpHfdscOX5an71nHd8WSUH6GNOCf=V7wgDaTXdDd9=goN-gfA@mail.gmail.com Discussion: https://postgr.es/m/CAPpHfds1waRZ=NOmueYq0sx1ZSCnt+5QJvizT8ndT2=etZEeAQ@mail.gmail.com
2020-04-06 21:33:28 +02:00
#include "executor/nodeIncrementalSort.h"
#include "executor/nodeIndexonlyscan.h"
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#include "executor/nodeIndexscan.h"
#include "executor/nodeLimit.h"
#include "executor/nodeLockRows.h"
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#include "executor/nodeMaterial.h"
#include "executor/nodeMergeAppend.h"
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#include "executor/nodeMergejoin.h"
#include "executor/nodeModifyTable.h"
#include "executor/nodeNamedtuplestorescan.h"
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#include "executor/nodeNestloop.h"
Move targetlist SRF handling from expression evaluation to new executor node. Evaluation of set returning functions (SRFs_ in the targetlist (like SELECT generate_series(1,5)) so far was done in the expression evaluation (i.e. ExecEvalExpr()) and projection (i.e. ExecProject/ExecTargetList) code. This meant that most executor nodes performing projection, and most expression evaluation functions, had to deal with the possibility that an evaluated expression could return a set of return values. That's bad because it leads to repeated code in a lot of places. It also, and that's my (Andres's) motivation, made it a lot harder to implement a more efficient way of doing expression evaluation. To fix this, introduce a new executor node (ProjectSet) that can evaluate targetlists containing one or more SRFs. To avoid the complexity of the old way of handling nested expressions returning sets (e.g. having to pass up ExprDoneCond, and dealing with arguments to functions returning sets etc.), those SRFs can only be at the top level of the node's targetlist. The planner makes sure (via split_pathtarget_at_srfs()) that SRF evaluation is only necessary in ProjectSet nodes and that SRFs are only present at the top level of the node's targetlist. If there are nested SRFs the planner creates multiple stacked ProjectSet nodes. The ProjectSet nodes always get input from an underlying node. We also discussed and prototyped evaluating targetlist SRFs using ROWS FROM(), but that turned out to be more complicated than we'd hoped. While moving SRF evaluation to ProjectSet would allow to retain the old "least common multiple" behavior when multiple SRFs are present in one targetlist (i.e. continue returning rows until all SRFs are at the end of their input at the same time), we decided to instead only return rows till all SRFs are exhausted, returning NULL for already exhausted ones. We deemed the previous behavior to be too confusing, unexpected and actually not particularly useful. As a side effect, the previously prohibited case of multiple set returning arguments to a function, is now allowed. Not because it's particularly desirable, but because it ends up working and there seems to be no argument for adding code to prohibit it. Currently the behavior for COALESCE and CASE containing SRFs has changed, returning multiple rows from the expression, even when the SRF containing "arm" of the expression is not evaluated. That's because the SRFs are evaluated in a separate ProjectSet node. As that's quite confusing, we're likely to instead prohibit SRFs in those places. But that's still being discussed, and the code would reside in places not touched here, so that's a task for later. There's a lot of, now superfluous, code dealing with set return expressions around. But as the changes to get rid of those are verbose largely boring, it seems better for readability to keep the cleanup as a separate commit. Author: Tom Lane and Andres Freund Discussion: https://postgr.es/m/20160822214023.aaxz5l4igypowyri@alap3.anarazel.de
2017-01-18 21:46:50 +01:00
#include "executor/nodeProjectSet.h"
#include "executor/nodeRecursiveunion.h"
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#include "executor/nodeResult.h"
#include "executor/nodeSamplescan.h"
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#include "executor/nodeSeqscan.h"
#include "executor/nodeSetOp.h"
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#include "executor/nodeSort.h"
#include "executor/nodeSubplan.h"
#include "executor/nodeSubqueryscan.h"
#include "executor/nodeTableFuncscan.h"
#include "executor/nodeTidscan.h"
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#include "executor/nodeUnique.h"
#include "executor/nodeValuesscan.h"
#include "executor/nodeWindowAgg.h"
#include "executor/nodeWorktablescan.h"
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#include "miscadmin.h"
#include "nodes/nodeFuncs.h"
static TupleTableSlot *ExecProcNodeFirst(PlanState *node);
static TupleTableSlot *ExecProcNodeInstr(PlanState *node);
/* ------------------------------------------------------------------------
* ExecInitNode
*
* Recursively initializes all the nodes in the plan tree rooted
* at 'node'.
*
* Inputs:
* 'node' is the current node of the plan produced by the query planner
* 'estate' is the shared execution state for the plan tree
* 'eflags' is a bitwise OR of flag bits described in executor.h
*
* Returns a PlanState node corresponding to the given Plan node.
* ------------------------------------------------------------------------
*/
PlanState *
ExecInitNode(Plan *node, EState *estate, int eflags)
{
PlanState *result;
List *subps;
ListCell *l;
/*
* do nothing when we get to the end of a leaf on tree.
