Distributed Hypertables

In most cases, using a distributed hypertable is similar to using a regular hypertable, including inserting, querying, and altering it. Thus, for basic usage, please review the documentation for regular hypertables.

For inserts and queries, however, a distributed hypertable has unique performance characteristics and there are also additional limitations due to its distributed nature. For instance, query performance is heavily dependent on the ability to push down work to data nodes, which in turn ties into how data is partitioned across the nodes. If it is not possible to push down computations, or the query does not involve many data nodes, the query performance of a distributed hypertable will likely be worse than that of a regular hypertable due to the additional network overhead.

Note, also, that distributed hypertables can live alongside non-distributed tables and other objects; in fact, no objects are distributed by default. Interactions between distributed hypertables and non-distributed objects might not have the expected behavior. For instance, setting permissions on a distributed hypertable only works if the roles involved exist identically on all data nodes. Further, joins between a local table and a distributed hypertable requires fetching the raw data from data nodes and performing the join locally.

multinode channel in our community Slack.

Creating a distributed hypertable is very similar to creating a regular hypertable. The main difference is that you must use the function instead of the regular create_hypertable.

This creates a multi-dimensional distributed hypertable across all data nodes, partitioned along time and location. With the given partitioning configuration, each data node will be responsible for a specific subset of the data based on the value of location, allowing concurrent and parallel execution of a query across the data nodes.

You can now insert data into the distributed hypertable and the data will automatically be routed to the correct data node.

Like a regular hypertable, a distributed hypertable needs to be partitioned along a “time” dimension (e.g., a timestamptz column). However, for best performance with most distributed workloads, we recommend multi-dimensional partitioning with an additional “space” dimension that consistently partitions the data over the data nodes, similar to traditional ).

If your data set has a column called something similar to customerID, deviceID, or location (as in the example above), and it figures frequently in the GROUP BY clause of queries, then it is likely a good candidate column for space partitioning. For instance, a query like the following one would work well on the example distributed hypertable above:

  1. SELECT time_bucket('1 hour', time) AS hour, location, avg(temperature)
  2. FROM conditions
  3. GROUP BY hour, location
  4. ORDER BY hour, location

as this query would execute in parallel on all data nodes. A query that would not make the best use of space partitioning, however, would be:

  1. SELECT time_bucket('1 hour', time) AS hour, avg(temperature)
  2. FROM conditions
  3. WHERE location = 'office_1'
  4. GROUP BY hour
  5. ORDER BY hour
  6. LIMIT 100;

as this query would only involve a single data node. Still, there are other factors to consider as well. For instance, if the latter example query is executed concurrently by many different client sessions, each filtering on a different location, then that would also spread the load evenly across the distributed hypertable.

Inserts also benefit from space partitioning; the additional space dimension makes it more likely that a multi-row insert uniformly spreads across the data nodes, leading to increased insert performance. In contrast, with a single time dimension it is likely that in-order inserts write to only one data node and chunk at a time. Chunks would then be created on data nodes in round-robin fashion.

Inserting into a Distributed Hypertable

Just like a regular hypertable, it is important to batch data when inserting into a distributed hypertable. Each insert statement is often its own transaction, and the overhead and cost of this transaction is good to amortize over many rows of data. With a distributed hypertable, the transaction has additional costs due to the coordination that needs to happen across data nodes (e.g., two-phase commit protocol). In such cases, a single insert transaction to the access node involving many rows of data is processed by the access node, such that the access node (a) splits the input set into several smaller batches of rows (with each batch having those rows that belong to a specific data node based on the distributed hypertable’s partitioning), and then (b) writes each batch of rows to its corresponding data node.

There are two ways to insert data to the access node (which similarly uses corresponding methods when interacting with its data nodes):

  • By tuning the insert batch size, throughput can be optimized. The maximum insert batch size is, however, limited by the maximum number of parameters allowed in a prepared statement (32767), and the number of columns in each row. For example, if a distributed hypertable has 10 columns, the max insert batch size is capped at 3276 rows.

