5.11. Table Partitioning

5.11. Table Partitioning
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5.11.1. Overview
5.11.2. Declarative Partitioning
5.11.3. Partitioning Using Inheritance
5.11.4. Partition Pruning
5.11.5. Partitioning and Constraint Exclusion
5.11.6. Best Practices for Declarative Partitioning

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

5.11.1. 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. Partitioning + effectively substitutes for the upper tree levels of indexes, + 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 using a + sequential scan of that partition instead of using an + index, which would require random-access reads scattered across the + whole table. +
+ Bulk loads and deletes can be accomplished by adding or removing + partitions, if the usage pattern is accounted for in the + partitioning design. Dropping an individual partition + using DROP TABLE, or doing ALTER TABLE + DETACH PARTITION, is far faster than a bulk + operation. These commands also entirely avoid the + VACUUM overhead caused by a bulk DELETE. +
+ Seldom-used data can be migrated to cheaper and slower storage media. +

+ + These 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. +

+ PostgreSQL offers built-in support for the + following forms of partitioning: + +

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. + Each range's bounds are understood as being inclusive at the + lower end and exclusive at the upper end. For example, if one + partition's range is from 1 + to 10, and the next one's range is + from 10 to 20, then + value 10 belongs to the second partition not + the first. +
List Partitioning: + The table is partitioned by explicitly listing which key value(s) + appear in each partition. +
Hash Partitioning: + The table is partitioned by specifying a modulus and a remainder for + each partition. Each partition will hold the rows for which the hash + value of the partition key divided by the specified modulus will + produce the specified remainder. +

+ + If your application needs to use other forms of partitioning not listed + above, alternative methods such as inheritance and + UNION ALL views can be used instead. Such methods + offer flexibility but do not have some of the performance benefits + of built-in declarative partitioning. +

5.11.2. Declarative Partitioning

+ PostgreSQL allows you to declare + that a table is divided into partitions. The table that is divided + is referred to as a partitioned table. The + declaration includes the partitioning method + as described above, plus a list of columns or expressions to be used + as the partition key. +

+ The partitioned table itself is a “virtual” table having + no storage of its own. Instead, the storage belongs + to partitions, which are otherwise-ordinary + tables associated with the partitioned table. + Each partition stores a subset of the data as defined by its + partition bounds. + All rows inserted into a partitioned table will be routed to the + appropriate one of the partitions based on the values of the partition + key column(s). + Updating the partition key of a row will cause it to be moved into a + different partition if it no longer satisfies the partition bounds + of its original partition. +

+ Partitions may themselves be defined as partitioned tables, resulting + in sub-partitioning. Although all partitions + must have the same columns as their partitioned parent, partitions may + have their + own indexes, constraints and default values, distinct from those of other + partitions. See CREATE TABLE for more details on + creating partitioned tables and partitions. +

+ It is not possible to turn a regular table into a partitioned table or + vice versa. However, it is possible to add an existing regular or + partitioned table as a partition of a partitioned table, or remove a + partition from a partitioned table turning it into a standalone table; + this can simplify and speed up many maintenance processes. + See ALTER TABLE to learn more about the + ATTACH PARTITION and DETACH PARTITION + sub-commands. +

+ Partitions can also be foreign + tables, although considerable care is needed because it is then + the user's responsibility that the contents of the foreign table + satisfy the partitioning rule. There are some other restrictions as + well. See CREATE FOREIGN TABLE for more + information. +

5.11.2.1. 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: + +

+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 keep only 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. +