*/
if (node == NULL)
return NULL;
/*
* Make sure there's enough stack available. Need to check here, in
* addition to ExecProcNode() (via ExecProcNodeFirst()), to ensure the
* stack isn't overrun while initializing the node tree.
*/
check_stack_depth();
switch (nodeTag(node))
{
/*
* control nodes
*/
case T_Result:
result = (PlanState *) ExecInitResult((Result *) node,
estate, eflags);
break;
Move targetlist SRF handling from expression evaluation to new executor node. Evaluation of set returning functions (SRFs_ in the targetlist (like SELECT generate_series(1,5)) so far was done in the expression evaluation (i.e. ExecEvalExpr()) and projection (i.e. ExecProject/ExecTargetList) code. This meant that most executor nodes performing projection, and most expression evaluation functions, had to deal with the possibility that an evaluated expression could return a set of return values. That's bad because it leads to repeated code in a lot of places. It also, and that's my (Andres's) motivation, made it a lot harder to implement a more efficient way of doing expression evaluation. To fix this, introduce a new executor node (ProjectSet) that can evaluate targetlists containing one or more SRFs. To avoid the complexity of the old way of handling nested expressions returning sets (e.g. having to pass up ExprDoneCond, and dealing with arguments to functions returning sets etc.), those SRFs can only be at the top level of the node's targetlist. The planner makes sure (via split_pathtarget_at_srfs()) that SRF evaluation is only necessary in ProjectSet nodes and that SRFs are only present at the top level of the node's targetlist. If there are nested SRFs the planner creates multiple stacked ProjectSet nodes. The ProjectSet nodes always get input from an underlying node. We also discussed and prototyped evaluating targetlist SRFs using ROWS FROM(), but that turned out to be more complicated than we'd hoped. While moving SRF evaluation to ProjectSet would allow to retain the old "least common multiple" behavior when multiple SRFs are present in one targetlist (i.e. continue returning rows until all SRFs are at the end of their input at the same time), we decided to instead only return rows till all SRFs are exhausted, returning NULL for already exhausted ones. We deemed the previous behavior to be too confusing, unexpected and actually not particularly useful. As a side effect, the previously prohibited case of multiple set returning arguments to a function, is now allowed. Not because it's particularly desirable, but because it ends up working and there seems to be no argument for adding code to prohibit it. Currently the behavior for COALESCE and CASE containing SRFs has changed, returning multiple rows from the expression, even when the SRF containing "arm" of the expression is not evaluated. That's because the SRFs are evaluated in a separate ProjectSet node. As that's quite confusing, we're likely to instead prohibit SRFs in those places. But that's still being discussed, and the code would reside in places not touched here, so that's a task for later. There's a lot of, now superfluous, code dealing with set return expressions around. But as the changes to get rid of those are verbose largely boring, it seems better for readability to keep the cleanup as a separate commit. Author: Tom Lane and Andres Freund Discussion: https://postgr.es/m/20160822214023.aaxz5l4igypowyri@alap3.anarazel.de
2017-01-18 21:46:50 +01:00
case T_ProjectSet:
result = (PlanState *) ExecInitProjectSet((ProjectSet *) node,
estate, eflags);
break;
case T_ModifyTable:
result = (PlanState *) ExecInitModifyTable((ModifyTable *) node,
estate, eflags);
break;
case T_Append:
result = (PlanState *) ExecInitAppend((Append *) node,
estate, eflags);
break;
case T_MergeAppend:
result = (PlanState *) ExecInitMergeAppend((MergeAppend *) node,
estate, eflags);
break;
case T_RecursiveUnion:
result = (PlanState *) ExecInitRecursiveUnion((RecursiveUnion *) node,
estate, eflags);
break;
case T_BitmapAnd:
result = (PlanState *) ExecInitBitmapAnd((BitmapAnd *) node,
estate, eflags);
break;
case T_BitmapOr:
result = (PlanState *) ExecInitBitmapOr((BitmapOr *) node,
estate, eflags);
break;
/*
* scan nodes
*/
case T_SeqScan:
result = (PlanState *) ExecInitSeqScan((SeqScan *) node,
estate, eflags);
break;
case T_SampleScan:
result = (PlanState *) ExecInitSampleScan((SampleScan *) node,
estate, eflags);
break;
case T_IndexScan:
result = (PlanState *) ExecInitIndexScan((IndexScan *) node,
estate, eflags);
break;
case T_IndexOnlyScan:
result = (PlanState *) ExecInitIndexOnlyScan((IndexOnlyScan *) node,
estate, eflags);
break;
case T_BitmapIndexScan:
result = (PlanState *) ExecInitBitmapIndexScan((BitmapIndexScan *) node,
estate, eflags);
break;
case T_BitmapHeapScan:
result = (PlanState *) ExecInitBitmapHeapScan((BitmapHeapScan *) node,
estate, eflags);
break;
case T_TidScan:
result = (PlanState *) ExecInitTidScan((TidScan *) node,
estate, eflags);
break;
case T_SubqueryScan:
result = (PlanState *) ExecInitSubqueryScan((SubqueryScan *) node,
estate, eflags);
