SHOW STATISTICS - CockroachDB

The SHOW STATISTICS lists used by the . By default, CockroachDB on all indexed columns and up to 100 non-indexed columns, and automatically collects on the columns that prefix each index.

Synopsis

Required Privileges

To list table statistics, the user must have any on the table being inspected.

Parameters

Parameter	Description
`table\_name`	The name of the table to view statistics for.
`opt\_with\_options`	Control the behavior of `SHOW STATISTICS` with these options.

Options

Option	Value	Description
`FORECAST`	N/A	Display forecasted statistics along with the existing table statistics.

Output

Column	Description
`statistics\_name`	The name of the statistics. If `\_\_auto\_\_`, the statistics were created automatically. If `\_\_forecast\_\_`, the statistics are forecasted.
`column\_names`	The name of the columns on which the statistics were created.
`created`	The when the statistics were created.
`row\_count`	The number of rows for which the statistics were computed.
`distinct\_count`	The number of distinct values for which the statistics were computed.
`null\_count`	The number of null values for which the statistics were computed.
`avg\_size`	The average size in bytes of the values of the columns for which the statistics were computed.
`histogram\_id`	The ID of the used to compute statistics.

Examples

Setup

To follow along, run to start a temporary, in-memory cluster with the sample dataset preloaded:

$ cockroach demo

List table statistics

> SHOW STATISTICS FOR TABLE rides;

  statistics_name |       column_names        |          created           | row_count | distinct_count | null_count | avg_size |    histogram_id
------------------+---------------------------+----------------------------+-----------+----------------+------------+----------+---------------------
  __auto__        | {id}                      | 2022-09-22 16:45:29.957103 |       500 |            500 |          0 |       26 | 798866571146067969
  __auto__        | {id,city}                 | 2022-09-22 16:45:29.957103 |       500 |            500 |          0 |       37 |               NULL
  __auto__        | {city}                    | 2022-09-22 16:45:29.957103 |       500 |              9 |          0 |       12 | 798866571134304257
  __auto__        | {city,rider_id}           | 2022-09-22 16:45:29.957103 |       500 |             50 |          0 |       29 |               NULL
  __auto__        | {vehicle_city}            | 2022-09-22 16:45:29.957103 |       500 |              9 |          0 |       11 | 798866571197153281
  __auto__        | {vehicle_city,vehicle_id} | 2022-09-22 16:45:29.957103 |       500 |             15 |          0 |       28 |               NULL
  __auto__        | {rider_id}                | 2022-09-22 16:45:29.957103 |       500 |             50 |          0 |       17 | 798866571173724161
  __auto__        | {vehicle_id}              | 2022-09-22 16:45:29.957103 |       500 |             15 |          0 |       17 | 798866571208720385
  __auto__        | {start_address}           | 2022-09-22 16:45:29.957103 |       500 |            500 |          0 |       25 | 798866571232575489
  __auto__        | {end_address}             | 2022-09-22 16:45:29.957103 |       500 |            500 |          0 |       25 | 798866571245191169
  __auto__        | {start_time}              | 2022-09-22 16:45:29.957103 |       500 |             30 |          0 |        7 | 798866571257315329
  __auto__        | {end_time}                | 2022-09-22 16:45:29.957103 |       500 |            367 |          0 |        7 | 798866571269537793
  __auto__        | {revenue}                 | 2022-09-22 16:45:29.957103 |       500 |            100 |          0 |        6 | 798866571283103745
(13 rows)

Display forecasted statistics

The WITH FORECAST option calculates and displays along with the existing table statistics. The following example shows 3 historical statistics collections and the subsequent forecast:

SHOW STATISTICS FOR TABLE rides WITH FORECAST;

  statistics_name |       column_names        |          created           | row_count | distinct_count | null_count | avg_size |    histogram_id
------------------+---------------------------+----------------------------+-----------+----------------+------------+----------+---------------------
  __auto__        | {id}                      | 2022-09-22 18:57:19.254073 |       500 |            500 |          0 |       26 | 798892488327364609
  __auto__        | {id,city}                 | 2022-09-22 18:57:19.254073 |       500 |            500 |          0 |       37 |               NULL
  __auto__        | {city}                    | 2022-09-22 18:57:19.254073 |       500 |              9 |          0 |       12 | 798892488315830273
  __auto__        | {city,rider_id}           | 2022-09-22 18:57:19.254073 |       500 |             50 |          0 |       29 |               NULL
  __auto__        | {vehicle_city}            | 2022-09-22 18:57:19.254073 |       500 |              9 |          0 |       11 | 798892488400011265
  __auto__        | {vehicle_city,vehicle_id} | 2022-09-22 18:57:19.254073 |       500 |             15 |          0 |       28 |               NULL
  __auto__        | {rider_id}                | 2022-09-22 18:57:19.254073 |       500 |             50 |          0 |       17 | 798892488351875073
  __auto__        | {vehicle_id}              | 2022-09-22 18:57:19.254073 |       500 |             15 |          0 |       17 | 798892488412004353
  __auto__        | {start_address}           | 2022-09-22 18:57:19.254073 |       500 |            500 |          0 |       25 | 798892488436908033
  __auto__        | {end_address}             | 2022-09-22 18:57:19.254073 |       500 |            500 |          0 |       25 | 798892488447590401
  __auto__        | {start_time}              | 2022-09-22 18:57:19.254073 |       500 |             30 |          0 |        7 | 798892488458928129
  __auto__        | {end_time}                | 2022-09-22 18:57:19.254073 |       500 |            367 |          0 |        7 | 798892488472920065
  __auto__        | {revenue}                 | 2022-09-22 18:57:19.254073 |       500 |            100 |          0 |        6 | 798892488485011457
  __auto__        | {id}                      | 2022-09-22 19:35:13.274435 |       500 |            500 |          0 |       26 | 798899939842326529
  __auto__        | {id,city}                 | 2022-09-22 19:35:13.274435 |       500 |            500 |          0 |       37 |               NULL
  __auto__        | {city}                    | 2022-09-22 19:35:13.274435 |       500 |              9 |          0 |       12 | 798899939828039681
  __auto__        | {city,rider_id}           | 2022-09-22 19:35:13.274435 |       500 |             50 |          0 |       29 |               NULL
  __auto__        | {vehicle_city}            | 2022-09-22 19:35:13.274435 |       500 |              9 |          0 |       11 | 798899939903963137
  __auto__        | {vehicle_city,vehicle_id} | 2022-09-22 19:35:13.274435 |       500 |             15 |          0 |       28 |               NULL
  __auto__        | {rider_id}                | 2022-09-22 19:35:13.274435 |       500 |             50 |          0 |       17 | 798899939874242561
  __auto__        | {vehicle_id}              | 2022-09-22 19:35:13.274435 |       500 |             15 |          0 |       17 | 798899939921068033
  __auto__        | {start_address}           | 2022-09-22 19:35:13.274435 |       500 |            500 |          0 |       25 | 798899939972808705
  __auto__        | {end_address}             | 2022-09-22 19:35:13.274435 |       500 |            500 |          0 |       25 | 798899939987783681
  __auto__        | {start_time}              | 2022-09-22 19:35:13.274435 |       500 |             30 |          0 |        7 | 798899939956031489
  __auto__        | {end_time}                | 2022-09-22 19:35:13.274435 |       500 |            367 |          0 |        7 | 798899940003348481
  __auto__        | {revenue}                 | 2022-09-22 19:35:13.274435 |       500 |            100 |          0 |        6 | 798899940018978817
  __auto__        | {id}                      | 2022-09-22 19:37:13.395095 |       500 |            500 |          0 |       26 | 798900333460258817
  __auto__        | {id,city}                 | 2022-09-22 19:37:13.395095 |       500 |            500 |          0 |       37 |               NULL
  __auto__        | {city}                    | 2022-09-22 19:37:13.395095 |       500 |              9 |          0 |       12 | 798900333442203649
  __auto__        | {city,rider_id}           | 2022-09-22 19:37:13.395095 |       500 |             50 |          0 |       29 |               NULL
  __auto__        | {vehicle_city}            | 2022-09-22 19:37:13.395095 |       500 |              9 |          0 |       11 | 798900333525762049
  __auto__        | {vehicle_city,vehicle_id} | 2022-09-22 19:37:13.395095 |       500 |             15 |          0 |       28 |               NULL
  __auto__        | {rider_id}                | 2022-09-22 19:37:13.395095 |       500 |             50 |          0 |       17 | 798900333491945473
  __auto__        | {vehicle_id}              | 2022-09-22 19:37:13.395095 |       500 |             15 |          0 |       17 | 798900333540900865
  __auto__        | {start_address}           | 2022-09-22 19:37:13.395095 |       500 |            500 |          0 |       25 | 798900333573799937
  __auto__        | {end_address}             | 2022-09-22 19:37:13.395095 |       500 |            500 |          0 |       25 | 798900333588676609
  __auto__        | {start_time}              | 2022-09-22 19:37:13.395095 |       500 |             30 |          0 |        7 | 798900333605093377
  __auto__        | {end_time}                | 2022-09-22 19:37:13.395095 |       500 |            367 |          0 |        7 | 798900333623869441
  __auto__        | {revenue}                 | 2022-09-22 19:37:13.395095 |       500 |            100 |          0 |        6 | 798900333639041025
  __forecast__    | {id}                      | 2022-09-22 19:57:10.465606 |       500 |            500 |          0 |       26 |                  0
  __forecast__    | {id,city}                 | 2022-09-22 19:57:10.465606 |       500 |            500 |          0 |       37 |               NULL
  __forecast__    | {city}                    | 2022-09-22 19:57:10.465606 |       500 |              9 |          0 |       12 |                  0
  __forecast__    | {city,rider_id}           | 2022-09-22 19:57:10.465606 |       500 |             50 |          0 |       29 |               NULL
  __forecast__    | {vehicle_city}            | 2022-09-22 19:57:10.465606 |       500 |              9 |          0 |       11 |                  0
  __forecast__    | {vehicle_city,vehicle_id} | 2022-09-22 19:57:10.465606 |       500 |             15 |          0 |       28 |               NULL
  __forecast__    | {rider_id}                | 2022-09-22 19:57:10.465606 |       500 |             50 |          0 |       17 |                  0
  __forecast__    | {vehicle_id}              | 2022-09-22 19:57:10.465606 |       500 |             15 |          0 |       17 |                  0
  __forecast__    | {start_address}           | 2022-09-22 19:57:10.465606 |       500 |            500 |          0 |       25 |                  0
  __forecast__    | {end_address}             | 2022-09-22 19:57:10.465606 |       500 |            500 |          0 |       25 |                  0
  __forecast__    | {start_time}              | 2022-09-22 19:57:10.465606 |       500 |             30 |          0 |        7 |                  0
  __forecast__    | {end_time}                | 2022-09-22 19:57:10.465606 |       500 |            367 |          0 |        7 |                  0
  __forecast__    | {revenue}                 | 2022-09-22 19:57:10.465606 |       500 |            100 |          0 |        6 |                  0
(52 rows)

Delete statistics

To delete statistics for all tables in all databases:

DELETE FROM system.table_statistics WHERE true;

To delete a named set of statistics (e.g, one named “users_stats”), run a query like the following:

DELETE FROM system.table_statistics WHERE name = 'users_stats';

For more information about the DELETE statement, see .

​Synopsis

​Required Privileges

​Parameters

​Options

​Output

​Examples

​Setup

​List table statistics

​Display forecasted statistics

​Delete statistics

​See also