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14.1. Using EXPLAIN #

   14.1.1. EXPLAIN Basics
   14.1.2. EXPLAIN ANALYZE
   14.1.3. Caveats

   PostgreSQL devises a query plan 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 planner that tries to choose good plans. You can use
   the EXPLAIN command to see what query plan the planner creates for any
   query. Plan-reading is an art that requires some experience to master,
   but this section attempts to cover the basics.

   Examples in this section are drawn from the regression test database
   after doing a VACUUM ANALYZE, using v18 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
   ANALYZE's statistics are random samples rather than exact, and because
   costs are inherently somewhat platform-dependent.

   The examples use EXPLAIN's default “text” output format, which is
   compact and convenient for humans to read. If you want to feed
   EXPLAIN's output to a program for further analysis, you should use one
   of its machine-readable output formats (XML, JSON, or YAML) instead.

14.1.1. EXPLAIN Basics #

   The structure of a query plan is a tree of plan nodes. Nodes at the
   bottom level of the tree are 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.
   There are also non-table row sources, such as VALUES clauses and
   set-returning functions in FROM, which have their own scan node types.
   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 EXPLAIN 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.
   Additional lines might appear, indented from the node's summary line,
   to show additional properties of the node. The very first line (the
   summary line for the topmost node) has the estimated total execution
   cost for the plan; it is this number that the planner seeks to
   minimize.

   Here is a trivial example, just to show what the output looks like:
EXPLAIN SELECT * FROM tenk1;

                         QUERY PLAN
-------------------------------------------------------------
 Seq Scan on tenk1  (cost=0.00..445.00 rows=10000 width=244)

   Since this query has no WHERE clause, it must scan all the rows of the
   table, so the planner has chosen to use a simple sequential scan plan.
   The numbers that are quoted in parentheses are (left to right):
     * Estimated start-up cost. This is the time expended before the
       output phase can begin, e.g., time to do the sorting in a sort
       node.
     * Estimated total cost. This is stated on the assumption that the
       plan node is run to completion, i.e., all available rows are
       retrieved. In practice a node's parent node might stop short of
       reading all available rows (see the LIMIT example below).
     * Estimated number of rows output by this plan node. Again, the node
       is assumed to be run to completion.
     * Estimated average width of rows output by this plan node (in
       bytes).

   The costs are measured in arbitrary units determined by the planner's
   cost parameters (see Section 19.7.2). Traditional practice is to
   measure the costs in units of disk page fetches; that is, seq_page_cost
   is conventionally set to 1.0 and the other cost parameters are set
   relative to that. The examples in this section are run with the default
   cost parameters.

   It's important to understand 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 to
   convert output values to text form or to transmit them to the client,
   which could be important factors in the real elapsed time; but the
   planner ignores those costs because it cannot change them by altering
   the plan. (Every correct plan will output the same row set, we trust.)

   The rows value is a little tricky because it is not the number of rows
   processed or scanned by the plan node, but rather the number emitted by
   the node. This is often less than the number scanned, as a result of
   filtering by any WHERE-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.

   Returning to our example:
EXPLAIN SELECT * FROM tenk1;

                         QUERY PLAN
-------------------------------------------------------------
 Seq Scan on tenk1  (cost=0.00..445.00 rows=10000 width=244)

   These numbers are derived very straightforwardly. If you do:
SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1';

   you will find that tenk1 has 345 disk pages and 10000 rows. The
   estimated cost is computed as (disk pages read * seq_page_cost) + (rows
   scanned * cpu_tuple_cost). By default, seq_page_cost is 1.0 and
   cpu_tuple_cost is 0.01, so the estimated cost is (345 * 1.0) + (10000 *
   0.01) = 445.

   Now let's modify the query to add a WHERE condition:
EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 7000;

                         QUERY PLAN
------------------------------------------------------------
 Seq Scan on tenk1  (cost=0.00..470.00 rows=7000 width=244)
   Filter: (unique1 < 7000)

   Notice that the EXPLAIN output shows the WHERE clause being applied as
   a “filter” condition attached to the Seq Scan plan node. 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 WHERE 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 * cpu_operator_cost, to be
   exact) to reflect the extra CPU time spent checking the WHERE
   condition.

   The actual number of rows this query would select is 7000, but the rows
   estimate is only approximate. If you try to duplicate this experiment,
   you may well get a slightly different estimate; moreover, it can change
   after each ANALYZE command, because the statistics produced by ANALYZE
   are taken from a randomized sample of the table.

   Now, let's make the condition more restrictive:
EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100;

                                  QUERY PLAN
-------------------------------------------------------------------​-----------
 Bitmap Heap Scan on tenk1  (cost=5.06..224.98 rows=100 width=244)
   Recheck Cond: (unique1 < 100)
   ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=100 width=0)
         Index Cond: (unique1 < 100)

   Here the planner has decided to use a two-step plan: the child 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 rows separately is much more expensive
   than reading them sequentially, 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 “bitmap”
   mentioned in the node names is the mechanism that does the sorting.)