break;
case T_FunctionScan:
result = (PlanState *) ExecInitFunctionScan((FunctionScan *) node,
estate, eflags);
break;
case T_TableFuncScan:
result = (PlanState *) ExecInitTableFuncScan((TableFuncScan *) node,
estate, eflags);
break;
case T_ValuesScan:
result = (PlanState *) ExecInitValuesScan((ValuesScan *) node,
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estate, eflags);
break;
case T_CteScan:
result = (PlanState *) ExecInitCteScan((CteScan *) node,
estate, eflags);
break;
case T_NamedTuplestoreScan:
result = (PlanState *) ExecInitNamedTuplestoreScan((NamedTuplestoreScan *) node,
estate, eflags);
break;
case T_WorkTableScan:
result = (PlanState *) ExecInitWorkTableScan((WorkTableScan *) node,
estate, eflags);
break;
case T_ForeignScan:
result = (PlanState *) ExecInitForeignScan((ForeignScan *) node,
estate, eflags);
break;
case T_CustomScan:
result = (PlanState *) ExecInitCustomScan((CustomScan *) node,
estate, eflags);
break;
/*
* join nodes
*/
case T_NestLoop:
result = (PlanState *) ExecInitNestLoop((NestLoop *) node,
estate, eflags);
break;
case T_MergeJoin:
result = (PlanState *) ExecInitMergeJoin((MergeJoin *) node,
estate, eflags);
break;
case T_HashJoin:
result = (PlanState *) ExecInitHashJoin((HashJoin *) node,
estate, eflags);
break;
/*
* materialization nodes
*/
case T_Material:
result = (PlanState *) ExecInitMaterial((Material *) node,
estate, eflags);
break;
case T_Sort:
result = (PlanState *) ExecInitSort((Sort *) node,
estate, eflags);
break;
Implement Incremental Sort Incremental Sort is an optimized variant of multikey sort for cases when the input is already sorted by a prefix of the requested sort keys. For example when the relation is already sorted by (key1, key2) and we need to sort it by (key1, key2, key3) we can simply split the input rows into groups having equal values in (key1, key2), and only sort/compare the remaining column key3. This has a number of benefits: - Reduced memory consumption, because only a single group (determined by values in the sorted prefix) needs to be kept in memory. This may also eliminate the need to spill to disk. - Lower startup cost, because Incremental Sort produce results after each prefix group, which is beneficial for plans where startup cost matters (like for example queries with LIMIT clause). We consider both Sort and Incremental Sort, and decide based on costing. The implemented algorithm operates in two different modes: - Fetching a minimum number of tuples without check of equality on the prefix keys, and sorting on all columns when safe. - Fetching all tuples for a single prefix group and then sorting by comparing only the remaining (non-prefix) keys. We always start in the first mode, and employ a heuristic to switch into the second mode if we believe it's beneficial - the goal is to minimize the number of unnecessary comparions while keeping memory consumption below work_mem. This is a very old patch series. The idea was originally proposed by Alexander Korotkov back in 2013, and then revived in 2017. In 2018 the patch was taken over by James Coleman, who wrote and rewrote most of the current code. There were many reviewers/contributors since 2013 - I've done my best to pick the most active ones, and listed them in this commit message. Author: James Coleman, Alexander Korotkov Reviewed-by: Tomas Vondra, Andreas Karlsson, Marti Raudsepp, Peter Geoghegan, Robert Haas, Thomas Munro, Antonin Houska, Andres Freund, Alexander Kuzmenkov Discussion: https://postgr.es/m/CAPpHfdscOX5an71nHd8WSUH6GNOCf=V7wgDaTXdDd9=goN-gfA@mail.gmail.com Discussion: https://postgr.es/m/CAPpHfds1waRZ=NOmueYq0sx1ZSCnt+5QJvizT8ndT2=etZEeAQ@mail.gmail.com
2020-04-06 21:33:28 +02:00
case T_IncrementalSort:
result = (PlanState *) ExecInitIncrementalSort((IncrementalSort *) node,
estate, eflags);
break;
case T_Group:
result = (PlanState *) ExecInitGroup((Group *) node,
estate, eflags);
break;
case T_Agg:
result = (PlanState *) ExecInitAgg((Agg *) node,
estate, eflags);
break;
case T_WindowAgg:
result = (PlanState *) ExecInitWindowAgg((WindowAgg *) node,
estate, eflags);
break;
case T_Unique:
result = (PlanState *) ExecInitUnique((Unique *) node,
estate, eflags);
break;
case T_Gather:
result = (PlanState *) ExecInitGather((Gather *) node,
estate, eflags);
break;
case T_GatherMerge:
result = (PlanState *) ExecInitGatherMerge((GatherMerge *) node,
estate, eflags);
break;
case T_Hash:
result = (PlanState *) ExecInitHash((Hash *) node,
estate, eflags);
break;
case T_SetOp:
result = (PlanState *) ExecInitSetOp((SetOp *) node,
estate, eflags);
break;
case T_LockRows:
result = (PlanState *) ExecInitLockRows((LockRows *) node,
estate, eflags);
break;
case T_Limit:
result = (PlanState *) ExecInitLimit((Limit *) node,
estate, eflags);
break;
default:
elog(ERROR, "unrecognized node type: %d", (int) nodeTag(node));
result = NULL; /* keep compiler quiet */
break;
}
ExecSetExecProcNode(result, result->ExecProcNode);
/*
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* Initialize any initPlans present in this node. The planner put them in
* a separate list for us.
