[data] Read->SplitBlocks to ensure requested read parallelism is alwa…

…ys met (ray-project#36352)

Today, the number of initial blocks of a dataset is limited to the number of input files of the datasource, regardless of the requested parallelism. This is problematic as it means to increase the number of blocks requires a `repartition()` call, which is not always practical in the streaming setting.

This PR inserts a streaming SplitBlocks operator that is fused with read tasks in this case to allow for arbitrarily high requested parallelism (up to number of individual records) without needing a blocking repartition.

Before:
```
ray.data.read_parquet([list, of, 100, parquet, files], parallelism=2000)
# -> num_blocks = 100
```

After:
```
ray.data.read_parquet([list, of, 100, parquet, files], parallelism=2000)
# -> num_blocks = 2000
```

Limitations:
- Until ray-project#36071 merges and is integrated with Ray Data, downstream operators of the read may still block until the entire file is read, even if the read would produce multiple blocks.
- The SplitBlocks operator cannot be fused with downstream Map stages, since it is changing the physical partitioning of the stream. If we fused it, then the parallelism increase would not be realized as we could not split the read output to multiple processes.

doc/source/data/getting-started.rst

@@ -66,7 +66,7 @@ transform datasets. Ray executes transformations in parallel for performance.
 .. testoutput::
 
  MaterializedDataset(
- num_blocks=1,
+ num_blocks=...,
  num_rows=150,
  schema={
  sepal length (cm): double,
@@ -147,7 +147,7 @@ or remote filesystems.
 
  import os
 
- transformed_ds.write_parquet("/tmp/iris")
+ transformed_ds.repartition(1).write_parquet("/tmp/iris")
 
  print(os.listdir("/tmp/iris"))
 

doc/source/data/key-concepts.rst

@@ -30,15 +30,15 @@ blocks need to be materialized in the cluster memory at once.
 Reading Data
 ============
 
-Dataset uses Ray tasks to read data from remote storage in parallel. Each read task reads one or more files and produces an output block:
+Dataset uses Ray tasks to read data from remote storage in parallel. Each read task reads one or more files and produces one or more output blocks:
 
 .. image:: images/dataset-read.svg
  :align: center
 
 ..
  https://docs.google.com/drawings/d/15B4TB8b5xN15Q9S8-s0MjW6iIvo_PrH7JtV1fL123pU/edit
 
-You can manually specify the number of read tasks, but the final parallelism is always capped by the number of files in the underlying dataset.
+You can increase or decrease the number of output blocks by changing the ``parallelism`` parameter.
 
 For an in-depth guide on creating datasets, read :ref:`Loading Data <loading_data>`.
 

doc/source/data/performance-tips.rst

@@ -135,7 +135,8 @@ By default, Ray Data automatically selects the read ``parallelism`` according to
 4. The parallelism is truncated to ``min(num_files, parallelism)``.
 
 Occasionally, it is advantageous to manually tune the parallelism to optimize the application. This can be done when loading data via the ``parallelism`` parameter.
-For example, use ``ray.data.read_parquet(path, parallelism=1000)`` to force up to 1000 read tasks to be created.
+For example, use ``ray.data.read_parquet(path, parallelism=1000)`` to force up to 1000 read tasks to be created. Note that read tasks can produce multiple output
+blocks per file in order to satisfy the requested parallelism.
 