   Now let's add another condition to the WHERE clause:
EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND stringu1 = 'xxx';

                                  QUERY PLAN
-------------------------------------------------------------------​-----------
 Bitmap Heap Scan on tenk1  (cost=5.04..225.20 rows=1 width=244)
   Recheck Cond: (unique1 < 100)
   Filter: (stringu1 = 'xxx'::name)
   ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=100 width=0)
         Index Cond: (unique1 < 100)

   The added condition stringu1 = 'xxx' reduces the output row count
   estimate, but not the cost because we still have to visit the same set
   of rows. That's because the stringu1 clause cannot be applied as an
   index condition, since this index is only on the unique1 column.
   Instead it is applied as a filter on the rows retrieved using the
   index. Thus the cost has actually gone up slightly to reflect this
   extra checking.

   In some cases the planner will prefer a “simple” index scan plan:
EXPLAIN SELECT * FROM tenk1 WHERE unique1 = 42;

                                 QUERY PLAN
-------------------------------------------------------------------​----------
 Index Scan using tenk1_unique1 on tenk1  (cost=0.29..8.30 rows=1 width=244)
   Index Cond: (unique1 = 42)

   In this type of plan 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. It's
   also often used for queries that have an ORDER BY condition that
   matches the index order, because then no extra sorting step is needed
   to satisfy the ORDER BY. In this example, adding ORDER BY unique1 would
   use the same plan because the index already implicitly provides the
   requested ordering.

   The planner may implement an ORDER BY clause in several ways. The above
   example shows that such an ordering clause may be implemented
   implicitly. The planner may also add an explicit Sort step:
EXPLAIN SELECT * FROM tenk1 ORDER BY unique1;

                            QUERY PLAN
-------------------------------------------------------------------
 Sort  (cost=1109.39..1134.39 rows=10000 width=244)
   Sort Key: unique1
   ->  Seq Scan on tenk1  (cost=0.00..445.00 rows=10000 width=244)

   If a part of the plan guarantees an ordering on a prefix of the
   required sort keys, then the planner may instead decide to use an
   Incremental Sort step:
EXPLAIN SELECT * FROM tenk1 ORDER BY hundred, ten LIMIT 100;

                                              QUERY PLAN
-------------------------------------------------------------------​------------
-----------------
 Limit  (cost=19.35..39.49 rows=100 width=244)
   ->  Incremental Sort  (cost=19.35..2033.39 rows=10000 width=244)
         Sort Key: hundred, ten
         Presorted Key: hundred
         ->  Index Scan using tenk1_hundred on tenk1  (cost=0.29..1574.20 rows=1
0000 width=244)

   Compared to regular sorts, sorting incrementally allows returning
   tuples before the entire result set has been sorted, which particularly
   enables optimizations with LIMIT queries. It may also reduce memory
   usage and the likelihood of spilling sorts to disk, but it comes at the
   cost of the increased overhead of splitting the result set into
   multiple sorting batches.

   If there are separate indexes on several of the columns referenced in
   WHERE, the planner might choose to use an AND or OR combination of the
   indexes:
EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000;

                                     QUERY PLAN
-------------------------------------------------------------------​------------
------
 Bitmap Heap Scan on tenk1  (cost=25.07..60.11 rows=10 width=244)
   Recheck Cond: ((unique1 < 100) AND (unique2 > 9000))
   ->  BitmapAnd  (cost=25.07..25.07 rows=10 width=0)
         ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=100 width
=0)
               Index Cond: (unique1 < 100)
         ->  Bitmap Index Scan on tenk1_unique2  (cost=0.00..19.78 rows=999 widt
h=0)
               Index Cond: (unique2 > 9000)

   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.

   Here is an example showing the effects of LIMIT:
EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000 LIMIT 2;

                                     QUERY PLAN
-------------------------------------------------------------------​------------
------
 Limit  (cost=0.29..14.28 rows=2 width=244)
   ->  Index Scan using tenk1_unique2 on tenk1  (cost=0.29..70.27 rows=10 width=
244)
         Index Cond: (unique2 > 9000)
         Filter: (unique1 < 100)

   This is the same query as above, but we added a LIMIT so that not all
   the rows need be retrieved, and the planner changed its mind about what
   to do. Notice that the total cost and row count of the Index Scan node
   are shown as if it were run to completion. However, the Limit node is
   expected to stop after retrieving only a fifth of those rows, so its
   total cost is only a fifth as much, and that's the actual estimated
   cost of the query. This plan is preferred over adding a Limit node to
   the previous plan because the Limit could not avoid paying the startup
   cost of the bitmap scan, so the total cost would be something over 25
   units with that approach.

