Tiger Data
Oct 20, 2026
One CREATE TABLE change that makes Postgres fast at scale
How to get both transactional and analytical speed from the same table
Why it works
- Lead with a specific technical pain point, then introduce the core concept as the solution.
- Explain the underlying mechanism before showing a practical code example, and close with clear setup paths for different workflows.
How it is built
- Personalized greeting
- Performance problem
- Hypertable explanation
- Storage workflow
Email copy
Hi [First name], Plain Postgres tables slow down at scale: inserts lag, queries scan too many rows, and indexes break. Hypertables solve this by automatically partitioning your data by time, so queries touch only the chunks they need. Here's a deep dive on how to make the most out of them. Get started in the Console Use the CLI/MCP Here’s how they work: Hypertables run on Hypercore, our hybrid row-columnar storage engine. New data lands in the rowstore for fast inserts, updates, and late-arriving rows. As data cools, it automatically converts to the columnstore, where analytical queries read fewer bytes and run dramatically faster. This combination gives you both transactional and analytical speed from the same table, without building a separate database. That’s it. Partitioning is automatic on the first timestamp column. Creating one is a single command: CREATE TABLE sensor_readings ( "time" TIMESTAMPTZ, device_id TEXT, temperature DOUBLE PRECISION, battery_pct NUMERIC ) WITH ( timescaledb.hypertable, timescaledb.segmentby = 'device_id', timescaledb.orderby = 'time DESC' ); The fastest path is using our CLI/MCP. If you prefer the console, head to the “Goals” section and select “Create a hypertable”. It’s as simple as that. Get started in the Console Use the CLI/MCP Log in Support Docs Blog Timescale, Inc., d/b/a Tiger Data, 335 Madison Ave, New York, NY, 10017, United States Unsubscribe Manage Preferences Privacy Policy