 Tuning Read Resources
 ~~~~~~~~~~~~~~~~~~~~~

doc/source/data/transforming-data.rst

@@ -335,9 +335,9 @@ Repartitioning data
 
 A :class:`~ray.data.dataset.Dataset` operates on a sequence of distributed data
 :term:`blocks <block>`. If you want to achieve more fine-grained parallelization,
-increase the number of blocks.
+increase the number of blocks by setting a higher ``parallelism`` at read time.
 
-To change the number of blocks, call
+To change the number of blocks for an existing Dataset, call
 :meth:`Dataset.repartition() <ray.data.Dataset.repartition>`.
 
 .. testcode::

python/ray/air/tests/test_legacy_dataset_config.py

@@ -358,7 +358,7 @@ def checker(shard, results):
  assert len(results[0]) == 5, results
  assert results[0] != results[1], results
  stats = shard.stats()
- assert "RandomizeBlockOrder: 5/5 blocks executed in" in stats, stats
+ assert "RandomizeBlockOrder:" in stats, stats
 
  ds = ray.data.range(5)
  test = TestStream(
@@ -385,7 +385,7 @@ def checker(shard, results):
  # we eliminate the ordering in comparison.
  assert set(results[0]) == set(results[1]), results
  stats = shard.stats()
- assert "RandomizeBlockOrder: 5/5 blocks executed" in stats, stats
+ assert "RandomizeBlockOrder:" in stats, stats
 
  ds = ray.data.range(5)
  test = TestBatch(
@@ -400,7 +400,7 @@ def checker(shard, results):
  assert len(results[0]) == 5, results
  assert results[0] != results[1], results
  stats = shard.stats()
- assert "RandomizeBlockOrder: 5/5 blocks executed in" in stats, stats
+ assert "RandomizeBlockOrder:" in stats, stats
 
  ds = ray.data.range(5)
  test = TestStream(

python/ray/data/BUILD

@@ -289,6 +289,14 @@ py_test(
  deps = ["//:ray_lib", ":conftest"],
 )
 
+py_test(
+ name = "test_splitblocks",
+ size = "medium",
+ srcs = ["tests/test_splitblocks.py"],
+ tags = ["team:data", "exclusive"],
+ deps = ["//:ray_lib", ":conftest"],
+)
+
 py_test(
  name = "test_execution_optimizer",
  size = "medium",

python/ray/data/_internal/block_list.py

@@ -32,6 +32,9 @@ def __init__(
  # Whether the block list is owned by consuming APIs, and if so it can be
  # eagerly deleted after read by the consumer.
  self._owned_by_consumer = owned_by_consumer
+ # This field can be set to indicate the number of estimated output blocks,
+ # since each read task may produce multiple output blocks after splitting.
+ self._estimated_num_blocks = None
 
  def __repr__(self):
  return f"BlockList(owned_by_consumer={self._owned_by_consumer})"
@@ -217,6 +220,10 @@ def initial_num_blocks(self) -> int:
  """Returns the number of blocks of this BlockList."""
  return self._num_blocks
 
+ def estimated_num_blocks(self) -> int:
+ """Estimate of `executed_num_blocks()`, without triggering actual execution."""
+ return self._estimated_num_blocks or self._num_blocks
+
  def executed_num_blocks(self) -> int:
  """Returns the number of output blocks after execution.
 

python/ray/data/_internal/execution/operators/input_data_buffer.py

@@ -19,12 +19,15 @@ def __init__(
  self,
  input_data: Optional[List[RefBundle]] = None,
  input_data_factory: Callable[[], List[RefBundle]] = None,
+ num_output_blocks: Optional[int] = None,
  ):
  """Create an InputDataBuffer.
 
  Args:
  input_data: The list of bundles to output from this operator.
  input_data_factory: The factory to get input data, if input_data is None.
+ num_output_blocks: The number of output blocks. If not specified, progress
+ bars total will be set based on num output bundles instead.
  """
  if input_data is not None:
  assert input_data_factory is None
@@ -37,6 +40,7 @@ def __init__(
  assert input_data_factory is not None
  self._input_data_factory = input_data_factory
  self._is_input_initialized = False
+ self._num_output_blocks = num_output_blocks
