About Database Statistics in Greenplum Database
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About Database Statistics in Greenplum Database
An overview of statistics gathered by the ANALYZE command in Greenplum Database.
Statistics are metadata that describe the data stored in the database. The query optimizer needs up-to-date statistics to choose the best execution plan for a query. For example, if a query joins two tables and one of them must be broadcast to all segments, the optimizer can choose the smaller of the two tables to minimize network traffic.
- You can run the ANALYZE command directly.
- You can run the analyzedb management utility outside of the database, at the command line.
- An automatic analyze operation can be triggered when DML operations are performed on tables that have no statistics or when a DML operation modifies a number of rows greater than a specified threshold.
Calculating statistics consumes time and resources, so Greenplum Database produces estimates by calculating statistics on samples of large tables. In most cases, the default settings provide the information needed to generate correct execution plans for queries. If the statistics produced are not producing optimal query execution plans, the administrator can tune configuration parameters to produce more accurate stastistics by increasing the sample size or the granularity of statistics saved in the system catalog. Producing more accurate statistics has CPU and storage costs and may not produce better plans, so it is important to view explain plans and test query performance to ensure that the additional statistics-related costs result in better query performance.
System Statistics
Table Size
The query planner seeks to minimize the disk I/O and network traffic required to execute a query, using estimates of the number of rows that must be processed and the number of disk pages the query must access. The data from which these estimates are derived are the pg_class system table columns reltuples and relpages, which contain the number of rows and pages at the time a VACUUM or ANALYZE command was last run. As rows are added or deleted, the numbers become less accurate. However, an accurate count of disk pages is always available from the operating system, so as long as the ratio of reltuples to relpages does not change significantly, the optimizer can produce an estimate of the number of rows that is sufficiently accurate to choose the correct query execution plan.
When the reltuples column differs significantly from the row count returned by SELECT COUNT(*), an analyze should be performed to update the statistics.When a REINDEX command finishes recreating an index, the relpages and reltuples columns are set to zero. The ANALYZE command should be run on the base table to update these columns.
The pg_statistic System Table and pg_stats View
- starelid
- The object ID of the table or index the column belongs to.
- staattnum
- The number of the described column, beginning with 1.
- stainherit
- If true, the statistics include inheritance child columns, not just the values in the specified relation.
- stanullfrac
- The fraction of the column's entries that are null.
- stawidth
- The average stored width, in bytes, of non-null entries.
- stadistinct
- A positive number is an estimate of the number of distinct values in the column; the number is not expected to vary with the number of rows. A negative value is the number of distinct values divided by the number of rows, that is, the ratio of rows with distinct values for the column, negated. This form is used when the number of distinct values increases with the number of rows. A unique column, for example, has an n_distinct value of -1.0. Columns with an average width greater than 1024 are considered unique.
- stakindN
- A code number indicating the kind of statistics stored in the Nth slot of the pg_statistic row.
- staopN
- An operator used to derive the statistics stored in the Nth slot. For example, a histogram slot would show the < operator that defines the sort order of the data.
- stanumbersN
- float4 array containing numerical statistics of the appropriate kind for the Nth slot, or NULL if the slot kind does not involve numerical values.
- stavaluesN
- Column data values of the appropriate kind for the Nth slot, or NULL if the slot kind does not store any data values. Each array's element values are actually of the specific column's data type, so there is no way to define these columns' types more specifically than anyarray.
The statistics collected for a column vary for different data types, so the pg_statistic table stores statistics that are appropriate for the data type in four slots, consisting of four columns per slot. For example, the first slot, which normally contains the most common values for a column, consists of the columns stakind1, staop1, stanumbers1, and stavalues1.
stakind Code | Description |
---|---|
1 |
Most CommonValues (MCV) Slot
|
2 |
Histogram Slot – describes the distribution of scalar data.
If a Most Common Values slot is also provided, then the histogram describes the data distribution after removing the values listed in the MCV array. (It is a compressed histogram in the technical parlance). This allows a more accurate representation of the distribution of a column with some very common values. In a column with only a few distinct values, it is possible that the MCV list describes the entire data population; in this case the histogram reduces to empty and should be omitted. |
3 |
Correlation Slot – describes the correlation between the physical
order of table tuples and the ordering of data values of this column.
|
4 |
Most Common Elements Slot - is similar to a Most Common Values (MCV)
Slot, except that it stores the most common non-null elements of the
column values. This is useful when the column datatype is an array or some other
type with identifiable elements (for instance, tsvector).
