arch-dev.sgml

PostgreSQL 8.1.4的源码 适用于Linux下的开源数据库系统

     call.  This may be transformed to either a <structname>FuncExpr</>
     or <structname>Aggref</> node depending on whether the referenced
     name turns out to be an ordinary function or an aggregate function.
     Also, information about the actual data types of columns and expression
     results is added to the query tree.
    </para>
   </sect2>
  </sect1>

  <sect1 id="rule-system">
   <title>The <productname>PostgreSQL</productname> Rule System</title>

   <para>
    <productname>PostgreSQL</productname> supports a powerful
    <firstterm>rule system</firstterm> for the specification
    of <firstterm>views</firstterm> and ambiguous <firstterm>view updates</firstterm>.
    Originally the <productname>PostgreSQL</productname>
    rule system consisted of two implementations:

    <itemizedlist>
     <listitem>
      <para>
       The first one worked using <firstterm>row level</firstterm> processing and was
       implemented deep in the <firstterm>executor</firstterm>. The rule system was
       called whenever an individual row had been accessed. This
       implementation was removed in 1995 when the last official release
       of the <productname>Berkeley Postgres</productname> project was
       transformed into <productname>Postgres95</productname>. 
      </para>
     </listitem>

     <listitem>
      <para>
       The second implementation of the rule system is a technique
       called <firstterm>query rewriting</firstterm>.
       The <firstterm>rewrite system</firstterm> is a module
       that exists between the <firstterm>parser stage</firstterm> and the
       <firstterm>planner/optimizer</firstterm>. This technique is still implemented.
      </para>
     </listitem>
    </itemizedlist>
   </para>

   <para>
    The query rewriter is discussed in some detail in
    <xref linkend="rules">, so there is no need to cover it here.
    We will only point out that both the input and the output of the
    rewriter are query trees, that is, there is no change in the
    representation or level of semantic detail in the trees.  Rewriting
    can be thought of as a form of macro expansion.
   </para>

  </sect1>

  <sect1 id="planner-optimizer">
   <title>Planner/Optimizer</title>

   <para>
    The task of the <firstterm>planner/optimizer</firstterm> is to
    create an optimal execution plan. A given SQL query (and hence, a
    query tree) can be actually executed in a wide variety of
    different ways, each of which will produce the same set of
    results.  If it is computationally feasible, the query optimizer
    will examine each of these possible execution plans, ultimately
    selecting the execution plan that is expected to run the fastest.
   </para>

   <note>
    <para>
     In some situations, examining each possible way in which a query
     may be executed would take an excessive amount of time and memory
     space. In particular, this occurs when executing queries
     involving large numbers of join operations. In order to determine
     a reasonable (not optimal) query plan in a reasonable amount of
     time, <productname>PostgreSQL</productname> uses a <xref
     linkend="geqo" endterm="geqo-title">.
    </para>
   </note>

   <para>
    The planner's search procedure actually works with data structures
    called <firstterm>paths</>, which are simply cut-down representations of
    plans containing only as much information as the planner needs to make
    its decisions. After the cheapest path is determined, a full-fledged
    <firstterm>plan tree</> is built to pass to the executor.  This represents
    the desired execution plan in sufficient detail for the executor to run it.
    In the rest of this section we'll ignore the distinction between paths
    and plans.
   </para>

   <sect2>
    <title>Generating Possible Plans</title>

    <para>
     The planner/optimizer starts by generating plans for scanning each
     individual relation (table) used in the query.  The possible plans
     are determined by the available indexes on each relation.
     There is always the possibility of performing a
     sequential scan on a relation, so a sequential scan plan is always
     created. Assume an index is defined on a
     relation (for example a B-tree index) and a query contains the
     restriction
     <literal>relation.attribute OPR constant</literal>. If
     <literal>relation.attribute</literal> happens to match the key of the B-tree
     index and <literal>OPR</literal> is one of the operators listed in
     the index's <firstterm>operator class</>, another plan is created using
     the B-tree index to scan the relation. If there are further indexes
     present and the restrictions in the query happen to match a key of an
     index further plans will be considered.
    </para>

    <para>
     After all feasible plans have been found for scanning single relations,
     plans for joining relations are created. The planner/optimizer
     preferentially considers joins between any two relations for which there
     exist a corresponding join clause in the <literal>WHERE</literal> qualification (i.e. for
     which a restriction like <literal>where rel1.attr1=rel2.attr2</literal>
     exists). Join pairs with no join clause are considered only when there
     is no other choice, that is, a particular relation has no available
     join clauses to any other relation. All possible plans are generated for
     every join pair considered
     by the planner/optimizer. The three possible join strategies are:

     <itemizedlist>
      <listitem>
       <para>
        <firstterm>nested loop join</firstterm>: The right relation is scanned
        once for every row found in the left relation. This strategy
        is easy to implement but can be very time consuming.  (However,
        if the right relation can be scanned with an index scan, this can
        be a good strategy.  It is possible to use values from the current
        row of the left relation as keys for the index scan of the right.)
       </para>
      </listitem>

