import collections
import logging
import time
from dataclasses import dataclass, fields
from enum import Enum
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterator,
List,
Optional,
Protocol,
Tuple,
TypeVar,
Union,
)
import numpy as np
import ray
from ray.air.util.tensor_extensions.arrow import ArrowConversionError
from ray.data._internal.util import _check_pyarrow_version, _truncated_repr
from ray.types import ObjectRef
from ray.util import log_once
from ray.util.annotations import DeveloperAPI
if TYPE_CHECKING:
import pandas
import pyarrow
from ray.data._internal.block_builder import BlockBuilder
from ray.data._internal.planner.exchange.sort_task_spec import SortKey
from ray.data.aggregate import AggregateFn
T = TypeVar("T", contravariant=True)
U = TypeVar("U", covariant=True)
KeyType = TypeVar("KeyType")
AggType = TypeVar("AggType")
# Represents a batch of records to be stored in the Ray object store.
#
# Block data can be accessed in a uniform way via ``BlockAccessors`` like`
# ``ArrowBlockAccessor``.
Block = Union["pyarrow.Table", "pandas.DataFrame"]
# Represents a single column of the ``Block``
BlockColumn = Union["pyarrow.ChunkedArray", "pyarrow.Array", "pandas.Series"]
logger = logging.getLogger(__name__)
@DeveloperAPI
class BlockType(Enum):
ARROW = "arrow"
PANDAS = "pandas"
# User-facing data batch type. This is the data type for data that is supplied to and
# returned from batch UDFs.
DataBatch = Union["pyarrow.Table", "pandas.DataFrame", Dict[str, np.ndarray]]
# User-facing data column type. This is the data type for data that is supplied to and
# returned from column UDFs.
DataBatchColumn = Union[BlockColumn, np.ndarray]
# A class type that implements __call__.
CallableClass = type
class _CallableClassProtocol(Protocol[T, U]):
def __call__(self, __arg: T) -> Union[U, Iterator[U]]:
...
# A user defined function passed to map, map_batches, ec.
UserDefinedFunction = Union[
Callable[[T], U],
Callable[[T], Iterator[U]],
"_CallableClassProtocol",
]
# A list of block references pending computation by a single task. For example,
# this may be the output of a task reading a file.
BlockPartition = List[Tuple[ObjectRef[Block], "BlockMetadata"]]
# The metadata that describes the output of a BlockPartition. This has the
# same type as the metadata that describes each block in the partition.
BlockPartitionMetadata = List["BlockMetadata"]
VALID_BATCH_FORMATS = ["pandas", "pyarrow", "numpy", None]
DEFAULT_BATCH_FORMAT = "numpy"
def _apply_batch_format(given_batch_format: Optional[str]) -> str:
if given_batch_format == "default":
given_batch_format = DEFAULT_BATCH_FORMAT
if given_batch_format not in VALID_BATCH_FORMATS:
raise ValueError(
f"The given batch format {given_batch_format} isn't allowed (must be one of"
f" {VALID_BATCH_FORMATS})."
)
return given_batch_format
@DeveloperAPI
def to_stats(metas: List["BlockMetadata"]) -> List["BlockStats"]:
return [m.to_stats() for m in metas]
[docs]
@DeveloperAPI
class BlockExecStats:
"""Execution stats for this block.
Attributes:
wall_time_s: The wall-clock time it took to compute this block.
cpu_time_s: The CPU time it took to compute this block.
node_id: A unique id for the node that computed this block.
max_uss_bytes: An estimate of the maximum amount of physical memory that the
process was using while computing this block.
"""
def __init__(self):
self.start_time_s: Optional[float] = None
self.end_time_s: Optional[float] = None
self.wall_time_s: Optional[float] = None
self.udf_time_s: Optional[float] = 0
self.cpu_time_s: Optional[float] = None
self.node_id = ray.runtime_context.get_runtime_context().get_node_id()
self.max_uss_bytes: int = 0
self.task_idx: Optional[int] = None
@staticmethod
def builder() -> "_BlockExecStatsBuilder":
return _BlockExecStatsBuilder()
def __repr__(self):
return repr(
{
"wall_time_s": self.wall_time_s,
"cpu_time_s": self.cpu_time_s,
"udf_time_s": self.udf_time_s,
"node_id": self.node_id,
}
)
class _BlockExecStatsBuilder:
"""Helper class for building block stats.
