--- title: add_dimension() | Tiger Data Docs description: Add a space-partitioning dimension to a hypertable --- Since [0.1.0](https://github.com/timescale/timescaledb/releases/tag/0.1.0) Add an additional partitioning dimension to a TimescaleDB hypertable. You can only execute this `add_dimension` command on an empty hypertable. To convert a normal table to a hypertable, call [create hypertable](/docs/reference/timescaledb/hypertables/create_hypertable/index.md). The column you select as the dimension can use either: - [Interval partitions](/docs/reference/timescaledb/hypertables/add_dimension#by_range/index.md): for example, for a second range partition. - [hash partitions](/docs/reference/timescaledb/hypertables/add_dimension#by_hash/index.md): to enable parallelization across multiple disks. Note **These instructions are for self-hosted TimescaleDB deployments** Best practice is to not use additional dimensions. However, Tiger Cloud transparently provides seamless storage scaling, both in terms of storage capacity and available storage IOPS/bandwidth. This page describes the generalized hypertable API introduced in TimescaleDB v2.13.0. For information about the deprecated interface, see [add\_dimension(), deprecated interface](/docs/reference/timescaledb/hypertables/add_dimension_old/index.md). ## Samples First convert table `conditions` to a 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)); ``` The `by_range` and `by_hash` dimension builders are an addition to TimescaleDB 2.13. Convert table `conditions` to a 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 partitioning 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); ``` ## Arguments The syntax is: ``` SELECT add_dimension( hypertable = '', dimension = by_range('', '') | by_hash('', ), if_not_exists = true | false ); ``` | Name | Type | Default | Required | Description | | --------------- | ------------------------------------------------------------------------------------------------ | ------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `hypertable` | REGCLASS | - | ✔ | The hypertable to add the dimension to. | | `dimension` | [DIMENSION\_INFO](/docs/reference/timescaledb/hypertables/add_dimension#dimension-info/index.md) | - | ✔ | To create a `_timescaledb_internal.dimension_info` instance to partition a hypertable, you call [`by_range`](/docs/reference/timescaledb/hypertables/add_dimension#by_range/index.md) and [`by_hash`](/docs/reference/timescaledb/hypertables/add_dimension#by_hash/index.md). | | `if_not_exists` | BOOLEAN | `false` | ✖ | Set to `true` to print an error if a dimension for the column already exists. By default an exception is raised. | ### Dimension info To create a `_timescaledb_internal.dimension_info` instance, you call [add\_dimension](/docs/reference/timescaledb/hypertables/add_dimension/index.md) to an existing hypertable. #### Samples Hypertables must always have a primary range dimension, followed by an arbitrary number of additional dimensions that can be either range or hash. Typically, this is just one hash. For example: ``` SELECT add_dimension('conditions', by_range('time')); SELECT add_dimension('conditions', by_hash('location', 2)); ``` For incompatible data types such as `jsonb`, you can specify a function to the `partition_func` argument of the dimension build to extract a compatible data type. Look in the example section below. #### Custom partitioning By default, TimescaleDB calls PostgreSQL‘s internal hash function for the given type. You use a custom partitioning function for value types that do not have a native PostgreSQL hash function. You can specify a custom partitioning function for both range and hash partitioning. A partitioning function should take a `anyelement` argument as the only parameter and return a positive `integer` hash value. This hash value is *not* a partition identifier, but rather the inserted value’s position in the dimension’s key space, which is then divided across the partitions. #### by\_range() Create a by-range dimension builder. You can partition `by_range` on it’s own. ##### Samples - Partition on time using `CREATE TABLE` The simplest usage is to partition on a time column: ``` 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 ); ``` When you create a hypertable using [CREATE TABLE … WITH …](/docs/reference/timescaledb/hypertables/create_table/index.md), the default partitioning column is automatically the first column with a timestamp data type. Also, TimescaleDB creates a [columnstore policy](/docs/reference/timescaledb/hypercore/add_columnstore_policy/index.md) that automatically converts your data to the columnstore, after an interval equal to the value of the [chunk\_interval](/docs/reference/timescaledb/hypertables/set_chunk_time_interval/index.md), defined through `after` in the policy. 