  • COPY: the access node switches each data node to “copy mode” and then routes each row to the correct data node in a stream. COPY typically delivers better performance than insert statements, although it doesn’t support features like conflict handling (ON CONFLICT clause) that are used for .

The query performance of a distribute hypertable depends heavily on the ability of the planner to push down computations to data nodes. Without a way to push down computations, the access node will fetch the raw data from each data node and then perform any necessary computations locally. Therefore, queries that can be pushed down and involve many data nodes are more likely to see better performance on a distributed hypertable compared to a regular hypertable. In particular, an aggregate computation that down-samples data is a good candidate to push down since it both distributes the computational workload across data nodes and reduces the amount of data that needs to be transferred in the result set.

The planner can consider two methods to push down aggregates:

  1. FULL: fully pushing down an aggregate offloads all the computation to data nodes and the access node only appends the individual results. To fully push down an aggregate computation, the GROUP BY clause must include all the partition keys (dimension columns) or only the first space partition key. An example is computing the max temperature for each location (office_1, office_2, etc.):

    Given that we use location as a space partition, each data node can compute the max on its own distinct subset of the data.

  2. PARTIAL: if it is not possible to fully push down the aggregate, the planner can instead consider a partial aggregation on data nodes. In this case, the access node offloads most of the computation to data nodes, yielding a partial result that is sent back and finalized on the access node by combining all the partials from each data node. An example is computing the max temperature; each data node computes a local max and then the access node finalizes the result by computing the max over all the data nodes’ max results:

    1. SELECT max(temperature) FROM conditions;

Other types of computations that can be pushed down include sorting operations, groupings, and joins. Joins on data nodes are currently unsupported, however. To see how the a query is pushed down to a data node, use EXPLAIN VERBOSE on the query and inspect the query plan and the remote SQL statement sent to each data node.

The query planner might not always be able to push down queries, however, or it can only push down parts of it:

  • If the query involves a region that saw a change in the partitioning configuration (e.g., the number of space partitions increased because the system was elastically expanded to include additional data nodes, and the number of space partitions was correspondingly increased), then it might not be possible to fully push down aggregates. In our example distributed hypertable, the change in partitioning might lead to data for the same location existing in two chunks that are stored on different data nodes. The planner must therefore avoid full aggregation on data nodes whenever the query covers the repartitioning boundary. It is still possible to do partial aggregation on data nodes, however.
  • If the query includes non-immutable functions and expressions, the planner cannot push down those parts of the query since they cannot be guaranteed to generate a consistent result across each data node. For instance, the function depends on the current seed and the state of the pseudo-random sequence. If the function was to be pushed down to each data node, it would not generate a valid pseudo-random sequence from the point of view of the access node that executes the query. Another example is the now() function to get the current transaction time. This function depends on the current time zone setting on each node.
  • If the query includes a user-defined function (UDF) the planner assumes that the function does not exist on the data nodes and therefore will not push it down.

TimescaleDB employs several optimizations to increase the likelihood of being able to push down queries and getting around some of the above limitations. For instance, to get around the limitation of not pushing down the now() function, the function is constified on the access node so that the resulting timestamp is instead pushed down to the data nodes.

There are a number of configuration parameters that are important for good query performance on a distributed hypertable. The following settings apply to the access node.

  • jit should be set to off as JIT compilation currently doesn’t work well with distributed hypertables.
  • timescaledb.remote_data_fetcher defaults to cursor, which is the recommended setting for most use cases. However, the cursor fetcher doesn’t support parallel query execution on data nodes, which might prevent faster query execution. It is possible to change this value to rowbyrow for better query performance, although this might not work with some queries that include sub-queries or common table expressions (CTEs).

Data Node Management

When a distributed hypertable is created, it will use all the available data nodes by default. It is possible, however, to use only a subset of data nodes with a particular distributed hypertable. This is useful to, e.g., tie a distributed hypertable to data nodes that have a specific performance profile.