When this class is created, we record the start time. When build() is
called, the time delta is saved as part of the stats.
"""
def __init__(self):
self._start_time = time.perf_counter()
self._start_cpu = time.process_time()
def build(self) -> "BlockExecStats":
# Record end times.
end_time = time.perf_counter()
end_cpu = time.process_time()
# Build the stats.
stats = BlockExecStats()
stats.start_time_s = self._start_time
stats.end_time_s = end_time
stats.wall_time_s = end_time - self._start_time
stats.cpu_time_s = end_cpu - self._start_cpu
return stats
@DeveloperAPI
@dataclass
class BlockStats:
"""Statistics about the block produced"""
#: The number of rows contained in this block, or None.
num_rows: Optional[int]
#: The approximate size in bytes of this block, or None.
size_bytes: Optional[int]
#: Execution stats for this block.
exec_stats: Optional[BlockExecStats]
def __post_init__(self):
if self.size_bytes is not None:
# Require size_bytes to be int, ray.util.metrics objects
# will not take other types like numpy.int64
assert isinstance(self.size_bytes, int)
_BLOCK_STATS_FIELD_NAMES = {f.name for f in fields(BlockStats)}
[docs]
@DeveloperAPI
class BlockAccessor:
"""Provides accessor methods for a specific block.
Ideally, we wouldn't need a separate accessor classes for blocks. However,
this is needed if we want to support storing ``pyarrow.Table`` directly
as a top-level Ray object, without a wrapping class (issue #17186).
"""
[docs]
def num_rows(self) -> int:
"""Return the number of rows contained in this block."""
raise NotImplementedError
[docs]
def iter_rows(self, public_row_format: bool) -> Iterator[T]:
"""Iterate over the rows of this block.
Args:
public_row_format: Whether to cast rows into the public Dict row
format (this incurs extra copy conversions).
"""
raise NotImplementedError
[docs]
def slice(self, start: int, end: int, copy: bool = False) -> Block:
"""Return a slice of this block.
Args:
start: The starting index of the slice (inclusive).
end: The ending index of the slice (exclusive).
copy: Whether to perform a data copy for the slice.
Returns:
The sliced block result.
"""
raise NotImplementedError
[docs]
def take(self, indices: List[int]) -> Block:
"""Return a new block containing the provided row indices.
Args:
indices: The row indices to return.
Returns:
A new block containing the provided row indices.
"""
raise NotImplementedError
[docs]
def select(self, columns: List[Optional[str]]) -> Block:
"""Return a new block containing the provided columns."""
raise NotImplementedError
[docs]
def rename_columns(self, columns_rename: Dict[str, str]) -> Block:
"""Return the block reflecting the renamed columns."""
raise NotImplementedError
[docs]
def random_shuffle(self, random_seed: Optional[int]) -> Block:
"""Randomly shuffle this block."""
raise NotImplementedError
[docs]
def to_pandas(self) -> "pandas.DataFrame":
"""Convert this block into a Pandas dataframe."""
raise NotImplementedError
[docs]
def to_numpy(
self, columns: Optional[Union[str, List[str]]] = None
) -> Union[np.ndarray, Dict[str, np.ndarray]]:
"""Convert this block (or columns of block) into a NumPy ndarray.
Args:
columns: Name of columns to convert, or None if converting all columns.
