Distributed hypertables

TimescaleDB supports distributing hypertables across multiple nodes (that is, a cluster) by leveraging the same hypertable and chunk primitives as described above. This allows TimescaleDB to scale inserts and queries beyond the capabilities of a single TimescaleDB instance.

Distributed hypertables and regular hypertables look very similar, with the main difference being that distributed chunks are not stored locally. There are also some features of regular hypertables that distributed hypertables do not support (see section on current limitations).

Distributed databases and nodes

A distributed hypertable exists in a distributed database that consists of multiple databases stored across one or more TimescaleDB instances. A database that is part of a distributed database can assume the role of either an access node or a data node (but not both).

A client connects to an access node database. The access node then distributes the requests and queries appropriately to data nodes, and aggregates the results received from the data nodes. Access nodes store cluster-wide information about the different data nodes as well as how chunks are distributed across those data nodes. Access nodes can also store non-distributed hypertables, as well as regular PostgreSQL tables.

Data nodes do not store cluster-wide information, and otherwise look just as if they were stand-alone TimescaleDB instances. You should not directly access hypertables or chunks on data nodes. Doing so might lead to inconsistent distributed hypertables.

It is important to note that access nodes and data nodes both run TimescaleDB, and for all intents and purposes, act just like a single instance of TimescaleDB from an operational perspective.

Configuring distributed hypertables

To ensure best performance, you should partition a distributed hypertable by both time and space. If you only partition data by time, that chunk has to fill up before the access node chooses another data node to store the next chunk, so during that chunk's time interval, all writes to the latest interval is handled by a single data node, rather than load balanced across all available data nodes. On the other hand, if you specify a space partition, the access node distributes chunks across multiple data nodes based on the space partition so that multiple chunks are created for a given chunk time interval, and both reads and writes to that recent time interval are load balanced across the cluster.

By default, we automatically set the number of space partitions equal to the number of data nodes if a value is not specified. The system also increases the number of space partitions, if necessary, when adding new data nodes. If setting manually, we recommend that the number of space partitions are equal or a multiple of the number of data nodes associated with the distributed hypertable for optimal data distribution across data nodes. In case of multiple space partitions, only the first space partition is used to determine how chunks are distributed across servers.

Scaling distributed hypertables

As time-series data grows, a common use case is to add data nodes to expand the storage and compute capacity of distributed hypertables. Thus, TimescaleDB can be elastically scaled out by simply adding data nodes to a distributed database.

As mentioned earlier, TimescaleDB adjusts the number of space partitions as new data nodes are added. Although existing chunks do not have their space partitions updated, the new settings are applied to newly created chunks. Because of this behavior, we do not need to move data between data nodes when the cluster size is increased, and simply update how data is distributed for the next time interval. Writes for new incoming data leverage the new partitioning settings, while the access node can still support queries across all chunks (even those that were created using the old partitioning settings). Do note that although the number of space partitions can be changed, the column on which the data is partitioned can not be changed.

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