Hypertables are PostgreSQL tables with special features that make it easy to handle time-series data. Anything you can do with a regular PostgreSQL table, you can do with a hypertable. In addition, you get the benefits of improved performance and user experience for time-series data.

With hypertables, TimescaleDB makes it easy to improve insert and query performance by partitioning time-series data on its time parameter. Behind the scenes, the database performs the work of setting up and maintaining the hypertable's partitions. Meanwhile, you insert and query your data as if it all lives in a single, regular PostgreSQL table.

A hypertable's behind-the-scenes partitions, called chunks, also make other features of TimescaleDB possible. These include continuous aggregates, compression, retention policies, and more.

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Hypertables compared to partitioning in regular PostgreSQL

If you use regular PostgreSQL, you can also partition your time-series data by writing the partitioning logic yourself. But handling child tables, constraints, chunk indexes, and other details gets complex. And that's before you get into more advanced features such as compression, continuous aggregates, and retention policies.

TimescaleDB handles all this for you so you can focus on your application.

When to use a hypertable in TimescaleDB

You can have both hypertables and regular PostgreSQL tables in the same database. Choose a hypertable for time-series data, and choose a regular PostgreSQL table for relational data.

For example, you might have:

  • A hypertable to record sensor readings over time, and a regular table to record sensor location and other metadata
  • A hypertable to record stock asset prices over time, and a regular table to record ticker symbols and names for each asset
  • A hypertable to record truck GPS coordinates over time, and a regular table to record the make, model, and age of each truck

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