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><DIV
CLASS="SECT1"
><H1
CLASS="SECT1"
><A
NAME="USING-EXPLAIN"
>14.1. Using <TT
CLASS="COMMAND"
>EXPLAIN</TT
></A
></H1
><P
>    <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> devises a <I
CLASS="FIRSTTERM"
>query
    plan</I
> for each query it receives.  Choosing the right
    plan to match the query structure and the properties of the data
    is absolutely critical for good performance, so the system includes
    a complex <I
CLASS="FIRSTTERM"
>planner</I
> that tries to choose good plans.
    You can use the
    <A
HREF="sql-explain.html"
>EXPLAIN</A
> command
    to see what query plan the planner creates for any query.
    Plan-reading is an art that deserves an extensive tutorial, which
    this is not; but here is some basic information.
   </P
><P
>    The structure of a query plan is a tree of <I
CLASS="FIRSTTERM"
>plan nodes</I
>.
    Nodes at the bottom level of the tree are table scan nodes: they return raw rows
    from a table.  There are different types of scan nodes for different
    table access methods: sequential scans, index scans, and bitmap index
    scans.  If the query requires joining, aggregation, sorting, or other
    operations on the raw rows, then there will be additional nodes
    above the scan nodes to perform these operations.  Again,
    there is usually more than one possible way to do these operations,
    so different node types can appear here too.  The output
    of <TT
CLASS="COMMAND"
>EXPLAIN</TT
> has one line for each node in the plan
    tree, showing the basic node type plus the cost estimates that the planner
    made for the execution of that plan node.  The first line (topmost node)
    has the estimated total execution cost for the plan; it is this number
    that the planner seeks to minimize.
   </P
><P
>    Here is a trivial example, just to show what the output looks like:
    <A
NAME="AEN23006"
HREF="#FTN.AEN23006"
><SPAN
CLASS="footnote"
>[1]</SPAN
></A
>

</P><PRE
CLASS="PROGRAMLISTING"
>EXPLAIN SELECT * FROM tenk1;

                         QUERY PLAN
-------------------------------------------------------------
 Seq Scan on tenk1  (cost=0.00..458.00 rows=10000 width=244)</PRE
><P>
   </P
><P
>    The numbers that are quoted by <TT
CLASS="COMMAND"
>EXPLAIN</TT
> are (left
    to right):

    <P
></P
></P><UL
><LI
><P
>       Estimated start-up cost (time expended before the output scan can start,
       e.g., time to do the sorting in a sort node)
      </P
></LI
><LI
><P
>       Estimated total cost (if all rows are retrieved, though they might
       not be; e.g., a query with a <TT
CLASS="LITERAL"
>LIMIT</TT
> clause will stop
       short of paying the total cost of the <TT
CLASS="LITERAL"
>Limit</TT
> plan node's
       input node)
      </P
></LI
><LI
><P
>       Estimated number of rows output by this plan node (again, only if
       executed to completion)
      </P
></LI
><LI
><P
>       Estimated average width (in bytes) of rows output by this plan
       node
      </P
></LI
></UL
><P>
   </P
><P
>    The costs are measured in arbitrary units determined by the planner's
    cost parameters (see <A
HREF="runtime-config-query.html#RUNTIME-CONFIG-QUERY-CONSTANTS"
>Section 18.7.2</A
>).
    Traditional practice is to measure the costs in units of disk page
    fetches; that is, <A
HREF="runtime-config-query.html#GUC-SEQ-PAGE-COST"
>seq_page_cost</A
> is conventionally
    set to <TT
CLASS="LITERAL"
>1.0</TT
> and the other cost parameters are set relative
    to that.  (The examples in this section are run with the default cost
    parameters.)
   </P
><P
>    It's important to note that the cost of an upper-level node includes
    the cost of all its child nodes.  It's also important to realize that
    the cost only reflects things that the planner cares about.
    In particular, the cost does not consider the time spent transmitting
    result rows to the client, which could be an important
    factor in the real elapsed time; but the planner ignores it because
    it cannot change it by altering the plan.  (Every correct plan will
    output the same row set, we trust.)
   </P
><P
>    The <TT
CLASS="LITERAL"
>rows</TT
> value is a little tricky
    because it is <SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>not</I
></SPAN
> the
    number of rows processed or scanned by the plan node.  It is usually less,
    reflecting the estimated selectivity of any <TT
CLASS="LITERAL"
>WHERE</TT
>-clause
    conditions that are being
    applied at the node.  Ideally the top-level rows estimate will
    approximate the number of rows actually returned, updated, or deleted
    by the query.
   </P
><P
>    Returning to our example:

</P><PRE
CLASS="PROGRAMLISTING"
>EXPLAIN SELECT * FROM tenk1;

                         QUERY PLAN
-------------------------------------------------------------
 Seq Scan on tenk1  (cost=0.00..458.00 rows=10000 width=244)</PRE
><P>
   </P
><P
>    This is about as straightforward as it gets.  If you do:

</P><PRE
CLASS="PROGRAMLISTING"
>SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1';</PRE
><P>

    you will find that <CODE
CLASS="CLASSNAME"
>tenk1</CODE
> has 358 disk
    pages and 10000 rows.  The estimated cost is computed as (disk pages read *
    <A
HREF="runtime-config-query.html#GUC-SEQ-PAGE-COST"
>seq_page_cost</A
>) + (rows scanned *
    <A
HREF="runtime-config-query.html#GUC-CPU-TUPLE-COST"
>cpu_tuple_cost</A
>).  By default,
    <TT
CLASS="VARNAME"
>seq_page_cost</TT
> is 1.0 and <TT
CLASS="VARNAME"
>cpu_tuple_cost</TT
> is 0.01,
    so the estimated cost is (358 * 1.0) + (10000 * 0.01) = 458.
   </P
><P
>    Now let's modify the original query to add a <TT
CLASS="LITERAL"
>WHERE</TT
> condition:

</P><PRE
CLASS="PROGRAMLISTING"
>EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 7000;

                         QUERY PLAN
------------------------------------------------------------
 Seq Scan on tenk1  (cost=0.00..483.00 rows=7033 width=244)
   Filter: (unique1 &lt; 7000)</PRE
><P>

    Notice that the <TT
CLASS="COMMAND"
>EXPLAIN</TT
> output shows the <TT
CLASS="LITERAL"
>WHERE</TT
>
    clause being applied as a <SPAN
CLASS="QUOTE"
>"filter"</SPAN
> condition; this means that
    the plan node checks the condition for each row it scans, and outputs
    only the ones that pass the condition.
    The estimate of output rows has been reduced because of the <TT
CLASS="LITERAL"
>WHERE</TT
>
    clause.
    However, the scan will still have to visit all 10000 rows, so the cost
    hasn't decreased; in fact it has gone up a bit (by 10000 * <A
HREF="runtime-config-query.html#GUC-CPU-OPERATOR-COST"
>cpu_operator_cost</A
>, to be exact) to reflect the extra CPU
    time spent checking the <TT
CLASS="LITERAL"
>WHERE</TT
> condition.
   </P
><P
>    The actual number of rows this query would select is 7000, but the <TT
CLASS="LITERAL"
>rows</TT
>
    estimate is only approximate.  If you try to duplicate this experiment,
    you will probably get a slightly different estimate; moreover, it will
    change after each <TT
CLASS="COMMAND"
>ANALYZE</TT
> command, because the
    statistics produced by <TT
CLASS="COMMAND"
>ANALYZE</TT
> are taken from a
    randomized sample of the table.
   </P
><P
>    Now, let's make the condition more restrictive:

</P><PRE
CLASS="PROGRAMLISTING"
>EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 100;

                                  QUERY PLAN
------------------------------------------------------------------------------
 Bitmap Heap Scan on tenk1  (cost=2.37..232.35 rows=106 width=244)
   Recheck Cond: (unique1 &lt; 100)
   -&#62;  Bitmap Index Scan on tenk1_unique1  (cost=0.00..2.37 rows=106 width=0)
         Index Cond: (unique1 &lt; 100)</PRE
><P>

    Here the planner has decided to use a two-step plan: the bottom plan
    node visits an index to find the locations of rows matching the index
    condition, and then the upper plan node actually fetches those rows
    from the table itself.  Fetching the rows separately is much more
    expensive than sequentially reading them, but because not all the pages
    of the table have to be visited, this is still cheaper than a sequential
    scan.  (The reason for using two plan levels is that the upper plan
    node sorts the row locations identified by the index into physical order
    before reading them, to minimize the cost of separate fetches.
    The <SPAN
CLASS="QUOTE"
>"bitmap"</SPAN
> mentioned in the node names is the mechanism that
    does the sorting.)
   </P
><P
>    If the <TT
CLASS="LITERAL"
>WHERE</TT
> condition is selective enough, the planner might
    switch to a <SPAN
CLASS="QUOTE"
>"simple"</SPAN
> index scan plan:

</P><PRE
CLASS="PROGRAMLISTING"
>EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 3;

                                  QUERY PLAN
------------------------------------------------------------------------------
 Index Scan using tenk1_unique1 on tenk1  (cost=0.00..10.00 rows=2 width=244)
   Index Cond: (unique1 &lt; 3)</PRE
><P>