      <listitem>
       <para>
        <firstterm>merge sort join</firstterm>: Each relation is sorted on the join
        attributes before the join starts. Then the two relations are
        scanned in parallel, and matching rows are combined to form
        join rows. This kind of join is more
        attractive because each relation has to be scanned only once.
        The required sorting may be achieved either by an explicit sort
        step, or by scanning the relation in the proper order using an
        index on the join key.
       </para>
      </listitem>

      <listitem>
       <para>
        <firstterm>hash join</firstterm>: the right relation is first scanned
        and loaded into a hash table, using its join attributes as hash keys.
        Next the left relation is scanned and the
        appropriate values of every row found are used as hash keys to
        locate the matching rows in the table.
       </para>
      </listitem>
     </itemizedlist>
    </para>

    <para>
     When the query involves more than two relations, the final result
     must be built up by a tree of join steps, each with two inputs.
     The planner examines different possible join sequences to find the
     cheapest one.
    </para>

    <para>
     The finished plan tree consists of sequential or index scans of
     the base relations, plus nested-loop, merge, or hash join nodes as
     needed, plus any auxiliary steps needed, such as sort nodes or
     aggregate-function calculation nodes.  Most of these plan node
     types have the additional ability to do <firstterm>selection</>
     (discarding rows that do not meet a specified boolean condition)
     and <firstterm>projection</> (computation of a derived column set
     based on given column values, that is, evaluation of scalar
     expressions where needed).  One of the responsibilities of the
     planner is to attach selection conditions from the
     <literal>WHERE</literal> clause and computation of required
     output expressions to the most appropriate nodes of the plan
     tree.
    </para>
   </sect2>
  </sect1>

  <sect1 id="executor">
   <title>Executor</title>

   <para>
    The <firstterm>executor</firstterm> takes the plan handed back by the
    planner/optimizer and recursively processes it to extract the required set
    of rows.  This is essentially a demand-pull pipeline mechanism.
    Each time a plan node is called, it must deliver one more row, or
    report that it is done delivering rows.
   </para>

   <para>
    To provide a concrete example, assume that the top
    node is a <literal>MergeJoin</literal> node. 
    Before any merge can be done two rows have to be fetched (one from
    each subplan). So the executor recursively calls itself to
    process the subplans (it starts with the subplan attached to
    <literal>lefttree</literal>). The new top node (the top node of the left
    subplan) is, let's say, a
    <literal>Sort</literal> node and again recursion is needed to obtain
    an input row.  The child node of the <literal>Sort</literal> might
    be a <literal>SeqScan</> node, representing actual reading of a table.
    Execution of this node causes the executor to fetch a row from the
    table and return it up to the calling node.  The <literal>Sort</literal>
    node will repeatedly call its child to obtain all the rows to be sorted.
    When the input is exhausted (as indicated by the child node returning
    a NULL instead of a row), the <literal>Sort</literal> code performs
    the sort, and finally is able to return its first output row, namely
    the first one in sorted order.  It keeps the remaining rows stored so
    that it can deliver them in sorted order in response to later demands.
   </para>

   <para>
    The <literal>MergeJoin</literal> node similarly demands the first row
    from its right subplan.  Then it compares the two rows to see if they
    can be joined; if so, it returns a join row to its caller.  On the next
    call, or immediately if it cannot join the current pair of inputs,
    it advances to the next row of one table
    or the other (depending on how the comparison came out), and again
    checks for a match.  Eventually, one subplan or the other is exhausted,
    and the <literal>MergeJoin</literal> node returns NULL to indicate that
    no more join rows can be formed.
   </para>

   <para>
    Complex queries may involve many levels of plan nodes, but the general
    approach is the same: each node computes and returns its next output
    row each time it is called.  Each node is also responsible for applying
    any selection or projection expressions that were assigned to it by
    the planner.
   </para>

   <para>
    The executor mechanism is used to evaluate all four basic SQL query types:
    <command>SELECT</>, <command>INSERT</>, <command>UPDATE</>, and
    <command>DELETE</>.  For <command>SELECT</>, the top-level executor
    code only needs to send each row returned by the query plan tree off
    to the client.  For <command>INSERT</>, each returned row is inserted
    into the target table specified for the <command>INSERT</>.  (A simple
    <command>INSERT ... VALUES</> command creates a trivial plan tree
    consisting of a single <literal>Result</> node, which computes just one
    result row.  But <command>INSERT ... SELECT</> may demand the full power
    of the executor mechanism.)  For <command>UPDATE</>, the planner arranges
    that each computed row includes all the updated column values, plus
    the <firstterm>TID</> (tuple ID, or row ID) of the original target row;
    the executor top level uses this information to create a new updated row
    and mark the old row deleted.  For <command>DELETE</>, the only column
    that is actually returned by the plan is the TID, and the executor top
    level simply uses the TID to visit each target row and mark it deleted.
   </para>

  </sect1>

 </chapter>

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