Important

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Introduction to Statistics

TiDB uses statistics to decide which index to choose. The tidb_analyze_version variable controls the statistics collected by TiDB. Currently, two versions of statistics are supported: tidb_analyze_version = 1 and tidb_analyze_version = 2. In versions before v5.1.0, the default value of this variable is 1. In v5.3.0 and later versions, the default value of this variable is 2, which serves as an experimental feature. If your cluster is upgraded from a version earlier than v5.3.0 to v5.3.0 or later, the default value of tidb_analyze_version does not change.

Note

When tidb_analyze_version = 2, if memory overflow occurs after ANALYZE is executed, you need to set tidb_analyze_version = 1 and perform one of the following operations:

If the ANALYZE statement is executed manually, manually analyze every table to be analyzed.

SELECT DISTINCT(CONCAT('ANALYZE TABLE ', table_schema, '.', table_name, ';')) FROM information_schema.tables, mysql.stats_histograms WHERE stats_ver = 2 AND table_id = tidb_table_id;

If TiDB automatically executes the ANALYZE statement because the auto-analysis has been enabled, execute the following statement that generates the DROP STATS statement:

SELECT DISTINCT(CONCAT('DROP STATS ', table_schema, '.', table_name, ';')) FROM information_schema.tables, mysql.stats_histograms WHERE stats_ver = 2 AND table_id = tidb_table_id;

These two versions include different information in TiDB:

Information	Version 1	Version 2
The total number of rows in the table	√	√
Column Count-Min Sketch	√	×
Index Count-Min Sketch	√	×
Column Top-N	√	√ (Maintenance methods and precision are improved)
Index Top-N	√ (Insufficient maintenance precision might cause inaccuracy)	√ (Maintenance methods and precision are improved)
Column histogram	√	√ (The histogram does not include Top-N values.)
Index histogram	√	√ (The histogram buckets record the number of different values in each bucket, and the histogram does not include Top-N values.)
The number of `NULL`s in the column	√	√
The number of `NULL`s in the index	√	√
The average length of columns	√	√
The average length of indexes	√	√

Compared to Version 1, Version 2 statistics avoids the potential inaccuracy caused by hash collision when the data volume is huge. It also maintains the estimate precision in most scenarios.

This document briefly introduces the histogram, Count-Min Sketch, and Top-N, and details the collection and maintenance of statistics.

Histogram

A histogram is an approximate representation of the distribution of data. It divides the entire range of values into a series of buckets, and uses simple data to describe each bucket, such as the number of values falling in the bucket. In TiDB, an equal-depth histogram is created for the specific columns of each table. The equal-depth histogram can be used to estimate the interval query.

Here "equal-depth" means that the number of values falling into each bucket is as equal as possible. For example, for a given set {1.6, 1.9, 1.9, 2.0, 2.4, 2.6, 2.7, 2.7, 2.8, 2.9, 3.4, 3.5}, you want to generate 4 buckets. The equal-depth histogram is as follows. It contains four buckets [1.6, 1.9], [2.0, 2.6], [2.7, 2.8], [2.9, 3.5]. The bucket depth is 3.

Equal-depth Histogram Example

For details about the parameter that determines the upper limit to the number of histogram buckets, refer to Manual Collection. When the number of buckets is larger, the accuracy of the histogram is higher; however, higher accuracy is at the cost of the usage of memory resources. You can adjust this number appropriately according to the actual scenario.

Count-Min Sketch

Count-Min Sketch is a hash structure. When an equivalence query contains a = 1 or IN query (for example, a in (1, 2, 3)), TiDB uses this data structure for estimation.

