Partitioning

PostgreSQL 8.2.6 Documentation
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# 5.9. Partitioning

PostgreSQL supports basic table partitioning. This section describes why and how to implement partitioning as part of your database design.

## 5.9.1. Overview

Partitioning refers to splitting what is logically one large table into smaller physical pieces. Partitioning can provide several benefits:

- Query performance can be improved dramatically in certain situations, particularly when most of the heavily accessed rows of the table are in a single partition or a small number of partitions. The partitioning substitutes for leading columns of indexes, reducing index size and making it more likely that the heavily-used parts of the indexes fit in memory.

- When queries or updates access a large percentage of a single partition, performance can be improved by taking advantage of sequential scan of that partition instead of using an index and random access reads scattered across the whole table.

- Bulk loads and deletes may be accomplished by adding or removing partitions, if that requirement is planned into the partitioning design. `ALTER TABLE` is far faster than a bulk operation. It also entirely avoids the `VACUUM` overhead caused by a bulk `DELETE`.

- Seldom-used data can be migrated to cheaper and slower storage media.

The benefits will normally be worthwhile only when a table would otherwise be very large. The exact point at which a table will benefit from partitioning depends on the application, although a rule of thumb is that the size of the table should exceed the physical memory of the database server.

Currently, PostgreSQL supports partitioning via table inheritance. Each partition must be created as a child table of a single parent table. The parent table itself is normally empty; it exists just to represent the entire data set. You should be familiar with inheritance (see Section 5.8) before attempting to set up partitioning.

The following forms of partitioning can be implemented in PostgreSQL:

Range Partitioning

  The table is partitioned into "ranges" defined by a key column or set of columns, with no overlap between the ranges of values assigned to different partitions. For example one might partition by date ranges, or by ranges of identifiers for particular business objects.

List Partitioning

  The table is partitioned by explicitly listing which key values appear in each partition.

## 5.9.2. Implementing Partitioning

To set up a partitioned table, do the following:

1. Create the "master" table, from which all of the partitions will inherit.

   This table will contain no data. Do not define any check constraints on this table, unless you intend them to be applied equally to all partitions. There is no point in defining any indexes or unique constraints on it, either.

2. Create several "child" tables that each inherit from the master table. Normally, these tables will not add any columns to the set inherited from the master.

   We will refer to the child tables as partitions, though they are in every way normal PostgreSQL tables.

3. Add table constraints to the partition tables to define the allowed key values in each partition.

   Typical examples would be:

       CHECK ( x = 1 )
       CHECK ( county IN ( 'Oxfordshire', 'Buckinghamshire', 'Warwickshire' ))
       CHECK ( outletID >= 100 AND outletID < 200 )

   Ensure that the constraints guarantee that there is no overlap between the key values permitted in different partitions. A common mistake is to set up range constraints like this:

       CHECK ( outletID BETWEEN 100 AND 200 )
       CHECK ( outletID BETWEEN 200 AND 300 )

   This is wrong since it is not clear which partition the key value 200 belongs in.

   Note that there is no difference in syntax between range and list partitioning; those terms are descriptive only.

4. For each partition, create an index on the key column(s), as well as any other indexes you might want. (The key index is not strictly necessary, but in most scenarios it is helpful. If you intend the key values to be unique then you should always create a unique or primary-key constraint for each partition.)

5. Optionally, define a rule or trigger to redirect modifications of the master table to the appropriate partition.

6. Ensure that the constraint_exclusion configuration parameter is enabled in `postgresql.conf`. Without this, queries will not be optimized as desired.

For example, suppose we are constructing a database for a large ice cream company. The company measures peak temperatures every day as well as ice cream sales in each region. Conceptually, we want a table like this:

    CREATE TABLE measurement (
        city_id         int not null,
        logdate         date not null,
        peaktemp        int,
        unitsales       int
    );

We know that most queries will access just the last week's, month's or quarter's data, since the main use of this table will be to prepare online reports for management. To reduce the amount of old data that needs to be stored, we decide to only keep the most recent 3 years worth of data. At the beginning of each month we will remove the oldest month's data.

