I am using Postgres on a large system (recording approximately 20million transactions per day). We use partitioning by date to assist with both vacuum processing time and to archive old data. At the core of the system are records in 2 different tables detailing different types of activity for monetary transactions (e.g. money in and money out) -> a single transaction has entries in both tables, so to retrieve all details for a single transaction we need to join the 2 tables.
The use of partitioning however has a significant impact on the performance of retrieving this data. Being relatively new to Postgres I wanted to share my findings and understand how others address them. We run postgres version 9.6 on CentOS, but the same behaviour is apparent in postgres 10.6. The test case outputs are from version 10.6 running on my Ubuntu machine with default postgres configuration.
Below is an example script to populate the test data:
=======================
drop table if exists tablea cascade;
drop table if exists tableb cascade;
CREATE TABLE tablea (
id serial,
reference int not null,
created date not null
) PARTITION BY RANGE (created);
CREATE TABLE tablea_part1 PARTITION OF tablea
FOR VALUES FROM ('2018-01-01') TO ('2018-01-02');
CREATE TABLE tablea_part2 PARTITION OF tablea
FOR VALUES FROM ('2018-01-02') TO ('2018-01-03');
CREATE TABLE tablea_part3 PARTITION OF tablea
FOR VALUES FROM ('2018-01-03') TO ('2018-01-04');
CREATE TABLE tablea_part4 PARTITION OF tablea
FOR VALUES FROM ('2018-01-04') TO ('2018-01-05');
CREATE TABLE tablea_part5 PARTITION OF tablea
FOR VALUES FROM ('2018-01-05') TO ('2018-01-06');
CREATE INDEX tablea_id_1 ON tablea_part1 (id);
CREATE INDEX tablea_id_2 ON tablea_part2 (id);
CREATE INDEX tablea_id_3 ON tablea_part3 (id);
CREATE INDEX tablea_id_4 ON tablea_part4 (id);
CREATE INDEX tablea_id_5 ON tablea_part5 (id);
CREATE INDEX tablea_reference_1 ON tablea_part1 (reference);
CREATE INDEX tablea_reference_2 ON tablea_part2 (reference);
CREATE INDEX tablea_reference_3 ON tablea_part3 (reference);
CREATE INDEX tablea_reference_4 ON tablea_part4 (reference);
CREATE INDEX tablea_reference_5 ON tablea_part5 (reference);
CREATE INDEX tablea_created_1 ON tablea_part1 (created);
CREATE INDEX tablea_created_2 ON tablea_part2 (created);
CREATE INDEX tablea_created_3 ON tablea_part3 (created);
CREATE INDEX tablea_created_4 ON tablea_part4 (created);
CREATE INDEX tablea_created_5 ON tablea_part5 (created);
alter table tablea_part1 add CHECK ( created >= DATE '2018-01-01' AND created < DATE '2018-01-02');
alter table tablea_part2 add CHECK ( created >= DATE '2018-01-02' AND created < DATE '2018-01-03');
alter table tablea_part3 add CHECK ( created >= DATE '2018-01-03' AND created < DATE '2018-01-04');
alter table tablea_part4 add CHECK ( created >= DATE '2018-01-04' AND created < DATE '2018-01-05');
alter table tablea_part5 add CHECK ( created >= DATE '2018-01-05' AND created < DATE '2018-01-06');
create or replace function populate_tablea()
RETURNS integer AS
$BODY$
DECLARE
i integer;
v_created date;
BEGIN
i := 0;
WHILE (i < 50000)
loop
i := i + 1;
IF (mod(i,5) = 1) THEN
v_created = '2018-01-01';
ELSIF (mod(i,5) = 2) THEN
v_created = '2018-01-02';
ELSIF (mod(i,5) = 3) THEN
v_created = '2018-01-03';
ELSIF (mod(i,5) = 4) THEN
