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Thecolumn_statistics data dictionary table stores histogram statistics about column values, for use by the optimizer in constructing query execution plans. To perform histogram management, use theANALYZE TABLE statement.

Thecolumn_statistics table has these characteristics:

Thecolumn_statistics table is not directly accessible by users because it is part of the data dictionary. Histogram information is available usingINFORMATION_SCHEMA.COLUMN_STATISTICS, which is implemented as a view on the data dictionary table.COLUMN_STATISTICS has these columns:

Column histograms contain buckets for parts of the range of values stored in the column. Histograms areJSON objects to permit flexibility in the representation of column statistics. Here is a sample histogram object:

{  "buckets": [    [      1,      0.3333333333333333    ],    [      2,      0.6666666666666666    ],    [      3,      1    ]  ],  "null-values": 0,  "last-updated": "2017-03-24 13:32:40.000000",  "sampling-rate": 1,  "histogram-type": "singleton",  "number-of-buckets-specified": 128,  "data-type": "int",  "collation-id": 8}

Histogram objects have these keys:

To extract particular values from the histogram objects, you can useJSON operations. For example:

mysql> SELECT         TABLE_NAME, COLUMN_NAME,         HISTOGRAM->>'$."data-type"' AS 'data-type',         JSON_LENGTH(HISTOGRAM->>'$."buckets"') AS 'bucket-count'       FROM INFORMATION_SCHEMA.COLUMN_STATISTICS;+-----------------+-------------+-----------+--------------+| TABLE_NAME      | COLUMN_NAME | data-type | bucket-count |+-----------------+-------------+-----------+--------------+| country         | Population  | int       |          226 || city            | Population  | int       |         1024 || countrylanguage | Language    | string    |          457 |+-----------------+-------------+-----------+--------------+

The optimizer uses histogram statistics, if applicable, for columns of any data type for which statistics are collected. The optimizer applies histogram statistics to determine row estimates based on the selectivity (filtering effect) of column value comparisons against constant values. Predicates of these forms qualify for histogram use:

col_name =constantcol_name <>constantcol_name !=constantcol_name >constantcol_name <constantcol_name >=constantcol_name <=constantcol_name IS NULLcol_name IS NOT NULLcol_name BETWEENconstant ANDconstantcol_name NOT BETWEENconstant ANDconstantcol_name IN (constant[,constant] ...)col_name NOT IN (constant[,constant] ...)

For example, these statements contain predicates that qualify for histogram use:

SELECT * FROM orders WHERE amount BETWEEN 100.0 AND 300.0;SELECT * FROM tbl WHERE col1 = 15 AND col2 > 100;

The requirement for comparison against a constant value includes functions that are constant, such asABS() andFLOOR():

SELECT * FROM tbl WHERE col1 < ABS(-34);

Histogram statistics are useful primarily for nonindexed columns. Adding an index to a column for which histogram statistics are applicable might also help the optimizer make row estimates. The tradeoffs are:

The optimizer prefers range optimizer row estimates to those obtained from histogram statistics. If the optimizer determines that the range optimizer applies, it does not use histogram statistics.

For columns that are indexed, row estimates can be obtained for equality comparisons using index dives (seeSection 10.2.1.2, “Range Optimization”). In this case, histogram statistics are not necessarily useful because index dives can yield better estimates.

In some cases, use of histogram statistics may not improve query execution (for example, if the statistics are out of date). To check whether this is the case, useANALYZE TABLE to regenerate the histogram statistics, then run the query again.

Alternatively, to disable histogram statistics, useANALYZE TABLE to drop them. A different method of disabling histogram statistics is to turn off thecondition_fanout_filter flag of theoptimizer_switch system variable (although this may disable other optimizations as well):

SET optimizer_switch='condition_fanout_filter=off';

If histogram statistics are used, the resulting effect is visible usingEXPLAIN. Consider the following query, where no index is available for columncol1:

SELECT * FROM t1 WHERE col1 < 24;

If histogram statistics indicate that 57% of the rows int1 satisfy thecol1 < 24 predicate, filtering can occur even in the absence of an index, andEXPLAIN shows 57.00 in thefiltered column.