A hash collision might occur since Count-Min Sketch is a hash structure. In the EXPLAIN statement, if the estimate of the equivalent query deviates greatly from the actual value, it can be considered that a larger value and a smaller value have been hashed together. In this case, you can take one of the following ways to avoid the hash collision:

Modify the WITH NUM TOPN parameter. TiDB stores the high-frequency (top x) data separately, with the other data stored in Count-Min Sketch. Therefore, to prevent a larger value and a smaller value from being hashed together, you can increase the value of WITH NUM TOPN. In TiDB, its default value is 20. The maximum value is 1024. For more information about this parameter, see Full Collection.
Modify two parameters WITH NUM CMSKETCH DEPTH and WITH NUM CMSKETCH WIDTH. Both affect the number of hash buckets and the collision probability. You can increase the values of the two parameters appropriately according to the actual scenario to reduce the probability of hash collision, but at the cost of higher memory usage of statistics. In TiDB, the default value of WITH NUM CMSKETCH DEPTH is 5, and the default value of WITH NUM CMSKETCH WIDTH is 2048. For more information about the two parameters, see Full Collection.

Top-N values

Top-N values are values with the top N occurrences in a column or index. TiDB records the values and occurences of Top-N values.

Collect statistics

Manual collection

You can run the ANALYZE statement to collect statistics.

Note

The execution time of ANALYZE TABLE in TiDB is longer than that in MySQL or InnoDB. In InnoDB, only a small number of pages are sampled, while in TiDB a comprehensive set of statistics is completely rebuilt. Scripts that were written for MySQL may naively expect ANALYZE TABLE will be a short-lived operation.

For quicker analysis, you can set tidb_enable_fast_analyze to 1 to enable the Quick Analysis feature. The default value for this parameter is 0.

After Quick Analysis is enabled, TiDB randomly samples approximately 10,000 rows of data to build statistics. Therefore, in the case of uneven data distribution or a relatively small amount of data, the accuracy of statistical information is relatively poor. It might lead to poor execution plans, such as choosing the wrong index. If the execution time of the normal ANALYZE statement is acceptable, it is recommended to disable the Quick Analysis feature.

tidb_enable_fast_analyze is an experimental feature, which currently does not match exactly with the statistical information of tidb_analyze_version=2. Therefore, you need to set the value of tidb_analyze_version to 1 when tidb_enable_fast_analyze is enabled.

Full collection

You can perform full collection using the following syntax.

To collect statistics of all the tables in TableNameList:

ANALYZE TABLE TableNameList [WITH NUM BUCKETS|TOPN|CMSKETCH DEPTH|CMSKETCH WIDTH]|[WITH NUM SAMPLES|WITH FLOATNUM SAMPLERATE];

WITH NUM BUCKETS specifies the maximum number of buckets in the generated histogram.
WITH NUM TOPN specifies the maximum number of the generated TOPNs.
WITH NUM CMSKETCH DEPTH specifies the depth of the CM Sketch.
WITH NUM CMSKETCH WIDTH specifies the width of the CM Sketch.
WITH NUM SAMPLES specifies the number of samples.
WITH FLOAT_NUM SAMPLERATE specifies the sampling rate.

WITH NUM SAMPLES and WITH FLOAT_NUM SAMPLERATE correspond to two different algorithms of collecting samples.

WITH NUM SAMPLES specifies the size of the sampling set, which is implemented in the reservoir sampling method in TiDB. When a table is large, it is not recommended to use this method to collect statistics. Because the intermediate result set of the reservoir sampling contains redundant results, it causes additional pressure on resources such as memory.
WITH FLOAT_NUM SAMPLERATE is a sampling method introduced in v5.3.0. With the value range (0, 1], this parameter specifies the sampling rate. It is implemented in the way of Bernoulli sampling in TiDB, which is more suitable for sampling larger tables and performs better in collection efficiency and resource usage.

Before v5.3.0, TiDB uses the reservoir sampling method to collect statistics. Since v5.3.0, the TiDB Version 2 statistics uses the Bernoulli sampling method to collect statistics by default. To re-use the reservoir sampling method, you can use the WITH NUM SAMPLES statement.

Note

The current sampling rate is calculated based on an adaptive algorithm. When you can observe the number of rows in a table using SHOW STATS_META, you can use this number of rows to calculate the sampling rate corresponding to 100,000 rows. If you cannot observe this number, you can use the TABLE_KEYS column in the TABLE_STORAGE_STATS table as another reference to calculate the sampling rate.

