PostgreSQL Code Audit

Slow Queries, Bad Indexes, and Schema Debt

Query performance issues are the most visible. Queries that worked at 10,000 rows scan the entire table at 10 million. The ORM generates joins that produce cartesian products, subqueries that execute per row, and SELECT * fetching 30 columns when the app uses three. The database does exactly what it’s told. The problem is what it’s being told to do.

Index strategy is either absent or cargo-culted. Tables with no indexes beyond the primary key, forcing sequential scans on every WHERE clause. Or tables with 15 indexes, most unused, slowing every INSERT and UPDATE. Composite indexes in the wrong column order. Partial indexes that could reduce size by 90% never used.

Schema design problems compound over time. Nullable columns that should be NOT NULL. Foreign keys dropped to fix a deployment, never restored. JSON columns for data that should be relational. Timestamps without time zones. Connection management sits at the infrastructure-application boundary - pools sized wrong, PgBouncer in transaction mode with session-level features, connection leaks in error paths. N+1 queries are everywhere: 51 database round-trips where one join would do.

Profiling With pg_stat_statements and EXPLAIN

We start with pg_stat_statements to find your most expensive queries by total execution time. A 5ms query running 100,000 times daily costs more than a 2-second query running once. We rank by cumulative cost to prioritize what matters.

Every expensive query gets EXPLAIN ANALYZE against production-like data. We look at plan choices - sequential scans where index scans should occur, nested loops where hash joins would be faster, sorts spilling to disk. We correlate with table statistics to identify stale statistics causing bad plans.

Schema review checks constraint coverage, foreign key behaviors, and drift between migrations and the actual database. Connection pooling gets load-tested - we simulate production traffic and measure checkout times, exhaustion events, and the impact of long-running queries on pool availability.

From Table Scans to Millisecond Lookups

Query performance improves by orders of magnitude for the worst offenders. Queries taking seconds complete in milliseconds. Database CPU drops because index lookups replace table scans. Your app needs fewer connections because queries finish faster.

Index strategy matches your actual patterns. Unused indexes are removed, reducing write overhead. Missing indexes are added with measured impact. Composite indexes are correctly ordered. Your database maintains its own performance instead of degrading with every row.

Schema integrity tightens. NOT NULL constraints prevent impossible values. Foreign keys prevent orphaned records. Check constraints enforce business rules at the data layer. Connection management becomes reliable - pool sizes match workload, error paths return connections, and traffic spikes cause brief queuing instead of cascading failure.

Generated Index and Migration Recommendations

Our AI analysis scans every query your application executes against your schema. We detect missing index support, join patterns that need materialized CTEs, and SELECT statements fetching unused columns. Each finding includes the optimized query and required index creation.

We generate specific index recommendations - CREATE INDEX statements for your actual queries with estimated impact on read and write performance. Covering indexes for read-heavy paths. Partial indexes for common predicates. GIN indexes for JSONB containment queries. Each includes the queries it accelerates and the write cost it adds.

Schema analysis generates zero-downtime migration scripts. Nullable columns that should be NOT NULL get ALTER TABLE statements with safe defaults. Missing foreign keys and check constraints get added. N+1 detection analyzes ORM code and generates single-query equivalents using joins or lateral queries, with estimated round-trip reduction. The generated code integrates with your existing data access layer.