EXPLAIN

prepared statements

showing the query plan

cursor

showing the query plan

EXPLAIN

7

SQL - Language Statements

EXPLAIN

show the execution plan of a statement

EXPLAIN \[ (

option

\[, \...\] ) \]

statement

EXPLAIN \[ ANALYZE \] \[ VERBOSE \]

statement

where

option

can be one of:

ANALYZE \[

boolean

\] VERBOSE \[

boolean

\] COSTS \[

boolean

\] SETTINGS \[

boolean

\] BUFFERS \[

boolean

\] WAL \[

boolean

\] TIMING \[

boolean

\] SUMMARY \[

boolean

\] FORMAT { TEXT \| XML \| JSON \| YAML }

Description
===========

This command displays the execution plan that the PostgreSQL planner
generates for the supplied statement. The execution plan shows how the
table(s) referenced by the statement will be scanned MDASH by plain
sequential scan, index scan, etc. MDASH and if multiple tables are
referenced, what join algorithms will be used to bring together the
required rows from each input table.

The most critical part of the display is the estimated statement
execution cost, which is the planner\'s guess at how long it will take
to run the statement (measured in cost units that are arbitrary, but
conventionally mean disk page fetches). Actually two numbers are shown:
the start-up cost before the first row can be returned, and the total
cost to return all the rows. For most queries the total cost is what
matters, but in contexts such as a subquery in `EXISTS`, the planner
will choose the smallest start-up cost instead of the smallest total
cost (since the executor will stop after getting one row, anyway). Also,
if you limit the number of rows to return with a `LIMIT` clause, the
planner makes an appropriate interpolation between the endpoint costs to
estimate which plan is really the cheapest.

The `ANALYZE` option causes the statement to be actually executed, not
only planned. Then actual run time statistics are added to the display,
including the total elapsed time expended within each plan node (in
milliseconds) and the total number of rows it actually returned. This is
useful for seeing whether the planner\'s estimates are close to reality.

> **Important**
>
> Keep in mind that the statement is actually executed when the
> `ANALYZE` option is used. Although `EXPLAIN` will discard any output
> that a `SELECT` would return, other side effects of the statement will
> happen as usual. If you wish to use `EXPLAIN ANALYZE` on an `INSERT`,
> `UPDATE`, `DELETE`, `CREATE TABLE AS`, or `EXECUTE` statement without
> letting the command affect your data, use this approach:
>
>     BEGIN;
>     EXPLAIN ANALYZE ...;
>     ROLLBACK;

Only the `ANALYZE` and `VERBOSE` options can be specified, and only in
that order, without surrounding the option list in parentheses. Prior to
PostgreSQL 9.0, the unparenthesized syntax was the only one supported.
It is expected that all new options will be supported only in the
parenthesized syntax.

Parameters
==========

`ANALYZE`

:   Carry out the command and show actual run times and other
    statistics. This parameter defaults to `FALSE`.

`VERBOSE`

:   Display additional information regarding the plan. Specifically,
    include the output column list for each node in the plan tree,
    schema-qualify table and function names, always label variables in
    expressions with their range table alias, and always print the name
    of each trigger for which statistics are displayed. The query
    identifier will also be displayed if one has been computed, see
    [???](#guc-compute-query-id) for more details. This parameter
    defaults to `FALSE`.

`COSTS`

:   Include information on the estimated startup and total cost of each
    plan node, as well as the estimated number of rows and the estimated
    width of each row. This parameter defaults to `TRUE`.

`SETTINGS`

:   Include information on configuration parameters. Specifically,
    include options affecting query planning with value different from
    the built-in default value. This parameter defaults to `FALSE`.

`BUFFERS`

:   Include information on buffer usage. Specifically, include the
    number of shared blocks hit, read, dirtied, and written, the number
    of local blocks hit, read, dirtied, and written, the number of temp
    blocks read and written, and the time spent reading and writing data
    file blocks and temporary file blocks (in milliseconds) if
    [???](#guc-track-io-timing) is enabled. A *hit* means that a read
    was avoided because the block was found already in cache when
    needed. Shared blocks contain data from regular tables and indexes;
    local blocks contain data from temporary tables and indexes; while
    temporary blocks contain short-term working data used in sorts,
    hashes, Materialize plan nodes, and similar cases. The number of
    blocks *dirtied* indicates the number of previously unmodified
    blocks that were changed by this query; while the number of blocks
    *written* indicates the number of previously-dirtied blocks evicted
    from cache by this backend during query processing. The number of
    blocks shown for an upper-level node includes those used by all its
    child nodes. In text format, only non-zero values are printed. It
    defaults to `FALSE`.

`WAL`

:   Include information on WAL record generation. Specifically, include
    the number of records, number of full page images (fpi) and the
    amount of WAL generated in bytes. In text format, only non-zero
    values are printed. This parameter may only be used when `ANALYZE`
    is also enabled. It defaults to `FALSE`.

`TIMING`

:   Include actual startup time and time spent in each node in the
    output. The overhead of repeatedly reading the system clock can slow
    down the query significantly on some systems, so it may be useful to
    set this parameter to `FALSE` when only actual row counts, and not
    exact times, are needed. Run time of the entire statement is always
    measured, even when node-level timing is turned off with this
    option. This parameter may only be used when `ANALYZE` is also
    enabled. It defaults to `TRUE`.

`SUMMARY`

:   Include summary information (e.g., totaled timing information) after
    the query plan. Summary information is included by default when
    `ANALYZE` is used but otherwise is not included by default, but can
    be enabled using this option. Planning time in `EXPLAIN EXECUTE`
    includes the time required to fetch the plan from the cache and the
    time required for re-planning, if necessary.

