def build(self) -> Block:
if self._builder is None:
if self._empty_block is not None:
self._builder = BlockAccessor.for_block(self._empty_block).builder()
else:
self._builder = ArrowBlockBuilder()
return self._builder.build()
def add(self, item: Any) -> None:
if self._builder is None:
if isinstance(item, dict) or isinstance(item, ArrowRow):
import pyarrow
try:
check = ArrowBlockBuilder()
check.add(item)
check.build()
self._builder = ArrowBlockBuilder()
except (TypeError, pyarrow.lib.ArrowInvalid):
self._builder = SimpleBlockBuilder()
else:
self._builder = SimpleBlockBuilder()
self._builder.add(item)
class DelegatingBlockBuilder(BlockBuilder[T]):
def __init__(self):
self._builder = None
self._empty_block = None
def add(self, item: Any) -> None:
if self._builder is None:
# TODO (kfstorm): Maybe we can use Pandas block format for dict.
if isinstance(item, dict) or isinstance(item, ArrowRow):
import pyarrow
try:
check = ArrowBlockBuilder()
check.add(item)
check.build()
self._builder = ArrowBlockBuilder()
except (TypeError, pyarrow.lib.ArrowInvalid):
self._builder = SimpleBlockBuilder()
elif isinstance(item, PandasRow):
self._builder = PandasBlockBuilder()
else:
self._builder = SimpleBlockBuilder()
self._builder.add(item)
def add_block(self, block: Block) -> None:
accessor = BlockAccessor.for_block(block)
if accessor.num_rows() == 0:
# Don't infer types of empty lists. Store the block and use it if no
# other data is added. https://github.com/ray-project/ray/issues/20290
self._empty_block = block
return
if self._builder is None:
self._builder = accessor.builder()
self._builder.add_block(block)
def build(self) -> Block:
if self._builder is None:
if self._empty_block is not None:
self._builder = BlockAccessor.for_block(
self._empty_block).builder()
else:
self._builder = ArrowBlockBuilder()
return self._builder.build()
def num_rows(self) -> int:
return self._builder.num_rows() if self._builder is not None else 0
def get_estimated_memory_usage(self) -> int:
if self._builder is None:
return 0
return self._builder.get_estimated_memory_usage()
class DelegatingBlockBuilder(BlockBuilder[T]):
def __init__(self):
self._builder = None
def add(self, item: Any) -> None:
if self._builder is None:
# TODO (kfstorm): Maybe we can use Pandas block format for dict.
if isinstance(item, dict) or isinstance(item, ArrowRow):
import pyarrow
try:
check = ArrowBlockBuilder()
check.add(item)
check.build()
self._builder = ArrowBlockBuilder()
except (TypeError, pyarrow.lib.ArrowInvalid):
self._builder = SimpleBlockBuilder()
elif isinstance(item, PandasRow):
self._builder = PandasBlockBuilder()
else:
self._builder = SimpleBlockBuilder()
self._builder.add(item)
def add_block(self, block: Block) -> None:
if self._builder is None:
self._builder = BlockAccessor.for_block(block).builder()
self._builder.add_block(block)
def build(self) -> Block:
if self._builder is None:
self._builder = ArrowBlockBuilder()
return self._builder.build()
def num_rows(self) -> int:
return self._builder.num_rows() if self._builder is not None else 0
def get_estimated_memory_usage(self) -> int:
if self._builder is None:
return 0
return self._builder.get_estimated_memory_usage()
def test_arrow_size_diff_values(ray_start_regular_shared):
b = ArrowBlockBuilder()
assert b.get_estimated_memory_usage() == 0
b.add(ARROW_LARGE_VALUE)
assert b._num_compactions == 0
assert_close(b.get_estimated_memory_usage(), 10019)
b.add(ARROW_LARGE_VALUE)
assert b._num_compactions == 0
assert_close(b.get_estimated_memory_usage(), 20038)
for _ in range(10):
b.add(ARROW_SMALL_VALUE)
assert_close(b.get_estimated_memory_usage(), 25178)
for _ in range(100):
b.add(ARROW_SMALL_VALUE)
assert b._num_compactions == 0
assert_close(b.get_estimated_memory_usage(), 35394)
for _ in range(13000):
b.add(ARROW_LARGE_VALUE)
assert_close(b.get_estimated_memory_usage(), 130131680)
assert b._num_compactions == 2