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 up to 98%, and organized for efficient, large-scale queries. You can customize this policy later using [alter\_job](/docs/reference/timescaledb/jobs-automation/alter_job/index.md). However, to change `after` or `created_before`, the compression settings, or the hypertable the policy is acting on, you must [remove the columnstore policy](/docs/reference/timescaledb/hypercore/remove_columnstore_policy/index.md) and [add a new one](/docs/reference/timescaledb/hypercore/add_columnstore_policy/index.md). You can also manually [convert chunks](/docs/reference/timescaledb/hypercore/convert_to_columnstore/index.md) in a hypertable to the columnstore. This is the default partition, you do not need to add it explicitly. - Extract time from a non-time column using `create_hypertable` If you have a table with a non-time column containing the time, such as a JSON column, add a partition function to extract the time: ``` CREATE TABLE my_table ( metric_id serial not null, data jsonb, ); CREATE FUNCTION get_time(jsonb) RETURNS timestamptz AS $$ SELECT ($1->>'time')::timestamptz $$ LANGUAGE sql IMMUTABLE; SELECT create_hypertable('my_table', by_range('data', '1 day', 'get_time')); ``` ##### Arguments | Name | Type | Default | Required | Description | | -------------------- | ------------ | ------- | -------- | ------------------------------------------------------------- | | `column_name` | `NAME` | - | ✔ | Name of column to partition on. | | `partition_func` | `REGPROC` | - | ✖ | The function to use for calculating the partition of a value. | | `partition_interval` | `ANYELEMENT` | - | ✖ | Interval to partition column on. | If the column to be partitioned is a: - `TIMESTAMP`, `TIMESTAMPTZ`, or `DATE`: specify `partition_interval` either as an `INTERVAL` type or an integer value in *microseconds*. - Another integer type: specify `partition_interval` as an integer that reflects the column’s underlying semantics. For example, if this column is in UNIX time, specify `partition_interval` in milliseconds. The partition type and default value depending on column type is:[]() | Column Type | Partition Type | Default value | | ---------------------------- | ---------------- | ------------- | | `TIMESTAMP WITHOUT TIMEZONE` | INTERVAL/INTEGER | 1 week | | `TIMESTAMP WITH TIMEZONE` | INTERVAL/INTEGER | 1 week | | `DATE` | INTERVAL/INTEGER | 1 week | | `SMALLINT` | SMALLINT | 10000 | | `INT` | INT | 100000 | | `BIGINT` | BIGINT | 1000000 | #### by\_hash() The main purpose of hash partitioning is to enable parallelization across multiple disks within the same time interval. Every distinct item in hash partitioning is hashed to one of *N* buckets. By default, TimescaleDB uses flexible range intervals to manage chunk sizes. ### Parallelizing disk I/O You use Parallel I/O in the following scenarios: - Two or more concurrent queries should be able to read from different disks in parallel. - A single query should be able to use query parallelization to read from multiple disks in parallel. For the following options: - **RAID**: use a RAID setup across multiple physical disks, and expose a single logical disk to the hypertable. That is, using a single tablespace. Best practice is to use RAID when possible, as you do not need to manually manage tablespaces in the database. - **Multiple tablespaces**: for each physical disk, add a separate tablespace to the database. TimescaleDB allows you to add multiple tablespaces to a *single* hypertable. However, although under the hood, a hypertable’s chunks are spread across the tablespaces associated with that hypertable. When using multiple tablespaces, a best practice is to also add a second hash-partitioned dimension to your hypertable and to have at least one hash partition per disk. While a single time dimension would also work, it would mean that the first chunk is written to one tablespace, the second to another, and so on, and thus would parallelize only if a query’s time range exceeds a single chunk. When adding a hash partitioned dimension, set the number of partitions to a multiple of number of disks. For example, the number of partitions P=N\*Pd where N is the number of disks and Pd is the number of partitions per disk. This enables you to add more disks later and move partitions to the new disk from other disks. TimescaleDB 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 hash 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. ##### Samples ``` 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.chunk_interval='1 day' ); SELECT add_dimension('conditions', by_hash('location', 2)); ``` ##### Arguments | Name | Type | Default | Required | Description | | ------------------- | ------------ | ------- | -------- | ----------------------------------------------------------------------------------- | | `column_name` | `NAME` | - | ✔ | Name of column to partition on. | | `partition_func` | `REGPROC` | - | ✖ | The function to use to calcule the partition of a value. | | `number_partitions` | `ANYELEMENT` | - | ✔ | Number of hash partitions to use for `partitioning_column`. Must be greater than 0. | #### Returns `by_range` and `by-hash` return an opaque `_timescaledb_internal.dimension_info` instance, holding the dimension information used by this function. ## Returns | Column | Type | Description | | -------------- | ------- | ------------------------------------------------------------------------------------------------------------- | | `dimension_id` | INTEGER | ID of the dimension in the TimescaleDB internal catalog | | `created` | BOOLEAN | `true` if the dimension was added, `false` when you set `if_not_exists` to `true` and no dimension was added. | On failure, an error is returned: | Error | Description | | ------------------------------------------------------------ | ------------------------------------------------------------------------- | | table “{table\_name}” is not a hypertable | The specified table has not been converted to a hypertable | | column “{column\_name}” does not exist | The specified column does not exist in the hypertable | | column “{column\_name}” is already a dimension | A dimension already exists for this column | | cannot specify both the number of partitions and an interval | Both `number_partitions` and `chunk_time_interval` were provided | | invalid interval type for bigint dimension | An INTERVAL type was used for a BIGINT column instead of an integer value |