Distributed hypertables - create_distributed_hypertable - 《TimescaleDB v2.9 Documentation》

create_distributed_hypertable()

create_distributed_hypertable()

Creates a TimescaleDB hypertable distributed across a multinode environment. Use this function in place of create_hypertable when creating distributed hypertables.

Required arguments

Name	Type	Description
`relation`	REGCLASS	Identifier of table to convert to hypertable.
`time_column_name`	TEXT	Name of the column containing time values as well as the primary column to partition by.

Optional arguments

Name	Type	Description
`partitioning_column`	TEXT	Name of an additional column to partition by.
`number_partitions`	INTEGER	Number of hash partitions to use for `partitioning_column`. Must be > 0. Default is the number of `data_nodes`.
`associated_schema_name`	TEXT	Name of the schema for internal hypertable tables. Default is `_timescaledb_internal`.
`associated_table_prefix`	TEXT	Prefix for internal hypertable chunk names. Default is `_hyper`.
`chunk_time_interval`	INTERVAL	Interval in event time that each chunk covers. Must be > 0. Default is 7 days.
`create_default_indexes`	BOOLEAN	Boolean whether to create default indexes on time/partitioning columns. Default is TRUE.
`if_not_exists`	BOOLEAN	Boolean whether to print warning if table already converted to hypertable or raise exception. Default is FALSE.
`partitioning_func`	REGCLASS	The function to use for calculating a value’s partition.
`migrate_data`	BOOLEAN	Set to TRUE to migrate any existing data from the `relation` table to chunks in the new hypertable. A non-empty table generates an error without this option. Large tables may take significant time to migrate. Default is FALSE.
`time_partitioning_func`	REGCLASS	Function to convert incompatible primary time column values to compatible ones. The function must be `IMMUTABLE`.
`replication_factor`	INTEGER	The number of data nodes to which the same data is written to. This is done by creating chunk copies on this amount of data nodes. Must be >= 1; If not set, the default value is determined by the `timescaledb.hypertable_replication_factor_default` GUC. Read the best practices before changing the default.
`data_nodes`	ARRAY	The set of data nodes used for the distributed hypertable. If not present, defaults to all data nodes known by the access node (the node on which the distributed hypertable is created).

Returns

Column	Type	Description
`hypertable_id`	INTEGER	ID of the hypertable in TimescaleDB.
`schema_name`	TEXT	Schema name of the table converted to hypertable.
`table_name`	TEXT	Table name of the table converted to hypertable.
`created`	BOOLEAN	TRUE if the hypertable was created, FALSE when `if_not_exists` is TRUE and no hypertable was created.

Sample usage

Create a table conditions which is partitioned across data nodes by the ‘location’ column. Note that the number of space partitions is automatically equal to the number of data nodes assigned to this hypertable (all configured data nodes in this case, as data_nodes is not specified).


SELECT create_distributed_hypertable('conditions', 'time', 'location');

Create a table conditions using a specific set of data nodes.


SELECT create_distributed_hypertable('conditions', 'time', 'location',
    data_nodes => '{ "data_node_1", "data_node_2", "data_node_4", "data_node_7" }');

Best practices

Space partitions: As opposed to the normal create_hypertable best practices, space partitions are highly recommended for distributed hypertables. Incoming data is divided among data nodes based upon the space partition (the first one if multiple space partitions have been defined). If there is no space partition, all the data for each time slice is written to a single data node.

Time intervals: Follow the same guideline in setting the chunk_time_interval as with create_hypertable, bearing in mind that the calculation needs to be based on the memory capacity of the data nodes. However, one additional thing to consider, assuming space partitioning is being used, is that the hypertable is evenly distributed across the data nodes, allowing a larger time interval.

For example, assume you are ingesting 10 GB of data per day and you have five data nodes, each with 64 GB of memory. If this is the only table being served by these data nodes, then you should use a time interval of 1 week (7 * 10 GB / 5 * 64 GB ~= 22% main memory used for most recent chunks).

If space partitioning is not being used, the chunk_time_interval should be the same as the non-distributed case, as all of the incoming data is handled by a single node.

Replication factor: The hypertable’s replication_factor defines to how many data nodes a newly created chunk is replicated. That is, a chunk with a replication_factor of three exists on three separate data nodes, and rows written to that chunk are inserted (as part of a two-phase commit protocol) to all three chunk copies. For chunks replicated more than once, if a data node fails or is removed, no data is lost, and writes can continue to succeed on the remaining chunk copies. However, the chunks present on the lost data node are now under-replicated. Currently, it is not possible to restore under-replicated chunks, although this limitation might be removed in a future release. To avoid such inconsistency, we do not yet recommend using replication_factor > 1, and instead rely on physical replication of each data node if such fault-tolerance is required.