for _ in range(4000):
b.add(ARROW_LARGE_VALUE)
assert_close(b.get_estimated_memory_usage(), 170129189)
assert b._num_compactions == 3
assert b.build().num_rows == 17112
def test_arrow_size(ray_start_regular_shared):
b = ArrowBlockBuilder()
assert b.get_estimated_memory_usage() == 0
b.add(ARROW_SMALL_VALUE)
assert_close(b.get_estimated_memory_usage(), 118)
b.add(ARROW_SMALL_VALUE)
assert_close(b.get_estimated_memory_usage(), 236)
for _ in range(8):
b.add(ARROW_SMALL_VALUE)
assert_close(b.get_estimated_memory_usage(), 1180)
for _ in range(90):
b.add(ARROW_SMALL_VALUE)
assert_close(b.get_estimated_memory_usage(), 11800)
for _ in range(900):
b.add(ARROW_SMALL_VALUE)
assert_close(b.get_estimated_memory_usage(), 118000)
assert b.build().num_rows == 1000
def test_arrow_size_add_block(ray_start_regular_shared):
b = ArrowBlockBuilder()
for _ in range(2000):
b.add(ARROW_LARGE_VALUE)
block = b.build()
b2 = ArrowBlockBuilder()
for _ in range(5):
b2.add_block(block)
assert b2._num_compactions == 0
assert_close(b2.get_estimated_memory_usage(), 100040020)
assert b2.build().num_rows == 10000
def fast_repartition(blocks, num_blocks):
from ray.data.dataset import Dataset
wrapped_ds = Dataset(ExecutionPlan(blocks,
DatasetStats(stages={}, parent=None)),
0,
lazy=False)
# Compute the (n-1) indices needed for an equal split of the data.
count = wrapped_ds.count()
dataset_format = wrapped_ds._dataset_format()
indices = []
cur_idx = 0
for _ in range(num_blocks - 1):
cur_idx += count / num_blocks
indices.append(int(cur_idx))
assert len(indices) < num_blocks, (indices, num_blocks)
if indices:
splits = wrapped_ds.split_at_indices(indices)
else:
splits = [wrapped_ds]
# TODO(ekl) include stats for the split tasks. We may also want to
# consider combining the split and coalesce tasks as an optimization.
# Coalesce each split into a single block.
reduce_task = cached_remote_fn(
_ShufflePartitionOp.reduce).options(num_returns=2)
reduce_bar = ProgressBar("Repartition", position=0, total=len(splits))
reduce_out = [
reduce_task.remote(False, None, *s.get_internal_block_refs())
for s in splits if s.num_blocks() > 0
]
# Early-release memory.
del splits, blocks, wrapped_ds
new_blocks, new_metadata = zip(*reduce_out)
new_blocks, new_metadata = list(new_blocks), list(new_metadata)
new_metadata = reduce_bar.fetch_until_complete(new_metadata)
reduce_bar.close()
# Handle empty blocks.
if len(new_blocks) < num_blocks:
from ray.data.impl.arrow_block import ArrowBlockBuilder
from ray.data.impl.pandas_block import PandasBlockBuilder
from ray.data.impl.simple_block import SimpleBlockBuilder
num_empties = num_blocks - len(new_blocks)
if dataset_format == "arrow":
builder = ArrowBlockBuilder()
elif dataset_format == "pandas":
builder = PandasBlockBuilder()
else:
builder = SimpleBlockBuilder()
empty_block = builder.build()
empty_meta = BlockAccessor.for_block(empty_block).get_metadata(
input_files=None, exec_stats=None) # No stats for empty block.
empty_blocks, empty_metadata = zip(*[(ray.put(empty_block), empty_meta)
for _ in range(num_empties)])
new_blocks += empty_blocks
new_metadata += empty_metadata
return BlockList(new_blocks, new_metadata), {}
def add(self, item: Any) -> None:
if self._builder is None:
# TODO (kfstorm): Maybe we can use Pandas block format for dict.
if isinstance(item, dict) or isinstance(item, ArrowRow):
import pyarrow
try:
check = ArrowBlockBuilder()
check.add(item)
check.build()
self._builder = ArrowBlockBuilder()
except (TypeError, pyarrow.lib.ArrowInvalid):
self._builder = SimpleBlockBuilder()
elif isinstance(item, np.ndarray):
self._builder = ArrowBlockBuilder()
elif isinstance(item, PandasRow):
self._builder = PandasBlockBuilder()
else:
self._builder = SimpleBlockBuilder()
self._builder.add(item)
def build(self) -> Block:
if self._builder is None:
self._builder = ArrowBlockBuilder()
return self._builder.build()