v_created = '2018-01-04';
ELSIF (mod(i,5) = 0) THEN
v_created = '2018-01-05';
END IF;
insert into tablea values (i, i, v_created);
end loop;
RETURN i;
END;
$BODY$
LANGUAGE plpgsql VOLATILE
COST 100;
CREATE TABLE tableb(
id serial,
reference int not null,
created date not null
) PARTITION BY RANGE (created);
CREATE TABLE tableb_part1 PARTITION OF tableb
FOR VALUES FROM ('2018-01-01') TO ('2018-01-02');
CREATE TABLE tableb_part2 PARTITION OF tableb
FOR VALUES FROM ('2018-01-02') TO ('2018-01-03');
CREATE TABLE tableb_part3 PARTITION OF tableb
FOR VALUES FROM ('2018-01-03') TO ('2018-01-04');
CREATE TABLE tableb_part4 PARTITION OF tableb
FOR VALUES FROM ('2018-01-04') TO ('2018-01-05');
CREATE TABLE tableb_part5 PARTITION OF tableb
FOR VALUES FROM ('2018-01-05') TO ('2018-01-06');
CREATE INDEX tableb_id_1 ON tableb_part1 (id);
CREATE INDEX tableb_id_2 ON tableb_part2 (id);
CREATE INDEX tableb_id_3 ON tableb_part3 (id);
CREATE INDEX tableb_id_4 ON tableb_part4 (id);
CREATE INDEX tableb_id_5 ON tableb_part5 (id);
CREATE INDEX tableb_reference_1 ON tableb_part1 (reference);
CREATE INDEX tableb_reference_2 ON tableb_part2 (reference);
CREATE INDEX tableb_reference_3 ON tableb_part3 (reference);
CREATE INDEX tableb_reference_4 ON tableb_part4 (reference);
CREATE INDEX tableb_reference_5 ON tableb_part5 (reference);
CREATE INDEX tableb_created_1 ON tableb_part1 (created);
CREATE INDEX tableb_created_2 ON tableb_part2 (created);
CREATE INDEX tableb_created_3 ON tableb_part3 (created);
CREATE INDEX tableb_created_4 ON tableb_part4 (created);
CREATE INDEX tableb_created_5 ON tableb_part5 (created);
alter table tableb_part1 add CHECK ( created >= DATE '2018-01-01' AND created < DATE '2018-01-02');
alter table tableb_part2 add CHECK ( created >= DATE '2018-01-02' AND created < DATE '2018-01-03');
alter table tableb_part3 add CHECK ( created >= DATE '2018-01-03' AND created < DATE '2018-01-04');
alter table tableb_part4 add CHECK ( created >= DATE '2018-01-04' AND created < DATE '2018-01-05');
alter table tableb_part5 add CHECK ( created >= DATE '2018-01-05' AND created < DATE '2018-01-06');
create or replace function populate_tableb()
RETURNS integer AS
$BODY$
DECLARE
i integer;
v_created date;
BEGIN
i := 0;
WHILE (i < 50000)
loop
i := i + 1;
IF (mod(i,5) = 0) THEN
v_created = '2018-01-01';
ELSIF (mod(i,5) = 1) THEN
v_created = '2018-01-02';
ELSIF (mod(i,5) = 2) THEN
v_created = '2018-01-03';
ELSIF (mod(i,5) = 3) THEN
v_created = '2018-01-04';
ELSIF (mod(i,5) = 4) THEN
v_created = '2018-01-05';
END IF;
insert into tableb values (i, i, v_created);
end loop;
RETURN i;
END;
$BODY$
LANGUAGE plpgsql VOLATILE
COST 100;
select populate_tablea();
select populate_tableb();
vacuum analyze;
==================================
So it creates 2 tables, both with 5 partitions (using range partitioning on the created column). Each partition has 10000 rows in it.
Below are some example queries I have run, the outputs of explain analyze for each and notes on each of my findings/questions:
============
-- NOTICE IN THE BELOW THAT WE USE A SINGLE ID (ESSENTIALLY THE PRIMARY KEY) BUT WE HAVE ESTIMATED 5 ROWS RETURNED. WE SEEM TO BE BASING
-- ON PARTITION STATS ONLY AND SUMMING. SO EACH PARTITION ASSUMES ID IS UNIQUE, BUT WITH 5 PARTITIONS, THE TOTAL ROWS IS 5.