In this situation we can use partitioning to help us meet all of our different requirements for the measurements table. Following the steps outlined above, partitioning can be set up as follows:

1. The master table is the `measurement` table, declared exactly as above.

2. Next we create one partition for each active month:

       CREATE TABLE measurement_y2004m02 ( ) INHERITS (measurement);
       CREATE TABLE measurement_y2004m03 ( ) INHERITS (measurement);
       ...
       CREATE TABLE measurement_y2005m11 ( ) INHERITS (measurement);
       CREATE TABLE measurement_y2005m12 ( ) INHERITS (measurement);
       CREATE TABLE measurement_y2006m01 ( ) INHERITS (measurement);

   Each of the partitions are complete tables in their own right, but they inherit their definition from the `measurement` table.

   This solves one of our problems: deleting old data. Each month, all we will need to do is perform a `DROP TABLE` on the oldest child table and create a new child table for the new month's data.

3. We must add non-overlapping table constraints, so that our table creation script becomes:

       CREATE TABLE measurement_y2004m02 (
           CHECK ( logdate >= DATE '2004-02-01' AND logdate < DATE '2004-03-01' )
       ) INHERITS (measurement);
       CREATE TABLE measurement_y2004m03 (
           CHECK ( logdate >= DATE '2004-03-01' AND logdate < DATE '2004-04-01' )
       ) INHERITS (measurement);
       ...
       CREATE TABLE measurement_y2005m11 (
           CHECK ( logdate >= DATE '2005-11-01' AND logdate < DATE '2005-12-01' )
       ) INHERITS (measurement);
       CREATE TABLE measurement_y2005m12 (
           CHECK ( logdate >= DATE '2005-12-01' AND logdate < DATE '2006-01-01' )
       ) INHERITS (measurement);
       CREATE TABLE measurement_y2006m01 (
           CHECK ( logdate >= DATE '2006-01-01' AND logdate < DATE '2006-02-01' )
       ) INHERITS (measurement);

4. We probably need indexes on the key columns too:

       CREATE INDEX measurement_y2004m02_logdate ON measurement_y2004m02 (logdate);
       CREATE INDEX measurement_y2004m03_logdate ON measurement_y2004m03 (logdate);
       ...
       CREATE INDEX measurement_y2005m11_logdate ON measurement_y2005m11 (logdate);
       CREATE INDEX measurement_y2005m12_logdate ON measurement_y2005m12 (logdate);
       CREATE INDEX measurement_y2006m01_logdate ON measurement_y2006m01 (logdate);

   We choose not to add further indexes at this time.

5. If data will be added only to the latest partition, we can set up a very simple rule to insert data. We must redefine this each month so that it always points to the current partition.

       CREATE OR REPLACE RULE measurement_current_partition AS
       ON INSERT TO measurement
       DO INSTEAD
           INSERT INTO measurement_y2006m01 VALUES ( NEW.city_id,
                                                     NEW.logdate,
                                                     NEW.peaktemp,
                                                     NEW.unitsales );

   We might want to insert data and have the server automatically locate the partition into which the row should be added. We could do this with a more complex set of rules as shown below.

       CREATE RULE measurement_insert_y2004m02 AS
       ON INSERT TO measurement WHERE
           ( logdate >= DATE '2004-02-01' AND logdate < DATE '2004-03-01' )
       DO INSTEAD
           INSERT INTO measurement_y2004m02 VALUES ( NEW.city_id,
                                                     NEW.logdate,
                                                     NEW.peaktemp,
                                                     NEW.unitsales );
       ...
       CREATE RULE measurement_insert_y2005m12 AS
       ON INSERT TO measurement WHERE
           ( logdate >= DATE '2005-12-01' AND logdate < DATE '2006-01-01' )
       DO INSTEAD
           INSERT INTO measurement_y2005m12 VALUES ( NEW.city_id,
                                                     NEW.logdate,
                                                     NEW.peaktemp,
                                                     NEW.unitsales );
       CREATE RULE measurement_insert_y2006m01 AS
       ON INSERT TO measurement WHERE
           ( logdate >= DATE '2006-01-01' AND logdate < DATE '2006-02-01' )
       DO INSTEAD
           INSERT INTO measurement_y2006m01 VALUES ( NEW.city_id,
                                                     NEW.logdate,
                                                     NEW.peaktemp,
                                                     NEW.unitsales );

   Note that the `WHERE` clause in each rule exactly matches the `CHECK` constraint for its partition.