Normally, STATS_META is more credible than TABLE_KEYS. However, after importing data through the methods like TiDB Lightning, the result of STATS_META is 0. To handle this situation, you can use TABLE_KEYS to calculate the sampling rate when the result of STATS_META is much smaller than the result of TABLE_KEYS.

Collect statistics on some columns

In most cases, when executing SQL statements, the optimizer only uses statistics on some columns (such as columns in the WHERE, JOIN, ORDER BY, and GROUP BY statements). These columns are called PREDICATE COLUMNS.

If a table has many columns, collecting statistics on all the columns can cause a large overhead. To reduce the overhead, you can collect statistics on only specific columns or PREDICATE COLUMNS to be used by the optimizer.

Note

Collecting statistics on some columns is only applicable for tidb_analyze_version = 2.

To collect statistics on specific columns, use the following syntax:
```
ANALYZE TABLE TableName COLUMNS ColumnNameList [WITH NUM BUCKETS|TOPN|CMSKETCH DEPTH|CMSKETCH WIDTH]|[WITH NUM SAMPLES|WITH FLOATNUM SAMPLERATE];
```
In the syntax, ColumnNameList specifies the name list of the target columns. If you need to specify more than one column, use comma , to separate the column names. For example, ANALYZE table t columns a, b. Besides collecting statistics on the specific columns in a specific table, this syntax collects statistics on the indexed columns and all indexes in that table at the same time.
Note
The syntax above is a full collection. For example, after collecting statistics on columns a and b using this syntax, if you also want to collect statistics on column c, you need to specify all three columns using ANALYZE table t columns a, b, c, rather than only specifying the additional column c using ANALYZE TABLE t COLUMNS c.
To collect statistics on PREDICATE COLUMNS, do the following:
Warning
Currently, collecting statistics on PREDICATE COLUMNS is an experimental feature. It is not recommended that you use it in production environments.
1. Set the value of the tidb_enable_column_tracking system variable to ON to enable TiDB to collect PREDICATE COLUMNS.
  After the setting, TiDB writes the PREDICATE COLUMNS information to the mysql.column_stats_usage system table every 100 * stats-lease.
2. After the query pattern of your business is relatively stable, collect statistics on PREDICATE COLUMNS by using the following syntax:
```
ANALYZE TABLE TableName PREDICATE COLUMNS [WITH NUM BUCKETS|TOPN|CMSKETCH DEPTH|CMSKETCH WIDTH]|[WITH NUM SAMPLES|WITH FLOATNUM SAMPLERATE];
```
  Besides collecting statistics on PREDICATE COLUMNS in a specific table, this syntax collects statistics on indexed columns and all indexes in that table at the same time.
  Note
  If the mysql.column_stats_usage system table does not contain any PREDICATE COLUMNS record for that table, the preceding syntax collects statistics on all columns and all indexes in that table.
  After using this syntax to collect statistics, when executing a new type of SQL query, the optimizer might temporarily use the old or pseudo column statistics for this time, and TiDB will collect the statistics on the used columns from the next time.

To collect statistics on all columns and indexes, use the following syntax:

ANALYZE TABLE TableName ALL COLUMNS [WITH NUM BUCKETS|TOPN|CMSKETCH DEPTH|CMSKETCH WIDTH]|[WITH NUM SAMPLES|WITH FLOATNUM SAMPLERATE];

If you want to persist the column configuration in the ANALYZE statement (including COLUMNS ColumnNameList, PREDICATE COLUMNS, and ALL COLUMNS), set the value of the tidb_persist_analyze_options system variable to ON to enable the ANALYZE configuration persistence feature. After enabling the ANALYZE configuration persistence feature:

When TiDB collects statistics automatically or when you manually collect statistics by executing the ANALYZE statement without specifying the column configuration, TiDB continues using the previously persisted configuration for statistics collection.
When you manually execute the ANALYZE statement multiple times with column configuration specified, TiDB overwrites the previously recorded persistent configuration using the new configuration specified by the latest ANALYZE statement.