explain analyze select * from tablea where id = 101;
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------
Append (cost=0.29..41.51 rows=5 width=12) (actual time=0.027..0.066 rows=1 loops=1)
-> Index Scan using tablea_id_1 on tablea_part1 (cost=0.29..8.30 rows=1 width=12) (actual time=0.026..0.029 rows=1 loops=1)
Index Cond: (id = 101)
-> Index Scan using tablea_id_2 on tablea_part2 (cost=0.29..8.30 rows=1 width=12) (actual time=0.010..0.010 rows=0 loops=1)
Index Cond: (id = 101)
-> Index Scan using tablea_id_3 on tablea_part3 (cost=0.29..8.30 rows=1 width=12) (actual time=0.008..0.009 rows=0 loops=1)
Index Cond: (id = 101)
-> Index Scan using tablea_id_4 on tablea_part4 (cost=0.29..8.30 rows=1 width=12) (actual time=0.008..0.008 rows=0 loops=1)
Index Cond: (id = 101)
-> Index Scan using tablea_id_5 on tablea_part5 (cost=0.29..8.30 rows=1 width=12) (actual time=0.007..0.007 rows=0 loops=1)
Index Cond: (id = 101)
Planning time: 0.875 ms
Execution time: 0.176 ms
============
-- IF WE USE AN IN WITH 10 ID'S WE ESTIMATE 50 ROWS RETURNED INSTEAD OF THE ACTUAL 10. AGAIN SEEMS TO BE AGGREGATING PARTITION STATISTICS.
explain analyze select * from tablea where id in (101,102,103,104,105,106,107,108,109,110);
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------
Append (cost=0.29..215.13 rows=50 width=12) (actual time=0.040..0.283 rows=10 loops=1)
-> Index Scan using tablea_id_1 on tablea_part1 (cost=0.29..43.03 rows=10 width=12) (actual time=0.039..0.079 rows=2 loops=1)
Index Cond: (id = ANY ('{101,102,103,104,105,106,107,108,109,110}'::integer[]))
-> Index Scan using tablea_id_2 on tablea_part2 (cost=0.29..43.03 rows=10 width=12) (actual time=0.021..0.052 rows=2 loops=1)
Index Cond: (id = ANY ('{101,102,103,104,105,106,107,108,109,110}'::integer[]))
-> Index Scan using tablea_id_3 on tablea_part3 (cost=0.29..43.03 rows=10 width=12) (actual time=0.022..0.048 rows=2 loops=1)
Index Cond: (id = ANY ('{101,102,103,104,105,106,107,108,109,110}'::integer[]))
-> Index Scan using tablea_id_4 on tablea_part4 (cost=0.29..43.03 rows=10 width=12) (actual time=0.026..0.049 rows=2 loops=1)
Index Cond: (id = ANY ('{101,102,103,104,105,106,107,108,109,110}'::integer[]))
-> Index Scan using tablea_id_5 on tablea_part5 (cost=0.29..43.03 rows=10 width=12) (actual time=0.028..0.048 rows=2 loops=1)
Index Cond: (id = ANY ('{101,102,103,104,105,106,107,108,109,110}'::integer[]))
Planning time: 1.526 ms
Execution time: 0.397 ms
===========
-- IF WE USE A RANGE INSTEAD OF INDIVIDUAL ID'S, WE GET ESTIMATED 10 ROWS RETURNED (GOOD).
-- IS THIS USING THE GLOBAL TABLE STATISTICS INSTEAD? WHY DOES IT DIFFER FROM DISTINCT ID'S?
explain analyze select * from tablea where id >= 101 and id <= 110;
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------
Append (cost=0.29..41.62 rows=10 width=12) (actual time=0.022..0.074 rows=10 loops=1)
-> Index Scan using tablea_id_1 on tablea_part1 (cost=0.29..8.32 rows=2 width=12) (actual time=0.021..0.026 rows=2 loops=1)
Index Cond: ((id >= 101) AND (id <= 110))
-> Index Scan using tablea_id_2 on tablea_part2 (cost=0.29..8.32 rows=2 width=12) (actual time=0.010..0.012 rows=2 loops=1)
Index Cond: ((id >= 101) AND (id <= 110))
-> Index Scan using tablea_id_3 on tablea_part3 (cost=0.29..8.32 rows=2 width=12) (actual time=0.009..0.010 rows=2 loops=1)
Index Cond: ((id >= 101) AND (id <= 110))
-> Index Scan using tablea_id_4 on tablea_part4 (cost=0.29..8.32 rows=2 width=12) (actual time=0.009..0.010 rows=2 loops=1)
Index Cond: ((id >= 101) AND (id <= 110))
-> Index Scan using tablea_id_5 on tablea_part5 (cost=0.29..8.32 rows=2 width=12) (actual time=0.008..0.010 rows=2 loops=1)
Index Cond: ((id >= 101) AND (id <= 110))
Planning time: 1.845 ms
Execution time: 0.196 ms
==========
-- HERE ARE THE TABLE STATS, SHOWING THAT POSTGRES IS AWARE THAT ID'S ARE GLOBALLY UNIQUE IN THE TABLEA TABLE. CAN IT USE THEM?
