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<div class=nosearch><h1 align="center">SQL As Understood By SQLite</h1><p><a href="lang.html">[Top]</a></p><h2>ANALYZE</h2></div><p><b><a href="syntax/analyze-stmt.html">analyze-stmt:</a></b>
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<p> The ANALYZE command gathers statistics about tables and
indices and stores the collected information
in <a href="fileformat2.html#intschema">internal tables</a> of the database where the query optimizer can
access the information and use it to help make better query planning choices.
If no arguments are given, all attached databases are
analyzed.  If a schema name is given as the argument, then all tables
and indices in that one database are analyzed.  
If the argument is a table name, then only that table and the
indices associated with that table are analyzed.  If the argument
is an index name, then only that one index is analyzed.</p>

<p> The default implementation stores all statistics in a single
table named "<a href="fileformat2.html#stat1tab">sqlite_stat1</a>".  If SQLite is compiled with the
<a href="compile.html#enable_stat3">SQLITE_ENABLE_STAT3</a> option and without the <a href="compile.html#enable_stat4">SQLITE_ENABLE_STAT4</a>
option, then additional histogram data is
collected and stored in <a href="fileformat2.html#stat3tab">sqlite_stat3</a>.
 If SQLite is compiled with the
<a href="compile.html#enable_stat4">SQLITE_ENABLE_STAT4</a> option, then additional histogram data is
collected and stored in <a href="fileformat2.html#stat4tab">sqlite_stat4</a>.
Older versions of SQLite would make use of the <a href="fileformat2.html#stat2tab">sqlite_stat2</a> table
when compiled with <a href="compile.html#enable_stat2">SQLITE_ENABLE_STAT2</a> but all recent versions of
SQLite ignore the sqlite_stat2 table.
Future enhancements may create
additional <a href="fileformat2.html#intschema">internal tables</a> with the same name pattern except with
final digit larger than "4".
All of these tables are collectively referred to as "statistics tables".
</p>

<p> The content of the statistics tables can be queried using <a href="lang_select.html">SELECT</a>
and can be changed using the <a href="lang_delete.html">DELETE</a>, <a href="lang_insert.html">INSERT</a>, and <a href="lang_update.html">UPDATE</a> commands.
The <a href="lang_droptable.html">DROP TABLE</a> command works on statistics tables
as of SQLite version 3.7.9. (2011-11-01)
The <a href="lang_altertable.html">ALTER TABLE</a> command does not work on statistics tables.
Appropriate care should be used when changing the content of the statistics
tables as invalid content can cause SQLite to select inefficient
query plans.  Generally speaking, one should not modify the content of
the statistics tables by any mechanism other than invoking the
ANALYZE command.  
See "<a href="optoverview.html#manctrl">Manual Control Of Query Plans Using SQLITE_STAT Tables</a>" for
further information.</p>

<p> Statistics gathered by ANALYZE are not automatically updated as
the content of the database changes.  If the content of the database
changes significantly, or if the database schema changes, then one should
consider rerunning the ANALYZE command in order to update the statistics.</p>

<p> The query planner loads the content of the statistics tables
into memory when the schema is read.  Hence, when an application
changes the statistics tables directly, SQLite will not immediately
notice the changes. An application
can force the query planner to reread the statistics tables by running
<b>ANALYZE sqlite_master</b>. </p>

<a name="autoanalyze"></a>

<h2>Automatically Running ANALYZE</h2>

<p>The <a href="pragma.html#pragma_optimize">PRAGMA optimize</a> command will automatically run ANALYZE on individual
tables on an as-needed basis.  The recommended practice is for applications
to invoke the <a href="pragma.html#pragma_optimize">PRAGMA optimize</a> statement just before closing each database
connection.</p>

<p>Each SQLite <a href="c3ref/sqlite3.html">database connection</a> records cases when the query planner would
benefit from having accurate results of ANALYZE at hand.  These records
are held in memory and accumulate over the life of a database connection.
The <a href="pragma.html#pragma_optimize">PRAGMA optimize</a> command looks at those records and runs ANALYZE on only
those tables for which new or updated ANALYZE data seems likely to be useful.
In most cases <a href="pragma.html#pragma_optimize">PRAGMA optimize</a> will not run ANALYZE, but it will occasionally
do so either for tables that have never before been analyzed, or for tables
that have grown significantly since they were last analyzed.</p>

<p>Since the actions of <a href="pragma.html#pragma_optimize">PRAGMA optimize</a> are determined to some extent by
prior queries that have been evaluated on the same database connection, it
is recommended that <a href="pragma.html#pragma_optimize">PRAGMA optimize</a> be deferred until the database connection
is closing and has thus had an opportunity to accumulate as must usage information
as possible.  It is also reasonable to set a timer to run <a href="pragma.html#pragma_optimize">PRAGMA optimize</a>
every few hours, or every few days, for database connections that stay open
for a long time.</p>

<p>Applications that desire more control can run <a href="pragma.html#pragma_optimize">PRAGMA optimize(0x03)</a> to 
obtain a list of ANALYZE commands that SQLite thinks are appropriate to run,
but without actually running those commands.  If the returned set is 
non-empty, the application can then make a decision about whether or not
to run the suggested ANALYZE commands, perhaps after prompting the user
for guidance.</p>

<p>The <a href="pragma.html#pragma_optimize">PRAGMA optimize</a> command was first introduced with 
SQLite 3.18.0 (2017-03-28) and is a no-op for all prior releases
of SQLite.</p>

<h2>Anticipated Future Enhancements</h2>

<p>All existing versions of SQLite do a full table scan for ANALYZE.  This can be
slow for multi-gigabyte and larger databases.  Future versions of SQLite might
use random sampling rather than a full table scan to obtain estimates for the
database shape, especially on larger tables.  The results would approximate, but 
will be close enough for query planning purposes.  As of 2017-03-20, this
concept has been tested in experimental branches and appears to work well, but
has not been folded into an official release.</p>