   Let's try joining two tables, using the columns we have been
   discussing:
EXPLAIN SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2;

                                      QUERY PLAN
-------------------------------------------------------------------​------------
-------
 Nested Loop  (cost=4.65..118.50 rows=10 width=488)
   ->  Bitmap Heap Scan on tenk1 t1  (cost=4.36..39.38 rows=10 width=244)
         Recheck Cond: (unique1 < 10)
         ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..4.36 rows=10 width=
0)
               Index Cond: (unique1 < 10)
   ->  Index Scan using tenk2_unique2 on tenk2 t2  (cost=0.29..7.90 rows=1 width
=244)
         Index Cond: (unique2 = t1.unique2)

   In this plan, we have a nested-loop join node with two table scans as
   inputs, or children. The indentation of the node summary lines reflects
   the plan tree structure. The join's first, or “outer”, child is a
   bitmap scan similar to those we saw before. Its cost and row count are
   the same as we'd get from SELECT ... WHERE unique1 < 10 because we are
   applying the WHERE clause unique1 < 10 at that node. The t1.unique2 =
   t2.unique2 clause is not relevant yet, so it doesn't affect the row
   count of the outer scan. The nested-loop join node will run its second,
   or “inner” child once for each row obtained from the outer child.
   Column values from the current outer row can be plugged into the inner
   scan; here, the t1.unique2 value from the outer row is available, so we
   get a plan and costs similar to what we saw above for a simple SELECT
   ... WHERE t2.unique2 = constant case. (The estimated cost is actually a
   bit lower than what was seen above, as a result of caching that's
   expected to occur during the repeated index scans on t2.) 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 (10 * 7.90,
   here), plus a little CPU time for join processing.

   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
   there can be additional WHERE clauses that mention both tables and so
   can only be applied at the join point, not to either input scan. Here's
   an example:
EXPLAIN SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 < 10 AND t2.unique2 < 10 AND t1.hundred < t2.hundred;

                                         QUERY PLAN
-------------------------------------------------------------------​------------
--------------
 Nested Loop  (cost=4.65..49.36 rows=33 width=488)
   Join Filter: (t1.hundred < t2.hundred)
   ->  Bitmap Heap Scan on tenk1 t1  (cost=4.36..39.38 rows=10 width=244)
         Recheck Cond: (unique1 < 10)
         ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..4.36 rows=10 width=
0)
               Index Cond: (unique1 < 10)
   ->  Materialize  (cost=0.29..8.51 rows=10 width=244)
         ->  Index Scan using tenk2_unique2 on tenk2 t2  (cost=0.29..8.46 rows=1
0 width=244)
               Index Cond: (unique2 < 10)

   The condition t1.hundred < t2.hundred can't be tested in the
   tenk2_unique2 index, so it's applied at the join node. This reduces the
   estimated output row count of the join node, but does not change either
   input scan.

   Notice that here the planner has chosen to “materialize” the inner
   relation of the join, by putting a Materialize plan node atop it. This
   means that the t2 index scan will be done just once, even though the
   nested-loop join node needs to read that data ten times, once for each
   row from the outer relation. The Materialize node saves the data in
   memory as it's read, and then returns the data from memory on each
   subsequent pass.

   When dealing with outer joins, you might see join plan nodes with both
   “Join Filter” and plain “Filter” conditions attached. Join Filter
   conditions come from the outer join's ON clause, so a row that fails
   the Join Filter condition could still get emitted as a null-extended
   row. But a plain Filter condition is applied after the outer-join rules
   and so acts to remove rows unconditionally. In an inner join there is
   no semantic difference between these types of filters.

   If we change the query's selectivity a bit, we might get a very
   different join plan:
EXPLAIN SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;

                                        QUERY PLAN
-------------------------------------------------------------------​------------
-----------
 Hash Join  (cost=226.23..709.73 rows=100 width=488)
   Hash Cond: (t2.unique2 = t1.unique2)
   ->  Seq Scan on tenk2 t2  (cost=0.00..445.00 rows=10000 width=244)
   ->  Hash  (cost=224.98..224.98 rows=100 width=244)
         ->  Bitmap Heap Scan on tenk1 t1  (cost=5.06..224.98 rows=100 width=244
)
               Recheck Cond: (unique1 < 100)
               ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=100
 width=0)
                     Index Cond: (unique1 < 100)

   Here, the planner has chosen to use a hash join, in which rows of one
   table are entered into an in-memory hash table, after which the other
   table is scanned and the hash table is probed for matches to each row.
   Again note how the indentation reflects the plan structure: the bitmap
   scan on tenk1 is the input to the Hash node, which constructs the hash
   table. That's then returned to the Hash Join node, which reads rows
   from its outer child plan and searches the hash table for each one.

   Another possible type of join is a merge join, illustrated here:
EXPLAIN SELECT *
FROM tenk1 t1, onek t2
WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;

                                        QUERY PLAN
-------------------------------------------------------------------​------------
-----------
 Merge Join  (cost=0.56..233.49 rows=10 width=488)
   Merge Cond: (t1.unique2 = t2.unique2)
   ->  Index Scan using tenk1_unique2 on tenk1 t1  (cost=0.29..643.28 rows=100 w
idth=244)
         Filter: (unique1 < 100)
   ->  Index Scan using onek_unique2 on onek t2  (cost=0.28..166.28 rows=1000 wi
dth=244)

   Merge join requires its input data to be sorted on the join keys. In
   this example each input is sorted by using an index scan to visit the
   rows in the correct order; but a sequential scan and sort could also be
   used. (Sequential-scan-and-sort frequently beats an index scan for
   sorting many rows, because of the nonsequential disk access required by
   the index scan.)