select tablename,n_distinct from pg_stats where tablename like '%tablea%' and attname = 'id';
tablename | n_distinct
--------------+------------
tablea_part3 | -1
tablea | -1
tablea_part2 | -1
tablea_part4 | -1
tablea_part5 | -1
tablea_part1 | -1
==========
-- WHEN I JOIN, THE NUMBER OF ROWS MULTIPLIES. NOTICE THE SEQUENTIAL SCAN OF THE TABLEB PARTITION. THIS IS CAUSED BY THE OVERESTIMATION
-- OF ROWS RETURNED BY TABLEA
explain analyze select * from tablea a, tableb b where a.reference = b.reference and a.id in (101,102,103,104,105,106,107,108,109,110);
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------------------------
Hash Join (cost=215.75..1178.75 rows=50 width=24) (actual time=0.386..46.845 rows=10 loops=1)
Hash Cond: (b.reference = a.reference)
-> Append (cost=0.00..775.00 rows=50000 width=12) (actual time=0.024..26.527 rows=50000 loops=1)
-> Seq Scan on tableb_part1 b (cost=0.00..155.00 rows=10000 width=12) (actual time=0.022..4.006 rows=10000 loops=1)
-> Seq Scan on tableb_part2 b_1 (cost=0.00..155.00 rows=10000 width=12) (actual time=0.023..4.039 rows=10000 loops=1)
-> Seq Scan on tableb_part3 b_2 (cost=0.00..155.00 rows=10000 width=12) (actual time=0.023..3.247 rows=10000 loops=1)
-> Seq Scan on tableb_part4 b_3 (cost=0.00..155.00 rows=10000 width=12) (actual time=0.016..1.421 rows=10000 loops=1)
-> Seq Scan on tableb_part5 b_4 (cost=0.00..155.00 rows=10000 width=12) (actual time=0.007..1.113 rows=10000 loops=1)
-> Hash (cost=215.13..215.13 rows=50 width=12) (actual time=0.316..0.316 rows=10 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 9kB
-> Append (cost=0.29..215.13 rows=50 width=12) (actual time=0.034..0.301 rows=10 loops=1)
-> Index Scan using tablea_id_1 on tablea_part1 a (cost=0.29..43.03 rows=10 width=12) (actual time=0.033..0.074 rows=2 loops=1)
Index Cond: (id = ANY ('{101,102,103,104,105,106,107,108,109,110}'::integer[]))
-> Index Scan using tablea_id_2 on tablea_part2 a_1 (cost=0.29..43.03 rows=10 width=12) (actual time=0.020..0.051 rows=2 loops=1)
Index Cond: (id = ANY ('{101,102,103,104,105,106,107,108,109,110}'::integer[]))
-> Index Scan using tablea_id_3 on tablea_part3 a_2 (cost=0.29..43.03 rows=10 width=12) (actual time=0.021..0.048 rows=2 loops=1)
Index Cond: (id = ANY ('{101,102,103,104,105,106,107,108,109,110}'::integer[]))
-> Index Scan using tablea_id_4 on tablea_part4 a_3 (cost=0.29..43.03 rows=10 width=12) (actual time=0.025..0.072 rows=2 loops=1)
Index Cond: (id = ANY ('{101,102,103,104,105,106,107,108,109,110}'::integer[]))
-> Index Scan using tablea_id_5 on tablea_part5 a_4 (cost=0.29..43.03 rows=10 width=12) (actual time=0.028..0.049 rows=2 loops=1)
Index Cond: (id = ANY ('{101,102,103,104,105,106,107,108,109,110}'::integer[]))
Planning time: 2.642 ms
Execution time: 47.005 ms
===========
-- REPEAT, BUT USING A RANGE QUERY. NO LONGER SEQUENTIAL SCAN AS THE TABLEA ROW ESTIMATE DROPS TO 10 FROM 50.
