add_dimension()

Add an additional partitioning dimension to a Timescale hypertable. You can only execute this add_dimension command on an empty hypertable. To convert a normal table to a hypertable, call create hypertable.

The column you select as the dimension can use either:

• Interval partitions: For example, for a second range partition.
• hash partitions: for distributed hypertables

Note

This page describes the generalized hypertable API introduced in TimescaleDB v2.13.0.

For information about the deprecated interface, see add_dimension(), deprecated interface.

To achieve efficient scale-out performance, best practice is to use hash partitions for distributed hypertables. For regular hypertables that exist on a single node only, it is possible to configure additional partitioning for specialized use cases. However, this is is an expert option.

Every distinct item in hash partitioning is hashed to one of N buckets. Remember that we are already using (flexible) range intervals to manage chunk sizes; the main purpose of hash partitioning is to enable parallelization across multiple data nodes (in the case of distributed hypertables) or across multiple disks within the same time interval (in the case of single-node deployments).

In a distributed hypertable, hash partitioning enables inserts to be parallelized across data nodes, even while the inserted rows share timestamps from the same time interval, and thus increases the ingest rate. Query performance also benefits by being able to parallelize queries across nodes, particularly when full or partial aggregations can be "pushed down" to data nodes (for example, as in the query avg(temperature) FROM conditions GROUP BY hour, location when using location as a hash partition). Please see our best practices about partitioning in distributed hypertables for more information.

Parallel I/O can benefit in two scenarios: (a) two or more concurrent queries should be able to read from different disks in parallel, or (b) a single query should be able to use query parallelization to read from multiple disks in parallel.

Thus, users looking for parallel I/O have two options:

1. Use a RAID setup across multiple physical disks, and expose a single logical disk to the hypertable (that is, via a single tablespace).

2. For each physical disk, add a separate tablespace to the database. Timescale allows you to actually add multiple tablespaces to a single hypertable (although under the covers, a hypertable's chunks are spread across the tablespaces associated with that hypertable).

We recommend a RAID setup when possible, as it supports both forms of parallelization described above (that is, separate queries to separate disks, single query to multiple disks in parallel). The multiple tablespace approach only supports the former. With a RAID setup, no spatial partitioning is required.

That said, when using hash partitions, we recommend using 1 hash partition per disk.

Timescale does not benefit from a very large number of hash partitions (such as the number of unique items you expect in partition field). A very large number of such partitions leads both to poorer per-partition load balancing (the mapping of items to partitions using hashing), as well as much increased planning latency for some types of queries.

First convert table conditions to hypertable with just range partitioning on column time, then add an additional partition key on location with four partitions:

SELECT create_hypertable('conditions', by_range('time'));
SELECT add_dimension('conditions', by_hash('location', 4));

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Note

The by_range and by_hash dimension builders are an addition to TimescaleDB 2.13.

Convert table conditions to hypertable with range partitioning on time then add three additional dimensions: one hash partitioning on location, one range partition on time_received, and one hash partitionining on device_id.

SELECT create_hypertable('conditions', by_range('time'));
SELECT add_dimension('conditions', , by_hash('location', 2));
SELECT add_dimension('conditions', by_range('time_received', INTERVAL '1 day'));
SELECT add_dimension('conditions', by_hash('device_id', 2));
SELECT add_dimension('conditions', by_hash('device_id', 2), if_not_exists => true);

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Now in a multi-node example for distributed hypertables with a cluster of one access node and two data nodes, configure the access node for access to the two data nodes. Then, convert table conditions to a distributed hypertable with just range partitioning on column time, and finally add a hash partitioning dimension on location with two partitions (as the number of the attached data nodes).

SELECT add_data_node('dn1', host => 'dn1.example.com');
SELECT add_data_node('dn2', host => 'dn2.example.com');
SELECT create_distributed_hypertable('conditions', 'time');
SELECT add_dimension('conditions', by_hash('location', 2));

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