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src/backend/executor/README
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Amit Langote
Revert "Don't lock partitions pruned by initial pruning"
22 май 2025, 11:02
22 май 2025, 11:02
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src/backend/executor/README The Postgres Executor ===================== The executor processes a tree of "plan nodes". The plan tree is essentially a demand-pull pipeline of tuple processing operations. Each node, when called, will produce the next tuple in its output sequence, or NULL if no more tuples are available. If the node is not a primitive relation-scanning node, it will have child node(s) that it calls in turn to obtain input tuples. Refinements on this basic model include: * Choice of scan direction (forwards or backwards). Caution: this is not currently well-supported. It works for primitive scan nodes, but not very well for joins, aggregates, etc. * Rescan command to reset a node and make it generate its output sequence over again. * Parameters that can alter a node's results. After adjusting a parameter, the rescan command must be applied to that node and all nodes above it. There is a moderately intelligent scheme to avoid rescanning nodes unnecessarily (for example, Sort does not rescan its input if no parameters of the input have changed, since it can just reread its stored sorted data). For a SELECT, it is only necessary to deliver the top-level result tuples to the client. For INSERT/UPDATE/DELETE/MERGE, the actual table modification operations happen in a top-level ModifyTable plan node. If the query includes a RETURNING clause, the ModifyTable node delivers the computed RETURNING rows as output, otherwise it returns nothing. Handling INSERT is pretty straightforward: the tuples returned from the plan tree below ModifyTable are inserted into the correct result relation. For UPDATE, the plan tree returns the new values of the updated columns, plus "junk" (hidden) column(s) identifying which table row is to be updated. The ModifyTable node must fetch that row to extract values for the unchanged columns, combine the values into a new row, and apply the update. (For a heap table, the row-identity junk column is a CTID, but other things may be used for other table types.) For DELETE, the plan tree need only deliver junk row-identity column(s), and the ModifyTable node visits each of those rows and marks the row deleted. MERGE is described below. XXX a great deal more documentation needs to be written here... Plan Trees and State Trees -------------------------- The plan tree delivered by the planner contains a tree of Plan nodes (struct types derived from struct Plan). During executor startup we build a parallel tree of identical structure containing executor state nodes --- generally, every plan node type has a corresponding executor state node type. Each node in the state tree has a pointer to its corresponding node in the plan tree, plus executor state data as needed to implement that node type. This arrangement allows the plan tree to be completely read-only so far as the executor is concerned: all data that is modified during execution is in the state tree. Read-only plan trees make life much simpler for plan caching and reuse. A corresponding executor state node may not be created during executor startup if the executor determines that an entire subplan is not required due to execution time partition pruning determining that no matching records will be found there. This currently only occurs for Append and MergeAppend nodes. In this case the non-required subplans are ignored and the executor state's subnode array will become out of sequence to the plan's subplan list. Each Plan node may have expression trees associated with it, to represent its target list, qualification conditions, etc. These trees are also read-only to the executor, but the executor state for expression evaluation does not mirror the Plan expression's tree shape, as explained below. Rather, there's just one ExprState node per expression tree, although this may have sub-nodes for some complex expression node types. Altogether there are four classes of nodes used in these trees: Plan nodes, their corresponding PlanState nodes, Expr nodes, and ExprState nodes. (Actually, there are also List nodes, which are used as "glue" in all three tree-based representations.) Expression Trees and ExprState nodes ------------------------------------ Expression trees, in contrast to Plan trees, are not mirrored into a corresponding tree of state nodes. Instead each separately executable expression tree (e.g. a Plan's qual or targetlist) is represented by one ExprState node. The ExprState node contains the information needed to evaluate the expression in a compact, linear form. That compact form is stored as a flat array in ExprState->steps[] (an array of ExprEvalStep, not ExprEvalStep *). The reasons for choosing such a representation include: - commonly the amount of work needed to evaluate one Expr-type node is small enough that the overhead of having to perform a tree-walk during evaluation is significant. - the flat representation can be evaluated non-recursively within a single function, reducing stack depth and function call overhead. - such a representation is usable both for fast interpreted execution, and for compiling into native code. The Plan-tree representation of an expression is compiled into an ExprState node by ExecInitExpr(). As much complexity as possible should be handled by ExecInitExpr() (and helpers), instead of execution time where both interpreted and compiled versions would need