Querying a distributed hypertable

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.):

    SELECT location, max(temperature) FROM conditions
    GROUP BY location;

    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:

    SELECT max(temperature) FROM conditions;


The settings variable enable_partitionwise_aggregate must be set to on on the access node to make sure that the planner considers pushing down computations to data nodes (the default is off).

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.

Limitations of pushing down queries

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 random() 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.

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