Joining Data#

Note

This is a new feature released in Ray 2.46. Note that this is an experimental feature and some things might not work as expected.

Ray Data allows multiple Dataset instances to be joined using different join types based on the provided key columns as follows:

import ray

doubles_ds = ray.data.range(4).map(
    lambda row: {"id": row["id"], "double": int(row["id"]) * 2}
)

squares_ds = ray.data.range(4).map(
    lambda row: {"id": row["id"], "square": int(row["id"]) ** 2}
)

doubles_and_squares_ds = doubles_ds.join(
    squares_ds,
    join_type="inner",
    num_partitions=2,
    on=("id",),
)

Ray Data supports the following join types (check out Dataset.join docs for up-to-date list):

Inner/Outer Joins: - Inner, Left Outer, Right Outer, Full Outer

Semi Joins: - Left Semi, Right Semi (returns all rows that have at least one matching row in the other table, only returning columns from the requested side)

Anti Joins: - Left Anti, Right Anti (return rows that have no matching rows in the other table, only returning columns from the requested side)

Internally joins are currently powered by the hash-shuffle backend. Shuffle v2 (ShuffleStrategy.SHUFFLE_V2), currently in Alpha, provides an updated hash-shuffle implementation for joins. To use it, set the shuffle strategy before creating a Dataset: ray.data.DataContext.get_current().shuffle_strategy = ShuffleStrategy.SHUFFLE_V2. See Tuning shuffle v2 for memory-related knobs.

Configuring Joins#

Joins are generally memory-intensive operations that require accurate memory accounting and projection and hence are sensitive to skews and imbalances in the dataset.

Ray Data provides the following levers to allow tuning the performance of joins for your workload:

  • num_partitions: (required) specifies number of partitions both incoming datasets will be hash-partitioned into. Check out configuring number of partitions section for guidance on how to tune this up.

  • partition_size_hint: (deprecated) Hint to joining operator about the estimated avg expected size of the individual partition (in bytes). Ray Data ignores this parameter and a future release removes it. Passing a value emits a DeprecationWarning. The join path sizes reduce-task memory from observed partition sizes instead of from a hint.

Configuring number of partitions#

Number of partitions (also referred to as blocks) provide an important trade-off between the size of individual batch of rows handled by individual tasks against memory requirements of the operation performed on them

Rule of thumb: keep partitions large, but not too large to cause Out-of-Memory (OOM) errors

  1. It’s important to not “oversize” partitions for joins as that could lead to OOM errors (if joined partitions might be too large to fit in memory)

  2. It’s also important to not create too many small partitions as this creates an overhead of passing large amount of smaller objects

Configuring number of Aggregators#

“Aggregators” are worker actors that perform actual joins/aggregations/shuffling, they receive individual partition chunks from the incoming blocks and subsequently “aggregate” them in the way that’s required to perform given operation.

Following are important considerations for successfully configuring number of aggregators in your pool:

  • Defaults to the smallest of num_partitions, the number of CPUs in the cluster, and DataContext.max_hash_shuffle_aggregators (128 by default)

  • Individual Aggregators might be assigned to handle more than one partition (partitions are evenly split in round-robin fashion among the aggregators)

  • Aggregators are stateful components that hold the state (partitions) during shuffling in memory

Note

The rule of thumb is to avoid setting num_partitions >> number of aggregators as it might create bottlenecks

  1. Setting DataContext.max_hash_shuffle_aggregators caps the number of aggregators

  2. Setting it to large enough value has an effect of allocating 1 partition to 1 aggregator (when max_hash_shuffle_aggregators >= num_partitions)