to deal with the complexity. Besides duplicating effort between execution approaches, runtime initialization checks also have a small but noticeable cost every time the expression is evaluated. Therefore, we allow ExecInitExpr() to precompute information that we do not expect to vary across execution of a single query, for example the set of CHECK constraint expressions to be applied to a domain type. This could not be done at plan time without greatly increasing the number of events that require plan invalidation. (Previously, some information of this kind was rechecked on each expression evaluation, but that seems like unnecessary overhead.) Expression Initialization ------------------------- During ExecInitExpr() and similar routines, Expr trees are converted into the flat representation. Each Expr node might be represented by zero, one, or more ExprEvalSteps. Each ExprEvalStep's work is determined by its opcode (of enum ExprEvalOp) and it stores the result of its work into the Datum variable and boolean null flag variable pointed to by ExprEvalStep->resvalue/resnull. Complex expressions are performed by chaining together several steps. For example, "a + b" (one OpExpr, with two Var expressions) would be represented as two steps to fetch the Var values, and one step for the evaluation of the function underlying the + operator. The steps for the Vars would have their resvalue/resnull pointing directly to the appropriate args[].value .isnull elements in the FunctionCallInfoBaseData struct that is used by the function evaluation step, thus avoiding extra work to copy the result values around. The last entry in a completed ExprState->steps array is always an EEOP_DONE_RETURN or EEOP_DONE_NO_RETURN step; this removes the need to test for end-of-array while iterating. The former is used when the expression returns a value directly, the latter when side-effects of expression initialization are the goal (e.g. for projection or aggregate transition value computation). Also, if the expression contains any variable references (to user columns of the ExprContext's INNER, OUTER, or SCAN tuples), the steps array begins with EEOP_*_FETCHSOME steps that ensure that the relevant tuples have been deconstructed to make the required columns directly available (cf. slot_getsomeattrs()). This allows individual Var-fetching steps to be little more than an array lookup. Most of ExecInitExpr()'s work is done by the recursive function ExecInitExprRec() and its subroutines. ExecInitExprRec() maps one Expr node into the steps required for execution, recursing as needed for sub-expressions. Each ExecInitExprRec() call has to specify where that subexpression's results are to be stored (via the resv/resnull parameters). This allows the above scenario of evaluating a (sub-)expression directly into fcinfo->args[].value/isnull, but also requires some care: target Datum/isnull variables may not be shared with another ExecInitExprRec() unless the results are only needed by steps executing before further usages of those target Datum/isnull variables. Due to the non-recursiveness of the ExprEvalStep representation that's usually easy to guarantee. ExecInitExprRec() pushes new operations into the ExprState->steps array using ExprEvalPushStep(). To keep the steps as a consecutively laid out array, ExprEvalPushStep() has to repalloc the entire array when there's not enough space. Because of that it is *not* allowed to point directly into any of the steps during expression initialization. Therefore, the resv/resnull for a subexpression usually point to some storage that is palloc'd separately from the steps array. For instance, the FunctionCallInfoBaseData for a function call step is separately allocated rather than being part of the ExprEvalStep array. The overall result of a complete expression is typically returned into the resvalue/resnull fields of the ExprState node itself. Some steps, e.g. boolean expressions, allow skipping evaluation of certain subexpressions. In the flat representation this amounts to jumping to some later step rather than just continuing consecutively with the next step. The target for such a jump is represented by the integer index in the ExprState->steps array of the step to execute next. (Compare the EEO_NEXT and EEO_JUMP macros in execExprInterp.c.) Typically, ExecInitExprRec() has to push a jumping step into the steps array, then recursively generate steps for the subexpression that might get skipped over, then go back and fix up the jump target index using the now-known length of the subexpression's steps. This is handled by adjust_jumps lists in execExpr.c. The last step in constructing an ExprState is to apply ExecReadyExpr(), which readies it for execution using whichever execution method has been selected. Expression Evaluation --------------------- To allow for different methods of expression evaluation, and for better branch/jump target prediction, expressions are evaluated by calling ExprState->evalfunc (via ExecEvalExpr() and friends). ExecReadyExpr() can choose the method of interpretation by setting evalfunc to an appropriate function. The default execution function, ExecInterpExpr, is implemented in execExprInterp.c; see its header comment for details. Special-case evalfuncs are used for certain especially-simple expressions. Note that a lot of the more complex expression evaluation steps, which are less performance-critical than the simpler ones, are implemented as separate functions outside the fast-path of expression execution, allowing their implementation to be shared between interpreted and compiled expression evaluation. This means that these helper