As we can see, a complex partitioning scheme could require a substantial amount of DDL. In the above example we would be creating a new partition each month, so it may be wise to write a script that generates the required DDL automatically.

Partitioning can also be arranged using a `UNION ALL` view:

    CREATE VIEW measurement AS
              SELECT * FROM measurement_y2004m02
    UNION ALL SELECT * FROM measurement_y2004m03
    ...
    UNION ALL SELECT * FROM measurement_y2005m11
    UNION ALL SELECT * FROM measurement_y2005m12
    UNION ALL SELECT * FROM measurement_y2006m01;

However, the need to recreate the view adds an extra step to adding and dropping individual partitions of the data set.

## 5.9.3. Managing Partitions

Normally the set of partitions established when initially defining the table are not intended to remain static. It is common to want to remove old partitions of data and periodically add new partitions for new data. One of the most important advantages of partitioning is precisely that it allows this otherwise painful task to be executed nearly instantaneously by manipulating the partition structure, rather than physically moving large amounts of data around.

The simplest option for removing old data is simply to drop the partition that is no longer necessary:

    DROP TABLE measurement_y2003m02;

This can very quickly delete millions of records because it doesn't have to individually delete every record.

Another option that is often preferable is to remove the partition from the partitioned table but retain access to it as a table in its own right:

    ALTER TABLE measurement_y2003m02 NO INHERIT measurement;

This allows further operations to be performed on the data before it is dropped. For example, this is often a useful time to back up the data using `COPY`, pg_dump, or similar tools. It can also be a useful time to aggregate data into smaller formats, perform other data manipulations, or run reports.

Similarly we can add a new partition to handle new data. We can create an empty partition in the partitioned table just as the original partitions were created above.

    CREATE TABLE measurement_y2006m02 (
        CHECK ( logdate >= DATE '2006-02-01' AND logdate < DATE '2006-03-01' )
    ) INHERITS (measurement);

As an alternative, it is sometimes more convenient to create the new table outside the partition structure, and make it a proper partition later. This allows the data to be loaded, checked, and transformed prior to it appearing in the partitioned table.

    CREATE TABLE measurement_y2006m02
      (LIKE measurement INCLUDING DEFAULTS INCLUDING CONSTRAINTS);
    ALTER TABLE measurement_y2006m02 ADD CONSTRAINT y2006m02
       CHECK ( logdate >= DATE '2006-02-01' AND logdate < DATE '2006-03-01' );
    \copy measurement_y2006m02 from 'measurement_y2006m02'
    -- possibly some other data preparation work
    ALTER TABLE measurement_y2006m02 INHERIT measurement;

## 5.9.4. Partitioning and Constraint Exclusion

Constraint exclusion is a query optimization technique that improves performance for partitioned tables defined in the fashion described above. As an example:

    SET constraint_exclusion = on;
    SELECT count(*) FROM measurement WHERE logdate >= DATE '2006-01-01';

Without constraint exclusion, the above query would scan each of the partitions of the `measurement` table. With constraint exclusion enabled, the planner will examine the constraints of each partition and try to prove that the partition need not be scanned because it could not contain any rows meeting the query's `WHERE` clause. When the planner can prove this, it excludes the partition from the query plan.

