import copy
from typing import Callable, Dict, Generator, Iterable, List, Optional
import numpy as np
import pyarrow as pa
from ray.data._internal.util import _check_pyarrow_version
from ray.data.block import Block, BlockMetadata, Schema
from ray.data.datasource.util import _iter_sliced_blocks
from ray.data.expressions import Expr
from ray.util.annotations import Deprecated, DeveloperAPI, PublicAPI
class _DatasourceProjectionPushdownMixin:
"""Mixin for reading operators supporting projection pushdown"""
def supports_projection_pushdown(self) -> bool:
"""Returns ``True`` in case ``Datasource`` supports projection operation
being pushed down into the reading layer"""
return False
def get_projection_map(self) -> Optional[Dict[str, str]]:
"""Return the projection map (original column names -> final column names).
Returns:
Dict mapping original column names (in storage) to final column names
(after optional renames). Keys indicate which columns are selected.
None means all columns are selected with no renames.
Empty dict {} means no columns are selected.
"""
return self._projection_map
def get_column_renames(self) -> Optional[Dict[str, str]]:
"""Return the column renames from the projection map.
This is used by predicate pushdown to rewrite filter expressions
from renamed column names back to original column names.
Returns:
Dict mapping original column names to renamed names,
or None if no renaming has been applied.
"""
if self._projection_map is None:
return None
# Only include actual renames (where key != value)
renames = {k: v for k, v in self._projection_map.items() if k != v}
return renames if renames else None
def _get_data_columns(self) -> Optional[List[str]]:
"""Extract data columns from projection map.
Helper method for datasources that need to pass columns to legacy
read functions expecting separate columns and rename_map parameters.
Returns:
List of column names, or None if all columns should be read.
Empty list [] means no columns.
"""
return (
list(self._projection_map.keys())
if self._projection_map is not None
else None
)
@staticmethod
def _combine_projection_map(
prev_projection_map: Optional[Dict[str, str]],
new_projection_map: Optional[Dict[str, str]],
) -> Optional[Dict[str, str]]:
"""Combine two projection maps via transitive composition.
Args:
prev_projection_map: Previous projection (original -> intermediate names)
new_projection_map: New projection to apply (intermediate -> final names)
Returns:
Combined projection map (original -> final names)
Examples:
>>> # Select columns a, b with no renames
>>> prev = {"a": "a", "b": "b"}
>>> # Select only 'a', rename to 'x'
>>> new = {"a": "x"}
>>> _DatasourceProjectionPushdownMixin._combine_projection_map(prev, new)
{'a': 'x'}
>>> # First rename a->temp
>>> prev = {"a": "temp"}
>>> # Then rename temp->final
>>> new = {"temp": "final"}
>>> _DatasourceProjectionPushdownMixin._combine_projection_map(prev, new)
{'a': 'final'}
"""
# Handle None cases (None means "all columns, no renames")
if prev_projection_map is None:
return new_projection_map
elif new_projection_map is None:
return prev_projection_map
# Compose projections: for each original->intermediate mapping in prev,
# check if intermediate is selected by new projection
composed = {}
for orig_col, intermediate_name in prev_projection_map.items():
# If intermediate name is in new projection, follow the chain
if intermediate_name in new_projection_map:
final_name = new_projection_map[intermediate_name]
composed[orig_col] = final_name
# The composition already handles transitive chains correctly:
# prev {a: temp}, new {temp: final} -> composed {a: final}
# No need for collapse_transitive_map which would incorrectly remove
# identity mappings like {b: b}
return composed
def apply_projection(
self,
projection_map: Optional[Dict[str, str]],
) -> "Datasource":
"""Apply a projection to this datasource.
Args:
projection_map: Dict mapping original column names (in storage)
to final column names (after optional renames). Keys indicate
which columns to select. None means select all columns with no renames.
Returns:
A new datasource instance with the projection applied.
"""
clone = copy.copy(self)
# Combine projections via transitive map composition
clone._projection_map = self._combine_projection_map(
self._projection_map, projection_map
)
return clone
@staticmethod
def _apply_rename(
table: "pa.Table",
column_rename_map: Optional[Dict[str, str]],
) -> "pa.Table":
"""Apply column renaming to a PyArrow table.
