Source code for ray.data.datasource.datasink
from typing import Any, Iterable, List, Optional
import ray
from ray.data._internal.execution.interfaces import TaskContext
from ray.data.block import Block, BlockAccessor
from ray.util.annotations import DeveloperAPI
[docs]
@DeveloperAPI
class Datasink:
"""Interface for defining write-related logic.
If you want to write data to something that isn't built-in, subclass this class
and call :meth:`~ray.data.Dataset.write_datasink`.
"""
[docs]
def on_write_start(self) -> None:
"""Callback for when a write job starts.
Use this method to perform setup for write tasks. For example, creating a
staging bucket in S3.
"""
pass
[docs]
def write(
self,
blocks: Iterable[Block],
ctx: TaskContext,
) -> Any:
"""Write blocks. This is used by a single write task.
Args:
blocks: Generator of data blocks.
ctx: ``TaskContext`` for the write task.
Returns:
A user-defined output. Can be anything, and the returned value is passed to
:meth:`~ray.data.Datasink.on_write_complete`.
"""
raise NotImplementedError
[docs]
def on_write_complete(self, write_results: List[Any]) -> None:
"""Callback for when a write job completes.
This can be used to "commit" a write output. This method must
succeed prior to ``write_datasink()`` returning to the user. If this
method fails, then ``on_write_failed()`` is called.
Args:
write_results: The objects returned by every
:meth:`~ray.data.Datasink.write` task.
"""
pass
[docs]
def on_write_failed(self, error: Exception) -> None:
"""Callback for when a write job fails.
This is called on a best-effort basis on write failures.
Args:
error: The first error encountered.
"""
pass
[docs]
def get_name(self) -> str:
"""Return a human-readable name for this datasink.
This is used as the names of the write tasks.
"""
name = type(self).__name__
datasink_suffix = "Datasink"
if name.startswith("_"):
name = name[1:]
if name.endswith(datasink_suffix):
name = name[: -len(datasink_suffix)]
return name
@property
def supports_distributed_writes(self) -> bool:
"""If ``False``, only launch write tasks on the driver's node."""
return True
@property
def num_rows_per_write(self) -> Optional[int]:
"""The target number of rows to pass to each :meth:`~ray.data.Datasink.write` call.
If ``None``, Ray Data passes a system-chosen number of rows.
"""
return None
@DeveloperAPI
class DummyOutputDatasink(Datasink):
"""An example implementation of a writable datasource for testing.
Examples:
>>> import ray
>>> from ray.data.datasource import DummyOutputDatasink
>>> output = DummyOutputDatasink()
>>> ray.data.range(10).write_datasink(output)
>>> assert output.num_ok == 1
"""
def __init__(self):
ctx = ray.data.DataContext.get_current()
# Setup a dummy actor to send the data. In a real datasource, write
# tasks would send data to an external system instead of a Ray actor.
@ray.remote(scheduling_strategy=ctx.scheduling_strategy)
class DataSink:
def __init__(self):
self.rows_written = 0
self.enabled = True
def write(self, block: Block) -> str:
block = BlockAccessor.for_block(block)
self.rows_written += block.num_rows()
return "ok"
def get_rows_written(self):
return self.rows_written
self.data_sink = DataSink.remote()
self.num_ok = 0
self.num_failed = 0
self.enabled = True
def write(
self,
blocks: Iterable[Block],
ctx: TaskContext,
) -> Any:
tasks = []
if not self.enabled:
raise ValueError("disabled")
for b in blocks:
tasks.append(self.data_sink.write.remote(b))
ray.get(tasks)
return "ok"
def on_write_complete(self, write_results: List[Any]) -> None:
assert all(w == "ok" for w in write_results), write_results
self.num_ok += 1
def on_write_failed(self, error: Exception) -> None:
self.num_failed += 1