With slow query logging turned on, we see a lot of COMMITs taking upwards of multiple seconds to complete on our production database. On investigation, these are generally simple transactions: fetch a row, UPDATE the row, COMMIT. The SELECTs and UPDATEs in these particular transactions aren't being logged as slow. Is there anything we can do, or tools that we can use, to figure out the reason for these slow commits? We're running on an SSD, and are streaming to a slave, if that makes a difference.
Postgres commits are synchronous. This means they will wait for the WAL writes to complete before moving to the next one. You can adjust the WAL settings in the config file to adjust for this.
You can set the commit level to asynchronous at a session/user level or database wide with the synchronous_commit in the config file.
On the database side.
Vacuum your tables an update the statistics. This will get rid of dead tuples since your performing updates, there will be many.
VACUUM ANALYZE
Related
I'm getting the following error when running a query on a PostgreSQL db in standby mode. The query that causes the error works fine for 1 month but when you query for more than 1 month an error results.
ERROR: canceling statement due to conflict with recovery
Detail: User query might have needed to see row versions that must be removed
Any suggestions on how to resolve? Thanks
No need to touch hot_standby_feedback. As others have mentioned, setting it to on can bloat master. Imagine opening transaction on a slave and not closing it.
Instead, set max_standby_archive_delay and max_standby_streaming_delay to some sane value:
# /etc/postgresql/10/main/postgresql.conf on a slave
max_standby_archive_delay = 900s
max_standby_streaming_delay = 900s
This way queries on slaves with a duration less than 900 seconds won't be cancelled. If your workload requires longer queries, just set these options to a higher value.
Running queries on hot-standby server is somewhat tricky — it can fail, because during querying some needed rows might be updated or deleted on primary. As a primary does not know that a query is started on secondary it thinks it can clean up (vacuum) old versions of its rows. Then secondary has to replay this cleanup, and has to forcibly cancel all queries which can use these rows.
Longer queries will be canceled more often.
You can work around this by starting a repeatable read transaction on primary which does a dummy query and then sits idle while a real query is run on secondary. Its presence will prevent vacuuming of old row versions on primary.
More on this subject and other workarounds are explained in Hot Standby — Handling Query Conflicts section in documentation.
There's no need to start idle transactions on the master. In postgresql-9.1 the
most direct way to solve this problem is by setting
hot_standby_feedback = on
This will make the master aware of long-running queries. From the docs:
The first option is to set the parameter hot_standby_feedback, which prevents
VACUUM from removing recently-dead rows and so cleanup conflicts do not occur.
Why isn't this the default? This parameter was added after the initial
implementation and it's the only way that a standby can affect a master.
As stated here about hot_standby_feedback = on :
Well, the disadvantage of it is that the standby can bloat the master,
which might be surprising to some people, too
And here:
With what setting of max_standby_streaming_delay? I would rather
default that to -1 than default hot_standby_feedback on. That way what
you do on the standby only affects the standby
So I added
max_standby_streaming_delay = -1
And no more pg_dump error for us, nor master bloat :)
For AWS RDS instance, check http://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Appendix.PostgreSQL.CommonDBATasks.html
The table data on the hot standby slave server is modified while a long running query is running. A solution (PostgreSQL 9.1+) to make sure the table data is not modified is to suspend the replication and resume after the query:
select pg_xlog_replay_pause(); -- suspend
select * from foo; -- your query
select pg_xlog_replay_resume(); --resume
I'm going to add some updated info and references to #max-malysh's excellent answer above.
In short, if you do something on the master, it needs to be replicated on the slave. Postgres uses WAL records for this, which are sent after every logged action on the master to the slave. The slave then executes the action and the two are again in sync. In one of several scenarios, you can be in conflict on the slave with what's coming in from the master in a WAL action. In most of them, there's a transaction happening on the slave which conflicts with what the WAL action wants to change. In that case, you have two options:
Delay the application of the WAL action for a bit, allowing the slave to finish its conflicting transaction, then apply the action.
Cancel the conflicting query on the slave.
We're concerned with #1, and two values:
max_standby_archive_delay - this is the delay used after a long disconnection between the master and slave, when the data is being read from a WAL archive, which is not current data.
max_standby_streaming_delay - delay used for cancelling queries when WAL entries are received via streaming replication.
Generally, if your server is meant for high availability replication, you want to keep these numbers short. The default setting of 30000 (milliseconds if no units given) is sufficient for this. If, however, you want to set up something like an archive, reporting- or read-replica that might have very long-running queries, then you'll want to set this to something higher to avoid cancelled queries. The recommended 900s setting above seems like a good starting point. I disagree with the official docs on setting an infinite value -1 as being a good idea--that could mask some buggy code and cause lots of issues.
The one caveat about long-running queries and setting these values higher is that other queries running on the slave in parallel with the long-running one which is causing the WAL action to be delayed will see old data until the long query has completed. Developers will need to understand this and serialize queries which shouldn't run simultaneously.
For the full explanation of how max_standby_archive_delay and max_standby_streaming_delay work and why, go here.
It might be too late for the answer but we face the same kind of issue on the production.
Earlier we have only one RDS and as the number of users increases on the app side, we decided to add Read Replica for it. Read replica works properly on the staging but once we moved to the production we start getting the same error.
So we solve this by enabling hot_standby_feedback property in the Postgres properties.
We referred the following link
https://aws.amazon.com/blogs/database/best-practices-for-amazon-rds-postgresql-replication/
I hope it will help.
Likewise, here's a 2nd caveat to #Artif3x elaboration of #max-malysh's excellent answer, both above.