To locate PREDICATE COLUMNS and columns on which statistics have been collected, use the following syntax:

SHOW COLUMN_STATS_USAGE [ShowLikeOrWhere];

The SHOW COLUMN_STATS_USAGE statement returns the following 6 columns:

Column name	Description
`Db_name`	The database name
`Table_name`	The table name
`Partition_name`	The partition name
`Column_name`	The column name
`Last_used_at`	The last time when the column statistics were used in the query optimization
`Last_analyzed_at`	The last time when the column statistics were collected

In the following example, after executing ANALYZE TABLE t PREDICATE COLUMNS;, TiDB collects statistics on columns b, c, and d, where column b is a PREDICATE COLUMN and columns c and d are index columns.

SET GLOBAL tidb_enable_column_tracking = ON;
Query OK, 0 rows affected (0.00 sec)

CREATE TABLE t (a INT, b INT, c INT, d INT, INDEX idx_c_d(c, d));
Query OK, 0 rows affected (0.00 sec)

-- The optimizer uses the statistics on column b in this query.
SELECT * FROM t WHERE b > 1;
Empty set (0.00 sec)

-- After waiting for a period of time (100 * stats-lease), TiDB writes the collected `PREDICATE COLUMNS` to mysql.column_stats_usage.
-- Specify `last_used_at IS NOT NULL` to show the `PREDICATE COLUMNS` collected by TiDB.
SHOW COLUMN_STATS_USAGE WHERE db_name = 'test' AND table_name = 't' AND last_used_at IS NOT NULL;
+---------+------------+----------------+-------------+---------------------+------------------+
| Db_name | Table_name | Partition_name | Column_name | Last_used_at        | Last_analyzed_at |
+---------+------------+----------------+-------------+---------------------+------------------+
| test    | t          |                | b           | 2022-01-05 17:21:33 | NULL             |
+---------+------------+----------------+-------------+---------------------+------------------+
1 row in set (0.00 sec)

ANALYZE TABLE t PREDICATE COLUMNS;
Query OK, 0 rows affected, 1 warning (0.03 sec)

-- Specify `last_analyzed_at IS NOT NULL` to show the columns for which statistics have been collected.
SHOW COLUMN_STATS_USAGE WHERE db_name = 'test' AND table_name = 't' AND last_analyzed_at IS NOT NULL;
+---------+------------+----------------+-------------+---------------------+---------------------+
| Db_name | Table_name | Partition_name | Column_name | Last_used_at        | Last_analyzed_at    |
+---------+------------+----------------+-------------+---------------------+---------------------+
| test    | t          |                | b           | 2022-01-05 17:21:33 | 2022-01-05 17:23:06 |
| test    | t          |                | c           | NULL                | 2022-01-05 17:23:06 |
| test    | t          |                | d           | NULL                | 2022-01-05 17:23:06 |
+---------+------------+----------------+-------------+---------------------+---------------------+
3 rows in set (0.00 sec)

Collect statistics on indexes

To collect statistics on all indexes in IndexNameList in TableName, use the following syntax:

ANALYZE TABLE TableName INDEX [IndexNameList] [WITH NUM BUCKETS|TOPN|CMSKETCH DEPTH|CMSKETCH WIDTH]|[WITH NUM SAMPLES|WITH FLOATNUM SAMPLERATE];

When IndexNameList is empty, this syntax collects statistics on all indexes in TableName.

Note

To ensure that the statistical information before and after the collection is consistent, when tidb_analyze_version is 2, this syntax collects statistics on the entire table (including all columns and indexes), instead of only on indexes.