functions are not allowed to perform expression step dispatch themselves, as the method of dispatch will vary based on the caller. The helpers therefore cannot call for the execution of subexpressions; all subexpression results they need must be computed by earlier steps. And dispatch to the following expression step must be performed after returning from the helper. Targetlist Evaluation --------------------- ExecBuildProjectionInfo builds an ExprState that has the effect of evaluating a targetlist into ExprState->resultslot. A generic targetlist expression is executed by evaluating it as discussed above (storing the result into the ExprState's resvalue/resnull fields) and then using an EEOP_ASSIGN_TMP step to move the result into the appropriate tts_values[] and tts_isnull[] array elements of the result slot. There are special fast-path step types (EEOP_ASSIGN_*_VAR) to handle targetlist entries that are simple Vars using only one step instead of two. MERGE ----- MERGE is a multiple-table, multiple-action command: It specifies a target table and a source relation, and can contain multiple WHEN MATCHED and WHEN NOT MATCHED clauses, each of which specifies one UPDATE, INSERT, DELETE, or DO NOTHING actions. The target table is modified by MERGE, and the source relation supplies additional data for the actions. Each action optionally specifies a qualifying expression that is evaluated for each tuple. In the planner, transform_MERGE_to_join constructs a join between the target table and the source relation, with row-identifying junk columns from the target table. This join is an outer join if the MERGE command contains any WHEN NOT MATCHED clauses; the ModifyTable node fetches tuples from the plan tree of that join. If the row-identifying columns in the fetched tuple are NULL, then the source relation contains a tuple that is not matched by any tuples in the target table, so the qualifying expression for each WHEN NOT MATCHED clause is evaluated given that tuple as returned by the plan. If the expression returns true, the action indicated by the clause is executed, and no further clauses are evaluated. On the other hand, if the row-identifying columns are not NULL, then the matching tuple from the target table can be fetched; qualifying expression of each WHEN MATCHED clause is evaluated given both the fetched tuple and the tuple returned by the plan. If no WHEN NOT MATCHED clauses are present, then the join constructed by the planner is an inner join, and the row-identifying junk columns are always non NULL. If WHEN MATCHED ends up processing a row that is concurrently updated or deleted, EvalPlanQual (see below) is used to find the latest version of the row, and that is re-fetched; if it exists, the search for a matching WHEN MATCHED clause to use starts at the top. MERGE does not allow its own type of triggers, but instead fires UPDATE, DELETE, and INSERT triggers: row triggers are fired for each row when an action is executed for that row. Statement triggers are fired always, regardless of whether any rows match the corresponding clauses. Memory Management ----------------- A "per query" memory context is created during CreateExecutorState(); all storage allocated during an executor invocation is allocated in that context or a child context. This allows easy reclamation of storage during executor shutdown --- rather than messing with retail pfree's and probable storage leaks, we just destroy the memory context. In particular, the plan state trees and expression state trees described in the previous section are allocated in the per-query memory context. To avoid intra-query memory leaks, most processing while a query runs is done in "per tuple" memory contexts, which are so-called because they are typically reset to empty once per tuple. Per-tuple contexts are usually associated with ExprContexts, and commonly each PlanState node has its own ExprContext to evaluate its qual and targetlist expressions in. Query Processing Control Flow ----------------------------- This is a sketch of control flow for full query processing: CreateQueryDesc ExecutorStart CreateExecutorState creates per-query context switch to per-query context to run ExecInitNode AfterTriggerBeginQuery ExecInitNode --- recursively scans plan tree ExecInitNode recurse into subsidiary nodes CreateExprContext creates per-tuple context ExecInitExpr ExecutorRun ExecProcNode --- recursively called in per-query context ExecEvalExpr --- called in per-tuple context ResetExprContext --- to free memory ExecutorFinish ExecPostprocessPlan --- run any unfinished ModifyTable nodes AfterTriggerEndQuery ExecutorEnd ExecEndNode --- recursively releases resources FreeExecutorState frees per-query context and child contexts FreeQueryDesc Per above comments, it's not really critical for ExecEndNode to free any memory; it'll all go away in FreeExecutorState anyway. However, we do need to be careful to close relations, drop buffer pins, etc, so we do need to scan the plan state tree to find these sorts of resources. The executor can also be used to evaluate simple expressions without any Plan tree ("simple" meaning "no aggregates and no sub-selects", though such might be hidden inside function calls). This case has a flow of control like CreateExecutorState creates per-query context CreateExprContext -- or use GetPerTupleExprContext(estate) creates per-tuple context ExecPrepareExpr temporarily switch to per-query context run the expression through expression_planner ExecInitExpr Repeatedly do: ExecEvalExprSwitchContext ExecEvalExpr --- called in per-tuple context ResetExprContext --- to free memory FreeExecutorState frees