By
Marcus Gartner
Maintaining database performance is important, especially for applications that rely on speed and accuracy. Over time, PostgreSQL databases collect dead rows and unused space, which can slow things down. The VACUUM FULL command helps reclaim this space and improve efficiency.
Running VACUUM FULL on AWS RDS Postgres requires careful planning to avoid long downtimes or disruptions. This article explains step-by-step how to perform a full vacuum in a managed cloud environment, focusing on strategies to limit impact on your application.
This guide is written for anyone who wants to understand how to use Postgres VACUUM FULL, especially on AWS RDS, with a focus on minimizing downtime and keeping data accessible.
VACUUM FULL is a PostgreSQL maintenance command that completely rewrites your database tables to reclaim unused disk space. When you run Postgresql VACUUM FULL, the database creates a new copy of the table with only active data, then replaces the original table with this compacted version.
This process differs from regular VACUUM operations. A standard VACUUM command marks deleted rows as reusable space but doesn't actually shrink the table file. VACUUM FULL physically reduces the table size by removing all that dead space.
Key differences between VACUUM and VACUUM FULL:
The trade-off is significant. While Full VACUUM Postgresql operations recover more disk space, they require exclusive table locks that prevent any reads or writes during the process.
Over time, PostgreSQL tables accumulate “bloat” from deleted or updated rows. This bloat makes your database files larger than necessary and can slow down queries that have to scan through more data than needed.
Vacuum Full Postgresql operations become useful when regular maintenance isn't enough. Here are the main scenarios:
Common situations that benefit from VACUUM FULL:
You can check if your tables have significant bloat by querying the pg_stat_user_tables view. Look for tables where n_dead_tup (dead tuples) represents a large percentage of n_tup (total tuples).
VACUUM FULL operations come with significant limitations that make them unsuitable for routine maintenance. Understanding these drawbacks helps you plan better and consider alternatives.
Primary limitations of VACUUM FULL:
The disk space requirement is particularly important on AWS RDS. If you're running Vacuumdb Full on a large table, you temporarily need enough free space to hold two copies of that table.
For most routine maintenance, PostgreSQL's autovacuum feature handles cleanup automatically without these drawbacks. VACUUM FULL works best as an occasional deep-cleaning operation rather than regular maintenance.
Before running VACUUM FULL on AWS RDS, you'll want to prepare your environment to minimize downtime and avoid common issues.
Essential preparation steps:
pg_stat_user_tables to confirm which tables actually need VACUUM FULLYou can monitor table sizes and bloat with this query:
SELECT schemaname, tablename,
pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename)) AS size,
n_dead_tup, n_live_tup
FROM pg_stat_user_tables
WHERE n_dead_tup > 0
ORDER BY pg_total_relation_size(schemaname||'.'||tablename) DESC;
Consider temporarily increasing your RDS instance size or provisioned IOPS before the operation. Larger instances with more CPU and memory can complete VACUUM FULL operations significantly faster.
Running VACUUM FULL on AWS RDS follows the same basic process as standard PostgreSQL, with a few cloud-specific considerations.
Connect using your preferred PostgreSQL client with administrative privileges:
psql -h your-rds-endpoint.amazonaws.com -U your-admin-user -d your-database
Run the bloat monitoring query from the previous section to confirm which tables need attention. Focus on tables with high n_dead_tup values relative to their total size.
Target individual tables rather than running VACUUM FULL on the entire database:
VACUUM FULL your_table_name;
For verbose output that shows progress:
VACUUM (FULL, VERBOSE) your_table_name;
AWS RDS provides CloudWatch metrics to track the operation's progress. Watch for:
The operation completes when you see a message like “VACUUM completed” and CPU usage returns to normal levels.
While VACUUM FULL inherently requires table locks, you can minimize downtime through careful planning and alternative approaches.
Strategies to reduce downtime:
The pg_repack extension offers an alternative approach that rebuilds tables while allowing most read and write operations to continue. However, pg_repack isn't available by default on AWS RDS and requires careful configuration.
For most AWS RDS users, the most practical approach combines proper timing with temporary resource scaling. Running VACUUM FULL during your lowest traffic period while using a larger instance size can significantly reduce the impact.
After VACUUM FULL completes, verify that the operation achieved its goals and didn't cause any issues.
Immediate verification steps:
\dt+ in psql or the size query from earlierUse this query to see the space reclaimed:
SELECT schemaname, tablename,
pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename)) AS current_size
FROM pg_stat_user_tables
WHERE tablename = 'your_processed_table';
Ongoing monitoring:
If you don't see the expected space reduction, the table might not have had significant bloat, or other factors might be affecting disk usage.
Regular maintenance prevents the buildup of table bloat that makes VACUUM FULL operations necessary in the first place.
Effective maintenance strategies:
autovacuum_vacuum_scale_factor and related parameters for tables with high update/delete activityFor AWS RDS, you can modify autovacuum parameters through parameter groups. Tables with frequent updates might benefit from more aggressive autovacuum settings:
ALTER TABLE high_activity_table SET (autovacuum_vacuum_scale_factor = 0.1);
This approach reduces the frequency and severity of bloat, making VACUUM FULL operations less necessary over time.
VACUUM FULL isn't always the right solution for performance or storage issues. Understanding when to avoid it can save time and prevent unnecessary downtime.
Situations where alternatives work better:
For production systems that can't tolerate extended downtime, evaluate whether the space savings justify the operational impact. Sometimes accepting higher disk usage is more practical than scheduling regular VACUUM FULL operations.
Managing database maintenance becomes easier with platforms that handle infrastructure complexity automatically. Modern data operations focus on automation and reliability rather than manual intervention.
At Lob, we've found that automated data management reduces the operational overhead of maintaining high-performance databases. Our platform handles the underlying infrastructure so teams can focus on building features rather than managing maintenance windows.
Whether you're running VACUUM FULL operations or optimizing other database tasks, the right tooling makes these processes more predictable and less disruptive to your applications.
What is the difference between VACUUM and VACUUM FULL in PostgreSQL?
VACUUM marks dead rows as reusable space and can run alongside normal database operations, while VACUUM FULL physically rewrites the entire table to reclaim disk space but requires an exclusive lock that blocks all access to the table.
Does VACUUM FULL in PostgreSQL block other database queries?
Yes, VACUUM FULL takes an exclusive lock on the table being processed, which prevents all read and write operations on that table until the operation completes.
How long does VACUUM FULL take to run on large PostgreSQL tables?
The duration depends on table size, available system resources, and disk speed, but large tables can take several hours to process, which is why careful planning and scheduling during low-traffic periods is important.
Can you safely cancel a VACUUM FULL operation once it starts?
No, interrupting VACUUM FULL can leave your database in an inconsistent state, so you cannot safely cancel the operation once PostgreSQL begins rewriting the table.
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