How can you transform a complex problem of managing historical data into a simple and effective solution?
This is a common challenge faced by companies managing large databases. One of our clients encountered a typical yet serious issue: outdated data was clogging their storage, degrading database performance, and driving up disk costs. We devised a solution that not only resolved these issues but also automated data management, ensuring stability and resource efficiency.
The Problem.
The client approached us with a PostgreSQL database containing a large table designed to store historical data. According to their internal policies, this data had to be retained for three months, after which outdated records were to be deleted. The classic DELETE method was used for this purpose.
Once a month, the client’s specialists ran a cleanup procedure that took dozens of hours and heavily overloaded the database. After millions of rows were deleted, autovacuum was triggered to reclaim space occupied by “dead” records.
However, in practice, the freed disk space was not fully reclaimed, and fragmentation (“bloat”) in the table and indexes grew rapidly. This resulted in higher costs for premium disk storage.
Consequences
- Prolonged query execution times: Massive deletion in a single query locked the table and consumed enormous resources.
- Excessive autovacuum load: After each data cleanup, autovacuum worked to remove “dead” rows, further straining the server.
- Increasing bloat: The table expanded due to fragmentation and “empty” space left after row deletions. This required either manual “VACUUM FULL” operations or increased storage volumes, leading to higher costs.
Our Solution.
DBServ specialists proposed and implemented table partitioning. With this approach:
- Data is divided into partitions based on time (e.g., monthly).
- New data is automatically written to the corresponding “fresh” partition.
- Deleting outdated information is reduced to a DROP PARTITION operation, which takes only seconds and avoids creating “empty space” in the table.
- Automatic logic (via additional scripts or extensions) was configured to create new partitions and delete “closed” (no longer relevant) ones.

The Results.
- Reduced operation times: Thanks to the simple DROP PARTITION operation, there was no longer a need to manually delete millions of rows or endure lengthy autovacuum processes.
- Minimized load: The lengthy DELETE process and associated background “storms” were eliminated, resulting in a more stable database.
- No bloat: Partitions are removed entirely, leaving no “phantom” fragments in files. This saves disk space and avoids heavy cleanup operations.
- Automation: There’s no need to manually track deletion dates or run scripts—the system autonomously manages “fresh” and “aging” data partitions.
By transitioning to partitioning, the client resolved issues with prolonged deletions, excessive disk space usage, and database load during autovacuum. Optimizing historical data management freed up resources and, importantly, improved access to relevant data.