+ To use declarative partitioning in this case, use the following steps: + +

+ Create the measurement table as a partitioned + table by specifying the PARTITION BY clause, which + includes the partitioning method (RANGE in this + case) and the list of column(s) to use as the partition key. + +
```
+CREATE TABLE measurement (
+    city_id         int not null,
+    logdate         date not null,
+    peaktemp        int,
+    unitsales       int
+) PARTITION BY RANGE (logdate);
+
```
+
+ Create partitions. Each partition's definition must specify bounds + that correspond to the partitioning method and partition key of the + parent. Note that specifying bounds such that the new partition's + values would overlap with those in one or more existing partitions will + cause an error. +
+ Partitions thus created are in every way normal + PostgreSQL + tables (or, possibly, foreign tables). It is possible to specify a + tablespace and storage parameters for each partition separately. +
+ For our example, each partition should hold one month's worth of + data, to match the requirement of deleting one month's data at a + time. So the commands might look like: + +
```
+CREATE TABLE measurement_y2006m02 PARTITION OF measurement
+    FOR VALUES FROM ('2006-02-01') TO ('2006-03-01');
+
+CREATE TABLE measurement_y2006m03 PARTITION OF measurement
+    FOR VALUES FROM ('2006-03-01') TO ('2006-04-01');
+
+...
+CREATE TABLE measurement_y2007m11 PARTITION OF measurement
+    FOR VALUES FROM ('2007-11-01') TO ('2007-12-01');
+
+CREATE TABLE measurement_y2007m12 PARTITION OF measurement
+    FOR VALUES FROM ('2007-12-01') TO ('2008-01-01')
+    TABLESPACE fasttablespace;
+
+CREATE TABLE measurement_y2008m01 PARTITION OF measurement
+    FOR VALUES FROM ('2008-01-01') TO ('2008-02-01')
+    WITH (parallel_workers = 4)
+    TABLESPACE fasttablespace;
+
```
+ + (Recall that adjacent partitions can share a bound value, since + range upper bounds are treated as exclusive bounds.) +
+ If you wish to implement sub-partitioning, again specify the + PARTITION BY clause in the commands used to create + individual partitions, for example: + +
```
+CREATE TABLE measurement_y2006m02 PARTITION OF measurement
+    FOR VALUES FROM ('2006-02-01') TO ('2006-03-01')
+    PARTITION BY RANGE (peaktemp);
+
```
+ + After creating partitions of measurement_y2006m02, + any data inserted into measurement that is mapped to + measurement_y2006m02 (or data that is + directly inserted into measurement_y2006m02, + which is allowed provided its partition constraint is satisfied) + will be further redirected to one of its + partitions based on the peaktemp column. The partition + key specified may overlap with the parent's partition key, although + care should be taken when specifying the bounds of a sub-partition + such that the set of data it accepts constitutes a subset of what + the partition's own bounds allow; the system does not try to check + whether that's really the case. +
+ Inserting data into the parent table that does not map + to one of the existing partitions will cause an error; an appropriate + partition must be added manually. +
+ It is not necessary to manually create table constraints describing + the partition boundary conditions for partitions. Such constraints + will be created automatically. +
+ Create an index on the key column(s), as well as any other indexes you + might want, on the partitioned table. (The key index is not strictly + necessary, but in most scenarios it is helpful.) + This automatically creates a matching index on each partition, and + any partitions you create or attach later will also have such an + index. + An index or unique constraint declared on a partitioned table + is “virtual” in the same way that the partitioned table + is: the actual data is in child indexes on the individual partition + tables. + +
```
+CREATE INDEX ON measurement (logdate);
+
```
+
+ Ensure that the enable_partition_pruning + configuration parameter is not disabled in postgresql.conf. + If it is, queries will not be optimized as desired. +

+ In the above example we would be creating a new partition each month, so + it might be wise to write a script that generates the required DDL + automatically. +

5.11.2.2. Partition Maintenance

+ Normally the set of partitions established when initially defining the + table is not intended to remain static. It is common to want to + remove partitions holding old 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 to drop the partition that + is no longer necessary: +

+DROP TABLE measurement_y2006m02;
+

+ This can very quickly delete millions of records because it doesn't have + to individually delete every record. Note however that the above command + requires taking an ACCESS EXCLUSIVE lock on the parent + table. +

+ 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. This has two forms: + +

+ALTER TABLE measurement DETACH PARTITION measurement_y2006m02;
+ALTER TABLE measurement DETACH PARTITION measurement_y2006m02 CONCURRENTLY;
+

+ + These allow 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 might also be a useful time to aggregate data + into smaller formats, perform other data manipulations, or run + reports. The first form of the command requires an + ACCESS EXCLUSIVE lock on the parent table. + Adding the CONCURRENTLY qualifier as in the second + form allows the detach operation to require only + SHARE UPDATE EXCLUSIVE lock on the parent table, but see + ALTER TABLE ... DETACH PARTITION + for details on the restrictions. +

+ 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_y2008m02 PARTITION OF measurement
+    FOR VALUES FROM ('2008-02-01') TO ('2008-03-01')
+    TABLESPACE fasttablespace;
+

