Add an additional partitioning dimension to a Timescale hypertable. The column selected as the dimension can either use interval partitioning (for example, for a second time partition) or hash partitioning.
add_dimension command can only be executed after a table has been converted to a hypertable (via
create_hypertable), but must similarly be run only on an empty hypertable.
Space partitions: Using space partitions is highly recommended for distributed hypertables to achieve efficient scale-out performance. For regular hypertables that exist only on a single node, additional partitioning can be used for specialized use cases and not recommended for most users.
Space partitions use hashing: Every distinct item is hashed to one of N buckets. Remember that we are already using (flexible) time intervals to manage chunk sizes; the main purpose of space 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, space 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
location as a space 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:
Use a RAID setup across multiple physical disks, and expose a single logical disk to the hypertable (that is, via a single tablespace).
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 space partitions, we recommend using 1 space partition per disk.
Timescale does not benefit from a very large number of space 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.
|REGCLASS||Hypertable to add the dimension to|
|TEXT||Column to partition by|
|INTEGER||Number of hash partitions to use on |
|INTERVAL||Interval that each chunk covers. Must be > 0|
|REGCLASS||The function to use for calculating a value's partition (see |
|BOOLEAN||Set to true to avoid throwing an error if a dimension for the column already exists. A notice is issued instead. Defaults to false|
|INTEGER||ID of the dimension in the TimescaleDB internal catalog|
|TEXT||Schema name of the hypertable|
|TEXT||Table name of the hypertable|
|TEXT||Column name of the column to partition by|
|BOOLEAN||True if the dimension was added, false when |
When executing this function, either
chunk_time_interval must be supplied, which dictates if the
dimension uses hash or interval partitioning.
chunk_time_interval should be specified as follows:
If the column to be partitioned is a TIMESTAMP, TIMESTAMPTZ, or DATE, this length should be specified either as an INTERVAL type or an integer value in microseconds.
If the column is some other integer type, this length should be an integer that reflects the column's underlying semantics (for example, the
chunk_time_intervalshould be given in milliseconds if this column is the number of milliseconds since the UNIX epoch).
Supporting more than one additional dimension is currently experimental. For any production environments, users are recommended to use at most one "space" dimension.
First convert table
conditions to hypertable with just time
partitioning on column
time, then add an additional partition key on
location with four partitions:
SELECT create_hypertable('conditions', 'time');SELECT add_dimension('conditions', 'location', number_partitions => 4);
conditions to hypertable with time partitioning on
space partitioning (2 partitions) on
location, then add two additional dimensions.
SELECT create_hypertable('conditions', 'time', 'location', 2);SELECT add_dimension('conditions', 'time_received', chunk_time_interval => INTERVAL '1 day');SELECT add_dimension('conditions', 'device_id', number_partitions => 2);SELECT add_dimension('conditions', 'device_id', number_partitions => 2, if_not_exists => true);
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
a distributed hypertable with just time partitioning on column
and finally add a space partitioning dimension on
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', 'location', number_partitions => 2);
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