Collect statistics on partitions

To collect statistics on all partitions in PartitionNameList in TableName, use the following syntax:

ANALYZE TABLE TableName PARTITION PartitionNameList [WITH NUM BUCKETS|TOPN|CMSKETCH DEPTH|CMSKETCH WIDTH]|[WITH NUM SAMPLES|WITH FLOATNUM SAMPLERATE];

To collect index statistics on all partitions in PartitionNameList in TableName, use the following syntax:

ANALYZE TABLE TableName PARTITION PartitionNameList INDEX [IndexNameList] [WITH NUM BUCKETS|TOPN|CMSKETCH DEPTH|CMSKETCH WIDTH]|[WITH NUM SAMPLES|WITH FLOATNUM SAMPLERATE];

If you only need to collect statistics on some columns of some partitions in a table, use the following syntax:
Warning
Currently, collecting statistics on PREDICATE COLUMNS is an experimental feature. It is not recommended that you use it in production environments.
```
ANALYZE TABLE TableName PARTITION PartitionNameList [COLUMNS ColumnNameList|PREDICATE COLUMNS|ALL COLUMNS] [WITH NUM BUCKETS|TOPN|CMSKETCH DEPTH|CMSKETCH WIDTH]|[WITH NUM SAMPLES|WITH FLOATNUM SAMPLERATE];
```

Incremental collection

To improve the speed of analysis after full collection, incremental collection could be used to analyze the newly added sections in monotonically non-decreasing columns such as time columns.

Note

Currently, the incremental collection is only provided for index.
When using the incremental collection, you must ensure that only INSERT operations exist on the table, and that the newly inserted value on the index column is monotonically non-decreasing. Otherwise, the statistical information might be inaccurate, affecting the TiDB optimizer to select an appropriate execution plan.

You can perform incremental collection using the following syntax.

To incrementally collect statistics on index columns in all IndexNameLists in TableName:

ANALYZE INCREMENTAL TABLE TableName INDEX [IndexNameList] [WITH NUM BUCKETS|TOPN|CMSKETCH DEPTH|CMSKETCH WIDTH]|[WITH NUM SAMPLES|WITH FLOATNUM SAMPLERATE];

To incrementally collect statistics on index columns for partitions in all PartitionNameLists in TableName:

ANALYZE INCREMENTAL TABLE TableName PARTITION PartitionNameList INDEX [IndexNameList] [WITH NUM BUCKETS|TOPN|CMSKETCH DEPTH|CMSKETCH WIDTH]|[WITH NUM SAMPLES|WITH FLOATNUM SAMPLERATE];

Automatic update

For the INSERT, DELETE, or UPDATE statements, TiDB automatically updates the number of rows and updated rows. TiDB persists this information regularly and the update cycle is 20 * stats-lease. The default value of stats-lease is 3s. If you specify the value as 0, it does not update automatically.

Three system variables related to automatic update of statistics are as follows:

System Variable	Default Value	Description
`tidb_auto_analyze_ratio`	0.5	The threshold value of automatic update
`tidb_auto_analyze_start_time`	`00:00 +0000`	The start time in a day when TiDB can perform automatic update
`tidb_auto_analyze_end_time`	`23:59 +0000`	The end time in a day when TiDB can perform automatic update

When the ratio of the number of modified rows to the total number of rows of tbl in a table is greater than tidb_auto_analyze_ratio, and the current time is between tidb_auto_analyze_start_time and tidb_auto_analyze_end_time, TiDB executes the ANALYZE TABLE tbl statement in the background to automatically update the statistics on this table.

Note

Currently, the automatic update does not record the configuration items input at manual ANALYZE. Therefore, when you use the WITH syntax to control the collecting behavior of ANALYZE, you need to manually set scheduled tasks to collect statistics.

Before TiDB v5.0, when you execute a query, TiDB collects feedback with feedback-probability and updates the histogram and Count-Min Sketch based on the feedback. Since v5.0, this feature is disabled by default. Since v5.4, this feature has been deprecated, and it is not recommended to enable this feature.

Since TiDB v6.0, TiDB supports using the KILL statement to terminate an ANALYZE task running in the background. If you find that an ANALYZE task running in the background consumes a lot of resources and affects your application, you can terminate the ANALYZE task by taking the following steps:

Execute the following SQL statement to get the TiDB instance address and the ID of the background ANALYZE task:

SELECT ci.instance as instance, cp.id as id FROM information_schema.cluster_info ci, information_schema.cluster_processlist cp WHERE ci.status_address = cp.instance and ci.type = 'tidb' and cp.info like 'analyze table %' and cp.user = '' and cp.host = '';

If there is no result, no ANALYZE task is running in the background.

