I have a medium-size table (about 15 billion rows) that is made of roughly 200 sub-tables (i.e. each sub-table INHERITS the mother table, and there are constraint CHECKS to optimize the partitioning).
The plpgsql code that loads each table also produces indexes on each of the sub-tables, but unfortunately (because it is a function), it cannot also execute a vacuum analyze subtable; for the sub-table it has just loaded and indexed.
So, until the next vacuum analyze of the whole DB is done, all the queries on the mother table suck and don't use the indexes (as revealed by explain).
Note that this is all with PostgreSQL 8.2.14.
Questions
Is there a way to have the plpgsql function say, please pretty please with sugar on top, end transaction block and perform a vacuum analyze subtable?
Is there a way to vacuum/analyze many tables at once using some wildcard (vacuum analyze schema.subtables*)?
Alternatively, is it possible to vacuum/analyze just one schema?
Is there any other way to vacuum/analyze the 200 sub-tables in a programmatic fashion from within postgresql (yes, I can dump the name of all sub-tables, toss and season to taste with some perl into a psql script, and execute that, but it's kinda ugly).
Note that this is all with PostgreSQL 8.2.14.
8.2 went out of support last year, after a five year run, and 8.2.14 is missing lots of fixes security vulnerabilities and bugs which were discovered after 2009-03-16. You desperately need to move onto a version from this decade.
Is there any other way to vacuum/analyze the 200 sub-tables in a
programmatic fashion from within postgresql (yes, I can dump the name
of all sub-tables, toss and season to taste with some perl into a psql
script, and execute that, but it's kinda ugly).
You could create entries in pg_autovacuum and leave it to the autovacuum daemon. Of course, that daemon has gotten a lot better in recent years, so that's another reason to update.
Related
Is it possible to run PostgreSQL 11's VACUUM FULL for a short while and then get some benefit? Or does cancelling it midway cause all of its progress to be lost?
I've read about pg_repack (https://aws.amazon.com/blogs/database/remove-bloat-from-amazon-aurora-and-rds-for-postgresql-with-pg_repack/) but the way it works (creating new tables, copying data, etc.) sounds risky to me. Is that my paranoia or is it safe to use on a production database?
Backstory: I am working with a very large production database on AWS Aurora PostgreSQL 11. Many of the tables had tens of millions of records but have been pruned down significantly. The problem is that the table sizes on disk (and in the snapshots) have not decreased because DELETE and VACUUM (without FULL) do not shrink the files. These tables are in the hundreds of gigabytes range and I'm afraid running VACUUM FULL will take forever.
No. VACUUM FULL writes a new physical file for the table. Stopping it before it finishes voids the work done so far.
The manual:
VACUUM FULL rewrites the entire contents of the table into a new
disk file with no extra space, allowing unused space to be returned to
the operating system. This form is much slower and requires an ACCESS EXCLUSIVE lock on each table while it is being processed.
This is the main reason why community tools like pg_repack or pg_squeeze were created, which are more flexible, less blocking, and often faster, too. (I don't think pg_squeeze is available for Aurora, yet).
pg_repack might be a bit of overkill. You can instead just delete tuples from the end of the table and reinsert them towards the front of the table (reusing space already marked as free by an earlier VACUUM), at which point another ordinary VACUUM can truncate away the free space at the end of the table.
with d as (delete from mytable where ctid>='(50000,1)' returning *)
insert into mytable select * from d;
You can use pg_freespacemap to figure out where would be a good place to start the ctid criterion at.
This might not behave well if you have triggers or FK constraints, and it might bloat indexes such they would need to be rebuilt (but they probably do anyway). It will also lock a large number rows at a time, for the duration it takes for the re-insert to run and commit.
Improvements made since v11 will make the ctid scan more efficient than it will be in v11.
I process a table with ~10^7 rows the following way: take last N rows, update them in some way, and delete, then vacuum table. In the end I make a query for pg_total_relation_size. Loop repeats until the table is over. Each iteration last for several seconds. There are no any other queries for this table except mentioned above. The problem is that I get the same results for table size. It changes about once a several hours.
So the question is -- does postgres store somewhere the table size or does it calculate it every time the function is invoked? I.e., does my table size really stays the same in spite of its processing?
Your table really does stay the same size on disk despite the DELETEs and VACUUMing you're doing. As per the documentation on VACUUM, ordinary VACUUM only releases space back to the OS if it can do so by truncating free space from the end of the file without rearranging live rows.