To view the data nodes used by a distributed hypertable, use the following query:

  1. SELECT hypertable_name, data_nodes
  2. FROM timescaledb_information.hypertables
  3. WHERE hypertable_name = 'conditions';
  5.  hypertable_name |              data_nodes
  6. -----------------+---------------------------------------
  7.  conditions      | {data_node_1,data_node_2,data_node_3}

If additional data nodes are added to a distributed database, the data nodes are not automatically associated with existing distributed hypertables. Instead, you need to explicitly attach a data node using :

In a similar way, if you want to remove a data node from a distributed hypertable, you can use detach_data_node.

  1. SELECT detach_data_node('node1', hypertable => 'conditions');

Note that you cannot detach a data node that still holds data for the hypertable. To be able to detach a data node, ensure that either (1) all its data is deleted first, or (2) the data is replicated on other data nodes (see the next section on native replication).

A distributed hypertable can be configured to write each chunk to multiple data nodes in order to replicate data at the chunk level. This native replication ensures that a distributed hypertable is protected against data node failures and provides an alternative to fully replicating each data node using streaming replication.

While data nodes require no additional setup to use native replication, the access node continues to rely on streaming replication, however. In this regard, the access node has similar requirements for high availability as any regular PostgreSQL instance.

The number of data nodes a chunk is written to is determined by a distributed hypertable’s replication_factor (the default value is 1, i.e., no native replication), and it can be set when calling :

  1. SELECT create_distributed_hypertable('conditions', 'time', 'location',

Alternatively, the function set_replication_factor can be used to enable native replication on an existing distributed hypertable.

Once enabled, native replication happens as part of normal inserts, by writing each row to multiple data nodes, and therefore requires no additional mechanism for replicating newly inserted data. Existing chunks, which are not replicated according to the configured replication factor, needs to be re-replicated by a background job in order to achieve the set replication factor.

To view the data nodes each chunk is replicated to, the following query can be used:

When querying a distributed hypertable using native replication, the query planner knows how to include only one replica of each chunk in the query plan. The planner can employ different strategies to pick the set of chunk replicas in order to, e.g., evenly spread the query load across the data nodes.

Compared to streaming replication, native replication provides several advantages:

  • Built in: Native replication is built in and requires no additional configuration to use. TimescaleDB instances that serve as data nodes need not be configured with standby instances using streaming replication.
  • Elasticity: As long all chunks on a data node have at least one replica on another data node, it is possible to delete that node. The chunks that are under-replicated after deleting the data node would be re-replicated to new data nodes as long as there is still capacity (i.e., there remains at least as many data nodes as the configured replication factor). This flexibility allows not only scaling up a multi-node cluster during peek hours, but also allows scaling it down when the extra capacity is no longer needed.
  • Query optimizations: Native replication enables a number of interesting optimizations and features that go beyond just providing high availability (HA). For instance, when a chunk exists on multiple data nodes, the planner could balance queries across different chunk replicas for better resource utilization. Similarly, if the planner detects a non-responsive or slow data node, it could ensure that only responsive nodes are included in the query plan.

There are also disadvantages with native replication. For instance, if a data node holds data that is not part of a distributed hypertable, this data won’t be replicated. In such situations, streaming replication is recommended.

Transactions and Consistency Model

Transactions that involve distributed hypertables are atomic, just like those involving regular hypertables. In other words, a distributed transaction that involves multiple data nodes is guaranteed to either succeed on all nodes or none of them. This guarantee is provided by the , which is used to implement distributed transactions in TimescaleDB.

The read consistency of a distributed hypertable is a bit different from a regular hypertable, however. Since a distributed transaction is essentially a set of individual transactions across multiple nodes, each node might commit its local transaction at a slightly different time (due to network transmission delays, etc.). As a consequence, the access node will not be able to guarantee a fully consistent snapshot of the data across all data nodes. For example, a distributed read transaction might start when another concurrent write transaction is in its commit phase and has committed on some data nodes but not others. The read transaction can therefore use a snapshot on one node that includes the other transaction’s modifications, while the snapshots used on other data nodes might not include them.

Stronger read consistency can be achieved by ensuring that a distributed transaction is tied to consistent snapshots across all data nodes. However, since this requires a lot of coordination and bookkeeping, it is also slower and is therefore not implemented for distributed hypertables.