You can use the `EXPLAIN` command to show the difference between a plan with `constraint_exclusion` on and a plan with it off. A typical default plan for this type of table setup is:

    SET constraint_exclusion = off;
    EXPLAIN SELECT count(*) FROM measurement WHERE logdate >= DATE '2006-01-01';

                                              QUERY PLAN
    -----------------------------------------------------------------------------------------------
     Aggregate  (cost=158.66..158.68 rows=1 width=0)
       ->  Append  (cost=0.00..151.88 rows=2715 width=0)
             ->  Seq Scan on measurement  (cost=0.00..30.38 rows=543 width=0)
                   Filter: (logdate >= '2006-01-01'::date)
             ->  Seq Scan on measurement_y2004m02 measurement  (cost=0.00..30.38 rows=543 width=0)
                   Filter: (logdate >= '2006-01-01'::date)
             ->  Seq Scan on measurement_y2004m03 measurement  (cost=0.00..30.38 rows=543 width=0)
                   Filter: (logdate >= '2006-01-01'::date)
    ...
             ->  Seq Scan on measurement_y2005m12 measurement  (cost=0.00..30.38 rows=543 width=0)
                   Filter: (logdate >= '2006-01-01'::date)
             ->  Seq Scan on measurement_y2006m01 measurement  (cost=0.00..30.38 rows=543 width=0)
                   Filter: (logdate >= '2006-01-01'::date)

Some or all of the partitions might use index scans instead of full-table sequential scans, but the point here is that there is no need to scan the older partitions at all to answer this query. When we enable constraint exclusion, we get a significantly reduced plan that will deliver the same answer:

    SET constraint_exclusion = on;
    EXPLAIN SELECT count(*) FROM measurement WHERE logdate >= DATE '2006-01-01';
                                              QUERY PLAN
    -----------------------------------------------------------------------------------------------
     Aggregate  (cost=63.47..63.48 rows=1 width=0)
       ->  Append  (cost=0.00..60.75 rows=1086 width=0)
             ->  Seq Scan on measurement  (cost=0.00..30.38 rows=543 width=0)
                   Filter: (logdate >= '2006-01-01'::date)
             ->  Seq Scan on measurement_y2006m01 measurement  (cost=0.00..30.38 rows=543 width=0)
                   Filter: (logdate >= '2006-01-01'::date)

Note that constraint exclusion is driven only by `CHECK` constraints, not by the presence of indexes. Therefore it isn't necessary to define indexes on the key columns. Whether an index needs to be created for a given partition depends on whether you expect that queries that scan the partition will generally scan a large part of the partition or just a small part. An index will be helpful in the latter case but not the former.

## 5.9.5. Caveats

The following caveats apply to partitioned tables:

- There is currently no way to verify that all of the `CHECK` constraints are mutually exclusive. Care is required by the database designer.

- There is currently no simple way to specify that rows must not be inserted into the master table. A `CHECK (false)` constraint on the master table would be inherited by all child tables, so that cannot be used for this purpose. One possibility is to set up an `ON INSERT` trigger on the master table that always raises an error. (Alternatively, such a trigger could be used to redirect the data into the proper child table, instead of using a set of rules as suggested above.)

The following caveats apply to constraint exclusion:

- Constraint exclusion only works when the query's `WHERE` clause contains constants. A parameterized query will not be optimized, since the planner cannot know what partitions the parameter value might select at run time. For the same reason, "stable" functions such as `CURRENT_DATE` must be avoided.

- Avoid cross-data type comparisons in the `CHECK` constraints, as the planner will currently fail to prove such conditions false. For example, the following constraint will work if `x` is an `integer` column, but not if `x` is a `bigint`:

      CHECK ( x = 1 )

  For a `bigint` column we must use a constraint like:

      CHECK ( x = 1::bigint )

  The problem is not limited to the `bigint` data type — it can occur whenever the default data type of the constant does not match the data type of the column to which it is being compared. Cross-data type comparisons in the supplied queries are usually OK, just not in the `CHECK` conditions.

- All constraints on all partitions of the master table are considered for constraint exclusion, so large numbers of partitions are likely to increase query planning time considerably.

- Don't forget that you still need to run `ANALYZE` on each partition individually. A command like

      ANALYZE measurement;

  will only process the master table.

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