+ + 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 new data to be loaded, checked, and + transformed prior to it appearing in the partitioned table. + The CREATE TABLE ... LIKE option is helpful + to avoid tediously repeating the parent table's definition: + +

+CREATE TABLE measurement_y2008m02
+  (LIKE measurement INCLUDING DEFAULTS INCLUDING CONSTRAINTS)
+  TABLESPACE fasttablespace;
+
+ALTER TABLE measurement_y2008m02 ADD CONSTRAINT y2008m02
+   CHECK ( logdate >= DATE '2008-02-01' AND logdate < DATE '2008-03-01' );
+
+\copy measurement_y2008m02 from 'measurement_y2008m02'
+-- possibly some other data preparation work
+
+ALTER TABLE measurement ATTACH PARTITION measurement_y2008m02
+    FOR VALUES FROM ('2008-02-01') TO ('2008-03-01' );
+

+ The ATTACH PARTITION command requires taking a + SHARE UPDATE EXCLUSIVE lock on the partitioned table. +

+ Before running the ATTACH PARTITION command, it is + recommended to create a CHECK constraint on the table to + be attached that matches the expected partition constraint, as + illustrated above. That way, the system will be able to skip the scan + which is otherwise needed to validate the implicit + partition constraint. Without the CHECK constraint, + the table will be scanned to validate the partition constraint while + holding an ACCESS EXCLUSIVE lock on that partition. + It is recommended to drop the now-redundant CHECK + constraint after the ATTACH PARTITION is complete. If + the table being attached is itself a partitioned table, then each of its + sub-partitions will be recursively locked and scanned until either a + suitable CHECK constraint is encountered or the leaf + partitions are reached. +

+ Similarly, if the partitioned table has a DEFAULT + partition, it is recommended to create a CHECK + constraint which excludes the to-be-attached partition's constraint. If + this is not done then the DEFAULT partition will be + scanned to verify that it contains no records which should be located in + the partition being attached. This operation will be performed whilst + holding an ACCESS EXCLUSIVE lock on the + DEFAULT partition. If the DEFAULT partition + is itself a partitioned table, then each of its partitions will be + recursively checked in the same way as the table being attached, as + mentioned above. +

+ As explained above, it is possible to create indexes on partitioned tables + so that they are applied automatically to the entire hierarchy. + This is very + convenient, as not only will the existing partitions become indexed, but + also any partitions that are created in the future will. One limitation is + that it's not possible to use the CONCURRENTLY + qualifier when creating such a partitioned index. To avoid long lock + times, it is possible to use CREATE INDEX ON ONLY + the partitioned table; such an index is marked invalid, and the partitions + do not get the index applied automatically. The indexes on partitions can + be created individually using CONCURRENTLY, and then + attached to the index on the parent using + ALTER INDEX .. ATTACH PARTITION. Once indexes for all + partitions are attached to the parent index, the parent index is marked + valid automatically. Example: +

+CREATE INDEX measurement_usls_idx ON ONLY measurement (unitsales);
+
+CREATE INDEX measurement_usls_200602_idx
+    ON measurement_y2006m02 (unitsales);
+ALTER INDEX measurement_usls_idx
+    ATTACH PARTITION measurement_usls_200602_idx;
+...
+

+ + This technique can be used with UNIQUE and + PRIMARY KEY constraints too; the indexes are created + implicitly when the constraint is created. Example: +

+ALTER TABLE ONLY measurement ADD UNIQUE (city_id, logdate);
+
+ALTER TABLE measurement_y2006m02 ADD UNIQUE (city_id, logdate);
+ALTER INDEX measurement_city_id_logdate_key
+    ATTACH PARTITION measurement_y2006m02_city_id_logdate_key;
+...
+

5.11.2.3. Limitations

+ The following limitations apply to partitioned tables: +

+ Unique constraints (and hence primary keys) on partitioned tables must + include all the partition key columns. This limitation exists because + the individual indexes making up the constraint can only directly + enforce uniqueness within their own partitions; therefore, the + partition structure itself must guarantee that there are not + duplicates in different partitions. +
+ There is no way to create an exclusion constraint spanning the + whole partitioned table. It is only possible to put such a + constraint on each leaf partition individually. Again, this + limitation stems from not being able to enforce cross-partition + restrictions. +
+ BEFORE ROW triggers on INSERT + cannot change which partition is the final destination for a new row. +
+ Mixing temporary and permanent relations in the same partition tree is + not allowed. Hence, if the partitioned table is permanent, so must be + its partitions and likewise if the partitioned table is temporary. When + using temporary relations, all members of the partition tree have to be + from the same session. +

