Postgres now has parallel queries. Are parallel queries used when the table is partitioned, the query is on the master table, and more than one partitions (child tables) are involved.
For example, I partition by the hour of the day. Then I want to count a type of event over more than one hour. The aggregation can be done on each partition, with the results added up at the end.
The alternative is to use a union between the partitions (child tables). In this case Postgres does parallel execution.
No, partitions are not queried in parallel. At this time (9.6) only table scans use parallel execution. The table is divided among the available workers, and each worker scans part of the table. At the end the primary worker combines the partial results.
A side effect of this is that the optimizer is more likely to chose a full table scan when parallel query execution is enabled.
As far as I can tell, there is no plan to parallelize execution based on partitions (or union all). A suggestion to add this has been added here.
Edit: My original answer was wrong. This answer has been completely revised.
Related
Postgres doc tells that partitioned tables are not processed by autovacuum. But still I see that last_autovacuum column from pg_stat_user_tables is populated with recent timestamps for live partitions.
Does it mean that these timestamps are set by the background worker which only prevents transaction ID wraparound, without actually performing ANALYZE&VACUUM? Or whatever else could populate them?
Besides, taken that partitions are large and active enough, should I run the both ANALYZE and VACUUM manually on those partitions? If yes, does the order matter?
UPDATE
I'm trying to elaborate, thanks to the comments given.
Taking that vacuum should work the same way on partition as on the regular table, what could be a reason for much faster growth of the occupied disk space after partitioning? Before partitioning it was nearly a linear function of records count.
What is confusing as well, when looking for autovacuum processes running I see that those related to partitions are denoted with "to prevent wraparound", while others are not. Is it absolutely a coincidence or there is something to check?
Documentation describes partitioned table as rather a virtual entity, without its own storage. What is the point in denoting that it is not vacuumed?
The statement from the documentation is true, but misleading. Autovacuum does not process the partitioned table itself, but it processes the partitions, which are regular PostgreSQL tables. So dead tuples get removed, the visibility map gets updated, and so on. In short, there is nothing to worry about as far as vacuuming is concerned. Remember that the partitioned table itself does not hold any data!
What the documentation warns you about is ANALYZE. Autovacuum also launches automatic ANALYZE jobs to collect accurate table statistics. This will be work fine on the partitions, but there are no table statistics collected on the partitioned table itself, so you have to run ANALYZE manually on the partitioned table to get these data. In practice, I find that not to be a problem, since the optimizer generates plans for each individual partition anyway, and there it has accurate statistics.
in Postgres I have a table partitioned by date, I know that internally when I perform a search by range by specific date, Postgres will only analyze the partitions that are from that range.
But what happens if I no longer search by date but by another column such as an id, would it be the same as a seq scan as a normal table?
and I have another question:
If I have two tables with the same information in both
Normal table : users
Partition table: users_partitioned
At the performance level, which would be faster?
select * from users
select * from users_partitioned
my intention is to know if the partitioned tables search in parallel in the partitions, and thus the response speed can be improved
The query on the partitioned table will be slightly slower, because both the optimizer and the executor have more work to do. The overhead should increase linearly with the number of partitions.
No parallelism will be used, because rows found by parallel workers would have to be gathered at the parallel leader process, and that overhead would render a parallel plan inefficient.
I'm running:
select *
from pg_stat_activity
And it shows 2 rows with same query content (under query field), and in active state,
but one row show client_backed (backend_type) and the other row show parallel_worker (backend_type)
why do I have 2 instances of same query ? (I have run just one query in my app)
what is the different between client_backed and parallel_worker ?
Since PostgreSQL v10 there is parallel processing for queries:
If the optimizer decides it is a good idea and there are enough resources, PostgreSQL will start parallel worker processes that execute the query together with your client backend. Eventually, the client backend will gather all the information from the parallel workers and finish query processing.
This speeds up query processing, but uses more resources on the database server.
The parameters that govern this are, among others max_parallel_workers, which limits the total limit for parallel worker processes, and max_parallel_workers_per_gather, which limits the numbers of parallel workers for a single query.
We are on Postgresql 12 and looking to partition a group of tables that are all related by Data Source Name. A source can have tens of millions of records and the whole dataset makes up about 900GB of space across the 2000 data sources. We don't have a good way to update these records so we are looking at a full dump and reload any time we need to update data for a source. This is why we are looking at using partitioning so we can load the new data into a new partition, detach (and later drop) the partition that currently houses the data, and then attach the new partition with the latest data. Queries will be performed via a single ID field. My concern is that since we are partitioning by source name and querying by an ID that isn't used in the partition definition that we won't be able to utilize any partition pruning and our queries will suffer for it.
How concerned should we be with query performance for this use case? There will be an index defined on the ID that is being queried, but based on the Postgres documentation it can add a lot of planning time and use a lot of memory to service queries that look at many partitions.
Performance will suffer, but it will depend on the number of partitions how much. The more partitions you have, the slower both planning and execution time will get, so keep the number low.
You can save on query planning time by defining a prepared statement and reusing it.
Trying to see if I can design a job where I need both partitioning and remote chunking. We could have something like Table A holds rows (one of the columns in table A will be the partition key) and for every Row in Table A, we would have Table B that contains many child records for a given foreign/partition key in Table A . We would need to run a query that filters the partition keys from Table A based on a query and for every partition key, process all the child records in Table B (here again we would have several million records in Table B, so we would need parallelism for record processing and hence remote chunking)
What would be the right way to think through the spring batch job design for something like that?
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so we would need parallelism for record processing and hence remote chunking
Not necessarily. Nothing prevents you from using remote chunking in the workers of a partitioned step, but IMO this would complicate things.
A simpler approach is to use multiple jobs. Each job would handle a different partition and process items in parallel using a multi-threaded step. In other words, the partition key is a job parameter here. This approach has the following advantages:
Easier to scale: since you have parallelism at two levels:
run multiple jobs in parallel using multiple JVMs (either on the same machine or on different machines)
and with-in each JVM, use multiple threads to process items in parallel.
Easier to implement: Remote partitioning and chunking are not the easiest setups to configure. Running multiple jobs where each one reads select * from TableA where partitionKey = ? items and uses a multi-threaded step (it requires a single line of code, adding a task executor .taskExecutor(taskExecutor)) is much easier.