per-query context, as well as ExprContext (a separate FreeExprContext call is not necessary) EvalPlanQual (READ COMMITTED Update Checking) --------------------------------------------- For simple SELECTs, the executor need only pay attention to tuples that are valid according to the snapshot seen by the current transaction (ie, they were inserted by a previously committed transaction, and not deleted by any previously committed transaction). However, for UPDATE, DELETE, and MERGE it is not cool to modify or delete a tuple that's been modified by an open or concurrently-committed transaction. If we are running in SERIALIZABLE isolation level then we just raise an error when this condition is seen to occur. In READ COMMITTED isolation level, we must work a lot harder. The basic idea in READ COMMITTED mode is to take the modified tuple committed by the concurrent transaction (after waiting for it to commit, if need be) and re-evaluate the query qualifications to see if it would still meet the quals. If so, we regenerate the updated tuple (if we are doing an UPDATE) from the modified tuple, and finally update/delete the modified tuple. SELECT FOR UPDATE/SHARE behaves similarly, except that its action is just to lock the modified tuple and return results based on that version of the tuple. To implement this checking, we actually re-run the query from scratch for each modified tuple (or set of tuples, for SELECT FOR UPDATE), with the relation scan nodes tweaked to return only the current tuples --- either the original ones, or the updated (and now locked) versions of the modified tuple(s). If this query returns a tuple, then the modified tuple(s) pass the quals (and the query output is the suitably modified update tuple, if we're doing UPDATE). If no tuple is returned, then the modified tuple(s) fail the quals, so we ignore the current result tuple and continue the original query. In UPDATE/DELETE/MERGE, only the target relation needs to be handled this way. In SELECT FOR UPDATE, there may be multiple relations flagged FOR UPDATE, so we obtain lock on the current tuple version in each such relation before executing the recheck. It is also possible that there are relations in the query that are not to be locked (they are neither the UPDATE/DELETE/MERGE target nor specified to be locked in SELECT FOR UPDATE/SHARE). When re-running the test query we want to use the same rows from these relations that were joined to the locked rows. For ordinary relations this can be implemented relatively cheaply by including the row TID in the join outputs and re-fetching that TID. (The re-fetch is expensive, but we're trying to optimize the normal case where no re-test is needed.) We have also to consider non-table relations, such as a ValuesScan or FunctionScan. For these, since there is no equivalent of TID, the only practical solution seems to be to include the entire row value in the join output row. We disallow set-returning functions in the targetlist of SELECT FOR UPDATE, so as to ensure that at most one tuple can be returned for any particular set of scan tuples. Otherwise we'd get duplicates due to the original query returning the same set of scan tuples multiple times. Likewise, SRFs are disallowed in an UPDATE's targetlist. There, they would have the effect of the same row being updated multiple times, which is not very useful --- and updates after the first would have no effect anyway. Asynchronous Execution ---------------------- In cases where a node is waiting on an event external to the database system, such as a ForeignScan awaiting network I/O, it's desirable for the node to indicate that it cannot return any tuple immediately but may be able to do so at a later time. A process which discovers this type of situation can always handle it simply by blocking, but this may waste time that could be spent executing some other part of the plan tree where progress could be made immediately. This is particularly likely to occur when the plan tree contains an Append node. Asynchronous execution runs multiple parts of an Append node concurrently rather than serially to improve performance. For asynchronous execution, an Append node must first request a tuple from an async-capable child node using ExecAsyncRequest. Next, it must execute the asynchronous event loop using ExecAppendAsyncEventWait. Eventually, when a child node to which an asynchronous request has been made produces a tuple, the Append node will receive it from the event loop via ExecAsyncResponse. In the current implementation of asynchronous execution, the only node type that requests tuples from an async-capable child node is an Append, while the only node type that might be async-capable is a ForeignScan. Typically, the ExecAsyncResponse callback is the only one required for nodes that wish to request tuples asynchronously. On the other hand, async-capable nodes generally need to implement three methods: 1. When an asynchronous request is made, the node's ExecAsyncRequest callback will be invoked; it should use ExecAsyncRequestPending to indicate that the request is pending for a callback described below. Alternatively, it can instead use ExecAsyncRequestDone if a result is available immediately. 2. When the event loop wishes to wait or poll for file descriptor events, the node's ExecAsyncConfigureWait callback will be invoked to configure the file descriptor event for which the node wishes to wait. 3. When the file descriptor becomes ready, the node's ExecAsyncNotify callback will be invoked; like #1, it should use ExecAsyncRequestPending for another callback or ExecAsyncRequestDone to return a result immediately.