Args:
table: PyArrow table to rename
column_rename_map: Mapping from old column names to new names
Returns:
Table with renamed columns
"""
if not column_rename_map:
return table
new_names = [column_rename_map.get(col, col) for col in table.schema.names]
return table.rename_columns(new_names)
@staticmethod
def _apply_rename_to_tables(
tables: Iterable["pa.Table"],
column_rename_map: Optional[Dict[str, str]],
) -> Generator["pa.Table", None, None]:
"""Wrap a table generator to apply column renaming to each table.
This helper eliminates duplication across datasources that need to apply
column renames to tables yielded from generators.
Args:
tables: Iterator/generator yielding PyArrow tables
column_rename_map: Mapping from old column names to new names
Yields:
pa.Table: Tables with renamed columns
"""
for table in tables:
yield _DatasourceProjectionPushdownMixin._apply_rename(
table, column_rename_map
)
class _DatasourcePredicatePushdownMixin:
"""Mixin for reading operators supporting predicate pushdown"""
def __init__(self):
self._predicate_expr: Optional[Expr] = None
def supports_predicate_pushdown(self) -> bool:
return False
def get_current_predicate(self) -> Optional[Expr]:
return self._predicate_expr
def apply_predicate(
self,
predicate_expr: Expr,
) -> "Datasource":
"""Apply a predicate to this datasource.
Default implementation that combines predicates using AND.
Subclasses that support predicate pushdown should have a _predicate_expr
attribute to store the predicate.
Note: Column rebinding is handled by the PredicatePushdown rule
before this method is called, so the predicate_expr should already
reference the correct column names.
"""
import copy
clone = copy.copy(self)
# Combine with existing predicate using AND
clone._predicate_expr = (
predicate_expr
if clone._predicate_expr is None
else clone._predicate_expr & predicate_expr
)
return clone
[docs]
@PublicAPI
class Datasource(_DatasourceProjectionPushdownMixin, _DatasourcePredicatePushdownMixin):
"""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
[docs]
def __init__(self):
"""Initialize the datasource and its mixins."""
_DatasourcePredicatePushdownMixin.__init__(self)
[docs]
@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)
[docs]
@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
[docs]
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
[docs]
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
[docs]
def get_read_tasks(
self, parallelism: int, per_task_row_limit: Optional[int] = None
) -> 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.
per_task_row_limit: The per-task row limit for the read tasks.
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.
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, per_task_row_limit: Optional[int] = None
) -> 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.
per_task_row_limit: The per-task row limit for the read tasks.
Returns:
A list of read tasks that can be executed to read blocks from the
datasource in parallel.
"""
return self._datasource.prepare_read(parallelism, **self._read_args)
[docs]
@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,
schema: Optional["Schema"] = None,
per_task_row_limit: Optional[int] = None,
):
self._metadata = metadata
self._read_fn = read_fn
self._schema = schema
self._per_task_row_limit = per_task_row_limit
@property
def metadata(self) -> BlockMetadata:
return self._metadata
# TODO(justin): We want to remove schema from `ReadTask` later on
@property
def schema(self) -> Optional["Schema"]:
return self._schema
@property
def read_fn(self) -> Callable[[], Iterable[Block]]:
return self._read_fn
@property
def per_task_row_limit(self) -> Optional[int]:
"""Get the per-task row limit for this read task."""
return self._per_task_row_limit
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)
)
if self._per_task_row_limit is None:
yield from result
return
yield from _iter_sliced_blocks(result, self._per_task_row_limit)
@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):
"""Initialize the datasource that generates random-integer rows.
Args:
n: The number of rows to generate.
num_columns: The number of columns to generate.
"""
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,
per_task_row_limit: Optional[int] = None,
) -> 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,
input_files=None,
exec_stats=None,
)
read_tasks.append(
ReadTask(
lambda count=count, num_columns=num_columns: [
make_block(count, num_columns)
],
meta,
schema=schema,
per_task_row_limit=per_task_row_limit,
)
)
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"