Hydra

Billing reports running against tens of millions of transactional rows regularly trigger query timeouts and CPU spikes on production PostgreSQL databases. Instead of shipping data to a cloud warehouse like Snowflake and paying ongoing ETL subscriptions, teams can store those tables in a columnar format with Hydra. The extension executes analytical scans inside the existing database instance, eliminating separate data pipelines and warehouse compute credits.

Why Hydra instead of Snowflake

Snowflake separates compute from storage across managed cloud clusters, charging per-second warehouse credits alongside storage fees and pipeline tooling. Moving data out of an operational PostgreSQL database into Snowflake requires ongoing sync pipelines like Fivetran or custom change-data-capture workers, adding egress fees and schema synchronization overhead. Hydra removes that entire pipeline layer by keeping analytics inside Postgres. By implementing the native table access method interface, it stores analytical tables column-by-column on standard disks, allowing aggregation queries to skip irrelevant columns without copying datasets across services.

The economic difference is substantial for moderate data footprints. A team with fifty gigabytes of event data can run analytical queries on existing database instances without paying thousands of dollars annually in Snowflake minimums and ingestion tooling. What Hydra lacks is Snowflake's independent compute scaling and multi-cluster concurrency. If analytical queries saturate host CPU and disk I/O, transactional application traffic on the same instance suffers, forcing DevOps engineers to isolate instances or upgrade host hardware manually.

What it does well

• Columnar storage via table access methods: stores tables column-by-column using native access method syntax, allowing queries to read only the columns referenced in SELECT and WHERE clauses.
• Vectorized execution: processes column values in vectorized batches across CPU workers, accelerating aggregations on tables with tens of millions of rows.
• Hybrid table cohabitation: lets standard row-based heap tables and columnar analytical tables live in the same PostgreSQL database without data duplication.
• Standard SQL compatibility: works with standard PostgreSQL drivers, connection poolers, and functions without proprietary query dialects or driver changes.