"""
raise NotImplementedError
[docs]
def to_arrow(self) -> "pyarrow.Table":
"""Convert this block into an Arrow table."""
raise NotImplementedError
[docs]
def to_block(self) -> Block:
"""Return the base block that this accessor wraps."""
raise NotImplementedError
[docs]
def to_default(self) -> Block:
"""Return the default data format for this accessor."""
return self.to_block()
[docs]
def size_bytes(self) -> int:
"""Return the approximate size in bytes of this block."""
raise NotImplementedError
[docs]
def schema(self) -> Union[type, "pyarrow.lib.Schema"]:
"""Return the Python type or pyarrow schema of this block."""
raise NotImplementedError
[docs]
def zip(self, other: "Block") -> "Block":
"""Zip this block with another block of the same type and size."""
raise NotImplementedError
[docs]
@staticmethod
def builder() -> "BlockBuilder":
"""Create a builder for this block type."""
raise NotImplementedError
[docs]
@classmethod
def batch_to_block(
cls,
batch: DataBatch,
block_type: Optional[BlockType] = None,
) -> Block:
"""Create a block from user-facing data formats."""
if isinstance(batch, np.ndarray):
raise ValueError(
f"Error validating {_truncated_repr(batch)}: "
"Standalone numpy arrays are not "
"allowed in Ray 2.5. Return a dict of field -> array, "
"e.g., `{'data': array}` instead of `array`."
)
elif isinstance(batch, collections.abc.Mapping):
if block_type is None or block_type == BlockType.ARROW:
try:
return cls.batch_to_arrow_block(batch)
except ArrowConversionError as e:
if log_once("_fallback_to_pandas_block_warning"):
logger.warning(
f"Failed to convert batch to Arrow due to: {e}; "
f"falling back to Pandas block"
)
if block_type is None:
return cls.batch_to_pandas_block(batch)
else:
raise e
else:
assert block_type == BlockType.PANDAS
return cls.batch_to_pandas_block(batch)
return batch
[docs]
@classmethod
def batch_to_arrow_block(cls, batch: Dict[str, Any]) -> Block:
"""Create an Arrow block from user-facing data formats."""
from ray.data._internal.arrow_block import ArrowBlockBuilder
return ArrowBlockBuilder._table_from_pydict(batch)
[docs]
@classmethod
def batch_to_pandas_block(cls, batch: Dict[str, Any]) -> Block:
"""Create a Pandas block from user-facing data formats."""
from ray.data._internal.pandas_block import PandasBlockBuilder
return PandasBlockBuilder._table_from_pydict(batch)
[docs]
@staticmethod
def for_block(block: Block) -> "BlockAccessor[T]":
"""Create a block accessor for the given block."""
_check_pyarrow_version()
import pandas
import pyarrow
if isinstance(block, pyarrow.Table):
from ray.data._internal.arrow_block import ArrowBlockAccessor
return ArrowBlockAccessor(block)
elif isinstance(block, pandas.DataFrame):
from ray.data._internal.pandas_block import PandasBlockAccessor
return PandasBlockAccessor(block)
elif isinstance(block, bytes):
from ray.data._internal.arrow_block import ArrowBlockAccessor
return ArrowBlockAccessor.from_bytes(block)
elif isinstance(block, list):
raise ValueError(
f"Error validating {_truncated_repr(block)}: "
"Standalone Python objects are not "
"allowed in Ray 2.5. To use Python objects in a dataset, "
"wrap them in a dict of numpy arrays, e.g., "
"return `{'item': batch}` instead of just `batch`."
)
else:
raise TypeError("Not a block type: {} ({})".format(block, type(block)))
[docs]
def sample(self, n_samples: int, sort_key: "SortKey") -> "Block":
"""Return a random sample of items from this block."""