*/
subps = NIL;
foreach(l, node->initPlan)
{
SubPlan *subplan = (SubPlan *) lfirst(l);
SubPlanState *sstate;
Assert(IsA(subplan, SubPlan));
sstate = ExecInitSubPlan(subplan, result);
subps = lappend(subps, sstate);
}
result->initPlan = subps;
/* Set up instrumentation for this node if requested */
if (estate->es_instrument)
result->instrument = InstrAlloc(1, estate->es_instrument);
return result;
}
/*
* If a node wants to change its ExecProcNode function after ExecInitNode()
* has finished, it should do so with this function. That way any wrapper
* functions can be reinstalled, without the node having to know how that
* works.
*/
void
ExecSetExecProcNode(PlanState *node, ExecProcNodeMtd function)
{
/*
* Add a wrapper around the ExecProcNode callback that checks stack depth
* during the first execution and maybe adds an instrumentation wrapper.
* When the callback is changed after execution has already begun that
* means we'll superfluously execute ExecProcNodeFirst, but that seems ok.
*/
node->ExecProcNodeReal = function;
node->ExecProcNode = ExecProcNodeFirst;
}
/*
* ExecProcNode wrapper that performs some one-time checks, before calling
* the relevant node method (possibly via an instrumentation wrapper).
*/
static TupleTableSlot *
ExecProcNodeFirst(PlanState *node)
{
/*
* Perform stack depth check during the first execution of the node. We
* only do so the first time round because it turns out to not be cheap on
2017-08-14 23:29:33 +02:00
* some common architectures (eg. x86). This relies on the assumption
* that ExecProcNode calls for a given plan node will always be made at
* roughly the same stack depth.
*/
check_stack_depth();
/*
* If instrumentation is required, change the wrapper to one that just
* does instrumentation. Otherwise we can dispense with all wrappers and
* have ExecProcNode() directly call the relevant function from now on.
*/
if (node->instrument)
node->ExecProcNode = ExecProcNodeInstr;
else
node->ExecProcNode = node->ExecProcNodeReal;
return node->ExecProcNode(node);
}
/*
* ExecProcNode wrapper that performs instrumentation calls. By keeping
* this a separate function, we avoid overhead in the normal case where
* no instrumentation is wanted.
*/
static TupleTableSlot *
ExecProcNodeInstr(PlanState *node)
{
TupleTableSlot *result;
InstrStartNode(node->instrument);
result = node->ExecProcNodeReal(node);
InstrStopNode(node->instrument, TupIsNull(result) ? 0.0 : 1.0);
return result;
}
/* ----------------------------------------------------------------
* MultiExecProcNode
*
* Execute a node that doesn't return individual tuples
* (it might return a hashtable, bitmap, etc). Caller should
* check it got back the expected kind of Node.
*
* This has essentially the same responsibilities as ExecProcNode,
* but it does not do InstrStartNode/InstrStopNode (mainly because
* it can't tell how many returned tuples to count). Each per-node
* function must provide its own instrumentation support.
* ----------------------------------------------------------------
*/
Node *
MultiExecProcNode(PlanState *node)
{
2005-10-15 04:49:52 +02:00
Node *result;
check_stack_depth();
CHECK_FOR_INTERRUPTS();
if (node->chgParam != NULL) /* something changed */
ExecReScan(node); /* let ReScan handle this */
switch (nodeTag(node))
{
2005-10-15 04:49:52 +02:00
/*
* Only node types that actually support multiexec will be listed
*/
case T_HashState:
result = MultiExecHash((HashState *) node);
break;
case T_BitmapIndexScanState:
result = MultiExecBitmapIndexScan((BitmapIndexScanState *) node);
break;
case T_BitmapAndState:
result = MultiExecBitmapAnd((BitmapAndState *) node);
break;
case T_BitmapOrState:
result = MultiExecBitmapOr((BitmapOrState *) node);
break;
default:
elog(ERROR, "unrecognized node type: %d", (int) nodeTag(node));
result = NULL;
break;
}
return result;
}
/* ----------------------------------------------------------------
* ExecEndNode
*
* Recursively cleans up all the nodes in the plan rooted
* at 'node'.
*
* After this operation, the query plan will not be able to be
* processed any further. This should be called only after
* the query plan has been fully executed.
* ----------------------------------------------------------------
*/
void
ExecEndNode(PlanState *node)
{
/*
* do nothing when we get to the end of a leaf on tree.
*/
if (node == NULL)
return;
/*
* Make sure there's enough stack available. Need to check here, in
* addition to ExecProcNode() (via ExecProcNodeFirst()), because it's not
* guaranteed that ExecProcNode() is reached for all nodes.