Use a client to connect to the TiDB instance where the background ANALYZE task is running, and then execute the following KILL statement:
```
KILL TIDB ${id};
```
${id} is the ID of the background ANALYZE task obtained in the previous step.
Note
The above KILL statement only works on a TiDB instance that is executing a background ANALYZE task. Therefore, you must use a client to connect to that TiDB instance first. If you use a client to connect to another TiDB instance, or if there is a proxy between the client and TiDB, the KILL statement cannot terminate the background ANALYZE task. For more information, see KILL [TIDB].

Control `ANALYZE` concurrency

When you run the ANALYZE statement, you can adjust the concurrency using the following parameters, to control its effect on the system.

`tidb_build_stats_concurrency`

Currently, when you run the ANALYZE statement, the task is divided into multiple small tasks. Each task only works on one column or index. You can use the tidb_build_stats_concurrency parameter to control the number of simultaneous tasks. The default value is 4.

`tidb_distsql_scan_concurrency`

When you analyze regular columns, you can use the tidb_distsql_scan_concurrency parameter to control the number of Region to be read at one time. The default value is 15.

`tidb_index_serial_scan_concurrency`

When you analyze index columns, you can use the tidb_index_serial_scan_concurrency parameter to control the number of Region to be read at one time. The default value is 1.

Persist ANALYZE configurations

Since v5.4.0, TiDB supports persisting some ANALYZE configurations. With this feature, the existing configurations can be easily reused for future statistics collection.

The following are the ANALYZE configurations that support persistence:

Configurations	Corresponding ANALYZE syntax
The number of histogram buckets	WITH NUM BUCKETS
The number of Top-N	WITH NUM TOPN
The number of samples	WITH NUM SAMPLES
The sampling rate	WITH FLOATNUM SAMPLERATE
The `ANALYZE` column type	AnalyzeColumnOption ::= ( 'ALL COLUMNS' \| 'PREDICATE COLUMNS' \| 'COLUMNS' ColumnNameList )
The `ANALYZE` column	ColumnNameList ::= Identifier ( ',' Identifier )*

Enable ANALYZE configuration persistence

The ANALYZE configuration persistence feature is enabled by default (the system variable tidb_analyze_version is 2 and tidb_persist_analyze_options is ON by default). You can use this feature to record the persistence configurations specified in the ANALYZE statement when executing the statement manually. Once recorded, the next time TiDB automatically updates statistics or you manually collect statistics without specifying these configuration, TiDB will collect statistics according to the recorded configurations.

When you manually execute the ANALYZE statement multiple times with persistence configurations specified, TiDB overwrites the previously recorded persistent configuration using the new configurations specified by the latest ANALYZE statement.

Disable ANALYZE configuration persistence

To disable the ANALYZE configuration persistence feature, set the tidb_persist_analyze_options system variable to OFF. Because the ANALYZE configuration persistence feature is not applicable to tidb_analyze_version = 1, setting tidb_analyze_version = 1 can also disable the feature.

After disabling the ANALYZE configuration persistence feature, TiDB does not clear the persisted configuration records. Therefore, if you enable this feature again, TiDB continues to collect statistics using the previously recorded persistent configurations.

Note

When you enable the ANALYZE configuration persistence feature again, if the previously recorded persistence configurations are no longer applicable to the latest data, you need to execute the ANALYZE statement manually and specify the new persistence configurations.

View `ANALYZE` state

When executing the ANALYZE statement, you can view the current state of ANALYZE using the following SQL statement:

SHOW ANALYZE STATUS [ShowLikeOrWhere]

This statement returns the state of ANALYZE. You can use ShowLikeOrWhere to filter the information you need.

Currently, the SHOW ANALYZE STATUS statement returns the following 7 columns:

Column name	Description
table_schema	The database name
table_name	The table name
partition_name	The partition name
job_info	The task information. The element includes index names when index analysis is performed.
row_count	The number of rows that have been analyzed
start_time	The time at which the task starts
state	The state of a task, including `pending`, `running`, `finished`, and `failed`

View statistics

You can view the statistics status using the following statements.

