A Vacuum Riddle: What's Socks but Doesn't Suck? 

A vacuum!

(Except for the Postgres vacuum, which does—occasionally—suck.)

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In part 1, we learned about the importance of never disabling the vacuum process and how to prevent table bloat by ensuring dead rows are properly cleaned up. Now, we’ll take a closer look at how to monitor the vacuum process and some key strategies for optimizing its performance.

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Rule #2: Monitor the VACUUM Statistics

To keep your vacuum process running smoothly, it’s important to regularly check the vacuum statistics. You can do this by querying the pg_stat_user_tables view, which provides statistics about user tables, including the number of live and dead rows:
SELECT relname, n_live_tup, n_dead_tup FROM pg_stat_user_tables ORDER BY n_dead_tup DESC;
This query will return tables with the most dead rows. Here’s a simple rule of thumb for when you might have a problem:

• If n_dead_tup > 0.2 * n_live_tup, it’s time to investigate.
• If n_dead_tup > n_live_tup, you definitely have a problem.

High numbers of dead tuples typically indicate that the vacuum process isn’t keeping up with updates and deletes, leading to table bloat and degraded performance. Keep an eye on this!

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Rule #3: Enable log_vacuum_min_duration

To make it easier to detect long-running vacuum operations, enable the log_vacuum_min_duration parameter. This will log any vacuum process that runs longer than a specified duration.
For example, you can set the threshold to 100 milliseconds:
ALTER SYSTEM SET log_vacuum_min_duration = 100;
Once this is configured, you can use a simple grep command on the Postgres log to identify tables that are running too many vacuums (yes, there is such a thing as too many vacuums):
grep vacuum postgresql-Sun.log
This will give you a list of tables that may need some attention. If you see excessive vacuuming, you might need to adjust Postgres’ autovacuum settings, such as autovacuum_vacuum_cost_delay or autovacuum_vacuum_threshold, to better suit your workload.

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Rule #4: Avoid Overloading the Vacuum Process

Sometimes, users try to delete large amounts of data all at once, which can overload the vacuum process. Instead of issuing delete statements like this:
DELETE FROM mytable WHERE created_at < '2023-01-01';
Consider partitioning large tables and then simply dropping entire partitions instead of deleting millions of rows. Dropping a partition is much more efficient, as it removes the data file associated with the partition without needing to perform a vacuum.
Partitioning can significantly reduce the load on the vacuum process and improve performance when working with large datasets.

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Cluster Spotlight

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Your go-to source for all things 123cluster: platform news, step-by-step tutorials, and can’t-miss community events. Whether you’re spinning up a new cluster or fine-tuning an existing one, these resources will empower your workflow and spark new ideas.

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