# 10.2. Statistics Used by the Planner

As we saw in the previous section, the query planner needs to estimate the number of rows retrieved by a query in order to make good choices of query plans. This section provides a quick look at the statistics that the system uses for these estimates.

One component of the statistics is the total number of entries in each table and index, as well as the number of disk blocks occupied by each table and index. This information is kept in pg_class's reltuples and relpages columns. We can look at it with queries similar to this one:

```regression=# SELECT relname, relkind, reltuples, relpages FROM pg_class
regression-# WHERE relname LIKE 'tenk1%';
relname    | relkind | reltuples | relpages
---------------+---------+-----------+----------
tenk1         | r       |     10000 |      233
tenk1_hundred | i       |     10000 |       30
tenk1_unique1 | i       |     10000 |       30
tenk1_unique2 | i       |     10000 |       30
(4 rows)```

Here we can see that tenk1 contains 10000 rows, as do its indexes, but the indexes are (unsurprisingly) much smaller than the table.

For efficiency reasons, reltuples and relpages are not updated on-the-fly, and so they usually contain only approximate values (which is good enough for the planner's purposes). They are initialized with dummy values (presently 1000 and 10 respectively) when a table is created. They are updated by certain commands, presently VACUUM, ANALYZE, and CREATE INDEX. A stand-alone ANALYZE, that is one not part of VACUUM, generates an approximate reltuples value since it does not read every row of the table.

Most queries retrieve only a fraction of the rows in a table, due to having WHERE clauses that restrict the rows to be examined. The planner thus needs to make an estimate of the selectivity of WHERE clauses, that is, the fraction of rows that match each clause of the WHERE condition. The information used for this task is stored in the pg_statistic system catalog. Entries in pg_statistic are updated by ANALYZE and VACUUM ANALYZE commands, and are always approximate even when freshly updated.

Rather than look at pg_statistic directly, it's better to look at its view pg_stats when examining the statistics manually. pg_stats is designed to be more easily readable. Furthermore, pg_stats is readable by all, whereas pg_statistic is only readable by the superuser. (This prevents unprivileged users from learning something about the contents of other people's tables from the statistics. The pg_stats view is restricted to show only rows about tables that the current user can read.) For example, we might do:

```regression=# SELECT attname, n_distinct, most_common_vals FROM pg_stats WHERE tablename = 'road';
attname | n_distinct |                                                                                                                                                                                  most_common_vals
---------+------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
name    |  -0.467008 | {"I- 580                        Ramp","I- 880                        Ramp","Sp Railroad                       ","I- 580                            ","I- 680                        Ramp","I- 80                         Ramp","14th                          St  ","5th                           St  ","Mission                       Blvd","I- 880                            "}
thepath |         20 | {"[(-122.089,37.71),(-122.0886,37.711)]"}
(2 rows)
regression=#```

Table 10-1 shows the columns that exist in pg_stats.

Table 10-1. pg_stats Columns

NameTypeDescription
tablenamenameName of the table containing the column
attnamenameColumn described by this row
null_fracrealFraction of column's entries that are null
avg_widthintegerAverage width in bytes of the column's entries
n_distinctrealIf greater than zero, the estimated number of distinct values in the column. If less than zero, the negative of the number of distinct values divided by the number of rows. (The negated form is used when ANALYZE believes that the number of distinct values is likely to increase as the table grows; the positive form is used when the column seems to have a fixed number of possible values.) For example, -1 indicates a unique column in which the number of distinct values is the same as the number of rows.
most_common_valstext[]A list of the most common values in the column. (Omitted if no values seem to be more common than any others.)
most_common_freqsreal[]A list of the frequencies of the most common values, i.e., number of occurrences of each divided by total number of rows.
histogram_boundstext[]A list of values that divide the column's values into groups of approximately equal population. The most_common_vals, if present, are omitted from the histogram calculation. (Omitted if column data type does not have a < operator, or if the most_common_vals list accounts for the entire population.)
correlationrealStatistical correlation between physical row ordering and logical ordering of the column values. This ranges from -1 to +1. When the value is near -1 or +1, an index scan on the column will be estimated to be cheaper than when it is near zero, due to reduction of random access to the disk. (Omitted if column data type does not have a < operator.)

The maximum number of entries in the most_common_vals and histogram_bounds arrays can be set on a column-by-column basis using the ALTER TABLE SET STATISTICS command. The default limit is presently 10 entries. Raising the limit may allow more accurate planner estimates to be made, particularly for columns with irregular data distributions, at the price of consuming more space in pg_statistic and slightly more time to compute the estimates. Conversely, a lower limit may be appropriate for columns with simple data distributions.