+ Individual partitions are linked to their partitioned table using + inheritance behind-the-scenes. However, it is not possible to use + all of the generic features of inheritance with declaratively + partitioned tables or their partitions, as discussed below. Notably, + a partition cannot have any parents other than the partitioned table + it is a partition of, nor can a table inherit from both a partitioned + table and a regular table. That means partitioned tables and their + partitions never share an inheritance hierarchy with regular tables. +

+ Since a partition hierarchy consisting of the partitioned table and its + partitions is still an inheritance hierarchy, + tableoid and all the normal rules of + inheritance apply as described in Section 5.10, with + a few exceptions: + +

+ Partitions cannot have columns that are not present in the parent. It + is not possible to specify columns when creating partitions with + CREATE TABLE, nor is it possible to add columns to + partitions after-the-fact using ALTER TABLE. + Tables may be added as a partition with ALTER TABLE + ... ATTACH PARTITION only if their columns exactly match + the parent. +
+ Both CHECK and NOT NULL + constraints of a partitioned table are always inherited by all its + partitions. CHECK constraints that are marked + NO INHERIT are not allowed to be created on + partitioned tables. + You cannot drop a NOT NULL constraint on a + partition's column if the same constraint is present in the parent + table. +
+ Using ONLY to add or drop a constraint on only + the partitioned table is supported as long as there are no + partitions. Once partitions exist, using ONLY + will result in an error. Instead, constraints on the partitions + themselves can be added and (if they are not present in the parent + table) dropped. +
+ As a partitioned table does not have any data itself, attempts to use + TRUNCATE ONLY on a partitioned + table will always return an error. +

5.11.3. Partitioning Using Inheritance

+ While the built-in declarative partitioning is suitable for most + common use cases, there are some circumstances where a more flexible + approach may be useful. Partitioning can be implemented using table + inheritance, which allows for several features not supported + by declarative partitioning, such as: + +

+ For declarative partitioning, partitions must have exactly the same set + of columns as the partitioned table, whereas with table inheritance, + child tables may have extra columns not present in the parent. +
+ Table inheritance allows for multiple inheritance. +
+ Declarative partitioning only supports range, list and hash + partitioning, whereas table inheritance allows data to be divided in a + manner of the user's choosing. (Note, however, that if constraint + exclusion is unable to prune child tables effectively, query performance + might be poor.) +

5.11.3.1. Example

+ This example builds a partitioning structure equivalent to the + declarative partitioning example above. Use + the following steps: + +

+ Create the “root” table, from which all of the + “child” tables 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 child tables. There is no point in + defining any indexes or unique constraints on it, either. For our + example, the root table is the measurement + table as originally defined: + +
```
+CREATE TABLE measurement (
+    city_id         int not null,
+    logdate         date not null,
+    peaktemp        int,
+    unitsales       int
+);
+
```
+

+ Create several “child” tables that each inherit from + the root table. Normally, these tables will not add any columns + to the set inherited from the root. Just as with declarative + partitioning, these tables are in every way normal + PostgreSQL tables (or foreign tables). +

+CREATE TABLE measurement_y2006m02 () INHERITS (measurement);
+CREATE TABLE measurement_y2006m03 () INHERITS (measurement);
+...
+CREATE TABLE measurement_y2007m11 () INHERITS (measurement);
+CREATE TABLE measurement_y2007m12 () INHERITS (measurement);
+CREATE TABLE measurement_y2008m01 () INHERITS (measurement);
+

+ Add non-overlapping table constraints to the child tables to + define the allowed key values in each. +

+ 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 child tables. A common + mistake is to set up range constraints like: +

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

+ This is wrong since it is not clear which child table the key + value 200 belongs in. + Instead, ranges should be defined in this style: + +