   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 Section 19.7.1. (This is a crude tool, but useful.
   See also Section 14.3.) For example, if we're unconvinced that merge
   join is the best join type for the previous example, we could try
SET enable_mergejoin = off;

EXPLAIN SELECT *
FROM tenk1 t1, onek t2
WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;

                                        QUERY PLAN
-------------------------------------------------------------------​------------
-----------
 Hash Join  (cost=226.23..344.08 rows=10 width=488)
   Hash Cond: (t2.unique2 = t1.unique2)
   ->  Seq Scan on onek t2  (cost=0.00..114.00 rows=1000 width=244)
   ->  Hash  (cost=224.98..224.98 rows=100 width=244)
         ->  Bitmap Heap Scan on tenk1 t1  (cost=5.06..224.98 rows=100 width=244
)
               Recheck Cond: (unique1 < 100)
               ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=100
 width=0)
                     Index Cond: (unique1 < 100)

   which shows that the planner thinks that hash join would be nearly 50%
   more expensive than merge join for this case. Of course, the next
   question is whether it's right about that. We can investigate that
   using EXPLAIN ANALYZE, as discussed below.

   When using the enable/disable flags to disable plan node types, many of
   the flags only discourage the use of the corresponding plan node and
   don't outright disallow the planner's ability to use the plan node
   type. This is by design so that the planner still maintains the ability
   to form a plan for a given query. When the resulting plan contains a
   disabled node, the EXPLAIN output will indicate this fact.
SET enable_seqscan = off;
EXPLAIN SELECT * FROM unit;

                       QUERY PLAN
---------------------------------------------------------
 Seq Scan on unit  (cost=0.00..21.30 rows=1130 width=44)
   Disabled: true

   Because the unit table has no indexes, there is no other means to read
   the table data, so the sequential scan is the only option available to
   the query planner.

   Some query plans involve subplans, which arise from sub-SELECTs in the
   original query. Such queries can sometimes be transformed into ordinary
   join plans, but when they cannot be, we get plans like:
EXPLAIN VERBOSE SELECT unique1
FROM tenk1 t
WHERE t.ten < ALL (SELECT o.ten FROM onek o WHERE o.four = t.four);

                               QUERY PLAN
-------------------------------------------------------------------​------
 Seq Scan on public.tenk1 t  (cost=0.00..586095.00 rows=5000 width=4)
   Output: t.unique1
   Filter: (ALL (t.ten < (SubPlan 1).col1))
   SubPlan 1
     ->  Seq Scan on public.onek o  (cost=0.00..116.50 rows=250 width=4)
           Output: o.ten
           Filter: (o.four = t.four)

   This rather artificial example serves to illustrate a couple of points:
   values from the outer plan level can be passed down into a subplan
   (here, t.four is passed down) and the results of the sub-select are
   available to the outer plan. Those result values are shown by EXPLAIN
   with notations like (subplan_name).colN, which refers to the N'th
   output column of the sub-SELECT.

   In the example above, the ALL operator runs the subplan again for each
   row of the outer query (which accounts for the high estimated cost).
   Some queries can use a hashed subplan to avoid that:
EXPLAIN SELECT *
FROM tenk1 t
WHERE t.unique1 NOT IN (SELECT o.unique1 FROM onek o);

                                         QUERY PLAN
-------------------------------------------------------------------​------------
-------------
 Seq Scan on tenk1 t  (cost=61.77..531.77 rows=5000 width=244)
   Filter: (NOT (ANY (unique1 = (hashed SubPlan 1).col1)))
   SubPlan 1
     ->  Index Only Scan using onek_unique1 on onek o  (cost=0.28..59.27 rows=10
00 width=4)
(4 rows)

   Here, the subplan is run a single time and its output is loaded into an
   in-memory hash table, which is then probed by the outer ANY operator.
   This requires that the sub-SELECT not reference any variables of the
   outer query, and that the ANY's comparison operator be amenable to
   hashing.

   If, in addition to not referencing any variables of the outer query,
   the sub-SELECT cannot return more than one row, it may instead be
   implemented as an initplan:
EXPLAIN VERBOSE SELECT unique1
FROM tenk1 t1 WHERE t1.ten = (SELECT (random() * 10)::integer);

                             QUERY PLAN
------------------------------------------------------------​--------
 Seq Scan on public.tenk1 t1  (cost=0.02..470.02 rows=1000 width=4)
   Output: t1.unique1
   Filter: (t1.ten = (InitPlan 1).col1)
   InitPlan 1
     ->  Result  (cost=0.00..0.02 rows=1 width=4)
           Output: ((random() * '10'::double precision))::integer

   An initplan is run only once per execution of the outer plan, and its
   results are saved for re-use in later rows of the outer plan. So in
   this example random() is evaluated only once and all the values of
   t1.ten are compared to the same randomly-chosen integer. That's quite
   different from what would happen without the sub-SELECT construct.