Frequencies are measured as the fraction of non-null rows the element value appears in, not the frequency of all rows. Also, the values are sorted into the element type's default order (to support binary search for a particular value). Since this puts the minimum and maximum frequencies at unpredictable spots in stanumbers, there are two extra members of stanumbers that hold copies of the minimum and maximum frequencies. Optionally, there can be a third extra member that holds the frequency of null elements (the frequency is expressed in the same terms: the fraction of non-null rows that contain at least one null element). If this member is omitted, the column is presumed to contain no NULL elements.
Note: For tsvector columns, the stavalues
elements are of type text, even though their representation
within tsvector is not exactly
text.
|
5 |
Distinct Elements Count Histogram Slot - describes the distribution
of the number of distinct element values present in each row of an array-type
column. Only non-null rows are considered, and only non-null elements.
|
99 | Hyperloglog Slot - for child leaf partitions of a partitioned table, stores the hyperloglog_counter created for the sampled data. The hyperloglog_counter data structure is converted into a bytea and stored in a stavalues5 slot of the pg_statistic catalog table. |
- schemaname
- The name of the schema containing the table.
- tablename
- The name of the table.
- attname
- The name of the column this row describes.
- inherited
- If true, the statistics include inheritance child columns.
- null_frac
- The fraction of column entries that are null.
- avg_width
- The average storage width in bytes of the column's entries, calculated as avg(pg_column_size(column_name)).
- n_distinct
- A positive number is an estimate of the number of distinct values in the column; the number is not expected to vary with the number of rows. A negative value is the number of distinct values divided by the number of rows, that is, the ratio of rows with distinct values for the column, negated. This form is used when the number of distinct values increases with the number of rows. A unique column, for example, has an n_distinct value of -1.0. Columns with an average width greater than 1024 are considered unique.
- most_common_vals
- An array containing the most common values in the column, or null if no values seem to be more common. If the n_distinct column is -1, most_common_vals is null. The length of the array is the lesser of the number of actual distinct column values or the value of the default_statistics_target configuration parameter. The number of values can be overridden for a column using ALTER TABLE table SET COLUMN column SET STATISTICS N.
- most_common_freqs
- An array containing the frequencies of the values in the most_common_vals array. This is the number of occurrences of the value divided by the total number of rows. The array is the same length as the most_common_vals array. It is null if most_common_vals is null.
- histogram_bounds
- An array of values that divide the column values into groups of approximately the same size. A histogram can be defined only if there is a max() aggregate function for the column. The number of groups in the histogram is the same as the most_common_vals array size.
- correlation
- Greenplum Database computes correlation statistics for heap tables. These statistics are used by the Postgres Planner. Greenplum does not compute correlation statistics for AO and AOCO tables; the value in this column for these table types is undefined.
- most_common_elems
- An array that contains the most common element values.
- most_common_elem_freqs
- An array that contains common element frequencies.
- elem_count_histogram
- An array that describes the distribution of the number of distinct element values present in each row of an array-type column.
SELECT * from gp_toolkit.gp_stats_missing;
Sampling
When calculating statistics for large tables, Greenplum Database creates a smaller table by sampling the base table. If the table is partitioned, samples are taken from all partitions.
Updating Statistics
Running ANALYZE with no arguments updates statistics for all tables in the database. This could take a very long time, so it is better to analyze tables selectively after data has changed. You can also analyze a subset of the columns in a table, for example columns used in joins, WHERE clauses, SORT clauses, GROUP BY clauses, or HAVING clauses.
Analyzing a severely bloated table can generate poor statistics if the sample contains empty pages, so it is good practice to vacuum a bloated table before analyzing it.
See the SQL Command Reference in the Greenplum Database Reference Guide for details of running the ANALYZE command.
Refer to the Greenplum Database Management Utility Reference for details of running the analyzedb command.
Analyzing Partitioned Tables
When the ANALYZE command is run on a partitioned table, it analyzes each child leaf partition table, one at a time. You can run ANALYZE on just new or changed partition files to avoid analyzing partitions that have not changed.
The analyzedb command-line utility skips unchanged partitions automatically. It also runs concurrent sessions so it can analyze several partitions concurrently. It runs five sessions by default, but the number of sessions can be set from 1 to 10 with the -p command-line option. Each time analyzedb runs, it saves state information for append-optimized tables and partitions in the db_analyze directory in the master data directory. The next time it runs, analyzedb compares the current state of each table with the saved state and skips analyzing a table or partition if it is unchanged. Heap tables are always analyzed.