+CREATE TABLE measurement_y2006m02 (
+    CHECK ( logdate >= DATE '2006-02-01' AND logdate < DATE '2006-03-01' )
+) INHERITS (measurement);
+
+CREATE TABLE measurement_y2006m03 (
+    CHECK ( logdate >= DATE '2006-03-01' AND logdate < DATE '2006-04-01' )
+) INHERITS (measurement);
+
+...
+CREATE TABLE measurement_y2007m11 (
+    CHECK ( logdate >= DATE '2007-11-01' AND logdate < DATE '2007-12-01' )
+) INHERITS (measurement);
+
+CREATE TABLE measurement_y2007m12 (
+    CHECK ( logdate >= DATE '2007-12-01' AND logdate < DATE '2008-01-01' )
+) INHERITS (measurement);
+
+CREATE TABLE measurement_y2008m01 (
+    CHECK ( logdate >= DATE '2008-01-01' AND logdate < DATE '2008-02-01' )
+) INHERITS (measurement);
+

+ For each child table, create an index on the key column(s), + as well as any other indexes you might want. +

+CREATE INDEX measurement_y2006m02_logdate ON measurement_y2006m02 (logdate);
+CREATE INDEX measurement_y2006m03_logdate ON measurement_y2006m03 (logdate);
+CREATE INDEX measurement_y2007m11_logdate ON measurement_y2007m11 (logdate);
+CREATE INDEX measurement_y2007m12_logdate ON measurement_y2007m12 (logdate);
+CREATE INDEX measurement_y2008m01_logdate ON measurement_y2008m01 (logdate);
+

+ We want our application to be able to say INSERT INTO + measurement ... and have the data be redirected into the + appropriate child table. We can arrange that by attaching + a suitable trigger function to the root table. + If data will be added only to the latest child, we can + use a very simple trigger function: + +
```
+CREATE OR REPLACE FUNCTION measurement_insert_trigger()
+RETURNS TRIGGER AS $$
+BEGIN
+    INSERT INTO measurement_y2008m01 VALUES (NEW.*);
+    RETURN NULL;
+END;
+$$
+LANGUAGE plpgsql;
+
```
+
+ After creating the function, we create a trigger which + calls the trigger function: + +
```
+CREATE TRIGGER insert_measurement_trigger
+    BEFORE INSERT ON measurement
+    FOR EACH ROW EXECUTE FUNCTION measurement_insert_trigger();
+
```
+ + We must redefine the trigger function each month so that it always + inserts into the current child table. The trigger definition does + not need to be updated, however. +
+ We might want to insert data and have the server automatically + locate the child table into which the row should be added. We + could do this with a more complex trigger function, for example: + +
```
+CREATE OR REPLACE FUNCTION measurement_insert_trigger()
+RETURNS TRIGGER AS $$
+BEGIN
+    IF ( NEW.logdate >= DATE '2006-02-01' AND
+         NEW.logdate < DATE '2006-03-01' ) THEN
+        INSERT INTO measurement_y2006m02 VALUES (NEW.*);
+    ELSIF ( NEW.logdate >= DATE '2006-03-01' AND
+            NEW.logdate < DATE '2006-04-01' ) THEN
+        INSERT INTO measurement_y2006m03 VALUES (NEW.*);
+    ...
+    ELSIF ( NEW.logdate >= DATE '2008-01-01' AND
+            NEW.logdate < DATE '2008-02-01' ) THEN
+        INSERT INTO measurement_y2008m01 VALUES (NEW.*);
+    ELSE
+        RAISE EXCEPTION 'Date out of range.  Fix the measurement_insert_trigger() function!';
+    END IF;
+    RETURN NULL;
+END;
+$$
+LANGUAGE plpgsql;
+
```
+ + The trigger definition is the same as before. + Note that each IF test must exactly match the + CHECK constraint for its child table. +
+ While this function is more complex than the single-month case, + it doesn't need to be updated as often, since branches can be + added in advance of being needed. +
Note
+ In practice, it might be best to check the newest child first, + if most inserts go into that child. For simplicity, we have + shown the trigger's tests in the same order as in other parts + of this example. +
+ A different approach to redirecting inserts into the appropriate + child table is to set up rules, instead of a trigger, on the + root table. For example: + +
```
+CREATE RULE measurement_insert_y2006m02 AS
+ON INSERT TO measurement WHERE
+    ( logdate >= DATE '2006-02-01' AND logdate < DATE '2006-03-01' )
+DO INSTEAD
+    INSERT INTO measurement_y2006m02 VALUES (NEW.*);
+...
+CREATE RULE measurement_insert_y2008m01 AS
+ON INSERT TO measurement WHERE
+    ( logdate >= DATE '2008-01-01' AND logdate < DATE '2008-02-01' )
+DO INSTEAD
+    INSERT INTO measurement_y2008m01 VALUES (NEW.*);
+
```
+ + A rule has significantly more overhead than a trigger, but the + overhead is paid once per query rather than once per row, so this + method might be advantageous for bulk-insert situations. In most + cases, however, the trigger method will offer better performance. +
+ Be aware that COPY ignores rules. If you want to + use COPY to insert data, you'll need to copy into the + correct child table rather than directly into the root. COPY + does fire triggers, so you can use it normally if you use the trigger + approach. +
+ Another disadvantage of the rule approach is that there is no simple + way to force an error if the set of rules doesn't cover the insertion + date; the data will silently go into the root table instead. +
+ Ensure that the constraint_exclusion + configuration parameter is not disabled in + postgresql.conf; otherwise + child tables may be accessed unnecessarily. +