`FORMAT`

:   Specify the output format, which can be TEXT, XML, JSON, or YAML.
    Non-text output contains the same information as the text output
    format, but is easier for programs to parse. This parameter defaults
    to `TEXT`.

boolean

:   Specifies whether the selected option should be turned on or off.
    You can write `TRUE`, `ON`, or `1` to enable the option, and
    `FALSE`, `OFF`, or `0` to disable it. The boolean value can also be
    omitted, in which case `TRUE` is assumed.

statement

:   Any `SELECT`, `INSERT`, `UPDATE`, `DELETE`, `VALUES`, `EXECUTE`,
    `DECLARE`, `CREATE TABLE AS`, or `CREATE MATERIALIZED VIEW AS`
    statement, whose execution plan you wish to see.

Outputs
=======

The command\'s result is a textual description of the plan selected for
the statement, optionally annotated with execution statistics.
[???](#using-explain) describes the information provided.

Notes
=====

In order to allow the PostgreSQL query planner to make reasonably
informed decisions when optimizing queries, the
[pg\_statistic](#catalog-pg-statistic) data should be up-to-date for all
tables used in the query. Normally the [autovacuum daemon](#autovacuum)
will take care of that automatically. But if a table has recently had
substantial changes in its contents, you might need to do a manual
[`ANALYZE`](#sql-analyze) rather than wait for autovacuum to catch up
with the changes.

In order to measure the run-time cost of each node in the execution
plan, the current implementation of `EXPLAIN
   ANALYZE` adds profiling overhead to query execution. As a result,
running `EXPLAIN ANALYZE` on a query can sometimes take significantly
longer than executing the query normally. The amount of overhead depends
on the nature of the query, as well as the platform being used. The
worst case occurs for plan nodes that in themselves require very little
time per execution, and on machines that have relatively slow operating
system calls for obtaining the time of day.

Examples
========

To show the plan for a simple query on a table with a single `integer`
column and 10000 rows:

    EXPLAIN SELECT * FROM foo;

                           QUERY PLAN
    ---------------------------------------------------------
     Seq Scan on foo  (cost=0.00..155.00 rows=10000 width=4)
    (1 row)

Here is the same query, with JSON output formatting:

    EXPLAIN (FORMAT JSON) SELECT * FROM foo;
               QUERY PLAN
    --------------------------------
     [                             +
       {                           +
         "Plan": {                 +
           "Node Type": "Seq Scan",+
           "Relation Name": "foo", +
           "Alias": "foo",         +
           "Startup Cost": 0.00,   +
           "Total Cost": 155.00,   +
           "Plan Rows": 10000,     +
           "Plan Width": 4         +
         }                         +
       }                           +
     ]
    (1 row)

If there is an index and we use a query with an indexable `WHERE`
condition, `EXPLAIN` might show a different plan:

    EXPLAIN SELECT * FROM foo WHERE i = 4;

                             QUERY PLAN
    --------------------------------------------------------------
     Index Scan using fi on foo  (cost=0.00..5.98 rows=1 width=4)
       Index Cond: (i = 4)
    (2 rows)

Here is the same query, but in YAML format:

    EXPLAIN (FORMAT YAML) SELECT * FROM foo WHERE i='4';
              QUERY PLAN
    -------------------------------
     - Plan:                      +
         Node Type: "Index Scan"  +
         Scan Direction: "Forward"+
         Index Name: "fi"         +
         Relation Name: "foo"     +
         Alias: "foo"             +
         Startup Cost: 0.00       +
         Total Cost: 5.98         +
         Plan Rows: 1             +
         Plan Width: 4            +
         Index Cond: "(i = 4)"    
    (1 row)

XML format is left as an exercise for the reader.

Here is the same plan with cost estimates suppressed:

    EXPLAIN (COSTS FALSE) SELECT * FROM foo WHERE i = 4;

            QUERY PLAN
    ----------------------------
     Index Scan using fi on foo
       Index Cond: (i = 4)
    (2 rows)

Here is an example of a query plan for a query using an aggregate
function:

    EXPLAIN SELECT sum(i) FROM foo WHERE i < 10;

                                 QUERY PLAN
    -------------------------------------------------------------------ZWSP--
     Aggregate  (cost=23.93..23.93 rows=1 width=4)
       ->  Index Scan using fi on foo  (cost=0.00..23.92 rows=6 width=4)
             Index Cond: (i < 10)
    (3 rows)

Here is an example of using `EXPLAIN EXECUTE` to display the execution
plan for a prepared query:

    PREPARE query(int, int) AS SELECT sum(bar) FROM test
        WHERE id > $1 AND id < $2
        GROUP BY foo;

    EXPLAIN ANALYZE EXECUTE query(100, 200);

                                                           QUERY PLAN                                                       
    -------------------------------------------------------------------ZWSP-----------------------------------------------------
     HashAggregate  (cost=9.54..9.54 rows=1 width=8) (actual time=0.156..0.161 rows=11 loops=1)
       Group Key: foo
       ->  Index Scan using test_pkey on test  (cost=0.29..9.29 rows=50 width=8) (actual time=0.039..0.091 rows=99 loops=1)
             Index Cond: ((id > $1) AND (id < $2))
     Planning time: 0.197 ms
     Execution time: 0.225 ms
    (6 rows)

Of course, the specific numbers shown here depend on the actual contents
of the tables involved. Also note that the numbers, and even the
selected query strategy, might vary between PostgreSQL releases due to
planner improvements. In addition, the `ANALYZE` command uses random
sampling to estimate data statistics; therefore, it is possible for cost
estimates to change after a fresh run of `ANALYZE`, even if the actual
distribution of data in the table has not changed.

Compatibility
=============

There is no `EXPLAIN` statement defined in the SQL standard.

See Also
========
