Extensions
- pg_pathman 1.0.0
- Partitioning tool
Documentation
- README.rus
- pg_pathman
README
Contents
pg_pathman
The pg_pathman
module provides optimized partitioning mechanism and functions to manage partitions.
Overview
Partitioning means splitting one large table into smaller pieces. Each row in such table is moved to a single partition according to the partitioning key. PostgreSQL supports partitioning via table inheritance: each partition must be created as a child table with CHECK CONSTRAINT. For example:
CREATE TABLE test (id SERIAL PRIMARY KEY, title TEXT);
CREATE TABLE test_1 (CHECK ( id >= 100 AND id < 200 )) INHERITS (test);
CREATE TABLE test_2 (CHECK ( id >= 200 AND id < 300 )) INHERITS (test);
Despite the flexibility, this approach forces the planner to perform an exhaustive search and to check constraints on each partition to determine whether it should be present in the plan or not. Large amount of partitions may result in significant planning overhead.
The pg_pathman
module features partition managing functions and optimized planning mechanism which utilizes knowledge of the partitions' structure. It stores partitioning configuration in the pathman_config
table; each row contains a single entry for a partitioned table (relation name, partitioning column and its type). During the initialization stage the pg_pathman
module caches some information about child partitions in the shared memory, which is used later for plan construction. Before a SELECT query is executed, pg_pathman
traverses the condition tree in search of expressions like:
VARIABLE OP CONST
where VARIABLE
is a partitioning key, OP
is a comparison operator (supported operators are =, <, <=, >, >=), CONST
is a scalar value. For example:
WHERE id = 150
Based on the partitioning type and condition's operator, pg_pathman
searches for the corresponding partitions and builds the plan. Currently pg_pathman
supports two partitioning schemes:
- RANGE - maps rows to partitions using partitioning key ranges assigned to each partition. Optimization is achieved by using the binary search algorithm;
- HASH - maps rows to partitions using a generic hash function.
More interesting features are yet to come. Stay tuned!
Roadmap
- Provide a way to create user-defined partition creation\destruction callbacks (issue #22)
- Implement LIST partitioning scheme;
- Optimize hash join (both tables are partitioned by join key).
Installation guide
To install pg_pathman
, execute this in the module's directory:
make install USE_PGXS=1
Modify the shared_preload_libraries
parameter in postgresql.conf
as following:
shared_preload_libraries = 'pg_pathman'
It is essential to restart the PostgreSQL instance. After that, execute the following query in psql:
CREATE EXTENSION pg_pathman;
Done! Now it's time to setup your partitioning schemes.
Important: Don't forget to set the
PG_CONFIG
variable in case you want to testpg_pathman
on a custom build of PostgreSQL. Read more here.
Available functions
Partition creation
plpgsql
create_hash_partitions(relation REGCLASS,
attribute TEXT,
partitions_count INTEGER,
partition_name TEXT DEFAULT NULL)
Performs HASH partitioning for relation
by integer key attribute
. The partitions_count
parameter specifies the number of partitions to create; it cannot be changed afterwards. If partition_data
is true
then all the data will be automatically copied from the parent table to partitions. Note that data migration may took a while to finish and the table will be locked until transaction commits. See partition_table_concurrently()
for a lock-free way to migrate data.
```plpgsql create_range_partitions(relation REGCLASS, attribute TEXT, start_value ANYELEMENT, interval ANYELEMENT, count INTEGER DEFAULT NULL partition_data BOOLEAN DEFAULT true)
create_range_partitions(relation REGCLASS,
attribute TEXT,
start_value ANYELEMENT,
interval INTERVAL,
count INTEGER DEFAULT NULL,
partition_data BOOLEAN DEFAULT true)
``
Performs RANGE partitioning for
relationby partitioning key
attribute.
start_valueargument specifies initial value,
intervalsets the range of values in a single partition,
count` is the number of premade partitions (if not set then pathman tries to determine it based on attribute values).