*/
check_stack_depth();
if (node->chgParam != NULL)
{
bms_free(node->chgParam);
node->chgParam = NULL;
}
switch (nodeTag(node))
{
/*
* control nodes
*/
case T_ResultState:
ExecEndResult((ResultState *) node);
break;
Move targetlist SRF handling from expression evaluation to new executor node. Evaluation of set returning functions (SRFs_ in the targetlist (like SELECT generate_series(1,5)) so far was done in the expression evaluation (i.e. ExecEvalExpr()) and projection (i.e. ExecProject/ExecTargetList) code. This meant that most executor nodes performing projection, and most expression evaluation functions, had to deal with the possibility that an evaluated expression could return a set of return values. That's bad because it leads to repeated code in a lot of places. It also, and that's my (Andres's) motivation, made it a lot harder to implement a more efficient way of doing expression evaluation. To fix this, introduce a new executor node (ProjectSet) that can evaluate targetlists containing one or more SRFs. To avoid the complexity of the old way of handling nested expressions returning sets (e.g. having to pass up ExprDoneCond, and dealing with arguments to functions returning sets etc.), those SRFs can only be at the top level of the node's targetlist. The planner makes sure (via split_pathtarget_at_srfs()) that SRF evaluation is only necessary in ProjectSet nodes and that SRFs are only present at the top level of the node's targetlist. If there are nested SRFs the planner creates multiple stacked ProjectSet nodes. The ProjectSet nodes always get input from an underlying node. We also discussed and prototyped evaluating targetlist SRFs using ROWS FROM(), but that turned out to be more complicated than we'd hoped. While moving SRF evaluation to ProjectSet would allow to retain the old "least common multiple" behavior when multiple SRFs are present in one targetlist (i.e. continue returning rows until all SRFs are at the end of their input at the same time), we decided to instead only return rows till all SRFs are exhausted, returning NULL for already exhausted ones. We deemed the previous behavior to be too confusing, unexpected and actually not particularly useful. As a side effect, the previously prohibited case of multiple set returning arguments to a function, is now allowed. Not because it's particularly desirable, but because it ends up working and there seems to be no argument for adding code to prohibit it. Currently the behavior for COALESCE and CASE containing SRFs has changed, returning multiple rows from the expression, even when the SRF containing "arm" of the expression is not evaluated. That's because the SRFs are evaluated in a separate ProjectSet node. As that's quite confusing, we're likely to instead prohibit SRFs in those places. But that's still being discussed, and the code would reside in places not touched here, so that's a task for later. There's a lot of, now superfluous, code dealing with set return expressions around. But as the changes to get rid of those are verbose largely boring, it seems better for readability to keep the cleanup as a separate commit. Author: Tom Lane and Andres Freund Discussion: https://postgr.es/m/20160822214023.aaxz5l4igypowyri@alap3.anarazel.de
2017-01-18 21:46:50 +01:00
case T_ProjectSetState:
ExecEndProjectSet((ProjectSetState *) node);
break;
case T_ModifyTableState:
ExecEndModifyTable((ModifyTableState *) node);
break;
case T_AppendState:
ExecEndAppend((AppendState *) node);
break;
case T_MergeAppendState:
ExecEndMergeAppend((MergeAppendState *) node);
break;
case T_RecursiveUnionState:
ExecEndRecursiveUnion((RecursiveUnionState *) node);
break;
case T_BitmapAndState:
ExecEndBitmapAnd((BitmapAndState *) node);
break;
case T_BitmapOrState:
ExecEndBitmapOr((BitmapOrState *) node);
break;
/*
* scan nodes
*/
case T_SeqScanState:
ExecEndSeqScan((SeqScanState *) node);
break;
case T_SampleScanState:
ExecEndSampleScan((SampleScanState *) node);
break;
case T_GatherState:
ExecEndGather((GatherState *) node);
break;
case T_GatherMergeState:
ExecEndGatherMerge((GatherMergeState *) node);
break;
case T_IndexScanState:
ExecEndIndexScan((IndexScanState *) node);
break;
case T_IndexOnlyScanState:
ExecEndIndexOnlyScan((IndexOnlyScanState *) node);
break;
case T_BitmapIndexScanState:
ExecEndBitmapIndexScan((BitmapIndexScanState *) node);
break;
case T_BitmapHeapScanState:
ExecEndBitmapHeapScan((BitmapHeapScanState *) node);
break;
case T_TidScanState:
ExecEndTidScan((TidScanState *) node);
break;
case T_SubqueryScanState:
ExecEndSubqueryScan((SubqueryScanState *) node);
break;
case T_FunctionScanState:
ExecEndFunctionScan((FunctionScanState *) node);