+ As we can see, a complex table hierarchy could require a + substantial amount of DDL. In the above example we would be creating + a new child table each month, so it might be wise to write a script that + generates the required DDL automatically. +

5.11.3.2. Maintenance for Inheritance Partitioning

+ To remove old data quickly, simply drop the child table that is no longer + necessary: +

+DROP TABLE measurement_y2006m02;
+

+ To remove the child table from the inheritance hierarchy table but retain access to + it as a table in its own right: + +

+ALTER TABLE measurement_y2006m02 NO INHERIT measurement;
+

+ To add a new child table to handle new data, create an empty child table + just as the original children were created above: + +

+CREATE TABLE measurement_y2008m02 (
+    CHECK ( logdate >= DATE '2008-02-01' AND logdate < DATE '2008-03-01' )
+) INHERITS (measurement);
+

+ + Alternatively, one may want to create and populate the new child table + before adding it to the table hierarchy. This could allow data to be + loaded, checked, and transformed before being made visible to queries on + the parent table. + +

+CREATE TABLE measurement_y2008m02
+  (LIKE measurement INCLUDING DEFAULTS INCLUDING CONSTRAINTS);
+ALTER TABLE measurement_y2008m02 ADD CONSTRAINT y2008m02
+   CHECK ( logdate >= DATE '2008-02-01' AND logdate < DATE '2008-03-01' );
+\copy measurement_y2008m02 from 'measurement_y2008m02'
+-- possibly some other data preparation work
+ALTER TABLE measurement_y2008m02 INHERIT measurement;
+

5.11.3.3. Caveats

+ The following caveats apply to partitioning implemented using + inheritance: +

+ There is no automatic way to verify that all of the + CHECK constraints are mutually + exclusive. It is safer to create code that generates + child tables and creates and/or modifies associated objects than + to write each by hand. +
+ Indexes and foreign key constraints apply to single tables and not + to their inheritance children, hence they have some + caveats to be aware of. +
+ The schemes shown here assume that the values of a row's key column(s) + never change, or at least do not change enough to require it to move to another partition. + An UPDATE that attempts + to do that will fail because of the CHECK constraints. + If you need to handle such cases, you can put suitable update triggers + on the child tables, but it makes management of the structure + much more complicated. +
+ If you are using manual VACUUM or + ANALYZE commands, don't forget that + you need to run them on each child table individually. A command like: +
```
+ANALYZE measurement;
+
```
+ will only process the root table. +
+ INSERT statements with ON CONFLICT + clauses are unlikely to work as expected, as the ON CONFLICT + action is only taken in case of unique violations on the specified + target relation, not its child relations. +
+ Triggers or rules will be needed to route rows to the desired + child table, unless the application is explicitly aware of the + partitioning scheme. Triggers may be complicated to write, and will + be much slower than the tuple routing performed internally by + declarative partitioning. +

5.11.4. Partition Pruning

+ Partition pruning is a query optimization technique + that improves performance for declaratively partitioned tables. + As an example: + +

+SET enable_partition_pruning = on;                 -- the default
+SELECT count(*) FROM measurement WHERE logdate >= DATE '2008-01-01';
+

+ + Without partition pruning, the above query would scan each of the + partitions of the measurement table. With + partition pruning enabled, the planner will examine the definition + of each partition and 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 (prunes) the partition from the query + plan. +

