Small. Fast. Reliable.
Choose any three.
ANALYZE

# 1. Overview

analyze-stmt:

The ANALYZE command gathers statistics about tables and indices and stores the collected information in internal tables 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.

## 1.1. Recommended usage pattern

Applications with long-lived databases that use complex queries should consider running the following commands just prior to closing each database connection:

    PRAGMA analysis_limit=400;
    PRAGMA optimize;

The optimize pragma is usually a no-op but it will occasionally run ANALYZE if it seems like doing so will be useful to the query planner. The analysis_limit pragma limits the scope of any ANALYZE command that the optimize pragma runs so that it does not consume too many CPU cycles. The constant "400" can be adjusted as needed. Values between 100 and 1000 work well for most applications.

# 2. Details

The default implementation stores all statistics in a single table named "sqlite_stat1". If SQLite is compiled with the SQLITE_ENABLE_STAT4 option, then additional histogram data is collected and stored in sqlite_stat4. Older versions of SQLite would make use of the sqlite_stat2 table or sqlite_stat3 table when compiled with SQLITE_ENABLE_STAT2 or SQLITE_ENABLE_STAT3, but all recent versions of SQLite ignore the sqlite_stat2 and sqlite_stat3 tables. Future enhancements may create additional internal tables with the same name pattern except with final digit larger than "4". All of these tables are collectively referred to as "statistics tables".

The content of the statistics tables can be queried using SELECT and can be changed using the DELETE, INSERT, and UPDATE commands. The DROP TABLE command works on statistics tables as of SQLite version 3.7.9. (2011-11-01) The ALTER TABLE 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 "Manual Control Of Query Plans Using SQLITE_STAT Tables" for further information.

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.

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 ANALYZE sqlite_schema.

# 3. Automatically Running ANALYZE

The PRAGMA optimize command will automatically run ANALYZE on individual tables on an as-needed basis. The recommended practice is for applications to invoke the PRAGMA optimize statement just before closing each database connection.

Each SQLite database connection 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 PRAGMA optimize 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 PRAGMA optimize 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.

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

Applications that desire more control can run PRAGMA optimize(0x03) 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.

The PRAGMA optimize command was first introduced with SQLite 3.18.0 (2017-03-28) and is a no-op for all prior releases of SQLite.

# 4. Approximate ANALYZE For Large Databases

By default, ANALYZE does a full scan of every index. This can be slow for large databases. So beginning with SQLite version 3.32.0 (2020-05-22), the PRAGMA analysis_limit command can be used to limit the amount of scanning performed by ANALYZE, and thus help ANALYZE to run faster, even on very large database files. We call this running an "approximate ANALYZE".

The recommended usage pattern for the analysis_limit pragma is like this:

    PRAGMA analysis_limit=1000;

This pragma tells the ANALYZE command to start a full scan of the index as it normally would. But when the number of rows visited reaches 1000 (or whatever other limit is specified by the pragma), the ANALYZE command will begin taking actions to stop the scan. If the left-most column of the index has changed at least once during the previous 1000 steps, then the analysis stops immediately. But if the left-most column has always been the same, then ANALYZE skips ahead to the first entry with a different left-most column and reads an additional 1000 rows before terminating.

The details of the effects of the analysis limit described in the previous paragraph are subject to change in future versions of SQLite. But the core idea will remain the same. An analysis limit of N will strive to limit the number of rows visited in each index to approximately N.

Values of N between 100 and 1000 are recommended. Or, to disable the analysis limit, causing ANALYZE to do a complete scan of each index, set the analysis limit to 0. The default value for the analysis limit is 0 for backwards compatibility.

The values placed in the sqlite_stat1 table by an approximate ANALYZE are not exactly the same as what would be computed by an unrestricted analysis. But they are usually close enough. The index statistics in the sqlite_stat1 table are approximations in any case, so the fact that the results of an approximate ANALYZE are slightly different from a traditional full scan ANALYZE has little practical impact. It is possible to construct a pathological case where an approximate ANALYZE is noticeably inferior to a full-scan ANALYZE, but such cases rare in real-world problems.

A good rule of thumb seems to be to always set "PRAGMA analysis_limit=N" for N between 100 and 1000 prior to running either "ANALYZE" or "PRAGMA optimize". The results are not quite as precise, but they are precise enough, and the fact that the results are computed so much faster means that developers are more likely to compute them. An approximate ANALYZE is better than not running ANALYZE at all.

## 4.1. Limitations of approximate ANALYZE

The content in the sqlite_stat4 table cannot be computed with anything less than a full scan. Hence, if a non-zero analysis limit is specified, the sqlite_stat4 table is not computed.