Metadata of tables

You can use the SHOW STATS_META statement to view the total number of rows and the number of updated rows.

SHOW STATS_META [ShowLikeOrWhere];

The syntax of ShowLikeOrWhereOpt is as follows:

ShowLikeOrWhereOpt

Currently, the SHOW STATS_META statement returns the following 6 columns:

Column name	Description
`db_name`	The database name
`table_name`	The table name
`partition_name`	The partition name
`update_time`	The time of the update
`modify_count`	The number of modified rows
`row_count`	The total number of rows

Note

When TiDB automatically updates the total number of rows and the number of modified rows according to DML statements, update_time is also updated. Therefore, update_time does not necessarily indicate the last time when the ANALYZE statement is executed.

Health state of tables

You can use the SHOW STATS_HEALTHY statement to check the health state of tables and roughly estimate the accuracy of the statistics. When modify_count >= row_count, the health state is 0; when modify_count < row_count, the health state is (1 - modify_count/row_count) * 100.

The syntax is as follows:

SHOW STATS_HEALTHY [ShowLikeOrWhere];

The synopsis of SHOW STATS_HEALTHY is:

ShowStatsHealthy

Currently, the SHOW STATS_HEALTHY statement returns the following 4 columns:

Column name	Description
`db_name`	The database name
`table_name`	The table name
`partition_name`	The partition name
`healthy`	The health state of tables

Metadata of columns

You can use the SHOW STATS_HISTOGRAMS statement to view the number of different values and the number of NULL in all the columns.

Syntax as follows:

SHOW STATS_HISTOGRAMS [ShowLikeOrWhere]

This statement returns the number of different values and the number of NULL in all the columns. You can use ShowLikeOrWhere to filter the information you need.

Currently, the SHOW STATS_HISTOGRAMS statement returns the following 10 columns:

Column name	Description
`db_name`	The database name
`table_name`	The table name
`partition_name`	The partition name
`column_name`	The column name (when `is_index` is `0`) or the index name (when `is_index` is `1`)
`is_index`	Whether it is an index column or not
`update_time`	The time of the update
`distinct_count`	The number of different values
`null_count`	The number of `NULL`
`avg_col_size`	The average length of columns
correlation	The Pearson correlation coefficient of the column and the integer primary key, which indicates the degree of association between the two columns

Buckets of histogram

You can use the SHOW STATS_BUCKETS statement to view each bucket of the histogram.

The syntax is as follows:

SHOW STATS_BUCKETS [ShowLikeOrWhere]

The diagram is as follows:

SHOW STATS_BUCKETS

This statement returns information about all the buckets. You can use ShowLikeOrWhere to filter the information you need.

Currently, the SHOW STATS_BUCKETS statement returns the following 11 columns:

Column name	Description
`db_name`	The database name
`table_name`	The table name
`partition_name`	The partition name
`column_name`	The column name (when `is_index` is `0`) or the index name (when `is_index` is `1`)
`is_index`	Whether it is an index column or not
`bucket_id`	The ID of a bucket
`count`	The number of all the values that falls on the bucket and the previous buckets
`repeats`	The occurrence number of the maximum value
`lower_bound`	The minimum value
`upper_bound`	The maximum value
`ndv`	The number of different values in the bucket. When `tidb_analyze_version` = `1`, `ndv` is always `0`, which has no actual meaning.

Top-N information

You can use the SHOW STATS_TOPN statement to view the Top-N information currently collected by TiDB.

The syntax is as follows:

SHOW STATS_TOPN [ShowLikeOrWhere];

Currently, the SHOW STATS_TOPN statement returns the following 7 columns:

Column name	Description
`db_name`	The database name
`table_name`	The table name
`partition_name`	The partition name
`column_name`	The column name (when `is_index` is `0`) or the index name (when `is_index` is `1`)
`is_index`	Whether it is an index column or not
`value`	The value of this column
`count`	How many times the value appears

Delete statistics

You can run the DROP STATS statement to delete statistics.