14.1.2. EXPLAIN ANALYZE #

   It is possible to check the accuracy of the planner's estimates by
   using EXPLAIN's ANALYZE option. With this option, EXPLAIN actually
   executes the query, and then displays the true row counts and true run
   time accumulated within each plan node, along with the same estimates
   that a plain EXPLAIN shows. For example, we might get a result like
   this:
EXPLAIN ANALYZE SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2;

                                                           QUERY PLAN
-------------------------------------------------------------------​------------
--------------------------------------------------
 Nested Loop  (cost=4.65..118.50 rows=10 width=488) (actual time=0.017..0.051 ro
ws=10.00 loops=1)
   Buffers: shared hit=36 read=6
   ->  Bitmap Heap Scan on tenk1 t1  (cost=4.36..39.38 rows=10 width=244) (actua
l time=0.009..0.017 rows=10.00 loops=1)
         Recheck Cond: (unique1 < 10)
         Heap Blocks: exact=10
         Buffers: shared hit=3 read=5 written=4
         ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..4.36 rows=10 width=
0) (actual time=0.004..0.004 rows=10.00 loops=1)
               Index Cond: (unique1 < 10)
               Index Searches: 1
               Buffers: shared hit=2
   ->  Index Scan using tenk2_unique2 on tenk2 t2  (cost=0.29..7.90 rows=1 width
=244) (actual time=0.003..0.003 rows=1.00 loops=10)
         Index Cond: (unique2 = t1.unique2)
         Index Searches: 10
         Buffers: shared hit=24 read=6
 Planning:
   Buffers: shared hit=15 dirtied=9
 Planning Time: 0.485 ms
 Execution Time: 0.073 ms

   Note that the “actual time” values are in milliseconds of real time,
   whereas the cost estimates are expressed in arbitrary units; so they
   are unlikely to match up. The thing that's usually most important to
   look for is whether the estimated row counts are reasonably close to
   reality. In this example the estimates were all dead-on, but that's
   quite unusual in practice.

   In some query plans, it is possible for a subplan node to be executed
   more than once. For example, the inner index scan will be executed once
   per outer row in the above nested-loop plan. In such cases, the loops
   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 loops value to get the total time
   actually spent in the node. In the above example, we spent a total of
   0.030 milliseconds executing the index scans on tenk2.

   In some cases EXPLAIN ANALYZE shows additional execution statistics
   beyond the plan node execution times and row counts. For example, Sort
   and Hash nodes provide extra information:
EXPLAIN ANALYZE SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2 ORDER BY t1.fivethous;

                                                                 QUERY PLAN
-------------------------------------------------------------------​------------
-------------------------------------------------------​------
 Sort  (cost=713.05..713.30 rows=100 width=488) (actual time=2.995..3.002 rows=1
00.00 loops=1)
   Sort Key: t1.fivethous
   Sort Method: quicksort  Memory: 74kB
   Buffers: shared hit=440
   ->  Hash Join  (cost=226.23..709.73 rows=100 width=488) (actual time=0.515..2
.920 rows=100.00 loops=1)
         Hash Cond: (t2.unique2 = t1.unique2)
         Buffers: shared hit=437
         ->  Seq Scan on tenk2 t2  (cost=0.00..445.00 rows=10000 width=244) (act
ual time=0.026..1.790 rows=10000.00 loops=1)
               Buffers: shared hit=345
         ->  Hash  (cost=224.98..224.98 rows=100 width=244) (actual time=0.476..
0.477 rows=100.00 loops=1)
               Buckets: 1024  Batches: 1  Memory Usage: 35kB
               Buffers: shared hit=92
               ->  Bitmap Heap Scan on tenk1 t1  (cost=5.06..224.98 rows=100 wid
th=244) (actual time=0.030..0.450 rows=100.00 loops=1)
                     Recheck Cond: (unique1 < 100)
                     Heap Blocks: exact=90
                     Buffers: shared hit=92
                     ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 ro
ws=100 width=0) (actual time=0.013..0.013 rows=100.00 loops=1)
                           Index Cond: (unique1 < 100)
                           Index Searches: 1
                           Buffers: shared hit=2
 Planning:
   Buffers: shared hit=12
 Planning Time: 0.187 ms
 Execution Time: 3.036 ms

   The Sort node shows the sort method used (in particular, whether the
   sort was in-memory or on-disk) and the amount of memory or disk space
   needed. The Hash node shows the number of hash buckets and batches as
   well as the peak amount of memory used for the hash table. (If the
   number of batches exceeds one, there will also be disk space usage
   involved, but that is not shown.)