-- QUERY EXECUTION TIME DROPS FROM 47MS TO 0.7MS
explain analyze select * from tablea a, tableb b where a.reference = b.reference and a.id >= 101 and a. id <= 110;
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------------------------
Nested Loop (cost=0.57..437.25 rows=10 width=24) (actual time=0.063..0.543 rows=10 loops=1)
-> Append (cost=0.29..41.62 rows=10 width=12) (actual time=0.027..0.091 rows=10 loops=1)
-> Index Scan using tablea_id_1 on tablea_part1 a (cost=0.29..8.32 rows=2 width=12) (actual time=0.026..0.031 rows=2 loops=1)
Index Cond: ((id >= 101) AND (id <= 110))
-> Index Scan using tablea_id_2 on tablea_part2 a_1 (cost=0.29..8.32 rows=2 width=12) (actual time=0.010..0.013 rows=2 loops=1)
Index Cond: ((id >= 101) AND (id <= 110))
-> Index Scan using tablea_id_3 on tablea_part3 a_2 (cost=0.29..8.32 rows=2 width=12) (actual time=0.009..0.011 rows=2 loops=1)
Index Cond: ((id >= 101) AND (id <= 110))
-> Index Scan using tablea_id_4 on tablea_part4 a_3 (cost=0.29..8.32 rows=2 width=12) (actual time=0.009..0.012 rows=2 loops=1)
Index Cond: ((id >= 101) AND (id <= 110))
-> Index Scan using tablea_id_5 on tablea_part5 a_4 (cost=0.29..8.32 rows=2 width=12) (actual time=0.015..0.018 rows=2 loops=1)
Index Cond: ((id >= 101) AND (id <= 110))
-> Append (cost=0.29..39.51 rows=5 width=12) (actual time=0.021..0.041 rows=1 loops=10)
-> Index Scan using tableb_reference_1 on tableb_part1 b (cost=0.29..7.90 rows=1 width=12) (actual time=0.007..0.007 rows=0 loops=10)
Index Cond: (reference = a.reference)
-> Index Scan using tableb_reference_2 on tableb_part2 b_1 (cost=0.29..7.90 rows=1 width=12) (actual time=0.006..0.006 rows=0 loops=10)
Index Cond: (reference = a.reference)
-> Index Scan using tableb_reference_3 on tableb_part3 b_2 (cost=0.29..7.90 rows=1 width=12) (actual time=0.009..0.010 rows=0 loops=10)
Index Cond: (reference = a.reference)
-> Index Scan using tableb_reference_4 on tableb_part4 b_3 (cost=0.29..7.90 rows=1 width=12) (actual time=0.006..0.007 rows=0 loops=10)
Index Cond: (reference = a.reference)
-> Index Scan using tableb_reference_5 on tableb_part5 b_4 (cost=0.29..7.90 rows=1 width=12) (actual time=0.006..0.006 rows=0 loops=10)
Index Cond: (reference = a.reference)
Planning time: 3.629 ms
Execution time: 0.762 ms
===========
So to summarise the findings/questions from above:
- It seems like the Postgres optimizer sometimes uses the partition level statistics, and sometimes the global table level statistics? Or is it using something else?
- With partitioning tables with unique identifier and retrieving explicitly on those identifiers, at present the optimizer will always understimate the selectivity and overestimate the rows returned. This inaccuracy increases in proportion to the number of partitions.
- As a result, when joining to other tables, you are liable to hitting sequential scans. This becomes more likely as you have more partitions or if join to more partitioned tables (note I am aware I could try and tune random_page_cost to try and prevent this).
- To me in the examples queries described above, it makes sense to use the partition statistics for the partition level access strategy, but the global statistics when estimating the actual rows returned by all the individual partition queries. Is there a reason not to do this? Or do others believe the optimizer is doing the right thing here?
And then some general questions:
- How do other people use partitioning but without a significant performance disadvantage on reading the data? Is there something else I should be doing here to achieve the same thing without the overhead? At present my reads have increased optimization cost (as it needs to optimize access to each partition) and also execution cost (access the index on every partition). Even without the optimizer issues described above, the cost of reading simple data is extremely high relative to non-partitioned data (unless you use the partition key as a filter for each table to eliminate those partitions).
- Is there any chance/plan to add global indexes to postgres? If so would that impact significantly the cost of the partition drop e.g. to clean up the index.
Thanks in advance for any feedback/support,
Keith
