ray.data.Dataset.repartition#

Dataset.repartition(num_blocks: int, target_num_rows_per_block: int | None = None, *, shuffle: bool = False) → Dataset[source]#

Repartition the Dataset into exactly this number of blocks.

When target_num_rows_per_block is set, it repartitions Dataset to honor target number of rows per blocks. Note that the system will internally figure out the number of rows per blocks for optimal execution, based on the target_num_rows_per_block.

This method can be useful to tune the performance of your pipeline. To learn more, see Advanced: Performance Tips and Tuning.

If you’re writing data to files, you can also use this method to change the number of output files. To learn more, see Changing the number of output files.

Note

Repartition has two modes. If shuffle=False, Ray Data performs the minimal data movement needed to equalize block sizes. Otherwise, Ray Data performs a full distributed shuffle.

../../../_images/dataset-shuffle.svg

Note

This operation requires all inputs to be materialized in object store for it to execute.

Examples

>>> import ray
>>> ds = ray.data.range(100).repartition(10).materialize()
>>> ds.num_blocks()
10

Time complexity: O(dataset size / parallelism)

Parameters:

  • num_blocks – The number of blocks.

  • target_num_rows_per_block – The target number of rows per block to repartition.

  • shuffle – Whether to perform a distributed shuffle during the repartition. When shuffle is enabled, each output block contains a subset of data rows from each input block, which requires all-to-all data movement. When shuffle is disabled, output blocks are created from adjacent input blocks, minimizing data movement.

  • set (Note that either num_blocks or target_num_rows_per_block must be)

  • here

  • both. (but not)

Returns:

The repartitioned Dataset.