```plpgsql create_partitions_from_range(relation REGCLASS, attribute TEXT, start_value ANYELEMENT, end_value ANYELEMENT, interval ANYELEMENT, partition_data BOOLEAN DEFAULT true)
create_partitions_from_range(relation REGCLASS,
attribute TEXT,
start_value ANYELEMENT,
end_value ANYELEMENT,
interval INTERVAL,
partition_data BOOLEAN DEFAULT true)
``
Performs RANGE-partitioning from specified range for
relationby partitioning key
attribute`.
Data migration
plpgsql
partition_table_concurrently(relation REGCLASS)
Starts a background worker to move data from parent table to partitions. The worker utilizes short transactions to copy small batches of data (up to 10K rows per transaction) and thus doesn't significantly interfere with user's activity.
plpgsql
stop_concurrent_part_task(relation REGCLASS)
Stops a background worker performing a concurrent partitioning task. Note: worker will exit after it finishes relocating a current batch.
Triggers
plpgsql
create_hash_update_trigger(parent REGCLASS)
Creates the trigger on UPDATE for HASH partitions. The UPDATE trigger isn't created by default because of the overhead. It's useful in cases when the key attribute might change.
plpgsql
create_range_update_trigger(parent REGCLASS)
Same as above, but for a RANGE-partitioned table.
Post-creation partition management
plpgsql
split_range_partition(partition REGCLASS,
value ANYELEMENT,
partition_name TEXT DEFAULT NULL,)
Split RANGE partition
in two by value
.
plpgsql
merge_range_partitions(partition1 REGCLASS, partition2 REGCLASS)
Merge two adjacent RANGE partitions. First, data from partition2
is copied to partition1
, then partition2
is removed.
plpgsql
append_range_partition(p_relation REGCLASS,
partition_name TEXT DEFAULT NULL)
Append new RANGE partition.
plpgsql
prepend_range_partition(p_relation REGCLASS,
partition_name TEXT DEFAULT NULL)
Prepend new RANGE partition.
plpgsql
add_range_partition(relation REGCLASS,
start_value ANYELEMENT,
end_value ANYELEMENT,
partition_name TEXT DEFAULT NULL)
Create new RANGE partition for relation
with specified range bounds.
plpgsql
drop_range_partition(partition TEXT)
Drop RANGE partition and all its data.
plpgsql
attach_range_partition(relation REGCLASS,
partition REGCLASS,
start_value ANYELEMENT,
end_value ANYELEMENT)
Attach partition to the existing RANGE-partitioned relation. The attached table must have exactly the same structure as the parent table, including the dropped columns.
plpgsql
detach_range_partition(partition REGCLASS)
Detach partition from the existing RANGE-partitioned relation.
plpgsql
disable_pathman_for(relation TEXT)
Permanently disable pg_pathman
partitioning mechanism for the specified parent table and remove the insert trigger if it exists. All partitions and data remain unchanged.
plpgsql
drop_partitions(parent REGCLASS,
delete_data BOOLEAN DEFAULT FALSE)
Drop partitions of the parent
table. If delete_data
is false
then the data is copied to the parent table first. Default is false
.
Additional parameters
plpgsql
enable_parent(relation REGCLASS)
disable_parent(relation REGCLASS)
Include/exclude parent table into/from query plan. In original PostgreSQL planner parent table is always included into query plan even if it's empty which can lead to additional overhead. You can use disable_parent()
if you are never going to use parent table as a storage. Default value depends on the partition_data
parameter that was specified during initial partitioning in create_range_partitions()
or create_partitions_from_range()
functions. If the partition_data
parameter was true
then all data have already been migrated to partitions and parent table disabled. Otherwise it is enabled.
plpgsql
enable_auto(relation REGCLASS)
disable_auto(relation REGCLASS)
Enable/disable auto partition propagation (only for RANGE partitioning). It is enabled by default.
