I've got a system (WSO2SP) which uses PostgreSQL. It stores BLOBs and uses pg_largeobject. I don't have control over how the system uses this Postgres feature. The issue is that the table pg_largeobject is growing constantly and the only way to keep it from growing is cleaning the table using a scheduled task.
Is it possible to analyze requests, queries or another activity to understand why the table might be growing?
Answered by Mohandarshan on Siddhi Slack
As per the default behaviour, state persistence revisions are get cleaned automatically in Siddhi.
In Postgres, there is a separate table to handle the large object.
This is not cleaned even the actual data is removed. In our case, even state persistence data is removed respective pg_largeobject entry is not getting deleted.
It seems, Postgres recommends to remove them using the DB trigger or manual process. You can refer to the guide to get some understanding. Please get help from Postgres community, if you need further guidance on this.
Related
I know that in Apache Iceberg I can set limits on number and age of snapshots, and that "deleting" data from the table does not result in underlying data removal, it simply masks or deletes tracking information.
I would like to actually delete the underlying files on delete, however. I know this will make time-travel inconsistent, but it is still a business requirement.
https://iceberg.apache.org/docs/latest/configuration/
As best as I can tell, I'll have to track and manage the physical life-cycle every file independently. Am I missing something?
If you don't care about table history (or time travel) you can simply call the expire_snapshots procedure after each delete.
What you get is a common question for many iceberg users.
We often need an asynchronous task to delete and expire snapshots\data.
If you use spark, you can use https://iceberg.apache.org/docs/latest/spark-procedures/#expire_snapshots, as shay saied.
you can also do this using the java api provided by iceberg https://iceberg.apache.org/docs/latest/api/.
Starting a task for each table is difficult to manage. Tables often have different TTL. In this case, You can add custom configurations to a table. Manually scan all iceberg tables, then determines whether to delete expired snapshots and data based on these configurations.
If you are using Iceberg with Hive (4.0.0-alpha2 + version), you can try expire_snapshot command on beeline.
Like
ALTER TABLE test_table EXECUTE expire_snapshots('2021-12-09 05:39:18.689000000');
Can read:
https://docs.cloudera.com/cdw-runtime/cloud/iceberg-how-to/topics/iceberg-expiring-snapshots.html
Hive Jira adding support:
https://issues.apache.org/jira/browse/HIVE-26354
My postresql database is updated each night.
At the end of each nightly update, I need to know what data changed.
The update process is complex, taking a couple of hours and requires dozens of scripts, so I don't know if that influences how I could see what data has changed.
The database is around 1 TB in size, so any method that requires starting a temporary database may be very slow.
The database is an AWS instance (RDS). I have automated backups enabled (these are different to RDS snapshots which are user initiated). Is it possible to see the difference between two RDS automated backups?
I do not know if it is possible to see difference between RDS snapshots. But in the past we tested several solutions for similar problem. Maybe you can take some inspiration from it.
Obvious solution is of course auditing system. This way you can see in relatively simply way what was changed. Depending on granularity of your auditing system down to column values. Of course there is impact on your application due auditing triggers and queries into audit tables.
Another possibility is - for tables with primary keys you can store values of primary key and 'xmin' and 'ctid' hidden system columns (https://www.postgresql.org/docs/current/static/ddl-system-columns.html) for each row before updated and compare them with values after update. But this way you can identify only changed / inserted / deleted rows but not changes in different columns.
You can make streaming replica and set replication slots (and to be on the safe side also WAL log archiving ). Then stop replication on replica before updates and compare data after updates using dblink selects. But these queries can be very heavy.
I am interested in keeping a running history of every change which has happened on some tables in my database, thus being able to reconstruct historical states of the database for analysis purposes.
I am using Postgres, and this MVCC thing just seems like I should be able to exploit it for this purpose but I cannot find any documentation to support this. Can I do it? Is there a better way?
Any input is appreciated!
UPD
I have marked Denis' response as the answer, because he did in fact answer whether MVCC is what I want which was the question. However, the strategy I have settled on is detailed below in case anyone finds it useful:
The Postgres feature that does what I want: online backup/point in time recovery.
http://www.postgresql.org/docs/8.1/static/backup-online.html explains how to use this feature but essentially you can set this "write ahead log" to archive mode, take a snapshot of the database (say, before it goes live), then continually archive the WAL. You can then use log replay to recall the state of the database at any time, with the side benefit of having a warm standby if you choose (by continually replaying the new WALs on your standby server).
Perhaps this method is not as elegant as other ways of keeping a history, since you need to actually build the database for every point in time you wish to query, however it looks extremely easy to set up and loses zero information. That means when I have the time to improve my handling of historical data, I'll have everything and will therefore be able to transform my clunky system to a more elegant system.
