PostgreSQL supports basic table partitioning. This section describes why and how to implement partitioning as part of your database design.
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 can be accomplished by adding
or removing partitions, if that requirement is planned
into the partitioning design. Doing
ALTER TABLE DETACH PARTITION or dropping
an individual partition using
TABLE is far faster than a bulk operation. These
commands also entirely avoid the
VACUUM overhead caused by a bulk
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.
PostgreSQL offers built-in support for the following forms of 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.
The table is partitioned by explicitly listing which key values appear in each partition.
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
PostgreSQL offers a way to specify how to divide a table into pieces called partitions. The table that is divided is referred to as a partitioned table. The specification consists of the partitioning method and a list of columns or expressions to be used as the partition key.
All rows inserted into a partitioned table will be routed to one of the partitions based on the value of the partition key. Each partition has a subset of the data defined by its partition bounds. The currently supported partitioning methods are range, list, and hash.
Partitions may themselves be defined as partitioned tables, using what is called sub-partitioning. 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
a regular or partitioned table containing data as a partition
of a partitioned table, or remove a partition from a
partitioned table turning it into a standalone table; see
TABLE to learn more about the
ATTACH PARTITION and
DETACH PARTITION sub-commands.
Individual partitions are linked to the partitioned table with inheritance behind-the-scenes; however, it is not possible to use some of the generic features of inheritance (discussed below) with declaratively partitioned tables or their partitions. For example, a partition cannot have any parents other than the partitioned table it is a partition of, nor can a regular table inherit from a partitioned table making the latter its parent. That means partitioned tables and their partitions do not participate in inheritance with regular tables. Since a partition hierarchy consisting of the partitioned table and its partitions is still an inheritance hierarchy, all the normal rules of inheritance apply as described in Section 5.9 with some exceptions, most notably:
NOT NULL constraints of a
partitioned table are always inherited by all its
constraints that are marked
INHERIT are not allowed to be created on
ONLY to add or drop
a constraint on only the partitioned table is supported
as long as there are no partitions. Once partitions
result in an error as adding or dropping constraints on
only the partitioned table, when partitions exist, is not
supported. 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
directly, attempts to use
ONLY on a partitioned table will always
return an error.
Partitions cannot have columns that are not present in
the parent. It is not possible to specify columns when
creating partitions with
TABLE, nor is it possible to add columns to
partitions after-the-fact using
ALTER TABLE. Tables may be added as a
ALTER TABLE ...
ATTACH PARTITION only if their columns exactly
match the parent, including any
You cannot drop the
NULL constraint on a partition's column if the
constraint is present in the parent table.
Partitions can also be foreign tables, although they have some limitations that normal tables do not; see CREATE FOREIGN TABLE for more information.
Updating the partition key of a row might cause it to be moved into a different partition where this row satisfies the partition bounds.
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 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.
To use declarative partitioning in this case, use the following steps:
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);
You may decide to use multiple columns in the
partition key for range partitioning, if desired. Of
course, this will often result in a larger number of
partitions, each of which is individually smaller. On
the other hand, using fewer columns may lead to a
coarser-grained partitioning criteria with smaller
number of partitions. A query accessing the partitioned
table will have to scan fewer partitions if the
conditions involve some or all of these columns. For
example, consider a table range partitioned using
firstname (in that
order) as the partition key.
Create partitions. Each partition's definition must specify the bounds that correspond to the partitioning method and partition key of the parent. Note that specifying bounds such that the new partition's values will overlap with those in one or more existing partitions will cause an error. 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.
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.
It is not necessary to create table constraints describing partition boundary condition for partitions. Instead, partition constraints are generated implicitly from the partition bound specification whenever there is need to refer to them.
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;
To implement sub-partitioning, specify the
PARTITION BY clause in the
commands used to create individual partitions, for
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
measurement that is mapped to
(or data that is directly inserted into
measurement_y2006m02, provided it
satisfies its partition constraint) 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 allows; the system does not try
to check whether that's really the case.
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 one index on each partition, and any partitions you create or attach later will also contain the index.
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.
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 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
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 DETACH PARTITION measurement_y2006m02;
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 might 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_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 the data to be loaded, checked, and transformed prior to it appearing in the partitioned table:
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' );
Before running the
PARTITION command, it is recommended to create a
CHECK constraint on the table to
be attached describing the desired partition constraint. That
way, the system will be able to skip the scan to validate the
implicit partition constraint. Without such a constraint, the
table will be scanned to validate the partition constraint
while holding an
EXCLUSIVE lock on the parent table. One may then drop
the constraint after
PARTITION is finished, because it is no longer
The following limitations apply to partitioned tables:
There is no way to create an exclusion constraint spanning all partitions; it is only possible to constrain each leaf partition individually.
While primary keys are supported on partitioned tables, foreign keys referencing partitioned tables are not supported. (Foreign key references from a partitioned table to some other table are supported.)
UPDATE causes a
row to move from one partition to another, there is a
chance that another concurrent
DELETE misses this row. Suppose
session 1 is performing an
UPDATE on a partition key, and
meanwhile a concurrent session 2 for which this row is
visible performs an
DELETE operation on
this row. Session 2 can silently miss the row if the
row is deleted from the partition due to session 1's
activity. In such case, session 2's
DELETE, being unaware of the row
movement thinks that the row has just been deleted and
concludes that there is nothing to be done for this
row. In the usual case where the table is not
partitioned, or where there is no row movement, session
2 would have identified the newly updated row and
carried out the
DELETE on this new row version.
BEFORE ROW triggers, if
necessary, must be defined on individual partitions,
not the partitioned table.
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.
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.)
Some operations require a stronger lock when using
declarative partitioning than when using table
inheritance. For example, adding or removing a partition
to or from a partitioned table requires taking an
ACCESS EXCLUSIVE lock on the
parent table, whereas a
UPDATE EXCLUSIVE lock is enough in the case of
We use the same
measurement table we used above. To
implement partitioning using inheritance, use the following
Create the “master” 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 master table is the
measurement table as
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. 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.
It would be better to instead create child tables as follows:
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 master
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 points to 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
IF test must
exactly match the
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.
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 master 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
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 master.
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 master 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.
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;
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.
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
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
ANALYZE commands, don't forget that
you need to run them on each child table individually.
A command like:
will only process the master 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.
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) -> Append (cost=0.00..36.21 rows=617 width=0) -> 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
ANALYZE. It is possible to determine the number of
partitions which were removed during this phase by
observing the “Subplans Removed” property in the
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
nloops property in the
EXPLAIN ANALYZE output.
Partition pruning can be disabled using the enable_partition_pruning setting.
Currently, pruning of partitions during the planning of an
DELETE command is implemented using the
constraint exclusion method (however, it is controlled by the
constraint_exclusion) — see
the following section for details and caveats that apply.
Also, execution-time partition pruning currently only
occurs for the
Append node type,
Both of these behaviors are likely to be changed in a future release of PostgreSQL.
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
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, it cannot 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
cannot be optimized, since the planner cannot know which
child table the function's value might fall into at run
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.
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