The space is still "free" in that PostgreSQL can re-use it for other new rows. It is much, much faster to re-use space that PostgreSQL hasn't given back to the OS than it is to extend a relation with new space, so this is often preferable.
The other reason Pg doesn't just give this space back is that it can only give space back to the OS when it's a contiguous chunk with no visible rows until the end of the file. This doesn't happen much so in practice Pg needs to move some rows around to compact the table and allow it to free space at the end, kind of like a defrag on a file system. This is an inefficient and slow process that can counter-intuitively make the table slower to access instead of faster, so it's not always a good idea.
If you have a relation that's mostly but not entirely empty it can be worth doing a VACUUM FULL (Pg 9.0 and above) or CLUSTER (all versions) to free the space. If you expect to refill the table this is usually counter-productive; it's actually better to leave it as-is.
(For what I mean by terms like "live" and "visible" see the documentation on MVCC which will help you understand Pg's table organisation.)
Personally I'd skip the manual VACUUM in your case. Turn autovacuum up if you need to. If you really need to you could consider partitioning your table, processing it partition by partition and TRUNCATE each partition when you're done processing it.
We're using Postgresql 9.1.4 as our db server. I've been trying to speed up my test suite so I've stared profiling the db a bit to see exactly what's going on. We are using database_cleaner to truncate tables at the end of tests. YES I know transactions are faster, I can't use them in certain circumstances so I'm not concerned with that.
What I AM concerned with, is why TRUNCATION takes so long (longer than using DELETE) and why it takes EVEN LONGER on my CI server.
Right now, locally (on a Macbook Air) a full test suite takes 28 minutes. Tailing the logs, each time we truncate tables... ie:
TRUNCATE TABLE table1, table2 -- ... etc
it takes over 1 second to perform the truncation. Tailing the logs on our CI server (Ubuntu 10.04 LTS), take takes a full 8 seconds to truncate the tables and a build takes 84 minutes.
When I switched over to the :deletion strategy, my local build took 20 minutes and the CI server went down to 44 minutes. This is a significant difference and I'm really blown away as to why this might be. I've tuned the DB on the CI server, it has 16gb system ram, 4gb shared_buffers... and an SSD. All the good stuff. How is it possible:
a. that it's SO much slower than my Macbook Air with 2gb of ram
b. that TRUNCATION is so much slower than DELETE when the postgresql docs state explicitly that it should be much faster.
Any thoughts?
This has come up a few times recently, both on SO and on the PostgreSQL mailing lists.
The TL;DR for your last two points:
(a) The bigger shared_buffers may be why TRUNCATE is slower on the CI server. Different fsync configuration or the use of rotational media instead of SSDs could also be at fault.
(b) TRUNCATE has a fixed cost, but not necessarily slower than DELETE, plus it does more work. See the detailed explanation that follows.
UPDATE: A significant discussion on pgsql-performance arose from this post. See this thread.
UPDATE 2: Improvements have been added to 9.2beta3 that should help with this, see this post.
Detailed explanation of TRUNCATE vs DELETE FROM:
While not an expert on the topic, my understanding is that TRUNCATE has a nearly fixed cost per table, while DELETE is at least O(n) for n rows; worse if there are any foreign keys referencing the table being deleted.
I always assumed that the fixed cost of a TRUNCATE was lower than the cost of a DELETE on a near-empty table, but this isn't true at all.
TRUNCATE table; does more than DELETE FROM table;
The state of the database after a TRUNCATE table is much the same as if you'd instead run:
DELETE FROM table;
VACCUUM (FULL, ANALYZE) table; (9.0+ only, see footnote)
... though of course TRUNCATE doesn't actually achieve its effects with a DELETE and a VACUUM.
The point is that DELETE and TRUNCATE do different things, so you're not just comparing two commands with identical outcomes.
A DELETE FROM table; allows dead rows and bloat to remain, allows the indexes to carry dead entries, doesn't update the table statistics used by the query planner, etc.
A TRUNCATE gives you a completely new table and indexes as if they were just CREATEed. It's like you deleted all the records, reindexed the table and did a VACUUM FULL.
If you don't care if there's crud left in the table because you're about to go and fill it up again, you may be better off using DELETE FROM table;.