Custom plan nodes
pg_pathman
provides a couple of custom plan nodes which aim to reduce execution time, namely:
RuntimeAppend
(overridesAppend
plan node)RuntimeMergeAppend
(overridesMergeAppend
plan node)PartitionFilter
(drop-in replacement for INSERT triggers)
PartitionFilter
acts as a proxy node for INSERT's child scan, which means it can redirect output tuples to the corresponding partition:
``` EXPLAIN (COSTS OFF) INSERT INTO partitioned_table SELECT generate_series(1, 10), random();
QUERY PLAN
Insert on partitioned_table -> Custom Scan (PartitionFilter) -> Subquery Scan on "SELECT" -> Result (4 rows) ```
RuntimeAppend
and RuntimeMergeAppend
have much in common: they come in handy in a case when WHERE condition takes form of:
VARIABLE OP PARAM
This kind of expressions can no longer be optimized at planning time since the parameter's value is not known until the execution stage takes place. The problem can be solved by embedding the WHERE condition analysis routine into the original Append
's code, thus making it pick only required scans out of a whole bunch of planned partition scans. This effectively boils down to creation of a custom node capable of performing such a check.
There are at least several cases that demonstrate usefulness of these nodes:
``` /* create table we're going to partition */ CREATE TABLE partitioned_table(id INT NOT NULL, payload REAL);
/* insert some data */ INSERT INTO partitioned_table SELECT generate_series(1, 1000), random();
/* perform partitioning */ SELECT create_hash_partitions('partitioned_table', 'id', 100);
/* create ordinary table */ CREATE TABLE some_table AS SELECT generate_series(1, 100) AS VAL; ```
id = (select ... limit 1)
``` EXPLAIN (COSTS OFF, ANALYZE) SELECT * FROM partitioned_table WHERE id = (SELECT * FROM some_table LIMIT 1);QUERY PLAN
Custom Scan (RuntimeAppend) (actual time=0.030..0.033 rows=1 loops=1) InitPlan 1 (returns $0) -> Limit (actual time=0.011..0.011 rows=1 loops=1) -> Seq Scan on some_table (actual time=0.010..0.010 rows=1 loops=1) -> Seq Scan on partitioned_table_70 partitioned_table (actual time=0.004..0.006 rows=1 loops=1) Filter: (id = $0) Rows Removed by Filter: 9 Planning time: 1.131 ms Execution time: 0.075 ms (9 rows)
/* disable RuntimeAppend node */ SET pg_pathman.enable_runtimeappend = f;
EXPLAIN (COSTS OFF, ANALYZE) SELECT * FROM partitioned_table WHERE id = (SELECT * FROM some_table LIMIT 1);
QUERY PLAN
Append (actual time=0.196..0.274 rows=1 loops=1) InitPlan 1 (returns $0) -> Limit (actual time=0.005..0.005 rows=1 loops=1) -> Seq Scan on some_table (actual time=0.003..0.003 rows=1 loops=1) -> Seq Scan on partitioned_table_0 (actual time=0.014..0.014 rows=0 loops=1) Filter: (id = $0) Rows Removed by Filter: 6 -> Seq Scan on partitioned_table_1 (actual time=0.003..0.003 rows=0 loops=1) Filter: (id = $0) Rows Removed by Filter: 5 ... /* more plans follow */ Planning time: 1.140 ms Execution time: 0.855 ms (306 rows) ```
id = ANY (select ...)