  super().__init__("Input", [])
 
  def start(self, options: ExecutionOptions) -> None:
@@ -53,7 +57,7 @@ def get_next(self) -> RefBundle:
  return self._input_data.pop(0)
 
  def num_outputs_total(self) -> Optional[int]:
- return self._num_outputs
+ return self._num_output_blocks or self._num_output_bundles
 
  def get_stats(self) -> StatsDict:
  return {}
@@ -64,7 +68,7 @@ def add_input(self, refs, input_index) -> None:
  def _initialize_metadata(self):
  assert self._input_data is not None and self._is_input_initialized
 
- self._num_outputs = len(self._input_data)
+ self._num_output_bundles = len(self._input_data)
  block_metadata = []
  for bundle in self._input_data:
  block_metadata.extend([m for (_, m) in bundle.blocks])

python/ray/data/_internal/logical/operators/read_operator.py

@@ -11,8 +11,23 @@ def __init__(
  self,
  datasource: Datasource,
  read_tasks: List[ReadTask],
+ estimated_num_blocks: int,
  ray_remote_args: Optional[Dict[str, Any]] = None,
  ):
- super().__init__(f"Read{datasource.get_name()}", None, ray_remote_args)
+ if len(read_tasks) == estimated_num_blocks:
+ suffix = ""
+ else:
+ suffix = f"->SplitBlocks({int(estimated_num_blocks / len(read_tasks))})"
+ super().__init__(f"Read{datasource.get_name()}{suffix}", None, ray_remote_args)
  self._datasource = datasource
+ self._estimated_num_blocks = estimated_num_blocks
  self._read_tasks = read_tasks
+
+ def fusable(self) -> bool:
+ """Whether this should be fused with downstream operators.
+
+ When we are outputting multiple blocks per read task, we should disable fusion,
+ as fusion would prevent the blocks from being dispatched to multiple processes
+ for parallel processing in downstream operators.
+ """
+ return self._estimated_num_blocks == len(self._read_tasks)

python/ray/data/_internal/logical/rules/operator_fusion.py

@@ -24,6 +24,7 @@
  Repartition,
 )
 from ray.data._internal.logical.operators.map_operator import AbstractUDFMap
+from ray.data._internal.logical.operators.read_operator import Read
 from ray.data._internal.stats import StatsDict
 from ray.data.block import Block
 
@@ -132,6 +133,9 @@ def _can_fuse(self, down_op: PhysicalOperator, up_op: PhysicalOperator) -> bool:
  down_logical_op = self._op_map[down_op]
  up_logical_op = self._op_map[up_op]
 
+ if isinstance(up_logical_op, Read) and not up_logical_op.fusable():
+ return False
+
  # If the downstream operator takes no input, it cannot be fused with
  # the upstream operator.
  if not down_logical_op._input_dependencies:

python/ray/data/_internal/plan.py

@@ -216,7 +216,7 @@ def get_plan_as_string(self, classname: str) -> str:
  if dataset_blocks is None:
  num_blocks = "?"
  else:
- num_blocks = dataset_blocks.initial_num_blocks()
+ num_blocks = dataset_blocks.estimated_num_blocks()
  dataset_str = "{}(num_blocks={}, num_rows={}, schema={})".format(
  classname, num_blocks, count, schema_str
  )

python/ray/data/_internal/planner/plan_read_op.py

@@ -58,7 +58,9 @@ def get_input_data() -> List[RefBundle]:
  for read_task in read_tasks
  ]
 
- inputs = InputDataBuffer(input_data_factory=get_input_data)
+ inputs = InputDataBuffer(
+ input_data_factory=get_input_data, num_output_blocks=op._estimated_num_blocks
+ )
 
  def do_read(blocks: Iterator[ReadTask], _: TaskContext) -> Iterator[Block]:
  for read_task in blocks:

python/ray/data/_internal/util.py

@@ -90,7 +90,7 @@ def _autodetect_parallelism(
  ctx: DataContext,
  reader: Optional["Reader"] = None,
  avail_cpus: Optional[int] = None,
-) -> (int, int):
+) -> (int, int, Optional[int]):
  """Returns parallelism to use and the min safe parallelism to avoid OOMs.
 
  This detects parallelism using the following heuristics, applied in order:
@@ -111,8 +111,9 @@ def _autodetect_parallelism(
