File size: 8,499 Bytes
b40cb95 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 |
from typing import Callable, Iterable, List, Optional
import numpy as np
from ray.data._internal.util import _check_pyarrow_version
from ray.data.block import Block, BlockMetadata
from ray.util.annotations import Deprecated, DeveloperAPI, PublicAPI
@PublicAPI
class Datasource:
"""Interface for defining a custom :class:`~ray.data.Dataset` datasource.
To read a datasource into a dataset, use :meth:`~ray.data.read_datasource`.
""" # noqa: E501
@Deprecated
def create_reader(self, **read_args) -> "Reader":
"""
Deprecated: Implement :meth:`~ray.data.Datasource.get_read_tasks` and
:meth:`~ray.data.Datasource.estimate_inmemory_data_size` instead.
"""
return _LegacyDatasourceReader(self, **read_args)
@Deprecated
def prepare_read(self, parallelism: int, **read_args) -> List["ReadTask"]:
"""
Deprecated: Implement :meth:`~ray.data.Datasource.get_read_tasks` and
:meth:`~ray.data.Datasource.estimate_inmemory_data_size` instead.
"""
raise NotImplementedError
def get_name(self) -> str:
"""Return a human-readable name for this datasource.
This will be used as the names of the read tasks.
"""
name = type(self).__name__
datasource_suffix = "Datasource"
if name.endswith(datasource_suffix):
name = name[: -len(datasource_suffix)]
return name
def estimate_inmemory_data_size(self) -> Optional[int]:
"""Return an estimate of the in-memory data size, or None if unknown.
Note that the in-memory data size may be larger than the on-disk data size.
"""
raise NotImplementedError
def get_read_tasks(self, parallelism: int) -> List["ReadTask"]:
"""Execute the read and return read tasks.
Args:
parallelism: The requested read parallelism. The number of read
tasks should equal to this value if possible.
Returns:
A list of read tasks that can be executed to read blocks from the
datasource in parallel.
"""
raise NotImplementedError
@property
def should_create_reader(self) -> bool:
has_implemented_get_read_tasks = (
type(self).get_read_tasks is not Datasource.get_read_tasks
)
has_implemented_estimate_inmemory_data_size = (
type(self).estimate_inmemory_data_size
is not Datasource.estimate_inmemory_data_size
)
return (
not has_implemented_get_read_tasks
or not has_implemented_estimate_inmemory_data_size
)
@property
def supports_distributed_reads(self) -> bool:
"""If ``False``, only launch read tasks on the driver's node."""
return True
@Deprecated
class Reader:
"""A bound read operation for a :class:`~ray.data.Datasource`.
This is a stateful class so that reads can be prepared in multiple stages.
For example, it is useful for :class:`Datasets <ray.data.Dataset>` to know the
in-memory size of the read prior to executing it.
"""
def estimate_inmemory_data_size(self) -> Optional[int]:
"""Return an estimate of the in-memory data size, or None if unknown.
Note that the in-memory data size may be larger than the on-disk data size.
"""
raise NotImplementedError
def get_read_tasks(self, parallelism: int) -> List["ReadTask"]:
"""Execute the read and return read tasks.
Args:
parallelism: The requested read parallelism. The number of read
tasks should equal to this value if possible.
read_args: Additional kwargs to pass to the datasource impl.
Returns:
A list of read tasks that can be executed to read blocks from the
datasource in parallel.
"""
raise NotImplementedError
class _LegacyDatasourceReader(Reader):
def __init__(self, datasource: Datasource, **read_args):
self._datasource = datasource
self._read_args = read_args
def estimate_inmemory_data_size(self) -> Optional[int]:
return None
def get_read_tasks(self, parallelism: int) -> List["ReadTask"]:
return self._datasource.prepare_read(parallelism, **self._read_args)
@DeveloperAPI
class ReadTask(Callable[[], Iterable[Block]]):
"""A function used to read blocks from the :class:`~ray.data.Dataset`.
Read tasks are generated by :meth:`~ray.data.Datasource.get_read_tasks`,
and return a list of ``ray.data.Block`` when called. Initial metadata about the read
operation can be retrieved via the ``metadata`` attribute prior to executing the
read. Final metadata is returned after the read along with the blocks.
Ray will execute read tasks in remote functions to parallelize execution.
Note that the number of blocks returned can vary at runtime. For example,
if a task is reading a single large file it can return multiple blocks to
avoid running out of memory during the read.
The initial metadata should reflect all the blocks returned by the read,
e.g., if the metadata says ``num_rows=1000``, the read can return a single
block of 1000 rows, or multiple blocks with 1000 rows altogether.
The final metadata (returned with the actual block) reflects the exact
contents of the block itself.
"""
def __init__(self, read_fn: Callable[[], Iterable[Block]], metadata: BlockMetadata):
self._metadata = metadata
self._read_fn = read_fn
@property
def metadata(self) -> BlockMetadata:
return self._metadata
@property
def read_fn(self) -> Callable[[], Iterable[Block]]:
return self._read_fn
def __call__(self) -> Iterable[Block]:
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)
)
yield from result
@DeveloperAPI
class RandomIntRowDatasource(Datasource):
"""An example datasource that generates rows with random int64 columns.
Examples:
>>> import ray
>>> from ray.data.datasource import RandomIntRowDatasource
>>> source = RandomIntRowDatasource() # doctest: +SKIP
>>> ray.data.read_datasource( # doctest: +SKIP
... source, n=10, num_columns=2).take()
{'c_0': 1717767200176864416, 'c_1': 999657309586757214}
{'c_0': 4983608804013926748, 'c_1': 1160140066899844087}
"""
def __init__(self, n: int, num_columns: int):
self._n = n
self._num_columns = num_columns
def estimate_inmemory_data_size(self) -> Optional[int]:
return self._n * self._num_columns * 8
def get_read_tasks(
self,
parallelism: int,
) -> List[ReadTask]:
_check_pyarrow_version()
import pyarrow
read_tasks: List[ReadTask] = []
n = self._n
num_columns = self._num_columns
block_size = max(1, n // parallelism)
def make_block(count: int, num_columns: int) -> Block:
return pyarrow.Table.from_arrays(
np.random.randint(
np.iinfo(np.int64).max, size=(num_columns, count), dtype=np.int64
),
names=[f"c_{i}" for i in range(num_columns)],
)
schema = pyarrow.Table.from_pydict(
{f"c_{i}": [0] for i in range(num_columns)}
).schema
i = 0
while i < n:
count = min(block_size, n - i)
meta = BlockMetadata(
num_rows=count,
size_bytes=8 * count * num_columns,
schema=schema,
input_files=None,
exec_stats=None,
)
read_tasks.append(
ReadTask(
lambda count=count, num_columns=num_columns: [
make_block(count, num_columns)
],
meta,
)
)
i += block_size
return read_tasks
def get_name(self) -> str:
"""Return a human-readable name for this datasource.
This will be used as the names of the read tasks.
Note: overrides the base `Datasource` method.
"""
return "RandomInt"
|