def _create_ml_dataset(name: str,
record_pieces: List[RecordPiece],
record_sizes: List[int],
num_shards: int,
shuffle: bool,
shuffle_seed: int,
RecordBatchCls,
node_hints: List[str] = None) -> MLDataset:
if node_hints is not None:
assert num_shards % len(node_hints) == 0,\
f"num_shards: {num_shards} should be a multiple of length of node_hints: {node_hints}"
if shuffle_seed:
np.random.seed(shuffle_seed)
else:
np.random.seed(0)
# split the piece into num_shards partitions
divided_blocks = divide_blocks(blocks=record_sizes,
world_size=num_shards,
shuffle=shuffle,
shuffle_seed=shuffle_seed)
record_batches = []
for rank, blocks in divided_blocks.items():
pieces = []
for index, num_samples in blocks:
record_size = record_sizes[index]
piece = record_pieces[index]
if num_samples != record_size:
assert num_samples < record_size
new_row_ids = np.random.choice(
record_size, size=num_samples).tolist()
piece = piece.with_row_ids(new_row_ids)
pieces.append(piece)
if shuffle:
np.random.shuffle(pieces)
record_batches.append(RecordBatchCls(shard_id=rank,
prefix=name,
record_pieces=pieces,
shuffle=shuffle,
shuffle_seed=shuffle_seed))
worker_cls = ray.remote(ParallelIteratorWorkerWithLen)
if node_hints is not None:
actors = []
multiplier = num_shards // len(node_hints)
resource_keys = [f"node:{node_hints[i // multiplier]}" for i in range(num_shards)]
for g, resource_key in zip(record_batches, resource_keys):
actor = worker_cls.options(resources={resource_key: 0.01}).remote(g, False, len(g))
actors.append(actor)
else:
worker_cls = ray.remote(ParallelIteratorWorkerWithLen)
actors = [worker_cls.remote(g, False, len(g)) for g in record_batches]
it = parallel_it.from_actors(actors, name)
ds = ml_dataset.from_parallel_iter(
it, need_convert=False, batch_size=0, repeated=False)
return ds
def _save_spark_df_to_object_store(df: sql.DataFrame,
num_shards: int) -> List["RecordBatchShard"]:
# call java function from python
jvm = df.sql_ctx.sparkSession.sparkContext._jvm
jdf = df._jdf
object_store_writer = jvm.org.apache.spark.sql.raydp.ObjectStoreWriter(jdf)
records = object_store_writer.save()
worker = ray.worker.global_worker
blocks: List[ray.ObjectRef] = []
block_sizes: List[int] = []
for record in records:
owner_address = record.ownerAddress()
object_ref = ray.ObjectRef(record.objectId())
num_records = record.numRecords()
# Register the ownership of the ObjectRef
worker.core_worker.deserialize_and_register_object_ref(
object_ref.binary(), ray.ObjectRef.nil(), owner_address)
blocks.append(object_ref)
block_sizes.append(num_records)
divided_blocks = divide_blocks(block_sizes, num_shards)
record_batch_set: List[RecordBatchShard] = []
for i in range(num_shards):
indexes = divided_blocks[i]
object_ids = [blocks[index] for index in indexes]
record_batch_set.append(RecordBatchShard(i, object_ids))
return record_batch_set
def test_divide_blocks():
blocks = [5, 10, 9]
world_size = 3
def _sum(packed_indexes) -> int:
return sum([len(i) for i in packed_indexes])
# no shuffle, no pack
divided_blocks = utils.divide_blocks(blocks, world_size, None, False,
False)
assert len(divided_blocks) == 3
block_indexes_0, block_size_0 = divided_blocks[0]
block_indexes_1, block_size_1 = divided_blocks[1]
block_indexes_2, block_size_2 = divided_blocks[2]
assert sum(block_size_0) == sum(block_size_1) == sum(block_size_2)
# no shuffle, pack
divided_blocks = utils.divide_blocks(blocks, world_size, 0, False, True)
block_indexes_0, block_size_0 = divided_blocks[0]
block_indexes_1, block_size_1 = divided_blocks[1]
block_indexes_2, block_size_2 = divided_blocks[2]
assert _sum(block_size_0) == _sum(block_size_1) == _sum(block_size_2)
# shuffle, no pack
divided_blocks = utils.divide_blocks(blocks, world_size, 0, True, False)
block_indexes_0, block_size_0 = divided_blocks[0]
block_indexes_1, block_size_1 = divided_blocks[1]
block_indexes_2, block_size_2 = divided_blocks[2]
assert sum(block_size_0) == sum(block_size_1) == sum(block_size_2)
# shuffle, pack
divided_blocks = utils.divide_blocks(blocks, world_size, 0, True, True)