break;
case T_TableFuncScanState:
ExecEndTableFuncScan((TableFuncScanState *) node);
break;
case T_ValuesScanState:
ExecEndValuesScan((ValuesScanState *) node);
break;
case T_CteScanState:
ExecEndCteScan((CteScanState *) node);
break;
case T_NamedTuplestoreScanState:
ExecEndNamedTuplestoreScan((NamedTuplestoreScanState *) node);
break;
case T_WorkTableScanState:
ExecEndWorkTableScan((WorkTableScanState *) node);
break;
case T_ForeignScanState:
ExecEndForeignScan((ForeignScanState *) node);
break;
case T_CustomScanState:
ExecEndCustomScan((CustomScanState *) node);
break;
/*
* join nodes
*/
case T_NestLoopState:
ExecEndNestLoop((NestLoopState *) node);
break;
case T_MergeJoinState:
ExecEndMergeJoin((MergeJoinState *) node);
break;
case T_HashJoinState:
ExecEndHashJoin((HashJoinState *) node);
break;
/*
* materialization nodes
*/
case T_MaterialState:
ExecEndMaterial((MaterialState *) node);
break;
case T_SortState:
ExecEndSort((SortState *) node);
break;
Implement Incremental Sort Incremental Sort is an optimized variant of multikey sort for cases when the input is already sorted by a prefix of the requested sort keys. For example when the relation is already sorted by (key1, key2) and we need to sort it by (key1, key2, key3) we can simply split the input rows into groups having equal values in (key1, key2), and only sort/compare the remaining column key3. This has a number of benefits: - Reduced memory consumption, because only a single group (determined by values in the sorted prefix) needs to be kept in memory. This may also eliminate the need to spill to disk. - Lower startup cost, because Incremental Sort produce results after each prefix group, which is beneficial for plans where startup cost matters (like for example queries with LIMIT clause). We consider both Sort and Incremental Sort, and decide based on costing. The implemented algorithm operates in two different modes: - Fetching a minimum number of tuples without check of equality on the prefix keys, and sorting on all columns when safe. - Fetching all tuples for a single prefix group and then sorting by comparing only the remaining (non-prefix) keys. We always start in the first mode, and employ a heuristic to switch into the second mode if we believe it's beneficial - the goal is to minimize the number of unnecessary comparions while keeping memory consumption below work_mem. This is a very old patch series. The idea was originally proposed by Alexander Korotkov back in 2013, and then revived in 2017. In 2018 the patch was taken over by James Coleman, who wrote and rewrote most of the current code. There were many reviewers/contributors since 2013 - I've done my best to pick the most active ones, and listed them in this commit message. Author: James Coleman, Alexander Korotkov Reviewed-by: Tomas Vondra, Andreas Karlsson, Marti Raudsepp, Peter Geoghegan, Robert Haas, Thomas Munro, Antonin Houska, Andres Freund, Alexander Kuzmenkov Discussion: https://postgr.es/m/CAPpHfdscOX5an71nHd8WSUH6GNOCf=V7wgDaTXdDd9=goN-gfA@mail.gmail.com Discussion: https://postgr.es/m/CAPpHfds1waRZ=NOmueYq0sx1ZSCnt+5QJvizT8ndT2=etZEeAQ@mail.gmail.com
2020-04-06 21:33:28 +02:00
case T_IncrementalSortState:
ExecEndIncrementalSort((IncrementalSortState *) node);
break;
case T_GroupState:
ExecEndGroup((GroupState *) node);
break;
case T_AggState:
ExecEndAgg((AggState *) node);
break;
case T_WindowAggState:
ExecEndWindowAgg((WindowAggState *) node);
break;
case T_UniqueState:
ExecEndUnique((UniqueState *) node);
break;
case T_HashState:
ExecEndHash((HashState *) node);
break;
case T_SetOpState:
ExecEndSetOp((SetOpState *) node);
break;
case T_LockRowsState:
ExecEndLockRows((LockRowsState *) node);
break;
case T_LimitState:
ExecEndLimit((LimitState *) node);
break;
default:
elog(ERROR, "unrecognized node type: %d", (int) nodeTag(node));
break;
}
}
/*
* ExecShutdownNode
*
* Give execution nodes a chance to stop asynchronous resource consumption
* and release any resources still held.
*/
bool
ExecShutdownNode(PlanState *node)
{
if (node == NULL)
return false;
check_stack_depth();
planstate_tree_walker(node, ExecShutdownNode, NULL);
/*
* Treat the node as running while we shut it down, but only if it's run
* at least once already. We don't expect much CPU consumption during
* node shutdown, but in the case of Gather or Gather Merge, we may shut
* down workers at this stage. If so, their buffer usage will get
* propagated into pgBufferUsage at this point, and we want to make sure
* that it gets associated with the Gather node. We skip this if the node
* has never been executed, so as to avoid incorrectly making it appear
* that it has.
