def from_items(items: List[Any], *, parallelism: int = 200) -> Dataset[Any]:
"""Create a dataset from a list of local Python objects.
Examples:
>>> ray.data.from_items([1, 2, 3, 4, 5])
Args:
items: List of local Python objects.
parallelism: The amount of parallelism to use for the dataset.
Returns:
Dataset holding the items.
"""
block_size = max(1, len(items) // parallelism)
blocks: List[ObjectRef[Block]] = []
metadata: List[BlockMetadata] = []
i = 0
while i < len(items):
builder = DelegatingArrowBlockBuilder()
for item in items[i:i + block_size]:
builder.add(item)
block = builder.build()
blocks.append(ray.put(block))
metadata.append(
BlockAccessor.for_block(block).get_metadata(input_files=None))
i += block_size
return Dataset(BlockList(blocks, metadata))
def __call__(self) -> MaybeBlockPartition:
context = DatasetContext.get_current()
result = self._read_fn()
if not hasattr(result, "__iter__"):
DeprecationWarning(
"Read function must return Iterable[Block], got {}. "
"Probably you need to return `[block]` instead of "
"`block`.".format(result))
if context.block_splitting_enabled:
partition: BlockPartition = []
for block in result:
metadata = BlockAccessor.for_block(block).get_metadata(
input_files=self._metadata.input_files)
assert context.block_owner
partition.append(
(ray.put(block, _owner=context.block_owner), metadata))
if len(partition) == 0:
raise ValueError("Read task must return non-empty list.")
return partition
else:
builder = DelegatingArrowBlockBuilder()
for block in result:
builder.add_block(block)
return builder.build()
def _map_block_nosplit(block: Block, fn: Any,
input_files: List[str]) -> Tuple[Block, BlockMetadata]:
builder = DelegatingArrowBlockBuilder()
for new_block in fn(block):
builder.add_block(new_block)
new_block = builder.build()
accessor = BlockAccessor.for_block(new_block)
return new_block, accessor.get_metadata(input_files=input_files)
def __init__(self, block_udf: Optional[Callable[[Block], Block]],
target_max_block_size: int):
from ray.data.impl.arrow_block import DelegatingArrowBlockBuilder
self._target_max_block_size = target_max_block_size
self._block_udf = block_udf
self._buffer = DelegatingArrowBlockBuilder()
self._returned_at_least_one_block = False
self._finalized = False
def next(self) -> Block:
"""Returns the next complete output block."""
assert self.has_next()
block = self._buffer.build()
if self._block_udf and block.num_rows > 0:
block = self._block_udf(block)
self._buffer = DelegatingArrowBlockBuilder()
self._returned_at_least_one_block = True
return block
def _shuffle_reduce(*mapper_outputs: List[Block]) -> (Block, BlockMetadata):
builder = DelegatingArrowBlockBuilder()
for block in mapper_outputs:
builder.add_block(block)
new_block = builder.build()
accessor = BlockAccessor.for_block(new_block)
new_metadata = BlockMetadata(num_rows=accessor.num_rows(),
size_bytes=accessor.size_bytes(),
schema=accessor.schema(),
input_files=None)
return new_block, new_metadata
def sample_boundaries(blocks: List[ObjectRef[Block]], key: SortKeyT,
num_reducers: int) -> List[T]:
"""
Return (num_reducers - 1) items in ascending order from the blocks that
partition the domain into ranges with approximately equally many elements.
"""
# TODO(Clark): Support multiple boundary sampling keys.
if isinstance(key, list) and len(key) > 1:
raise ValueError("Multiple boundary sampling keys not supported.")