   Index Scan nodes (as well as Bitmap Index Scan and Index-Only Scan
   nodes) show an “Index Searches” line that reports the total number of
   searches across all node executions/loops:
EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE thousand IN (1, 500, 700, 999);
                                                            QUERY PLAN
-------------------------------------------------------------------​------------
---------------------------------------------------
 Bitmap Heap Scan on tenk1  (cost=9.45..73.44 rows=40 width=244) (actual time=0.
012..0.028 rows=40.00 loops=1)
   Recheck Cond: (thousand = ANY ('{1,500,700,999}'::integer[]))
   Heap Blocks: exact=39
   Buffers: shared hit=47
   ->  Bitmap Index Scan on tenk1_thous_tenthous  (cost=0.00..9.44 rows=40 width
=0) (actual time=0.009..0.009 rows=40.00 loops=1)
         Index Cond: (thousand = ANY ('{1,500,700,999}'::integer[]))
         Index Searches: 4
         Buffers: shared hit=8
 Planning Time: 0.029 ms
 Execution Time: 0.034 ms

   Here we see a Bitmap Index Scan node that needed 4 separate index
   searches. The scan had to search the index from the
   tenk1_thous_tenthous index root page once per integer value from the
   predicate's IN construct. However, the number of index searches often
   won't have such a simple correspondence to the query predicate:
EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE thousand IN (1, 2, 3, 4);
                                                            QUERY PLAN
-------------------------------------------------------------------​------------
---------------------------------------------------
 Bitmap Heap Scan on tenk1  (cost=9.45..73.44 rows=40 width=244) (actual time=0.
009..0.019 rows=40.00 loops=1)
   Recheck Cond: (thousand = ANY ('{1,2,3,4}'::integer[]))
   Heap Blocks: exact=38
   Buffers: shared hit=40
   ->  Bitmap Index Scan on tenk1_thous_tenthous  (cost=0.00..9.44 rows=40 width
=0) (actual time=0.005..0.005 rows=40.00 loops=1)
         Index Cond: (thousand = ANY ('{1,2,3,4}'::integer[]))
         Index Searches: 1
         Buffers: shared hit=2
 Planning Time: 0.029 ms
 Execution Time: 0.026 ms

   This variant of our IN query performed only 1 index search. It spent
   less time traversing the index (compared to the original query) because
   its IN construct uses values matching index tuples stored next to each
   other, on the same tenk1_thous_tenthous index leaf page.

   The “Index Searches” line is also useful with B-tree index scans that
   apply the skip scan optimization to more efficiently traverse through
   an index:
EXPLAIN ANALYZE SELECT four, unique1 FROM tenk1 WHERE four BETWEEN 1 AND 3 AND u
nique1 = 42;
                                                              QUERY PLAN
-------------------------------------------------------------------​------------
---------------------------------------------------
 Index Only Scan using tenk1_four_unique1_idx on tenk1  (cost=0.29..6.90 rows=1
width=8) (actual time=0.006..0.007 rows=1.00 loops=1)
   Index Cond: ((four >= 1) AND (four <= 3) AND (unique1 = 42))
   Heap Fetches: 0
   Index Searches: 3
   Buffers: shared hit=7
 Planning Time: 0.029 ms
 Execution Time: 0.012 ms

   Here we see an Index-Only Scan node using tenk1_four_unique1_idx, a
   multi-column index on the tenk1 table's four and unique1 columns. The
   scan performs 3 searches that each read a single index leaf page: “four
   = 1 AND unique1 = 42”, “four = 2 AND unique1 = 42”, and “four = 3 AND
   unique1 = 42”. This index is generally a good target for skip scan,
   since, as discussed in Section 11.3, its leading column (the four
   column) contains only 4 distinct values, while its second/final column
   (the unique1 column) contains many distinct values.

   Another type of extra information is the number of rows removed by a
   filter condition:
EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE ten < 7;

                                               QUERY PLAN
-------------------------------------------------------------------​------------
--------------------------
 Seq Scan on tenk1  (cost=0.00..470.00 rows=7000 width=244) (actual time=0.030..
1.995 rows=7000.00 loops=1)
   Filter: (ten < 7)
   Rows Removed by Filter: 3000
   Buffers: shared hit=345
 Planning Time: 0.102 ms
 Execution Time: 2.145 ms

   These counts can be particularly valuable for filter conditions applied
   at join nodes. The “Rows Removed” line only appears when at least one
   scanned row, or potential join pair in the case of a join node, is
   rejected by the filter condition.

   A case similar to filter conditions occurs with “lossy” index scans.
   For example, consider this search for polygons containing a specific
   point:
EXPLAIN ANALYZE SELECT * FROM polygon_tbl WHERE f1 @> polygon '(0.5,2.0)';

                                              QUERY PLAN
-------------------------------------------------------------------​------------
-----------------------
 Seq Scan on polygon_tbl  (cost=0.00..1.09 rows=1 width=85) (actual time=0.023..
0.023 rows=0.00 loops=1)
   Filter: (f1 @> '((0.5,2))'::polygon)
   Rows Removed by Filter: 7
   Buffers: shared hit=1
 Planning Time: 0.039 ms
 Execution Time: 0.033 ms

   The planner thinks (quite correctly) that this sample table is too
   small to bother with an index scan, so we have a plain sequential scan
   in which all the rows got rejected by the filter condition. But if we
   force an index scan to be used, we see:
SET enable_seqscan TO off;