block_indexes_0, block_size_0 = divided_blocks[0]
block_indexes_1, block_size_1 = divided_blocks[1]
block_indexes_2, block_size_2 = divided_blocks[2]
assert _sum(block_size_0) == _sum(block_size_1) == _sum(block_size_2)
# special case
blocks = [10]
divided_blocks = utils.divide_blocks(blocks, world_size, 0, False, False)
block_indexes_0, block_size_0 = divided_blocks[0]
block_indexes_1, block_size_1 = divided_blocks[1]
block_indexes_2, block_size_2 = divided_blocks[2]
assert sum(block_size_0) == sum(block_size_1) == sum(block_size_2)
assert sum(block_size_0) == 4
def test_divide_blocks():
blocks = [5, 1, 2, 3, 5, 6, 2, 1, 2]
world_size = 3
def get_num_records(sub_blocks):
nums = 0
for index, num in sub_blocks:
assert num <= blocks[index]
nums += num
return nums
divided_blocks = utils.divide_blocks(blocks, world_size, False)
assert len(divided_blocks) == 3
blocks_0 = get_num_records(divided_blocks[0])
blocks_1 = get_num_records(divided_blocks[1])
blocks_2 = get_num_records(divided_blocks[2])
assert blocks_0 == blocks_1 == blocks_2
divided_blocks = utils.divide_blocks(blocks, world_size, True)
assert len(divided_blocks) == 3
blocks_0 = get_num_records(divided_blocks[0])
blocks_1 = get_num_records(divided_blocks[1])
blocks_2 = get_num_records(divided_blocks[2])
assert blocks_0 == blocks_1 == blocks_2
blocks = [5, 1, 2, 3, 5, 6, 2, 2, 2]
world_size = 3
divided_blocks = utils.divide_blocks(blocks, world_size, False)
assert len(divided_blocks) == 3
blocks_0 = get_num_records(divided_blocks[0])
blocks_1 = get_num_records(divided_blocks[1])
blocks_2 = get_num_records(divided_blocks[2])
assert blocks_0 == blocks_1 == blocks_2
divided_blocks = utils.divide_blocks(blocks, world_size, True)
assert len(divided_blocks) == 3
blocks_0 = get_num_records(divided_blocks[0])
blocks_1 = get_num_records(divided_blocks[1])
blocks_2 = get_num_records(divided_blocks[2])
assert blocks_0 == blocks_1 == blocks_2
def test_divide_blocks():
blocks = [5, 1, 2, 3, 5, 6, 2, 1, 2]
world_size = 3
divided_blocks = utils.divide_blocks(blocks, world_size)
assert len(divided_blocks) == 3
blocks_0 = [blocks[i] for i in divided_blocks[0]]
blocks_1 = [blocks[i] for i in divided_blocks[1]]
blocks_2 = [blocks[i] for i in divided_blocks[2]]
assert sum(blocks_0) == sum(blocks_1) == sum(blocks_2)
blocks = [5, 1, 2, 3, 5, 6, 2, 2, 2]
world_size = 3
divided_blocks = utils.divide_blocks(blocks, world_size)
assert len(divided_blocks) == 3
blocks_0 = [blocks[i] for i in divided_blocks[0]]
blocks_1 = [blocks[i] for i in divided_blocks[1]]
blocks_2 = [blocks[i] for i in divided_blocks[2]]
assert sum(blocks_1) == sum(blocks_2)
assert sum(blocks_0) == (sum(blocks_1) + 1)
def save_to_ray(self, df: pyspark.sql.DataFrame,
num_shards: int) -> PandasDataset:
# call java function from python
sql_context = df.sql_ctx
jvm = sql_context.sparkSession.sparkContext._jvm
jdf = df._jdf
object_store_writer = jvm.org.apache.spark.sql.raydp.ObjectStoreWriter(
jdf)
records = object_store_writer.save()
worker = ray.worker.global_worker
blocks: List[ray.ObjectRef] = []
block_sizes: List[int] = []
for record in records:
owner_address = record.ownerAddress()
object_id = ray.ObjectID(record.objectId())
num_records = record.numRecords()
# Register the ownership of the ObjectRef
worker.core_worker.deserialize_and_register_object_ref(
object_id.binary(), ray.ObjectRef.nil(), owner_address)
blocks.append(object_id)
block_sizes.append(num_records)
divided_blocks = divide_blocks(block_sizes, num_shards, None, False,
False)
record_batch_set: List[List[RecordBatch]] = []
for i in range(num_shards):
indexes, record_sizes = divided_blocks[i]
object_ids = [blocks[index] for index in indexes]
record_batch_set.append([RecordBatch(object_ids, record_sizes)])
# TODO: we should specify the resource spec for each shard
ds = parallel_dataset.from_iterators(generators=record_batch_set,
name="spark_df")
def resolve_fn(it: Iterable[RecordBatch]) -> Iterator[RecordBatch]:
for item in it:
item.resolve()
yield item
return ds.transform(resolve_fn,
".RecordBatch#resolve()").flatten().to_pandas(None)