``` EXPLAIN (COSTS OFF, ANALYZE) SELECT * FROM partitioned_table WHERE id = any (SELECT * FROM some_table limit 4);QUERY PLAN
Nested Loop (actual time=0.025..0.060 rows=4 loops=1) -> Limit (actual time=0.009..0.011 rows=4 loops=1) -> Seq Scan on some_table (actual time=0.008..0.010 rows=4 loops=1) -> Custom Scan (RuntimeAppend) (actual time=0.002..0.004 rows=1 loops=4) -> Seq Scan on partitioned_table_70 partitioned_table (actual time=0.001..0.001 rows=10 loops=1) -> Seq Scan on partitioned_table_26 partitioned_table (actual time=0.002..0.003 rows=9 loops=1) -> Seq Scan on partitioned_table_27 partitioned_table (actual time=0.001..0.002 rows=20 loops=1) -> Seq Scan on partitioned_table_63 partitioned_table (actual time=0.001..0.002 rows=9 loops=1) Planning time: 0.771 ms Execution time: 0.101 ms (10 rows)
/* disable RuntimeAppend node */ SET pg_pathman.enable_runtimeappend = f;
EXPLAIN (COSTS OFF, ANALYZE) SELECT * FROM partitioned_table WHERE id = any (SELECT * FROM some_table limit 4);
QUERY PLAN
Nested Loop Semi Join (actual time=0.531..1.526 rows=4 loops=1) Join Filter: (partitioned_table.id = some_table.val) Rows Removed by Join Filter: 3990 -> Append (actual time=0.190..0.470 rows=1000 loops=1) -> Seq Scan on partitioned_table (actual time=0.187..0.187 rows=0 loops=1) -> Seq Scan on partitioned_table_0 (actual time=0.002..0.004 rows=6 loops=1) -> Seq Scan on partitioned_table_1 (actual time=0.001..0.001 rows=5 loops=1) -> Seq Scan on partitioned_table_2 (actual time=0.002..0.004 rows=14 loops=1) ... /* 96 scans follow */ -> Materialize (actual time=0.000..0.000 rows=4 loops=1000) -> Limit (actual time=0.005..0.006 rows=4 loops=1) -> Seq Scan on some_table (actual time=0.003..0.004 rows=4 loops=1) Planning time: 2.169 ms Execution time: 2.059 ms (110 rows) ```
NestLoop
involving a partitioned table, which is omitted since it's occasionally shown above.
In case you're interested, you can read more about custom nodes at Alexander Korotkov's blog.
Examples
Common tips
You can easily add partition column containing the names of the underlying partitions using the system attribute called tableoid:
SELECT tableoid::regclass AS partition, * FROM partitioned_table;
Though indices on a parent table aren't particularly useful (since it's empty), they act as prototypes for indices on partitions. For each index on the parent table,
pg_pathman
will create a similar index on every partition.All running concurrent partitioning tasks can be listed using the
pathman_concurrent_part_tasks
view:plpgsql SELECT * FROM pathman_concurrent_part_tasks; userid | pid | dbid | relid | processed | status
--------+------+-------+-------+-----------+--------- dmitry | 7367 | 16384 | test | 472000 | working (1 row)
HASH partitioning
Consider an example of HASH partitioning. First create a table with some integer column: ``` CREATE TABLE items ( id SERIAL PRIMARY KEY, name TEXT, code BIGINT);
INSERT INTO items (id, name, code)
SELECT g, md5(g::text), random() * 100000
FROM generate_series(1, 100000) as g;
Now run the `create_hash_partitions()` function with appropriate arguments:
SELECT create_hash_partitions('items', 'id', 100);
```
This will create new partitions and move the data from parent to partitions.
Here's an example of the query performing filtering by partitioning key: ``` SELECT * FROM items WHERE id = 1234; id | name | code ------+----------------------------------+------ 1234 | 81dc9bdb52d04dc20036dbd8313ed055 | 1855 (1 row)
EXPLAIN SELECT * FROM items WHERE id = 1234;
QUERY PLAN
Append (cost=0.28..8.29 rows=0 width=0) -> Index Scan using items_34_pkey on items_34 (cost=0.28..8.29 rows=0 width=0) Index Cond: (id = 1234) ```
Notice that the Append
node contains only one child scan which corresponds to the WHERE clause.
Important: pay attention to the fact that
pg_pathman
excludes the parent table from the query plan.
