Why 'Postgres for Everything' Breaks Down Under Concurrent Analytics Workloads

The popular practice of using PostgreSQL for all data workloads — including analytics — runs into fundamental performance limits once dashboards become concurrent. Adding a read replica, a common fix, does not resolve the core issue because replicas share the same row-oriented storage architecture as the primary database, making them equally inefficient for analytical queries. Postgres stores data in 8KB pages with per-row headers, meaning even a two-column dashboard query must read entire rows, a cost that compounds sharply under concurrent load. Industry analyses from MotherDuck and ClickHouse in 2026 documented aggregation queries degrading from 50ms to over five seconds — and eventually timing out — as row counts and concurrency scaled. Experts argue the real solution is not bigger hardware but adopting column-oriented storage purpose-built for analytics, rather than forcing a row store to perform work it was never designed to handle.