n_samples = int(num_reducers * 10 / len(blocks))
sample_block = cached_remote_fn(_sample_block)
sample_results = [
sample_block.remote(block, n_samples, key) for block in blocks
]
sample_bar = ProgressBar("Sort Sample", len(sample_results))
sample_bar.block_until_complete(sample_results)
sample_bar.close()
samples = ray.get(sample_results)
samples = [s for s in samples if len(s) > 0]
# The dataset is empty
if len(samples) == 0:
return [None] * (num_reducers - 1)
builder = DelegatingArrowBlockBuilder()
for sample in samples:
builder.add_block(sample)
samples = builder.build()
column = key[0][0] if isinstance(key, list) else None
sample_items = BlockAccessor.for_block(samples).to_numpy(column)
sample_items = np.sort(sample_items)
ret = [
np.quantile(sample_items, q, interpolation="nearest")
for q in np.linspace(0, 1, num_reducers)
]
return ret[1:]
def read_files(read_paths: List[str],
fs: Union["pyarrow.fs.FileSystem",
_S3FileSystemWrapper]):
logger.debug(f"Reading {len(read_paths)} files.")
if isinstance(fs, _S3FileSystemWrapper):
fs = fs.unwrap()
builder = DelegatingArrowBlockBuilder()
for read_path in read_paths:
with fs.open_input_stream(read_path) as f:
data = read_file(f, read_path, **reader_args)
if isinstance(data, pa.Table) or isinstance(
data, np.ndarray):
builder.add_block(data)
else:
builder.add(data)
return builder.build()
def next_batch(self) -> Block:
"""Get the next batch from the block buffer.
Returns:
A batch represented as a Block.
"""
# If no batch size, short-circuit.
if self._batch_size is None:
assert len(self._buffer) == 1
block = self._buffer[0]
self._buffer = []
return block
output = DelegatingArrowBlockBuilder()
leftover = []
needed = self._batch_size
for block in self._buffer:
accessor = BlockAccessor.for_block(block)
if needed <= 0:
# We already have a full batch, so add this block to
# the leftovers.
leftover.append(block)
elif accessor.num_rows() <= needed:
# We need this entire block to fill out a batch.
output.add_block(block)
needed -= accessor.num_rows()
else:
# We only need part of the block to fill out a batch.
output.add_block(accessor.slice(0, needed, copy=False))
# Add the rest of the block to the leftovers.
leftover.append(
accessor.slice(needed, accessor.num_rows(), copy=False))
needed = 0
# Move the leftovers into the block buffer so they're the first
# blocks consumed on the next batch extraction.
self._buffer = leftover
return output.build()
class BlockOutputBuffer(object):
"""Generates output blocks of a given size given a stream of inputs.
This class is used to turn a stream of items / blocks of arbitrary size
into a stream of blocks of ``target_max_block_size``. The caller should
check ``has_next()`` after each ``add()`` call, and call ``next()`` to get
the next block when ``has_next()`` returns True.
When all items have been added, the caller must call ``finalize()`` and
then check ``has_next()`` one last time.
Examples:
>>> # Yield a stream of output blocks.
>>> output = BlockOutputBuffer(udf, 500 * 1024 * 1024)
>>> for item in generator():
... output.add(item)
... if output.has_next():
... yield output.next()
... output.finalize()
... if output.has_next()
... yield output.next()
"""
def __init__(self, block_udf: Optional[Callable[[Block], Block]],
target_max_block_size: int):
from ray.data.impl.arrow_block import DelegatingArrowBlockBuilder
self._target_max_block_size = target_max_block_size
self._block_udf = block_udf
self._buffer = DelegatingArrowBlockBuilder()
self._returned_at_least_one_block = False
self._finalized = False
def add(self, item: Any) -> None:
"""Add a single item to this output buffer."""
assert not self._finalized
self._buffer.add(item)
def add_block(self, block: Block) -> None:
"""Add a data block to this output buffer."""
assert not self._finalized
self._buffer.add_block(block)
def finalize(self) -> None:
"""Must be called once all items have been added."""
assert not self._finalized
self._finalized = True
def has_next(self) -> bool:
"""Returns true when a complete output block is produced."""
if self._finalized:
return not self._returned_at_least_one_block \
or self._buffer.num_rows() > 0
else:
return self._buffer.get_estimated_memory_usage() > \
self._target_max_block_size
def next(self) -> Block:
"""Returns the next complete output block."""
assert self.has_next()
block = self._buffer.build()
if self._block_udf and block.num_rows > 0:
block = self._block_udf(block)
self._buffer = DelegatingArrowBlockBuilder()
self._returned_at_least_one_block = True
return block