EXPLAIN ANALYZE SELECT * FROM polygon_tbl WHERE f1 @> polygon '(0.5,2.0)';

                                                        QUERY PLAN
-------------------------------------------------------------------​------------
-------------------------------------------
 Index Scan using gpolygonind on polygon_tbl  (cost=0.13..8.15 rows=1 width=85)
(actual time=0.074..0.074 rows=0.00 loops=1)
   Index Cond: (f1 @> '((0.5,2))'::polygon)
   Rows Removed by Index Recheck: 1
   Index Searches: 1
   Buffers: shared hit=1
 Planning Time: 0.039 ms
 Execution Time: 0.098 ms

   Here we can see that the index returned one candidate row, which was
   then rejected by a recheck of the index condition. This happens because
   a GiST index is “lossy” for polygon containment tests: it actually
   returns the rows with polygons that overlap the target, and then we
   have to do the exact containment test on those rows.

   EXPLAIN has a BUFFERS option which provides additional detail about I/O
   operations performed during the planning and execution of the given
   query. The buffer numbers displayed show the count of the non-distinct
   buffers hit, read, dirtied, and written for the given node and all of
   its child nodes. The ANALYZE option implicitly enables the BUFFERS
   option. If this is undesired, BUFFERS may be explicitly disabled:
EXPLAIN (ANALYZE, BUFFERS OFF) SELECT * FROM tenk1 WHERE unique1 < 100 AND uniqu
e2 > 9000;

                                                           QUERY PLAN
-------------------------------------------------------------------​------------
--------------------------------------------------
 Bitmap Heap Scan on tenk1  (cost=25.07..60.11 rows=10 width=244) (actual time=0
.105..0.114 rows=10.00 loops=1)
   Recheck Cond: ((unique1 < 100) AND (unique2 > 9000))
   Heap Blocks: exact=10
   ->  BitmapAnd  (cost=25.07..25.07 rows=10 width=0) (actual time=0.100..0.101
rows=0.00 loops=1)
         ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=100 width
=0) (actual time=0.027..0.027 rows=100.00 loops=1)
               Index Cond: (unique1 < 100)
               Index Searches: 1
         ->  Bitmap Index Scan on tenk1_unique2  (cost=0.00..19.78 rows=999 widt
h=0) (actual time=0.070..0.070 rows=999.00 loops=1)
               Index Cond: (unique2 > 9000)
               Index Searches: 1
 Planning Time: 0.162 ms
 Execution Time: 0.143 ms

   Keep in mind that because EXPLAIN ANALYZE actually runs the query, any
   side-effects will happen as usual, even though whatever results the
   query might output are discarded in favor of printing the EXPLAIN data.
   If you want to analyze a data-modifying query without changing your
   tables, you can roll the command back afterwards, for example:
BEGIN;

EXPLAIN ANALYZE UPDATE tenk1 SET hundred = hundred + 1 WHERE unique1 < 100;

                                                           QUERY PLAN
-------------------------------------------------------------------​------------
-------------------------------------------------
 Update on tenk1  (cost=5.06..225.23 rows=0 width=0) (actual time=1.634..1.635 r
ows=0.00 loops=1)
   ->  Bitmap Heap Scan on tenk1  (cost=5.06..225.23 rows=100 width=10) (actual
time=0.065..0.141 rows=100.00 loops=1)
         Recheck Cond: (unique1 < 100)
         Heap Blocks: exact=90
         Buffers: shared hit=4 read=2
         ->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=100 width
=0) (actual time=0.031..0.031 rows=100.00 loops=1)
               Index Cond: (unique1 < 100)
               Index Searches: 1
               Buffers: shared read=2
 Planning Time: 0.151 ms
 Execution Time: 1.856 ms

ROLLBACK;

   As seen in this example, when the query is an INSERT, UPDATE, DELETE,
   or MERGE command, the actual work of applying the table changes is done
   by a top-level Insert, Update, Delete, or Merge plan node. The plan
   nodes underneath this node perform the work of locating the old rows
   and/or computing the new data. So above, we see the same sort of bitmap
   table scan we've seen already, and its output is fed to an Update node
   that stores the updated rows. It's worth noting that although the
   data-modifying node can take a considerable amount of run time (here,
   it's consuming the lion's share of the time), the planner does not
   currently add anything to the cost estimates to account for that work.
   That's because the work to be done is the same for every correct query
   plan, so it doesn't affect planning decisions.