  avail_cpus: Override avail cpus detection (for testing only).
 
  Returns:
- Tuple of detected parallelism (only if -1 was specified), and the min safe
- parallelism (which can be used to generate warnings about large blocks).
+ Tuple of detected parallelism (only if -1 was specified), the min safe
+ parallelism (which can be used to generate warnings about large blocks),
+ and the estimated inmemory size of the dataset.
  """
  min_safe_parallelism = 1
  max_reasonable_parallelism = sys.maxsize
@@ -140,7 +141,7 @@ def _autodetect_parallelism(
  f"estimated_available_cpus={avail_cpus} and "
  f"estimated_data_size={mem_size}."
  )
- return parallelism, min_safe_parallelism
+ return parallelism, min_safe_parallelism, mem_size
 
 
 def _estimate_avail_cpus(cur_pg: Optional["PlacementGroup"]) -> int:

python/ray/data/dataset.py

@@ -480,7 +480,7 @@ def map_batches(
  >>> ds = ds.map_batches(map_fn_with_large_output)
  >>> ds
  MapBatches(map_fn_with_large_output)
- +- Dataset(num_blocks=1, num_rows=1, schema={item: int64})
+ +- Dataset(num_blocks=..., num_rows=1, schema={item: int64})
 
 
  Args:
@@ -753,7 +753,7 @@ def select_columns(
  >>> ds
  MapBatches(<lambda>)
  +- Dataset(
- num_blocks=10,
+ num_blocks=...,
  num_rows=10,
  schema={col1: int64, col2: int64, col3: int64}
  )
@@ -1700,7 +1700,7 @@ def groupby(self, key: Optional[str]) -> "GroupedData":
  ... {"A": x % 3, "B": x} for x in range(100)]).groupby(
  ... "A").count()
  Aggregate
- +- Dataset(num_blocks=100, num_rows=100, schema={A: int64, B: int64})
+ +- Dataset(num_blocks=..., num_rows=100, schema={A: int64, B: int64})
 
  Time complexity: O(dataset size * log(dataset size / parallelism))
 
@@ -3289,7 +3289,7 @@ def to_tf(
  >>> ds = ray.data.read_csv("s3:https://anonymous@air-example-data/iris.csv")
  >>> ds
  Dataset(
- num_blocks=1,
+ num_blocks=...,
  num_rows=150,
  schema={
  sepal length (cm): double,
@@ -3320,7 +3320,7 @@ def to_tf(
  >>> ds
  Concatenator
  +- Dataset(
- num_blocks=1,
+ num_blocks=...,
  num_rows=150,
  schema={
  sepal length (cm): double,

python/ray/data/datasource/datasource.py

@@ -195,6 +195,7 @@ class ReadTask(Callable[[], Iterable[Block]]):
  def __init__(self, read_fn: Callable[[], Iterable[Block]], metadata: BlockMetadata):
  self._metadata = metadata
  self._read_fn = read_fn
+ self._additional_output_splits = 1
 
  def get_metadata(self) -> BlockMetadata:
  return self._metadata
@@ -211,13 +212,27 @@ def __call__(self) -> Iterable[Block]:
 
  if context.block_splitting_enabled:
  for block in result:
- yield block
+ yield from self._do_additional_splits(block)
  else:
  builder = DelegatingBlockBuilder()
  for block in result:
  builder.add_block(block)
  yield builder.build()
 
+ def _set_additional_split_factor(self, k: int) -> None:
+ self._additional_output_splits = k
+
+ def _do_additional_splits(self, block: Block) -> Iterable[Block]:
+ if self._additional_output_splits > 1:
+ block = BlockAccessor.for_block(block)
+ offset = 0
+ split_sizes = _splitrange(block.num_rows(), self._additional_output_splits)
+ for size in split_sizes:
+ yield block.slice(offset, offset + size, copy=True)
+ offset += size
+ else:
+ yield block
+
 
 @PublicAPI
 class RangeDatasource(Datasource):
@@ -478,3 +493,21 @@ def make_block(count: int, num_columns: int) -> Block:
  i += block_size
 
  return read_tasks
+
+
+def _splitrange(n, k):
+ """Calculates array lens of np.array_split().
+
+ This is the equivalent of
+ `[len(x) for x in np.array_split(range(n), k)]`.
+ """
+ base = n // k
+ output = [base] * k
+ rem = n - sum(output)
+ for i in range(len(output)):
+ if rem > 0:
+ output[i] += 1
+ rem -= 1
+ assert rem == 0, (rem, output, n, k)
+ assert sum(output) == n, (output, n, k)
+ return output