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executor

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    Tom Lane authored
    EXPLAIN ANALYZE.  (Premature optimization is the root of all evil?)
    1dfbbd51
    History
    $Header: /cvsroot/pgsql/src/backend/executor/README,v 1.3 2002/12/15 16:17:45 tgl Exp $
    
    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).
    
    The plan tree concept implements SELECT directly: it is only necessary to
    deliver the top-level result tuples to the client, or insert them into
    another table in the case of INSERT ... SELECT.  (INSERT ... VALUES is
    handled similarly, but the plan tree is just a Result node with no source
    tables.)  For UPDATE, the plan tree selects the tuples that need to be
    updated (WHERE condition) and delivers a new calculated tuple value for each
    such tuple, plus a "junk" (hidden) tuple CTID identifying the target tuple.
    The executor's top level then uses this information to update the correct
    tuple.  DELETE is similar to UPDATE except that only a CTID need be
    delivered by the plan tree.
    
    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).  Each Plan node may have expression trees
    associated with it, to represent its target list, qualification conditions,
    etc.  During executor startup we build a parallel tree of identical structure
    containing executor state nodes --- every plan and expression 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 as 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.
    
    Altogether there are four classes of nodes used in these trees: Plan nodes,
    their corresponding PlanState nodes, Expr nodes, and their corresponding
    ExprState nodes.  (Actually, there are also List nodes, which are used as
    "glue" in all four kinds of tree.)
    
    
    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
    		ExecInitNode --- recursively scans plan tree
    			CreateExprContext
    				creates per-tuple context
    			ExecInitExpr
    
    	ExecutorRun
    		ExecProcNode --- recursively called in per-query context
    			ExecEvalExpr --- called in per-tuple context
    			ResetExprContext --- to free memory
    
    	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
    		switch to per-query context to run ExecInitExpr
    		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 and DELETE 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 behaves similarly, except that its action
    is just to mark the modified tuple for update by the current transaction.
    
    To implement this checking, we actually re-run the entire query from scratch
    for each modified tuple, but with the scan node that sourced the original
    tuple set to return only the modified tuple, not the original tuple or any
    of the rest of the relation.  If this query returns a tuple, then the
    modified tuple passes 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 fails the quals, so we ignore it and continue the
    original query.  (This is reasonably efficient for simple queries, but may
    be horribly slow for joins.  A better design would be nice; one thought for
    future investigation is to treat the tuple substitution like a parameter,
    so that we can avoid rescanning unrelated nodes.)
    
    Note a fundamental bogosity of this approach: if the relation containing
    the original tuple is being used in a self-join, the other instance(s) of
    the relation will be treated as still containing the original tuple, whereas
    logical consistency would demand that the modified tuple appear in them too.
    But we'd have to actually substitute the modified tuple for the original,
    while still returning all the rest of the relation, to ensure consistent
    answers.  Implementing this correctly is a task for future work.
    
    In UPDATE/DELETE, only the target relation needs to be handled this way,
    so only one special recheck query needs to execute at a time.  In SELECT FOR
    UPDATE, there may be multiple relations flagged FOR UPDATE, so it's possible
    that while we are executing a recheck query for one modified tuple, we will
    hit another modified tuple in another relation.  In this case we "stack up"
    recheck queries: a sub-recheck query is spawned in which both the first and
    second modified tuples will be returned as the only components of their
    relations.  (In event of success, all these modified tuples will be marked
    for update.)  Again, this isn't necessarily quite the right thing ... but in
    simple cases it works.  Potentially, recheck queries could get nested to the
    depth of the number of FOR UPDATE relations in the query.
    
    It should be noted also that UPDATE/DELETE expect at most one tuple to
    result from the modified query, whereas in the FOR UPDATE case it's possible
    for multiple tuples to result (since we could be dealing with a join in
    which multiple tuples join to the modified tuple).  We want FOR UPDATE to
    mark all relevant tuples, so we pass all tuples output by all the stacked
    recheck queries back to the executor toplevel for marking.