This linear trend is very important because it is a necessary precondition to use hundreds or thousands of CPUs at the same time. Each worker will aggregate data and the partial aggregates are then added up. In case your system contains at least 16 CPU cores, performance will basically increase in a linear way as worker processes are added. PostgreSQL will process the large table using 16 worker processes. Node=# EXPLAIN SELECT grp, unt(data) FROM public.t_demo GROUP BY grp įinalize GroupAggregate (cost=635314.26 rows=10 width=12) When running in parallel mode, the best plan produced by the optimizer possible with our version is the following: The query used for this test is pretty: It simply counts the number of rows for each group: Id | integer | | not null | nextval('t_demo_id_seq'::regclass) To show how a simple query can benefit from parallelism, we have compiled a simple data structure:Ĭolumn | Type | Collation | Nullable | Default As the project moves on, more and more parallelism will be available, and many more queries can benefit from multicore systems. Many things inside PostgreSQL can already be done in parallel. Of course this used to be a severe limitation, which has fortunately been removed. Traditionally PostgreSQL used a single CPU core for a query. The idea to scale PostgreSQL infinitely is of course not new but it is still exciting to push the limits of our beloved Open Source database further and further every day. Scaling PostgreSQL to “1 billion rows / second”Ĭurrently we have some projects going on here at Cybertec, which require PostgreSQL to scale well beyond a single server. Development has moved on and things are constantly improving to make PostgreSQL even better. PostgreSQL 9.6 was the first release, which allowed to run a query on many nodes. A lot of development has gone into scaling PostgreSQL and to running a single query on many CPUs. Running PostgreSQL in a server farm is something, which has not been possible for many years. A lot has been said and written about scaling PostgreSQL to many cores and subsequently to many machines.
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