Summary
I am using Postgres UPSERTs in our ETLs and I'm experiencing issues with fragmentation and bloat on the tables I am writing to, which is slowing down all operations including reads.
Context
I have hourly batch ETLs upserting into tables (tables ~ 10s of Millions, upserts ~ 10s of thousands) and we have auto vacuums set to thresholds on AWS.
I have had to run FULL vacuums to get the space back and prevent processes from hanging. This has been exacerbated now as the frequency of one of our ETLs has increased, which populates some core tables which are the source for a number of denormalised views.
It seems like what is happening is that tables don't have a chance to be vacuumed before the next ETL run, thus creating a spiral which eventually leads to a complete slow-down.
Question!
Does Upsert fundamentally have a negative impact on fragmentation and if so, what are other people using? I am keen to implement some materialised views and move most of our indexes to the new views while retaining only the PK index on the tables we are writing to, but I'm not confident that this will resolve the issue I'm seeing with bloat.
I've done a bit of reading on the issue but nothing conclusive, for example --> https://www.targeted.org/articles/databases/fragmentation.html
Thanks for your help
It depends. If there are no constraint violations, INSERT ... ON CONFLICT won't cause any bloat. If it performs an update, it will produce a dead row.
The measures you can take:
set autovacuum_vacuum_cost_delay = 0 for faster autovacuum
use a fillfactor somewhat less than 100 and have no index on the updated columns, so that you can get HOT updates, which make autovacuum unnecessary
It is not clear what you are actually seeing. Can you turn track_io_timing on, and then do an EXPLAIN (ANALYZE, BUFFERS) for the query that you think has been slowed down by bloat?
Bloat and fragmentation aren't the same thing. Fragmentation is more an issue with indexes under some conditions, not the tables themselves.
It seems like what is happening is that tables don't have a chance to be vacuumed before the next ETL run
This one could be very easy to fix. Run a "manual" VACUUM (not VACUUM FULL) at end or at the beginning of each ETL run. Since you have a well defined workflow, there is no need to try get autovacuum to do the right thing, as it should be very easy to inject manual vacuums into your workflow. Or do you think that one VACUUM per ETL is overkill?
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'm using Postgres version 9.6
Most of my tables are for queries, update, insert.
Most of them around 200K-700K.
There are bigger (millions) and smaller.
Is that a good idea to perform vacuum (and analyze?) operation once a day? once a week? regardless if there is an autovacuum..
Advantages vs disadvantages?
Autovacuum is done when needed and it only creates statistics that are used when planning a query.
Basically you never need to do this manually, unless you have made vast changes to a table (filled it with data for example), and want to use it in another query within a few milliseconds. In that scenario, old statistics will result in the query planner coming up with a very bad query plan and will lead to a significantly slower query.
What you might want to do once per day / per week, or whatever, is to cluster tables, recreate degraded indexes, on tables that were modified a lot. Research these topics more to decide if / when / how to do 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 with a few million tuples.
I perform updates in most of them.
The first update takes about a minute. The second, takes two minutes. The third update takes four minutes.
After that, I execute a VACUUM FULL.
Then, I execute the update again, which takes two minutes.
If I dump the database and recreate it, the first update will take one minute.
Why doesn't PostgreSQL performance get back to its maximum after a VACUUM FULL?
VACUUM FULL does not compact the indexes. In fact, indexes can be in worse shape after performing a VACUUM FULL. After a VACUUM FULL, you should REINDEX the table.
However, VACUUM FULL+REINDEX is quite slow. You can achieve the same effect of compacting the table and the indexes using the CLUSTER command which takes a fraction of the time. It has the added benefit that it will order your table based on the index you choose to CLUSTER on. This can improve query performance. The downsides to CLUSTER over VACUUM FULL+REINDEX is that it requires approximately twice the disk space while running. Also, be very careful with this command if you are running a version older than 8.3. It is not MVCC safe and you can lose data.
Also, you can do a no-op ALTER TABLE ... ALTER COLUMN statement to get rid of the table and index bloat, this is the quickest solution.
Finally, any VACUUM FULL question should also address the fact why you need to do this? This is almost always caused by incorrect vacuuming. You should be running autovacuum and tuning it properly so that you never have to run a VACUUM FULL.
The order of the tuples might be different, this results in different queryplans. If you want a fixed order, use CLUSTER. Lower the FILLFACTOR as well and turn on auto_vacuum. And did you ANALYZE as well?
Use EXPLAIN to see how a query is executed.