    In this case the table rows are fetched in index order, which makes them
    even more expensive to read, but there are so few that the extra cost
    of sorting the row locations is not worth it.  You'll most often see
    this plan type for queries that fetch just a single row, and for queries
    that have an <TT
CLASS="LITERAL"
>ORDER BY</TT
> condition that matches the index
    order.
   </P
><P
>    Add another condition to the <TT
CLASS="LITERAL"
>WHERE</TT
> clause:

</P><PRE
CLASS="PROGRAMLISTING"
>EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 3 AND stringu1 = 'xxx';

                                  QUERY PLAN
------------------------------------------------------------------------------
 Index Scan using tenk1_unique1 on tenk1  (cost=0.00..10.01 rows=1 width=244)
   Index Cond: (unique1 &lt; 3)
   Filter: (stringu1 = 'xxx'::name)</PRE
><P>

    The added condition <TT
CLASS="LITERAL"
>stringu1 = 'xxx'</TT
> reduces the
    output-rows estimate, but not the cost because we still have to visit the
    same set of rows.  Notice that the <TT
CLASS="LITERAL"
>stringu1</TT
> clause
    cannot be applied as an index condition (since this index is only on
    the <TT
CLASS="LITERAL"
>unique1</TT
> column).  Instead it is applied as a filter on
    the rows retrieved by the index.  Thus the cost has actually gone up
    slightly to reflect this extra checking.
   </P
><P
>    If there are indexes on several columns referenced in <TT
CLASS="LITERAL"
>WHERE</TT
>, the
    planner might choose to use an AND or OR combination of the indexes:

</P><PRE
CLASS="PROGRAMLISTING"
>EXPLAIN SELECT * FROM tenk1 WHERE unique1 &lt; 100 AND unique2 &gt; 9000;

                                     QUERY PLAN
-------------------------------------------------------------------------------------
 Bitmap Heap Scan on tenk1  (cost=11.27..49.11 rows=11 width=244)
   Recheck Cond: ((unique1 &lt; 100) AND (unique2 &gt; 9000))
   -&gt;  BitmapAnd  (cost=11.27..11.27 rows=11 width=0)
         -&gt;  Bitmap Index Scan on tenk1_unique1  (cost=0.00..2.37 rows=106 width=0)
               Index Cond: (unique1 &lt; 100)
         -&gt;  Bitmap Index Scan on tenk1_unique2  (cost=0.00..8.65 rows=1042 width=0)
               Index Cond: (unique2 &gt; 9000)</PRE
><P>

    But this requires visiting both indexes, so it's not necessarily a win
    compared to using just one index and treating the other condition as
    a filter.  If you vary the ranges involved you'll see the plan change
    accordingly.
   </P
><P
>    Let's try joining two tables, using the columns we have been discussing:

</P><PRE
CLASS="PROGRAMLISTING"
>EXPLAIN SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 &lt; 100 AND t1.unique2 = t2.unique2;

                                      QUERY PLAN
--------------------------------------------------------------------------------------
 Nested Loop  (cost=2.37..553.11 rows=106 width=488)
   -&gt;  Bitmap Heap Scan on tenk1 t1  (cost=2.37..232.35 rows=106 width=244)
         Recheck Cond: (unique1 &lt; 100)
         -&gt;  Bitmap Index Scan on tenk1_unique1  (cost=0.00..2.37 rows=106 width=0)
               Index Cond: (unique1 &lt; 100)
   -&gt;  Index Scan using tenk2_unique2 on tenk2 t2  (cost=0.00..3.01 rows=1 width=244)
         Index Cond: (unique2 = t1.unique2)</PRE
><P>
   </P
><P
>    In this nested-loop join, the outer (upper) scan is the same bitmap index scan we
    saw earlier, and so its cost and row count are the same because we are
    applying the <TT
CLASS="LITERAL"
>WHERE</TT
> clause <TT
CLASS="LITERAL"
>unique1 &lt; 100</TT
>
    at that node.
    The <TT
CLASS="LITERAL"
>t1.unique2 = t2.unique2</TT
> clause is not relevant yet,
    so it doesn't affect the row count of the outer scan.  For the inner (lower) scan, the
    <TT
CLASS="LITERAL"
>unique2</TT
> value of the current outer-scan row is plugged into
    the inner index scan to produce an index condition like
    <TT
CLASS="LITERAL"
>unique2 = <TT
CLASS="REPLACEABLE"
><I
>constant</I
></TT
></TT
>.
    So we get the same inner-scan plan and costs that we'd get from, say,
    <TT
CLASS="LITERAL"
>EXPLAIN SELECT * FROM tenk2 WHERE unique2 = 42</TT
>.  The
    costs of the loop node are then set on the basis of the cost of the outer
    scan, plus one repetition of the inner scan for each outer row (106 * 3.01,
    here), plus a little CPU time for join processing.
   </P
><P
>    In this example the join's output row count is the same as the product
    of the two scans' row counts, but that's not true in all cases because
    you can have <TT
CLASS="LITERAL"
>WHERE</TT
> clauses that mention both tables
    and so can only be applied at the join point, not to either input scan.
    For example, if we added
    <TT
CLASS="LITERAL"
>WHERE ... AND t1.hundred &lt; t2.hundred</TT
>,
    that would decrease the output row count of the join node, but not change
    either input scan.
   </P
><P
>    One way to look at variant plans is to force the planner to disregard
    whatever strategy it thought was the cheapest, using the enable/disable
    flags described in <A
HREF="runtime-config-query.html#RUNTIME-CONFIG-QUERY-ENABLE"
>Section 18.7.1</A
>.
    (This is a crude tool, but useful.  See
    also <A
HREF="explicit-joins.html"
>Section 14.3</A
>.)