+ By using the EXPLAIN command and the enable_partition_pruning configuration parameter, it's + possible to show the difference between a plan for which partitions have + been pruned and one for which they have not. A typical unoptimized + plan for this type of table setup is: +

+SET enable_partition_pruning = off;
+EXPLAIN SELECT count(*) FROM measurement WHERE logdate >= DATE '2008-01-01';
+                                    QUERY PLAN
+-----------------------------------------------------------------------------------
+ Aggregate  (cost=188.76..188.77 rows=1 width=8)
+   ->  Append  (cost=0.00..181.05 rows=3085 width=0)
+         ->  Seq Scan on measurement_y2006m02  (cost=0.00..33.12 rows=617 width=0)
+               Filter: (logdate >= '2008-01-01'::date)
+         ->  Seq Scan on measurement_y2006m03  (cost=0.00..33.12 rows=617 width=0)
+               Filter: (logdate >= '2008-01-01'::date)
+...
+         ->  Seq Scan on measurement_y2007m11  (cost=0.00..33.12 rows=617 width=0)
+               Filter: (logdate >= '2008-01-01'::date)
+         ->  Seq Scan on measurement_y2007m12  (cost=0.00..33.12 rows=617 width=0)
+               Filter: (logdate >= '2008-01-01'::date)
+         ->  Seq Scan on measurement_y2008m01  (cost=0.00..33.12 rows=617 width=0)
+               Filter: (logdate >= '2008-01-01'::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 partition pruning, we get a significantly + cheaper plan that will deliver the same answer: +

+SET enable_partition_pruning = on;
+EXPLAIN SELECT count(*) FROM measurement WHERE logdate >= DATE '2008-01-01';
+                                    QUERY PLAN
+-----------------------------------------------------------------------------------
+ Aggregate  (cost=37.75..37.76 rows=1 width=8)
+   ->  Seq Scan on measurement_y2008m01  (cost=0.00..33.12 rows=617 width=0)
+         Filter: (logdate >= '2008-01-01'::date)
+

+ Note that partition pruning is driven only by the constraints defined + implicitly by the partition keys, 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. +

+ Partition pruning can be performed not only during the planning of a + given query, but also during its execution. This is useful as it can + allow more partitions to be pruned when clauses contain expressions + whose values are not known at query planning time, for example, + parameters defined in a PREPARE statement, using a + value obtained from a subquery, or using a parameterized value on the + inner side of a nested loop join. Partition pruning during execution + can be performed at any of the following times: + +

+ During initialization of the query plan. Partition pruning can be + performed here for parameter values which are known during the + initialization phase of execution. Partitions which are pruned + during this stage will not show up in the query's + EXPLAIN or EXPLAIN ANALYZE. + It is possible to determine the number of partitions which were + removed during this phase by observing the + “Subplans Removed” property in the + EXPLAIN output. +
+ During actual execution of the query plan. Partition pruning may + also be performed here to remove partitions using values which are + only known during actual query execution. This includes values + from subqueries and values from execution-time parameters such as + those from parameterized nested loop joins. Since the value of + these parameters may change many times during the execution of the + query, partition pruning is performed whenever one of the + execution parameters being used by partition pruning changes. + Determining if partitions were pruned during this phase requires + careful inspection of the loops property in + the EXPLAIN ANALYZE output. Subplans + corresponding to different partitions may have different values + for it depending on how many times each of them was pruned during + execution. Some may be shown as (never executed) + if they were pruned every time. +

+ Partition pruning can be disabled using the + enable_partition_pruning setting. +

5.11.5. Partitioning and Constraint Exclusion

+ Constraint exclusion is a query optimization + technique similar to partition pruning. While it is primarily used + for partitioning implemented using the legacy inheritance method, it can be + used for other purposes, including with declarative partitioning. +

+ Constraint exclusion works in a very similar way to partition + pruning, except that it uses each table's CHECK + constraints — which gives it its name — whereas partition + pruning uses the table's partition bounds, which exist only in the + case of declarative partitioning. Another difference is that + constraint exclusion is only applied at plan time; there is no attempt + to remove partitions at execution time. +

+ The fact that constraint exclusion uses CHECK + constraints, which makes it slow compared to partition pruning, can + sometimes be used as an advantage: because constraints can be defined + even on declaratively-partitioned tables, in addition to their internal + partition bounds, constraint exclusion may be able + to elide additional partitions from the query plan. +

