Insights, updates, and best practices
Adding a 5-row lookup table to a 9-table billing query flips the plan from 0.3ms nested loops to 467ms hash joins. The cause is join_collapse_limit = 8, a default Postgres has shipped since 2005. SQL Server and MySQL don't have this cliff. Measured across three engines using ExoBench.
Ten enterprise customers own 80% of the orders. On uniform test data the query runs in ~1ms; on that real skew it runs ~1,000x slower. The fix doesn't transfer between engines: Postgres is rescued by one free session setting, SQL Server can't be touched by it and needs a completely different access-path fix. Measured on Neon and Azure SQL Database using ExoBench.
Clustered columnstore beats rowstore on inserts and bulk updates. The penalty is dead-row bloat that wrecks reads until you REORGANIZE.
Architects say 'covering indexes hurt write performance.' How much? Nobody ever has the number. With ExoBench you do!
...because your vendor's Cost Model Is from 2002
Columns beat JSONB+GIN at everything except multi-key containment on PostgreSQL 17
There's a query in every codebase that everyone knows about and nobody fixes. We benchmarked it at five data scales and found a 1,007x improvement hiding behind one composite index.