Because you aren't running VACUUM you will find that dead rows and index entries accumulate as bloat that must be scanned then ignored; this slows all your queries down. If your tests don't actually create and delete all that much data you may not notice or care, and you can always do a VACUUM or two part-way through your test run if you do. Better, let aggressive autovacuum settings ensure that autovacuum does it for you in the background.
You can still TRUNCATE all your tables after the whole test suite runs to make sure no effects build up across many runs. On 9.0 and newer, VACUUM (FULL, ANALYZE); globally on the table is at least as good if not better, and it's a whole lot easier.
IIRC Pg has a few optimisations that mean it might notice when your transaction is the only one that can see the table and immediately mark the blocks as free anyway. In testing, when I've wanted to create bloat I've had to have more than one concurrent connection to do it. I wouldn't rely on this, though.
DELETE FROM table; is very cheap for small tables with no f/k refs
To DELETE all records from a table with no foreign key references to it, all Pg has to do a sequential table scan and set the xmax of the tuples encountered. This is a very cheap operation - basically a linear read and a semi-linear write. AFAIK it doesn't have to touch the indexes; they continue to point to the dead tuples until they're cleaned up by a later VACUUM that also marks blocks in the table containing only dead tuples as free.
DELETE only gets expensive if there are lots of records, if there are lots of foreign key references that must be checked, or if you count the subsequent VACUUM (FULL, ANALYZE) table; needed to match TRUNCATE's effects within the cost of your DELETE .
In my tests here, a DELETE FROM table; was typically 4x faster than TRUNCATE at 0.5ms vs 2ms. That's a test DB on an SSD, running with fsync=off because I don't care if I lose all this data. Of course, DELETE FROM table; isn't doing all the same work, and if I follow up with a VACUUM (FULL, ANALYZE) table; it's a much more expensive 21ms, so the DELETE is only a win if I don't actually need the table pristine.
TRUNCATE table; does a lot more fixed-cost work and housekeeping than DELETE
By contrast, a TRUNCATE has to do a lot of work. It must allocate new files for the table, its TOAST table if any, and every index the table has. Headers must be written into those files and the system catalogs may need updating too (not sure on that point, haven't checked). It then has to replace the old files with the new ones or remove the old ones, and has to ensure the file system has caught up with the changes with a synchronization operation - fsync() or similar - that usually flushes all buffers to the disk. I'm not sure whether the the sync is skipped if you're running with the (data-eating) option fsync=off .
I learned recently that TRUNCATE must also flush all PostgreSQL's buffers related to the old table. This can take a non-trivial amount of time with huge shared_buffers. I suspect this is why it's slower on your CI server.
The balance
Anyway, you can see that a TRUNCATE of a table that has an associated TOAST table (most do) and several indexes could take a few moments. Not long, but longer than a DELETE from a near-empty table.
Consequently, you might be better off doing a DELETE FROM table;.
--
Note: on DBs before 9.0, CLUSTER table_id_seq ON table; ANALYZE table; or VACUUM FULL ANALYZE table; REINDEX table; would be a closer equivalent to TRUNCATE. The VACUUM FULL impl changed to a much better one in 9.0.
Brad, just to let you know. I've looked fairly deeply into a very similar question.
Related question: 30 tables with few rows - TRUNCATE the fastest way to empty them and reset attached sequences?
Please also look at this issue and this pull request:
https://github.com/bmabey/database_cleaner/issues/126
https://github.com/bmabey/database_cleaner/pull/127
Also this thread: http://archives.postgresql.org/pgsql-performance/2012-07/msg00047.php
I am sorry for writing this as an answer, but I didn't find any comment links, maybe because there are too much comments already there.
I've encountered similar issue lately, i.e.:
The time to run test suite which used DatabaseCleaner varied widely between different systems with comparable hardware,
Changing DatabaseCleaner strategy to :deletion provided ~10x improvement.
The root cause of the slowness was a filesystem with journaling (ext4) used for database storage. During TRUNCATE operation the journaling daemon (jbd2) was using ~90% of disk IO capacity. I am not sure if this is a bug, an edge case or actually normal behaviour in these circumstances. This explains however why TRUNCATE was a lot slower than DELETE - it generated a lot more disk writes. As I did not want to actually use DELETE I resorted to setting fsync=off and it was enough to mitigate this issue (data safety was not important in this case).
A couple of alternate approaches to consider:
Create a empty database with static "fixture" data in it, and run the tests in that. When you are done, just just drop the database, which should be fast.
