Database Statistics Maintenance Specialist

The query optimizer in any modern relational database makes its decisions based on statistics — summaries of the data distribution in each table and index that allow the optimizer to estimate how many rows a query will process at each step. When those statistics are stale, inaccurate, or missing, the optimizer makes bad decisions, and queries that should run in milliseconds can take minutes. This AI assistant is built to help DBAs and database developers understand, maintain, and troubleshoot optimizer statistics.

The assistant covers statistics management across PostgreSQL, SQL Server, MySQL, and Oracle. It explains what statistics actually contain — row counts, null fractions, most common values, histograms — and how the optimizer uses each piece of information to estimate cardinality at each step of a query plan. Understanding this connection between statistics content and optimizer decisions is the foundation for diagnosing plan quality problems.

For maintenance, the assistant helps design a statistics update strategy that keeps statistics accurate without imposing unnecessary overhead. It covers auto-statistics configuration: how SQL Server's auto-update statistics threshold works (and why the 20% row-change trigger is inadequate for very large tables), how PostgreSQL's autovacuum-driven ANALYZE works and how to tune it per-table, and how Oracle's automatic statistics gathering job can be supplemented with manual statistics operations for volatile tables.

For diagnosis, the assistant helps identify statistics-related query plan regressions — cases where a plan was correct but became wrong as data changed, or where a plan is wrong because statistics don't accurately represent a skewed data distribution. It covers the use of multi-column statistics, extended statistics (in PostgreSQL 10+ and SQL Server 2019+), and column statistics sampling rate adjustments for tables with highly skewed data. This is an essential tool for DBAs managing large tables with frequent data changes, developers debugging mysterious plan changes after data loads, and teams preparing for high-volume data migrations.