+ The default (and recommended) setting of + constraint_exclusion is neither + on nor off, but an intermediate setting + called partition, which causes the technique to be + applied only to queries that are likely to be working on inheritance partitioned + tables. The on setting causes the planner to examine + CHECK constraints in all queries, even simple ones that + are unlikely to benefit. +

+ The following caveats apply to constraint exclusion: + +

+ Constraint exclusion is only applied during query planning, unlike + partition pruning, which can also be applied during query execution. +
+ Constraint exclusion only works when the query's WHERE + clause contains constants (or externally supplied parameters). + For example, a comparison against a non-immutable function such as + CURRENT_TIMESTAMP cannot be optimized, since the + planner cannot know which child table the function's value might fall + into at run time. +
+ Keep the partitioning constraints simple, else the planner may not be + able to prove that child tables might not need to be visited. Use simple + equality conditions for list partitioning, or simple + range tests for range partitioning, as illustrated in the preceding + examples. A good rule of thumb is that partitioning constraints should + contain only comparisons of the partitioning column(s) to constants + using B-tree-indexable operators, because only B-tree-indexable + column(s) are allowed in the partition key. +
+ All constraints on all children of the parent table are examined + during constraint exclusion, so large numbers of children are likely + to increase query planning time considerably. So the legacy + inheritance based partitioning will work well with up to perhaps a + hundred child tables; don't try to use many thousands of children. +

5.11.6. Best Practices for Declarative Partitioning

+ The choice of how to partition a table should be made carefully, as the + performance of query planning and execution can be negatively affected by + poor design. +

+ One of the most critical design decisions will be the column or columns + by which you partition your data. Often the best choice will be to + partition by the column or set of columns which most commonly appear in + WHERE clauses of queries being executed on the + partitioned table. WHERE clauses that are compatible + with the partition bound constraints can be used to prune unneeded + partitions. However, you may be forced into making other decisions by + requirements for the PRIMARY KEY or a + UNIQUE constraint. Removal of unwanted data is also a + factor to consider when planning your partitioning strategy. An entire + partition can be detached fairly quickly, so it may be beneficial to + design the partition strategy in such a way that all data to be removed + at once is located in a single partition. +

+ Choosing the target number of partitions that the table should be divided + into is also a critical decision to make. Not having enough partitions + may mean that indexes remain too large and that data locality remains poor + which could result in low cache hit ratios. However, dividing the table + into too many partitions can also cause issues. Too many partitions can + mean longer query planning times and higher memory consumption during both + query planning and execution, as further described below. + When choosing how to partition your table, + it's also important to consider what changes may occur in the future. For + example, if you choose to have one partition per customer and you + currently have a small number of large customers, consider the + implications if in several years you instead find yourself with a large + number of small customers. In this case, it may be better to choose to + partition by HASH and choose a reasonable number of + partitions rather than trying to partition by LIST and + hoping that the number of customers does not increase beyond what it is + practical to partition the data by. +

+ Sub-partitioning can be useful to further divide partitions that are + expected to become larger than other partitions. + Another option is to use range partitioning with multiple columns in + the partition key. + Either of these can easily lead to excessive numbers of partitions, + so restraint is advisable. +

+ It is important to consider the overhead of partitioning during + query planning and execution. The query planner is generally able to + handle partition hierarchies with up to a few thousand partitions fairly + well, provided that typical queries allow the query planner to prune all + but a small number of partitions. Planning times become longer and memory + consumption becomes higher when more partitions remain after the planner + performs partition pruning. Another + reason to be concerned about having a large number of partitions is that + the server's memory consumption may grow significantly over + time, especially if many sessions touch large numbers of partitions. + That's because each partition requires its metadata to be loaded into the + local memory of each session that touches it. +

+ With data warehouse type workloads, it can make sense to use a larger + number of partitions than with an OLTP type workload. + Generally, in data warehouses, query planning time is less of a concern as + the majority of processing time is spent during query execution. With + either of these two types of workload, it is important to make the right + decisions early, as re-partitioning large quantities of data can be + painfully slow. Simulations of the intended workload are often beneficial + for optimizing the partitioning strategy. Never just assume that more + partitions are better than fewer partitions, nor vice-versa. +