Optimize time-series data in hypertables

Hypertables are designed for real-time analytics, they are PostgreSQL tables that automatically partition your data by time. Typically, you partition hypertables on columns that hold time values. Best practice is to use timestamptz column type. However, you can also partition on date, integer and timestamp types.

To follow the steps on this page:

• Create a target Timescale Cloud service with time-series and analytics enabled.

  You need your connection details. This procedure also works for self-hosted TimescaleDB.

Create a hypertable for your time-series data using CREATE TABLE. For efficient queries on data in the columnstore, remember to segmentby the column you will use most often to filter your data:

CREATE TABLE conditions (
   time        TIMESTAMPTZ       NOT NULL,
   location    TEXT              NOT NULL,
   device      TEXT              NOT NULL,
   temperature DOUBLE PRECISION  NULL,
   humidity    DOUBLE PRECISION  NULL
) WITH (
   tsdb.hypertable,
   tsdb.partition_column='time',
   tsdb.segmentby = 'device',
   tsdb.orderby = 'time DESC'
);

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If you are self-hosting TimescaleDB v2.19.3 and below, create a PostgreSQL relational table, then convert it using create_hypertable. You then enable hypercore with a call to ALTER TABLE.

To convert an existing table with data in it, call create_hypertable on that table with migrate_data to true. However, if you have a lot of data, this may take a long time. For more information about migrating data, see Migrate your data to Timescale Cloud.

As the data cools and becomes more suited for analytics, [add a columnstore policy][add_columnstore_policy] so your data is automatically converted to the columnstore after a specific time interval. This columnar format enables fast scanning and aggregation, optimizing performance for analytical workloads while also saving significant storage space. In the columnstore conversion, hypertable chunks are compressed by more than 90%, and organized for efficient, large-scale queries. This columnar format enables fast scanning and aggregation, optimizing performance for analytical workloads.

To optimize your data, add a columnstore policy:

CALL add_columnstore_policy('conditions', after => INTERVAL '1d');

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You can also manually convert chunks in a hypertable to the columnstore.

You can alter a hypertable, for example to add a column, by using the PostgreSQL ALTER TABLE command. This works for both regular and distributed hypertables.

You add a column to a hypertable using the ALTER TABLE command. In this example, the hypertable is named conditions and the new column is named humidity:

ALTER TABLE conditions
  ADD COLUMN sunshine DOUBLE PRECISION NULL;

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If the column you are adding has the default value set to NULL, or has no default value, then adding a column is relatively fast. If you set the default to a non-null value, it takes longer, because it needs to fill in this value for all existing rows of all existing chunks.

You can change the name of a hypertable using the ALTER TABLE command. In this example, the hypertable is called conditions, and is being changed to the new name, weather:

ALTER TABLE conditions
  RENAME TO weather;

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Drop a hypertable using a standard PostgreSQL DROP TABLE command:

DROP TABLE weather;

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All data chunks belonging to the hypertable are deleted.