Create a new table called "test_ids_to_delete" that contains columns for table names and primary key ids. Update your deletion logic to insert the ids/table names in this table instead, which will be much faster than running deletes. Then, write a script to run "offline" to actually delete the data, either after a entire test run has finished, or overnight.
The former is a "clean room" approach, while latter means there will be some test data will persist in database for longer. The "dirty" approach with offline deletes is what I'm using for a test suite with about 20,000 tests. Yes, there are sometimes problems due to having "extra" test data in the dev database but at times. But sometimes this "dirtiness" has helped us find and fixed bug because the "messiness" better simulated a real-world situation, in a way that clean-room approach never will.
I have a 9 million row table. I figured out that a large amount of it (around 90%) can be freed up. What actions are needed after the cleanup? Vacuum, reindex etc.
If you want to free up space on the file system, either VACUUM FULL or CLUSTER can help you. You may also want to run ANALYZE after these, to make sure the planner has up-to-date statistics but this is not specifically required.
It is important to note using VACUUM FULL places an ACCESS EXCLUSIVE lock on your table(s) (blocking any operation, writes & reads), so you probably want to take your application offline for the duration.
In PostgreSQL 8.2 and earlier, VACUUM FULL is probably your best bet.
In PostgreSQL 8.3 and 8.4, the CLUSTER command was significantly improved, so VACUUM FULL is not recommended -- it's slow and it will bloat your indexes. `CLUSTER will re-create indexes from scratch and without the bloat. In my experience, it's usually much faster too. CLUSTER will also sort the whole physical table using an index, so you must pick an index. If you don't know which, the primary key will work fine.
In PostgreSQL 9.0, VACUUM FULL was changed to work like CLUSTER, so both are good.
It's hard to make predictions, but on a properly tuned server with commodity hardware, 9 million rows shouldn't take longer than 20 minutes.
See the documentation for CLUSTER.
PostgreSQL wiki about VACUUM FULL and recovering dead space
You definitely want to run a VACUUM, to free up that space for future inserts. If you want to actually reclaim that space on disk, making it available to the OS, you'll need to run VACUUM FULL. Keep in mind that VACUUM can run concurrently, but VACUUM FULL requires an exclusive lock on the table.
You will also want to REINDEX, since the indexes will remain bloated even after the VACUUM runs. If possible, a much faster way to do this is to drop the index and create it again from scratch.
You'll also want to ANALYZE, which you can just combine with the VACUUM.
See the documentation for more info.
Hi
Don't it be more optimal to create a temporary table with 10% of needed records. Then drop original table and rename temporary to original ...
I'm relatively new to the world of Postgres, but I understand VACUUM ANALYZE is recommended. I think there's also a sub-option which just frees up space. I found reindex useful as well when doing batch inserts or deletes. Yes I've been working with tables with a similar number of rows, and the speed increase is very noticeable (UBuntu, Core 2 Quad)
I have a table in PostgreSQL that I need read into memory. It is a very small table, with only three columns and 200 rows, and I just do a select col1, col2, col3 from my_table on the whole thing.
On the development machine this is very fast (less than 1ms), even though that machine is a VirtualBox inside of a Mac OS FileVault.
But on the production server it consistently takes 600 ms. The production server may have lower specs, and the database version is older, too (7.3.x), but that alone cannot explain the huge difference, I think.
In both cases, I am running explain analyze on the db server itself, so it cannot be the network overhead. The query execution plan is in both cases a simple sequential full table scan. There was also nothing else going on on the production machine at the time, so contention is out, too.
How can find out why this is so slow, and what can I do about it?
Sounds like perhaps you haven't been VACUUMing this database properly? 7.3 is way too old to have AutoVacuum, so this is something you must do manually (cron job is recommended). If you have had many updates (over time) to this table and not run VACUUM, it will be very slow to access.
It's clearly table bloat. Run vacuum analyze of the table in question. Also - upgrade. 7.3 is not even supported anymore.
what does happen if you run the query several times ?
the fisrt run should be slow, but the others should be faster because the 1st execution put the data in the cache.
BTW : if you do a SELECT ... FROM without any restriction, it's 100% normal that you have a seq scan, you have to seq scan to retrieve the value, and since you have do restrictions, there is no need to do an index scan.
Don't hesitate to post the result of your Explain Analyze query.
PostgreSQL 7.3 is really old, no option to upgrade to a more modern version ?