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