*/
if (node->instrument && node->instrument->running)
InstrStartNode(node->instrument);
switch (nodeTag(node))
{
case T_GatherState:
ExecShutdownGather((GatherState *) node);
break;
case T_ForeignScanState:
ExecShutdownForeignScan((ForeignScanState *) node);
break;
case T_CustomScanState:
ExecShutdownCustomScan((CustomScanState *) node);
break;
case T_GatherMergeState:
ExecShutdownGatherMerge((GatherMergeState *) node);
break;
case T_HashState:
ExecShutdownHash((HashState *) node);
break;
Add parallel-aware hash joins. Introduce parallel-aware hash joins that appear in EXPLAIN plans as Parallel Hash Join with Parallel Hash. While hash joins could already appear in parallel queries, they were previously always parallel-oblivious and had a partial subplan only on the outer side, meaning that the work of the inner subplan was duplicated in every worker. After this commit, the planner will consider using a partial subplan on the inner side too, using the Parallel Hash node to divide the work over the available CPU cores and combine its results in shared memory. If the join needs to be split into multiple batches in order to respect work_mem, then workers process different batches as much as possible and then work together on the remaining batches. The advantages of a parallel-aware hash join over a parallel-oblivious hash join used in a parallel query are that it: * avoids wasting memory on duplicated hash tables * avoids wasting disk space on duplicated batch files * divides the work of building the hash table over the CPUs One disadvantage is that there is some communication between the participating CPUs which might outweigh the benefits of parallelism in the case of small hash tables. This is avoided by the planner's existing reluctance to supply partial plans for small scans, but it may be necessary to estimate synchronization costs in future if that situation changes. Another is that outer batch 0 must be written to disk if multiple batches are required. A potential future advantage of parallel-aware hash joins is that right and full outer joins could be supported, since there is a single set of matched bits for each hashtable, but that is not yet implemented. A new GUC enable_parallel_hash is defined to control the feature, defaulting to on. Author: Thomas Munro Reviewed-By: Andres Freund, Robert Haas Tested-By: Rafia Sabih, Prabhat Sahu Discussion: https://postgr.es/m/CAEepm=2W=cOkiZxcg6qiFQP-dHUe09aqTrEMM7yJDrHMhDv_RA@mail.gmail.com https://postgr.es/m/CAEepm=37HKyJ4U6XOLi=JgfSHM3o6B-GaeO-6hkOmneTDkH+Uw@mail.gmail.com
2017-12-21 08:39:21 +01:00
case T_HashJoinState:
ExecShutdownHashJoin((HashJoinState *) node);
break;
default:
break;
}
/* Stop the node if we started it above, reporting 0 tuples. */
if (node->instrument && node->instrument->running)
InstrStopNode(node->instrument, 0);
return false;
}
/*
* ExecSetTupleBound
*
* Set a tuple bound for a planstate node. This lets child plan nodes
* optimize based on the knowledge that the maximum number of tuples that
* their parent will demand is limited. The tuple bound for a node may
* only be changed between scans (i.e., after node initialization or just
* before an ExecReScan call).
*
* Any negative tuples_needed value means "no limit", which should be the
* default assumption when this is not called at all for a particular node.
*
* Note: if this is called repeatedly on a plan tree, the exact same set
* of nodes must be updated with the new limit each time; be careful that
* only unchanging conditions are tested here.
*/
void
ExecSetTupleBound(int64 tuples_needed, PlanState *child_node)
{
/*
* Since this function recurses, in principle we should check stack depth
* here. In practice, it's probably pointless since the earlier node
* initialization tree traversal would surely have consumed more stack.
*/
if (IsA(child_node, SortState))
{
/*
* If it is a Sort node, notify it that it can use bounded sort.
*
* Note: it is the responsibility of nodeSort.c to react properly to
* changes of these parameters. If we ever redesign this, it'd be a
* good idea to integrate this signaling with the parameter-change
* mechanism.
*/
SortState *sortState = (SortState *) child_node;
Implement Incremental Sort Incremental Sort is an optimized variant of multikey sort for cases when the input is already sorted by a prefix of the requested sort keys. For example when the relation is already sorted by (key1, key2) and we need to sort it by (key1, key2, key3) we can simply split the input rows into groups having equal values in (key1, key2), and only sort/compare the remaining column key3. This has a number of benefits: - Reduced memory consumption, because only a single group (determined by values in the sorted prefix) needs to be kept in memory. This may also eliminate the need to spill to disk. - Lower startup cost, because Incremental Sort produce results after each prefix group, which is beneficial for plans where startup cost matters (like for example queries with LIMIT clause). We consider both Sort and Incremental Sort, and decide based on costing. The implemented algorithm operates in two different modes: - Fetching a minimum number of tuples without check of equality on the prefix keys, and sorting on all columns when safe. - Fetching all tuples for a single prefix group and then sorting by comparing only the remaining (non-prefix) keys. We always start in the first mode, and employ a heuristic to switch into the second mode if we believe it's beneficial - the goal is to minimize the number of unnecessary comparions while keeping memory consumption below work_mem. This is a very old patch series. The idea was originally proposed by Alexander Korotkov back in 2013, and then revived in 2017. In 2018 the patch was taken over by James Coleman, who wrote and rewrote most of the current code. There were many reviewers/contributors since 2013 - I've done my best to pick the most active ones, and listed them in this commit message. Author: James Coleman, Alexander Korotkov Reviewed-by: Tomas Vondra, Andreas Karlsson, Marti Raudsepp, Peter Geoghegan, Robert Haas, Thomas Munro, Antonin Houska, Andres Freund, Alexander Kuzmenkov Discussion: https://postgr.es/m/CAPpHfdscOX5an71nHd8WSUH6GNOCf=V7wgDaTXdDd9=goN-gfA@mail.gmail.com Discussion: https://postgr.es/m/CAPpHfds1waRZ=NOmueYq0sx1ZSCnt+5QJvizT8ndT2=etZEeAQ@mail.gmail.com
2020-04-06 21:33:28 +02:00
if (tuples_needed < 0)
{
/* make sure flag gets reset if needed upon rescan */
sortState->bounded = false;
}
else
{
sortState->bounded = true;
sortState->bound = tuples_needed;
}
}
else if (IsA(child_node, IncrementalSortState))
{
/*
* If it is an IncrementalSort node, notify it that it can use bounded
* sort.
