Replicating production database performance issues in a local development environment is a perennial challenge for engineers. Often, the query planner—the part of a database engine that decides the most efficient way to execute a command—behaves differently in development because it lacks the massive scale and distribution of production data.

PostgreSQL 18 addresses this by introducing pg_restore_relation_stats() and pg_restore_attribute_stats(). These functions allow developers to import internal statistics, such as frequency of values and column widths, without needing to migrate sensitive or bulky production datasets. By simulating these statistics, the local database "thinks" it is dealing with production-scale data and generates matching query plans.

The efficiency of this approach is staggering; Radim Marek notes that statistics for a multi-hundred-gigabyte database can fit into a text file smaller than 1MB. This allows for rapid debugging of slow queries without the security risks or storage overhead of cloning entire databases.

Furthermore, SQLite creator D. Richard Hipp highlighted that SQLite has long supported this via its writable sqlite_stat tables. Developers can inject specific statistics to force or test query planner decisions, a technique documented for manual plan control. These updates represent a significant shift toward metadata-driven performance tuning across major relational database systems.

Replicating production database performance issues in a local development environment is a perennial challenge for engineers. Often, the query planner—the part of a database engine that decides the most efficient way to execute a command—behaves differently in development because it lacks the massive scale and distribution of production data.

PostgreSQL 18 addresses this by introducing pg_restore_relation_stats() and pg_restore_attribute_stats(). These functions allow developers to import internal statistics, such as frequency of values and column widths, without needing to migrate sensitive or bulky production datasets. By simulating these statistics, the local database "thinks" it is dealing with production-scale data and generates matching query plans.

The efficiency of this approach is staggering; Radim Marek notes that statistics for a multi-hundred-gigabyte database can fit into a text file smaller than 1MB. This allows for rapid debugging of slow queries without the security risks or storage overhead of cloning entire databases.

Furthermore, SQLite creator D. Richard Hipp highlighted that SQLite has long supported this via its writable sqlite_stat tables. Developers can inject specific statistics to force or test query planner decisions, a technique documented for manual plan control. These updates represent a significant shift toward metadata-driven performance tuning across major relational database systems.