To access parent table use ONLY modifier: ``` EXPLAIN SELECT * FROM ONLY items;
QUERY PLAN
Seq Scan on items (cost=0.00..0.00 rows=1 width=45) ```
RANGE partitioning
Consider an example of RANGE partitioning. Let's create a table containing some dummy logs: ``` CREATE TABLE journal ( id SERIAL, dt TIMESTAMP NOT NULL, level INTEGER, msg TEXT );
-- similar index will also be created for each partition CREATE INDEX ON journal(dt);
-- generate some data
INSERT INTO journal (dt, level, msg)
SELECT g, random() * 6, md5(g::text)
FROM generate_series('2015-01-01'::date, '2015-12-31'::date, '1 minute') as g;
Run the `create_range_partitions()` function to create partitions so that each partition would contain the data for one day:
SELECT create_range_partitions('journal', 'dt', '2015-01-01'::date, '1 day'::interval);
```
It will create 365 partitions and move the data from parent to partitions.
New partitions are appended automaticaly by insert trigger, but it can be done manually with the following functions: ``` -- append new partition with specified range SELECT add_range_partition('journal', '2016-01-01'::date, '2016-01-07'::date);
-- append new partition with default range
SELECT append_range_partition('journal');
The first one creates a partition with specified range. The second one creates a partition with default interval and appends it to the partition list. It is also possible to attach an existing table as partition. For example, we may want to attach an archive table (or even foreign table from another server) for some outdated data:
CREATE FOREIGN TABLE journal_archive (
id INTEGER NOT NULL,
dt TIMESTAMP NOT NULL,
level INTEGER,
msg TEXT
) SERVER archive_server;
SELECT attach_range_partition('journal', 'journal_archive', '2014-01-01'::date, '2015-01-01'::date); ```
Important: the definition of the attached table must match the one of the existing partitioned table, including the dropped columns.
To merge to adjacent partitions, use the merge_range_partitions()
function:
SELECT merge_range_partitions('journal_archive', 'journal_1');
To split partition by value, use the split_range_partition()
function:
SELECT split_range_partition('journal_366', '2016-01-03'::date);
To detach partition, use the detach_range_partition()
function:
SELECT detach_range_partition('journal_archive');
Here's an example of the query performing filtering by partitioning key: ``` SELECT * FROM journal WHERE dt >= '2015-06-01' AND dt < '2015-06-03'; id | dt | level | msg --------+---------------------+-------+---------------------------------- 217441 | 2015-06-01 00:00:00 | 2 | 15053892d993ce19f580a128f87e3dbf 217442 | 2015-06-01 00:01:00 | 1 | 3a7c46f18a952d62ce5418ac2056010c 217443 | 2015-06-01 00:02:00 | 0 | 92c8de8f82faf0b139a3d99f2792311d ... (2880 rows)
EXPLAIN SELECT * FROM journal WHERE dt >= '2015-06-01' AND dt < '2015-06-03';
QUERY PLAN
Append (cost=0.00..58.80 rows=0 width=0) -> Seq Scan on journal_152 (cost=0.00..29.40 rows=0 width=0) -> Seq Scan on journal_153 (cost=0.00..29.40 rows=0 width=0) (3 rows) ```
Disabling pg_pathman
There are several user-accessible GUC variables designed to toggle the whole module or specific custom nodes on and off:
pg_pathman.enable
--- disable (or enable)pg_pathman
completelypg_pathman.enable_runtimeappend
--- toggleRuntimeAppend
custom node on\offpg_pathman.enable_runtimemergeappend
--- toggleRuntimeMergeAppend
custom node on\offpg_pathman.enable_partitionfilter
--- togglePartitionFilter
custom node on\off
To permanently disable pg_pathman
for some previously partitioned table, use the disable_partitioning()
function:
SELECT disable_pathman_for('range_rel');
All sections and data will remain unchanged and will be handled by the standard PostgreSQL inheritance mechanism.
Feedback
Do not hesitate to post your issues, questions and new ideas at the issues page.
Authors
Ildar Musin i.musin@postgrespro.ru Postgres Professional Ltd., Russia
Alexander Korotkov a.korotkov@postgrespro.ru Postgres Professional Ltd., Russia
Dmitry Ivanov d.ivanov@postgrespro.ru Postgres Professional Ltd., Russia