</P><PRE
CLASS="PROGRAMLISTING"
>SET enable_nestloop = off;
EXPLAIN SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 &lt; 100 AND t1.unique2 = t2.unique2;

                                        QUERY PLAN
------------------------------------------------------------------------------------------
 Hash Join  (cost=232.61..741.67 rows=106 width=488)
   Hash Cond: (t2.unique2 = t1.unique2)
   -&gt;  Seq Scan on tenk2 t2  (cost=0.00..458.00 rows=10000 width=244)
   -&gt;  Hash  (cost=232.35..232.35 rows=106 width=244)
         -&gt;  Bitmap Heap Scan on tenk1 t1  (cost=2.37..232.35 rows=106 width=244)
               Recheck Cond: (unique1 &lt; 100)
               -&gt;  Bitmap Index Scan on tenk1_unique1  (cost=0.00..2.37 rows=106 width=0)
                     Index Cond: (unique1 &lt; 100)</PRE
><P>

    This plan proposes to extract the 100 interesting rows of <CODE
CLASS="CLASSNAME"
>tenk1</CODE
>
    using that same old index scan, stash them into an in-memory hash table,
    and then do a sequential scan of <CODE
CLASS="CLASSNAME"
>tenk2</CODE
>, probing into the hash table
    for possible matches of <TT
CLASS="LITERAL"
>t1.unique2 = t2.unique2</TT
> for each <CODE
CLASS="CLASSNAME"
>tenk2</CODE
> row.
    The cost to read <CODE
CLASS="CLASSNAME"
>tenk1</CODE
> and set up the hash table is a start-up
    cost for the hash join, since there will be no output until we can
    start reading <CODE
CLASS="CLASSNAME"
>tenk2</CODE
>.  The total time estimate for the join also
    includes a hefty charge for the CPU time to probe the hash table
    10000 times.  Note, however, that we are <SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>not</I
></SPAN
> charging 10000 times 232.35;
    the hash table setup is only done once in this plan type.
   </P
><P
>    It is possible to check the accuracy of the planner's estimated costs
    by using <TT
CLASS="COMMAND"
>EXPLAIN ANALYZE</TT
>.  This command actually executes the query,
    and then displays the true run time accumulated within each plan node
    along with the same estimated costs that a plain <TT
CLASS="COMMAND"
>EXPLAIN</TT
> shows.
    For example, we might get a result like this:

</P><PRE
CLASS="SCREEN"
>EXPLAIN ANALYZE SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 &lt; 100 AND t1.unique2 = t2.unique2;

                                                            QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------
 Nested Loop  (cost=2.37..553.11 rows=106 width=488) (actual time=1.392..12.700 rows=100 loops=1)
   -&gt;  Bitmap Heap Scan on tenk1 t1  (cost=2.37..232.35 rows=106 width=244) (actual time=0.878..2.367 rows=100 loops=1)
         Recheck Cond: (unique1 &lt; 100)
         -&gt;  Bitmap Index Scan on tenk1_unique1  (cost=0.00..2.37 rows=106 width=0) (actual time=0.546..0.546 rows=100 loops=1)
               Index Cond: (unique1 &lt; 100)
   -&gt;  Index Scan using tenk2_unique2 on tenk2 t2  (cost=0.00..3.01 rows=1 width=244) (actual time=0.067..0.078 rows=1 loops=100)
         Index Cond: (unique2 = t1.unique2)
 Total runtime: 14.452 ms</PRE
><P>