Syntax as follows:

DROP STATS TableName

The statement deletes statistics of all the tables in TableName.

Load statistics

By default, depending on the size of column statistics, TiDB loads statistics differently as follows:

For statistics that consume small space (such as count, distinctCount, and nullCount), as long as the column data is updated, TiDB automatically loads the corresponding statistics into memory for use in the SQL optimization stage.
For statistics that consume large space (such as histograms, TopN, and Count-Min Sketch), to ensure the performance of SQL execution, TiDB loads the statistics asynchronously on demand. Take histograms as an example. TiDB loads histogram statistics on a column into memory only when the optimizer uses the histogram statistics on that column. On-demand asynchronous statistics loading does not affect the performance of SQL execution but might provide incomplete statistics for SQL optimization.

Since v5.4.0, TiDB introduces the synchronously loading statistics feature. This feature allows TiDB to synchronously load large-sized statistics (such as histograms, TopN, and Count-Min Sketch statistics) into memory when you execute SQL statements, which improves the completeness of statistics for SQL optimization.

Warning

Currently, synchronously loading statistics is an experimental feature. It is not recommended that you use it in production environments.

The synchronously loading statistics feature is disabled by default. To enable this feature, set the value of the tidb_stats_load_sync_wait system variable to a timeout (in milliseconds) that SQL optimization can wait for at most to synchronously load complete column statistics. The default value of this variable is 0, indicating that the feature is disabled.

After enabling the synchronously loading statistics feature, you can further configure the feature as follows:

To control how TiDB behaves when the waiting time of SQL optimization reaches the timeout, modify the value of the tidb_stats_load_pseudo_timeout system variable. The default value of this variable is OFF, indicating that the SQL execution fails after the timeout. If you set this variable to ON, after the timeout, the SQL optimization process does not use any histogram, TopN, or CMSketch statistics on any columns, but gets back to using pseudo statistics.
To specify the maximum number of columns that the synchronously loading statistics feature can process concurrently, modify the value of the stats-load-concurrency option in the TiDB configuration file. The default value is 5.
To specify the maximum number of column requests that the synchronously loading statistics feature can cache, modify the value of the stats-load-queue-size option in the TiDB configuration file. The default value is 1000.

Import and export statistics

Export statistics

The interface to export statistics is as follows:

To obtain the JSON format statistics of the ${table_name} table in the ${db_name} database:

http://${tidb-server-ip}:${tidb-server-status-port}/stats/dump/${db_name}/${table_name}

For example:

curl -s http://127.0.0.1:10080/stats/dump/test/t1 -o /tmp/t1.json

To obtain the JSON format statistics of the ${table_name} table in the ${db_name} database at specific time:

http://${tidb-server-ip}:${tidb-server-status-port}/stats/dump/${db_name}/${table_name}/${yyyyMMddHHmmss}

Import statistics

Note

When you start the MySQL client, use the --local-infile=1 option.

Generally, the imported statistics refer to the JSON file obtained using the export interface.

Syntax:

LOAD STATS 'file_name'

file_name is the file name of the statistics to be imported.

Introduction to Statistics

Histogram

Count-Min Sketch

Top-N values

Collect statistics

Manual collection

Full collection

Collect statistics on some columns

Collect statistics on indexes

Collect statistics on partitions

Incremental collection

Automatic update

Control ANALYZE concurrency

tidb_build_stats_concurrency

tidb_distsql_scan_concurrency

tidb_index_serial_scan_concurrency

Persist ANALYZE configurations

Enable ANALYZE configuration persistence

Disable ANALYZE configuration persistence

View ANALYZE state

View statistics

Metadata of tables

Health state of tables

Metadata of columns

Buckets of histogram

Top-N information

Delete statistics

Load statistics

Import and export statistics

Export statistics

Import statistics

See also

Control `ANALYZE` concurrency

`tidb_build_stats_concurrency`

`tidb_distsql_scan_concurrency`

`tidb_index_serial_scan_concurrency`

View `ANALYZE` state