If GPORCA is enabled (the default), you also need to run ANALYZE or ANALYZE ROOTPARTITION to refresh the root partition statistics. GPORCA requires statistics at the root level for partitioned tables. The Postgres Planner does not use these statistics.
The time to analyze a partitioned table is similar to the time to analyze a non-partitioned table with the same data since ANALYZE ROOTPARTITION does not collect statistics on the leaf partitions (the data is only sampled).
The Greenplum Database server configuration parameter optimizer_analyze_root_partition affects when statistics are collected on the root partition of a partitioned table. If the parameter is on (the default), the ROOTPARTITION keyword is not required to collect statistics on the root partition when you run ANALYZE. Root partition statistics are collected when you run ANALYZE on the root partition, or when you run ANALYZE on a child leaf partition of the partitioned table and the other child leaf partitions have statistics. If the parameter is off, you must run ANALYZE ROOTPARTITION to collect root partition statistics.
If you do not intend to execute queries on partitioned tables with GPORCA (setting the server configuration parameter optimizer to off), you can also set the server configuration parameter optimizer_analyze_root_partition to off to limit when ANALYZE updates the root partition statistics.
Configuring Statistics
There are several options for configuring Greenplum Database statistics collection.
Statistics Target
The statistics target is the size of the most_common_vals, most_common_freqs, and histogram_bounds arrays for an individual column. By default, the target is 25. The default target can be changed by setting a server configuration parameter and the target can be set for any column using the ALTER TABLE command. Larger values increase the time needed to do ANALYZE, but may improve the quality of the Postgres Planner estimates.
gpconfig -c default_statistics_target -v 150
The statististics target for individual columns can be set with the ALTER TABLE command. For example, some queries can be improved by increasing the target for certain columns, especially columns that have irregular distributions. You can set the target to zero for columns that never contribute to query otpimization. When the target is 0, ANALYZE ignores the column. For example, the following ALTER TABLE command sets the statistics target for the notes column in the emp table to zero:
ALTER TABLE emp ALTER COLUMN notes SET STATISTICS 0;
The statistics target can be set in the range 0 to 1000, or set it to -1 to revert to using the system default statistics target.
Setting the statistics target on a parent partition table affects the child partitions. If you set statistics to 0 on some columns on the parent table, the statistics for the same columns are set to 0 for all children partitions. However, if you later add or exchange another child partition, the new child partition will use either the default statistics target or, in the case of an exchange, the previous statistics target. Therefore, if you add or exchange child partitions, you should set the statistics targets on the new child table.
Automatic Statistics Collection
Greenplum Database can be set to automatically run ANALYZE on a table that either has no statistics or has changed significantly when certain operations are performed on the table. For partitioned tables, automatic statistics collection is only triggered when the operation is run directly on a leaf table, and then only the leaf table is analyzed.
- none disables automatic statistics collection.
- on_no_stats triggers an analyze operation for a table with no existing statistics when any of the commands CREATE TABLE AS SELECT, INSERT, or COPY are executed on the table.
- on_change triggers an analyze operation when any of the commands CREATE TABLE AS SELECT, UPDATE, DELETE, INSERT, or COPY are executed on the table and the number of rows affected exceeds the threshold defined by the gp_autostats_on_change_threshold configuration parameter.
- The gp_autostats_mode configuration parameter controls automatic statistics collection behavior outside of functions and is set to on_no_stats by default.
- The gp_autostats_mode_in_functions parameter controls the behavior when table operations are performed within a procedural language function and is set to none by default.
With the on_change mode, ANALYZE is triggered only if the number of rows affected exceeds the threshold defined by the gp_autostats_on_change_threshold configuration parameter. The default value for this parameter is a very high value, 2147483647, which effectively disables automatic statistics collection; you must set the threshold to a lower number to enable it. The on_change mode could trigger large, unexpected analyze operations that could disrupt the system, so it is not recommended to set it globally. It could be useful in a session, for example to automatically analyze a table following a load.
gpconfigure -c gp_autostats_mode -v none
gpconfigure -c gp_autostats_mode_in_functions -v on_no_stats
Set the log_autostats system configuration parameter to on if you want to log automatic statistics collection operations.