Re: [POC] hash partitioning

Hello

Looking at your hash partitioning syntax, I implemented a hash partition in a more concise way, with no need to determine the number of sub-tables, and dynamically add partitions.

Description

The hash partition's implement is on the basis of the original range / list partition,and using similar syntax.

To create a partitioned table ,use:

CREATE TABLE h (id int) PARTITION BY HASH(id);

The partitioning key supports only one value, and I think the partition key can support multiple values,
which may be difficult to implement when querying, but it is not impossible.

A partition table can be create as bellow:

CREATE TABLE h1 PARTITION OF h;
CREATE TABLE h2 PARTITION OF h;
CREATE TABLE h3 PARTITION OF h;

FOR VALUES clause cannot be used, and the partition bound is calclulated automatically as partition index of single integer value.

An inserted record is stored in a partition whose index equals
DatumGetUInt32(OidFunctionCall1(lookup_type_cache(key->parttypid[0], TYPECACHE_HASH_PROC)->hash_proc, values[0])) % nparts/* Number of partitions */
;
In the above example, this is DatumGetUInt32(OidFunctionCall1(lookup_type_cache(key->parttypid[0], TYPECACHE_HASH_PROC)->hash_proc, id)) % 3;

postgres=# insert into h select generate_series(1,20);
INSERT 0 20
postgres=# select tableoid::regclass,* from h;
tableoid | id
----------+----
h1 | 3
h1 | 5
h1 | 17
h1 | 19
h2 | 2
h2 | 6
h2 | 7
h2 | 11
h2 | 12
h2 | 14
h2 | 15
h2 | 18
h2 | 20
h3 | 1
h3 | 4
h3 | 8
h3 | 9
h3 | 10
h3 | 13
h3 | 16
(20 rows)

The number of partitions here can be dynamically added, and if a new partition is created, the number of partitions changes, the calculated target partitions will change, and the same data is not reasonable in different partitions,So you need to re-calculate the existing data and insert the target partition when you create a new partition.

postgres=# create table h4 partition of h;
CREATE TABLE
postgres=# select tableoid::regclass,* from h;
tableoid | id
----------+----
h1 | 5
h1 | 17
h1 | 19
h1 | 6
h1 | 12
h1 | 8
h1 | 13
h2 | 11
h2 | 14
h3 | 1
h3 | 9
h3 | 2
h3 | 15
h4 | 3
h4 | 7
h4 | 18
h4 | 20
h4 | 4
h4 | 10
h4 | 16
(20 rows)

When querying the data, the hash partition uses the same algorithm as the insertion, and filters out the table that does not need to be scanned.

postgres=# explain analyze select * from h where id = 1;
QUERY PLAN
----------------------------------------------------------------------------------------------------
Append (cost=0.00..41.88 rows=13 width=4) (actual time=0.020..0.023 rows=1 loops=1)
-> Seq Scan on h3 (cost=0.00..41.88 rows=13 width=4) (actual time=0.013..0.016 rows=1 loops=1)
Filter: (id = 1)
Rows Removed by Filter: 3
Planning time: 0.346 ms
Execution time: 0.061 ms
(6 rows)

postgres=# explain analyze select * from h where id in (1,5);;
QUERY PLAN
----------------------------------------------------------------------------------------------------
Append (cost=0.00..83.75 rows=52 width=4) (actual time=0.016..0.028 rows=2 loops=1)
-> Seq Scan on h1 (cost=0.00..41.88 rows=26 width=4) (actual time=0.015..0.018 rows=1 loops=1)
Filter: (id = ANY ('{1,5}'::integer[]))
Rows Removed by Filter: 6
-> Seq Scan on h3 (cost=0.00..41.88 rows=26 width=4) (actual time=0.005..0.007 rows=1 loops=1)
Filter: (id = ANY ('{1,5}'::integer[]))
Rows Removed by Filter: 3
Planning time: 0.720 ms
Execution time: 0.074 ms
(9 rows)

postgres=# explain analyze select * from h where id = 1 or id = 5;;
QUERY PLAN
----------------------------------------------------------------------------------------------------
Append (cost=0.00..96.50 rows=50 width=4) (actual time=0.017..0.078 rows=2 loops=1)
-> Seq Scan on h1 (cost=0.00..48.25 rows=25 width=4) (actual time=0.015..0.019 rows=1 loops=1)
Filter: ((id = 1) OR (id = 5))
Rows Removed by Filter: 6
-> Seq Scan on h3 (cost=0.00..48.25 rows=25 width=4) (actual time=0.005..0.010 rows=1 loops=1)
Filter: ((id = 1) OR (id = 5))
Rows Removed by Filter: 3
Planning time: 0.396 ms
Execution time: 0.139 ms
(9 rows)

Can not detach / attach / drop partition table.

Best regards,
young

yonj1e.github.io
yangjie(at)highgo(dot)com