    Note that the <SPAN
CLASS="QUOTE"
>"actual time"</SPAN
> values are in milliseconds of
    real time, whereas the <TT
CLASS="LITERAL"
>cost</TT
> estimates are expressed in
    arbitrary units; so they are unlikely to match up.
    The thing to pay attention to is whether the ratios of actual time and
    estimated costs are consistent.
   </P
><P
>    In some query plans, it is possible for a subplan node to be executed more
    than once.  For example, the inner index scan is executed once per outer
    row in the above nested-loop plan.  In such cases, the
    <TT
CLASS="LITERAL"
>loops</TT
> value reports the
    total number of executions of the node, and the actual time and rows
    values shown are averages per-execution.  This is done to make the numbers
    comparable with the way that the cost estimates are shown.  Multiply by
    the <TT
CLASS="LITERAL"
>loops</TT
> value to get the total time actually spent in
    the node.
   </P
><P
>    The <TT
CLASS="LITERAL"
>Total runtime</TT
> shown by <TT
CLASS="COMMAND"
>EXPLAIN
    ANALYZE</TT
> includes executor start-up and shut-down time, but not
    parsing, rewriting, or planning time.  For <TT
CLASS="COMMAND"
>INSERT</TT
>,
    <TT
CLASS="COMMAND"
>UPDATE</TT
>, and <TT
CLASS="COMMAND"
>DELETE</TT
> commands, the time spent
    applying the table changes is charged to a top-level Insert, Update,
    or Delete plan node.  (The plan nodes underneath this node represent
    the work of locating the old rows and/or computing the new ones.)
    Time spent executing <TT
CLASS="LITERAL"
>BEFORE</TT
> triggers, if any, is charged to
    the related Insert, Update, or Delete node, although time spent executing
    <TT
CLASS="LITERAL"
>AFTER</TT
> triggers is not.  The time spent in each trigger
    (either <TT
CLASS="LITERAL"
>BEFORE</TT
> or <TT
CLASS="LITERAL"
>AFTER</TT
>) is also shown separately
    and is included in total run time.
    Note, however, that deferred constraint triggers will not be executed
    until end of transaction and are thus not shown by
    <TT
CLASS="COMMAND"
>EXPLAIN ANALYZE</TT
>.
   </P
><P
>    There are two significant ways in which run times measured by
    <TT
CLASS="COMMAND"
>EXPLAIN ANALYZE</TT
> can deviate from normal execution of
    the same query.  First, since no output rows are delivered to the client,
    network transmission costs and I/O formatting costs are not included.
    Second, the overhead added by <TT
CLASS="COMMAND"
>EXPLAIN ANALYZE</TT
> can be
    significant, especially on machines with slow <CODE
CLASS="FUNCTION"
>gettimeofday()</CODE
>
    kernel calls.
   </P
><P
>    It is worth noting that <TT
CLASS="COMMAND"
>EXPLAIN</TT
> results should not be extrapolated
    to situations other than the one you are actually testing; for example,
    results on a toy-sized table cannot be assumed to apply to large tables.
    The planner's cost estimates are not linear and so it might choose
    a different plan for a larger or smaller table.  An extreme example
    is that on a table that only occupies one disk page, you'll nearly
    always get a sequential scan plan whether indexes are available or not.
    The planner realizes that it's going to take one disk page read to
    process the table in any case, so there's no value in expending additional
    page reads to look at an index.
   </P
></DIV
><H3
CLASS="FOOTNOTES"
>Notes</H3
><TABLE
BORDER="0"
CLASS="FOOTNOTES"
WIDTH="100%"
><TR
><TD
ALIGN="LEFT"
VALIGN="TOP"
WIDTH="5%"
><A
NAME="FTN.AEN23006"
HREF="using-explain.html#AEN23006"
><SPAN
CLASS="footnote"
>[1]</SPAN
></A
></TD
><TD
ALIGN="LEFT"
VALIGN="TOP"
WIDTH="95%"
><P
>      Examples in this section are drawn from the regression test database
      after doing a <TT
CLASS="COMMAND"
>VACUUM ANALYZE</TT
>, using 8.2 development sources.
      You should be able to get similar results if you try the examples yourself,
      but your estimated costs and row counts might vary slightly
      because <TT
CLASS="COMMAND"
>ANALYZE</TT
>'s statistics are random samples rather
      than exact.
     </P
></TD
></TR
></TABLE
><DIV
CLASS="NAVFOOTER"
><HR
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><A
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>Prev</A
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>Performance Tips</TD
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