With any delayed application of transactions from the master the follower(s) will have an older, stale view of the data. Therefore while providing time for the query on the follower to finish by setting max_standby_archive_delay and max_standby_streaming_delay makes sense, keep both of these caveats in mind:
the value of the follower as a standby / backup diminishes
any other queries running on the follower may return stale data.
If the value of the follower for backup ends up being too much in conflict with hosting queries, one solution would be multiple followers, each optimized for one or the other.
Also, note that several queries in a row can cause the application of wal entries to keep being delayed. So when choosing the new values, it’s not just the time for a single query, but a moving window that starts whenever a conflicting query starts, and ends when the wal entry is finally applied.
I have a very large multi-million row transaction that I ended up needing to kill.
This transaction scanned a very large number of rows and created new rows in a new table if certain conditions were met.
This was in a commit block and did not complete before I killed the process— are there any repercussions to killing the process and restarting the server? I do not even see the tables in the db (presumably because the commit never happened). Can I just immediately try to do my migration again?
The answer depends on how you “killed” the transaction.
If you hit Ctrl+C or canceled the query with pg_cancel_backend or pg_terminate_backend, the transaction will have rolled back normally.
Any table you created in the session will be gone.
If you modified rows in pre-existing tables, the new rows will be dead and aotovacuum will remove them.
At worst, you will have some bloat in some tables that will be reused by the next attempt at your transaction.
Similarly, if you used a regular kill to kill the backend process of the session, everything will be fine.
If you used kill -9 to kill the session's backend process, PostgreSQL will have gone into crash recovery.
Your database will be consistent after crash recovery, but it is possible that some files (belonging to newly created tables) will be left behind. Such orphans take up space and are never removed, and the only safe way to get rid of that wasted space is to dump the database and restore it to a new database cluster.
Theoretically, yes. You should be able to just go ahead and try again. It might mean that some of the cleanup hasn't been performed yet, so there are some partial tables floating around, taking up memory, but nothing that should impact your data quality.
I have a script that performs a bunch of updates on a moderately large (approximately 6 million rows) table, based on data read from a file.
It currently begins and then commits a transaction for each row it updates and I wanted to improve its performance somehow. I wonder if starting a single transaction at the beginning of the script's run and then rollbacking to individual savepoints in case any validation error occurs would actually result in a performance increase.
I looked online but haven't had much luck finding any documentation or benchmarks.
COMMIT is mostly an I/O problem, because the transaction log (WAL) has to be synchronized to disk.
So using subtransactions (savepoints) will verylikely boost performance. But beware that using more than 64 subtransactions per transaction will again hurt performance if you have concurrent transactions.
If you can live with losing some committed transactions in the event of a database server crash (which is rare), you could simply set synchronous_commit to off and stick with many small transactions.
Another, more complicated method is to process the rows in batches without using subtransactions and repeating the whole batch in case of a problem.
Having a single transaction with only 1 COMMIT should be faster than having multiple single row update transactions because each COMMIT must synchronize WAL writing to disk. But how really faster it is in a given environment depends a lot of the environment (number of transactions, table structure, index structure, UPDATE statement, PostgreSQL configuration, system configuration etc.): only you can benchmark in your environment.
My long running SELECT queries against Hot stand-by are failing apparently due to replay on the standby leading to vacuum of some of the rows matching my query.
Is there support for an option where I can ask the Hot stand-by server to not bother about such changes to the rows (even the rows that were updated/deleted) and continue with the scan, for my query?
Or, is dropping all queries where a matching row was cleaned up during replay, vaccumm something the server always does and there's no other way supported.
You can use hot_standby_feedback to tell the primary server to not vacuum rows that the standby server is still using. If you are concerned about affecting the primary in this way, you could instead use one of max_standby_streaming_delay or max_standby_archive_delay (depending on if you are streaming or copying log files).
These are all detailed here: https://www.postgresql.org/docs/current/runtime-config-replication.html
I have found a bug in my application code where I have started a transaction, but never commit or do a rollback. The connection is used periodically, just reading some data every 10s or so. In the pg_stat_activity table, its state is reported as "idle in transaction", and its backend_start time is over a week ago.
What is the impact on the database of this? Does it cause additional CPU and RAM usage? Will it impact other connections? How long can it persist in this state?
I'm using postgresql 9.1 and 9.4.
Since you only SELECT, the impact is limited. It is more severe for any write operations, where the changes are not visible to any other transaction until committed - and lost if never committed.
It does cost some RAM and permanently occupies one of your allowed connections (which may or may not matter).
One of the more severe consequences of very long running transactions: It blocks VACUUM from doing it's job, since there is still an old transaction that can see old rows. The system will start bloating.
In particular, SELECT acquires an ACCESS SHARE lock (the least blocking of all) on all referenced tables. This does not interfere with other DML commands like INSERT, UPDATE or DELETE, but it will block DDL commands as well as TRUNCATE or VACUUM (including autovacuum jobs). See "Table-level Locks" in the manual.
It can also interfere with various replication solutions and lead to transaction ID wraparound in the long run if it stays open long enough / you burn enough XIDs fast enough. More about that in the manual on "Routine Vacuuming".
Blocking effects can mushroom if other transactions are blocked from committing and those have acquired locks of their own. Etc.
You can keep transactions open (almost) indefinitely - until the connection is closed (which also happens when the server is restarted, obviously.)
But never leave transactions open longer than needed.
There are two major impacts to the system.
The tables that have been used in those transactions:
are not vacuumed which means they are not "cleaned up" and their statistics aren't updated which might lead to bad (=slow) execution plans
cannot be changed using ALTER TABLE