   When an UPDATE, DELETE, or MERGE command affects a partitioned table or
   inheritance hierarchy, the output might look like this:
EXPLAIN UPDATE gtest_parent SET f1 = CURRENT_DATE WHERE f2 = 101;

                                       QUERY PLAN
-------------------------------------------------------------------​------------
---------
 Update on gtest_parent  (cost=0.00..3.06 rows=0 width=0)
   Update on gtest_child gtest_parent_1
   Update on gtest_child2 gtest_parent_2
   Update on gtest_child3 gtest_parent_3
   ->  Append  (cost=0.00..3.06 rows=3 width=14)
         ->  Seq Scan on gtest_child gtest_parent_1  (cost=0.00..1.01 rows=1 wid
th=14)
               Filter: (f2 = 101)
         ->  Seq Scan on gtest_child2 gtest_parent_2  (cost=0.00..1.01 rows=1 wi
dth=14)
               Filter: (f2 = 101)
         ->  Seq Scan on gtest_child3 gtest_parent_3  (cost=0.00..1.01 rows=1 wi
dth=14)
               Filter: (f2 = 101)

   In this example the Update node needs to consider three child tables,
   but not the originally-mentioned partitioned table (since that never
   stores any data). So there are three input scanning subplans, one per
   table. For clarity, the Update node is annotated to show the specific
   target tables that will be updated, in the same order as the
   corresponding subplans.

   The Planning time shown by EXPLAIN ANALYZE is the time it took to
   generate the query plan from the parsed query and optimize it. It does
   not include parsing or rewriting.

   The Execution time shown by EXPLAIN ANALYZE includes executor start-up
   and shut-down time, as well as the time to run any triggers that are
   fired, but it does not include parsing, rewriting, or planning time.
   Time spent executing BEFORE triggers, if any, is included in the time
   for the related Insert, Update, or Delete node; but time spent
   executing AFTER triggers is not counted there because AFTER triggers
   are fired after completion of the whole plan. The total time spent in
   each trigger (either BEFORE or AFTER) is also shown separately. Note
   that deferred constraint triggers will not be executed until end of
   transaction and are thus not considered at all by EXPLAIN ANALYZE.

   The time shown for the top-level node does not include any time needed
   to convert the query's output data into displayable form or to send it
   to the client. While EXPLAIN ANALYZE will never send the data to the
   client, it can be told to convert the query's output data to
   displayable form and measure the time needed for that, by specifying
   the SERIALIZE option. That time will be shown separately, and it's also
   included in the total Execution time.

14.1.3. Caveats #

   There are two significant ways in which run times measured by EXPLAIN
   ANALYZE can deviate from normal execution of the same query. First,
   since no output rows are delivered to the client, network transmission
   costs are not included. I/O conversion costs are not included either
   unless SERIALIZE is specified. Second, the measurement overhead added
   by EXPLAIN ANALYZE can be significant, especially on machines with slow
   gettimeofday() operating-system calls. You can use the pg_test_timing
   tool to measure the overhead of timing on your system.

   EXPLAIN results should not be extrapolated to situations much different
   from 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. (We saw this happening in the polygon_tbl
   example above.)

   There are cases in which the actual and estimated values won't match up
   well, but nothing is really wrong. One such case occurs when plan node
   execution is stopped short by a LIMIT or similar effect. For example,
   in the LIMIT query we used before,
EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000 LIMIT
 2;

                                                          QUERY PLAN
-------------------------------------------------------------------​------------
------------------------------------------------
 Limit  (cost=0.29..14.33 rows=2 width=244) (actual time=0.051..0.071 rows=2.00
loops=1)
   Buffers: shared hit=16
   ->  Index Scan using tenk1_unique2 on tenk1  (cost=0.29..70.50 rows=10 width=
244) (actual time=0.051..0.070 rows=2.00 loops=1)
         Index Cond: (unique2 > 9000)
         Filter: (unique1 < 100)
         Rows Removed by Filter: 287
         Index Searches: 1
         Buffers: shared hit=16
 Planning Time: 0.077 ms
 Execution Time: 0.086 ms

   the estimated cost and row count for the Index Scan node are shown as
   though it were run to completion. But in reality the Limit node stopped
   requesting rows after it got two, so the actual row count is only 2 and
   the run time is less than the cost estimate would suggest. This is not
   an estimation error, only a discrepancy in the way the estimates and
   true values are displayed.

   Merge joins also have measurement artifacts that can confuse the
   unwary. A merge join will stop reading one input if it's exhausted the
   other input and the next key value in the one input is greater than the
   last key value of the other input; in such a case there can be no more
   matches and so no need to scan the rest of the first input. This
   results in not reading all of one child, with results like those
   mentioned for LIMIT. Also, if the outer (first) child contains rows
   with duplicate key values, the inner (second) child is backed up and
   rescanned for the portion of its rows matching that key value. EXPLAIN
   ANALYZE counts these repeated emissions of the same inner rows as if
   they were real additional rows. When there are many outer duplicates,
   the reported actual row count for the inner child plan node can be
   significantly larger than the number of rows that are actually in the
   inner relation.

   BitmapAnd and BitmapOr nodes always report their actual row counts as
   zero, due to implementation limitations.

   Normally, EXPLAIN will display every plan node created by the planner.
   However, there are cases where the executor can determine that certain
   nodes need not be executed because they cannot produce any rows, based
   on parameter values that were not available at planning time.
   (Currently this can only happen for child nodes of an Append or
   MergeAppend node that is scanning a partitioned table.) When this
   happens, those plan nodes are omitted from the EXPLAIN output and a
   Subplans Removed: N annotation appears instead.