*
* Note: it is the responsibility of nodeIncrementalSort.c to react
* properly to changes of these parameters. If we ever redesign this,
* it'd be a good idea to integrate this signaling with the
* parameter-change mechanism.
*/
IncrementalSortState *sortState = (IncrementalSortState *) child_node;
if (tuples_needed < 0)
{
/* make sure flag gets reset if needed upon rescan */
sortState->bounded = false;
}
else
{
sortState->bounded = true;
sortState->bound = tuples_needed;
}
}
Use Append rather than MergeAppend for scanning ordered partitions. If we need ordered output from a scan of a partitioned table, but the ordering matches the partition ordering, then we don't need to use a MergeAppend to combine the pre-ordered per-partition scan results: a plain Append will produce the same results. This both saves useless comparison work inside the MergeAppend proper, and allows us to start returning tuples after istarting up just the first child node not all of them. However, all is not peaches and cream, because if some of the child nodes have high startup costs then there will be big discontinuities in the tuples-returned-versus-elapsed-time curve. The planner's cost model cannot handle that (yet, anyway). If we model the Append's startup cost as being just the first child's startup cost, we may drastically underestimate the cost of fetching slightly more tuples than are available from the first child. Since we've had bad experiences with over-optimistic choices of "fast start" plans for ORDER BY LIMIT queries, that seems scary. As a klugy workaround, set the startup cost estimate for an ordered Append to be the sum of its children's startup costs (as MergeAppend would). This doesn't really describe reality, but it's less likely to cause a bad plan choice than an underestimated startup cost would. In practice, the cases where we really care about this optimization will have child plans that are IndexScans with zero startup cost, so that the overly conservative estimate is still just zero. David Rowley, reviewed by Julien Rouhaud and Antonin Houska Discussion: https://postgr.es/m/CAKJS1f-hAqhPLRk_RaSFTgYxd=Tz5hA7kQ2h4-DhJufQk8TGuw@mail.gmail.com
2019-04-06 01:20:30 +02:00
else if (IsA(child_node, AppendState))
{
/*
* If it is an Append, we can apply the bound to any nodes that are
* children of the Append, since the Append surely need read no more
* than that many tuples from any one input.
*/
AppendState *aState = (AppendState *) child_node;
int i;
for (i = 0; i < aState->as_nplans; i++)
ExecSetTupleBound(tuples_needed, aState->appendplans[i]);
}
else if (IsA(child_node, MergeAppendState))
{
/*
* If it is a MergeAppend, we can apply the bound to any nodes that
* are children of the MergeAppend, since the MergeAppend surely need
* read no more than that many tuples from any one input.
*/
MergeAppendState *maState = (MergeAppendState *) child_node;
int i;
for (i = 0; i < maState->ms_nplans; i++)
ExecSetTupleBound(tuples_needed, maState->mergeplans[i]);
}
else if (IsA(child_node, ResultState))
{
/*
* Similarly, for a projecting Result, we can apply the bound to its
* child node.
*
* If Result supported qual checking, we'd have to punt on seeing a
* qual. Note that having a resconstantqual is not a showstopper: if
* that condition succeeds it affects nothing, while if it fails, no
* rows will be demanded from the Result child anyway.
*/
if (outerPlanState(child_node))
ExecSetTupleBound(tuples_needed, outerPlanState(child_node));
}
else if (IsA(child_node, SubqueryScanState))
{
/*
* We can also descend through SubqueryScan, but only if it has no
* qual (otherwise it might discard rows).
*/
SubqueryScanState *subqueryState = (SubqueryScanState *) child_node;
if (subqueryState->ss.ps.qual == NULL)
ExecSetTupleBound(tuples_needed, subqueryState->subplan);
}
else if (IsA(child_node, GatherState))
{
/*
* A Gather node can propagate the bound to its workers. As with
* MergeAppend, no one worker could possibly need to return more
* tuples than the Gather itself needs to.
*
* Note: As with Sort, the Gather node is responsible for reacting
* properly to changes to this parameter.
*/
GatherState *gstate = (GatherState *) child_node;
gstate->tuples_needed = tuples_needed;
/* Also pass down the bound to our own copy of the child plan */
ExecSetTupleBound(tuples_needed, outerPlanState(child_node));
}
else if (IsA(child_node, GatherMergeState))
{
/* Same comments as for Gather */
GatherMergeState *gstate = (GatherMergeState *) child_node;
gstate->tuples_needed = tuples_needed;
ExecSetTupleBound(tuples_needed, outerPlanState(child_node));
}
/*
* In principle we could descend through any plan node type that is
* certain not to discard or combine input rows; but on seeing a node that
* can do that, we can't propagate the bound any further. For the moment
* it's unclear that any other cases are worth checking here.
*/
}