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. 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:
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.
The table is partitioned by explicitly listing which key value(s) 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 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.
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.
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:
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 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' );
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 the parent table.
It is recommended to drop the now-redundant CHECK
constraint after ATTACH PARTITION is finished.
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;
...
The following limitations apply to partitioned tables:
To create a unique or primary key constraint on a partitioned table, the partition keys must not include any expressions or function calls and the constraint's columns must include all of 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.
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.)
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.
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 5.9, 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, including any oid column.
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.
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 SHARE UPDATE EXCLUSIVE lock is enough
in the case of regular inheritance.
This example builds a partitioning structure equivalent to the declarative partitioning example above. Use the following steps:
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 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 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. 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 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 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.
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 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 master. 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 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 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 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
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.
Execution-time partition pruning currently only occurs for the
Append node type, not
for MergeAppend or ModifyTable
nodes. That is 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 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.
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 hundred 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 as more partitions are added. This is
particularly true for the UPDATE and
DELETE commands. 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.