One key fact that makes this so perfect is that my "valid time" is the same as my "transaction time" for the specific application- if this were not the case I would only be capturing "transaction time".
Before I found out about the WAL, I was considering just taking daily snapshots or something but the large size requirement and data loss involved did not sit well with me.
For a quick way to get up and running without compromising my data retention from the outset, this seems like the perfect solution.
Time Travel
PostgreSQL used to have just this feature, and called it "Time Travel". See the old documentation.
There's somewhat similar functionality in the spi contrib module that you might want to check out.
Composite type audit trigger
What I usually do instead is to use triggers to log changes along with timestamps to archival tables, and query against those. If the table structure isn't going to change you can use something like:
CREATE TABLE sometable_history(
command_tag text not null check (command_tag IN ('INSERT','DELETE','UPDATE','TRUNCATE')),
new_content sometable,
change_time timestamp with time zone
);
and your versioning trigger can just insert into sometable_history(TG_OP,NEW,current_timestamp) (with a different CASE for DELETE, where NEW is not defined).
hstore audit trigger
That gets painful if the schema changes to add new NOT NULL columns though. If you expect to do anything like that consider using a hstore to archive the columns, instead of a composite type. I've already added an implementation of that on the PostgreSQL wiki already.
PITR
If you want to avoid impact on your master database (growing tables, etc), you can alternately use continuous archiving and point-in-time recovery to log WAL files that can, using a recovery.conf, be replayed to any moment in time. Note that WAL files are big and they include not only the tuples you changed, but VACUUM activity and other details. You'll want to run them through clearxlogtail since they can have garbage data on the end if they're partial segments from an archive timeout, then you'll want to compress them heavily for long term storage.
I am using Postgres, and this MVCC thing just seems like I should be able to exploit it for this purpose but I cannot find any documentation to support this. Can I do it?
Not really. There are tools to see dead rows, because auto-vacuuming is so that will eventually be reclaimed.
Is there a better way?
If I get your question right, you're looking into logging slowly changing dimensions.
You might find this recent related thread interesting:
Temporal database design, with a twist (live vs draft rows)
I'm not aware of any tools/products that are built for that purpose.
While this may not be exactly what you're asking for, you can configure Postgresql to log ddl changes. Setting the log_line_prefix parameter (try including %d, %m, and %u) and setting the log_statement parameter to ddl should give you a reasonable history of who made what ddl changes and when.
Having said that, I don't believe logging ddl to be foolproof. For example, consider a situation where:
Multiple schemas have a table with the same name,
one of the tables is altered, and
the ddl doesn't fully qualify the table name (relying on the search path to get it right),
then it may not be possible to know from the log which table was actually altered.
Another option might be to log ddl as above but then have a watcher program perform a pg_dump of the database schema whenever a ddl entry get's logged. You could even compare the new dump with the previous dump and extract just the objects that were changed.
Does anyone here knows how to get the name
of the table that was changed,updated or deleted
in SQLite?..i found the function changes() and totalChanges()
but they only return the number of database rows that were
changed or inserted or deleted by the most recently completed SQL statement.
In most RDBMS's you have some kind of journaling that captures all database transactions for data backup and recovery. In Oracle, it's called a redo log. That is where you would go to check if a table name has changed.
But I'm not familiar enough with SqlLite to know if this is available. I did find a thread where a similar question was asked, and it was recommended to implement it yourself. Try reading through the this link and see if this satisfies your requirements:
But aside from all of that, I would also recommend that your app use views, that way you protect the model from changes.
I am considering log-shipping of Write Ahead Logs (WAL) in PostgreSQL to create a warm-standby database. However I have one table in the database that receives a huge amount of INSERT/DELETEs each day, but which I don't care about protecting the data in it. To reduce the amount of WALs produced I was wondering, is there a way to prevent any activity on one table from being recorded in the WALs?
Ran across this old question, which now has a better answer. Postgres 9.1 introduced "Unlogged Tables", which are tables that don't log their DML changes to WAL. See the docs for more info, but at least now there is a solution for this problem.
See Waiting for 9.1 - UNLOGGED tables by depesz, and the 9.1 docs.
Unfortunately, I don't believe there is. The WAL logging operates on the page level, which is much lower than the table level and doesn't even know which page holds data from which table. In fact, the WAL files don't even know which pages belong to which database.
You might consider moving your high activity table to a completely different instance of PostgreSQL. This seems drastic, but I can't think of another way off the top of my head to avoid having that activity show up in your WAL files.
To offer one option to my own question. There are temp tables - "temporary tables are automatically dropped at the end of a session, or optionally at the end of the current transaction (see ON COMMIT below)" - which I think don't generate WALs. Even so, this might not be ideal as the table creation & design will be have to be in the code.
I'd consider memcached for use-cases like this. You can even spread the load over a bunch of cheap machines too.