raise NotImplementedError
[docs]
def count(self, on: str, ignore_nulls: bool = False) -> Optional[U]:
"""Returns a count of the distinct values in the provided column"""
raise NotImplementedError
[docs]
def sum(self, on: str, ignore_nulls: bool) -> Optional[U]:
"""Returns a sum of the values in the provided column"""
raise NotImplementedError
[docs]
def min(self, on: str, ignore_nulls: bool) -> Optional[U]:
"""Returns a min of the values in the provided column"""
raise NotImplementedError
[docs]
def max(self, on: str, ignore_nulls: bool) -> Optional[U]:
"""Returns a max of the values in the provided column"""
raise NotImplementedError
[docs]
def mean(self, on: str, ignore_nulls: bool) -> Optional[U]:
"""Returns a mean of the values in the provided column"""
raise NotImplementedError
[docs]
def sum_of_squared_diffs_from_mean(
self,
on: str,
ignore_nulls: bool,
mean: Optional[U] = None,
) -> Optional[U]:
"""Returns a sum of diffs (from mean) squared for the provided column"""
raise NotImplementedError
[docs]
def sort(self, sort_key: "SortKey") -> "Block":
"""Returns new block sorted according to provided `sort_key`"""
raise NotImplementedError
[docs]
def sort_and_partition(
self, boundaries: List[T], sort_key: "SortKey"
) -> List["Block"]:
"""Return a list of sorted partitions of this block."""
raise NotImplementedError
def _aggregate(self, key: "SortKey", aggs: Tuple["AggregateFn"]) -> Block:
"""Combine rows with the same key into an accumulator."""
raise NotImplementedError
[docs]
@staticmethod
def merge_sorted_blocks(
blocks: List["Block"], sort_key: "SortKey"
) -> Tuple[Block, BlockMetadata]:
"""Return a sorted block by merging a list of sorted blocks."""
raise NotImplementedError
@staticmethod
def _combine_aggregated_blocks(
blocks: List[Block],
sort_key: "SortKey",
aggs: Tuple["AggregateFn"],
finalize: bool = True,
) -> Tuple[Block, BlockMetadata]:
"""Aggregate partially combined and sorted blocks."""
raise NotImplementedError
def _find_partitions_sorted(
self,
boundaries: List[Tuple[Any]],
sort_key: "SortKey",
) -> List[Block]:
"""NOTE: PLEASE READ CAREFULLY
Returns dataset partitioned using list of boundaries
This method requires that
- Block being sorted (according to `sort_key`)
- Boundaries is a sorted list of tuples
"""
raise NotImplementedError
[docs]
def block_type(self) -> BlockType:
"""Return the block type of this block."""
raise NotImplementedError
def _get_group_boundaries_sorted(self, keys: List[str]) -> np.ndarray:
"""
NOTE: THIS METHOD ASSUMES THAT PROVIDED BLOCK IS ALREADY SORTED
Compute boundaries of the groups within a block based on provided
key (a column or a list of columns)
NOTE: In each column, NaNs/None are considered to be the same group.
Args:
block: sorted block for which grouping of rows will be determined
based on provided key
keys: list of columns determining the key for every row based on
which the block will be grouped
Returns:
A list of starting indices of each group and an end index of the last
group, i.e., there are ``num_groups + 1`` entries and the first and last
entries are 0 and ``len(array)`` respectively.
"""
if self.num_rows() == 0:
return np.array([], dtype=np.int32)
elif not keys:
# If no keys are specified, whole block is considered a single group
return np.array([0, self.num_rows()])
# Convert key columns to Numpy (to perform vectorized
# ops on them)
projected_block = self.to_numpy(keys)
return _get_group_boundaries_sorted_numpy(list(projected_block.values()))
@DeveloperAPI(stability="beta")
class BlockColumnAccessor:
"""Provides vendor-neutral interface to apply common operations
to block's (Pandas/Arrow) columns"""
def __init__(self, col: BlockColumn):
self._column = col
def count(self, *, ignore_nulls: bool, as_py: bool = True) -> Optional[U]:
"""Returns a count of the distinct values in the column"""
raise NotImplementedError()
def sum(self, *, ignore_nulls: bool, as_py: bool = True) -> Optional[U]:
"""Returns a sum of the values in the column"""
return NotImplementedError()
def min(self, *, ignore_nulls: bool, as_py: bool = True) -> Optional[U]:
"""Returns a min of the values in the column"""
raise NotImplementedError()
def max(self, *, ignore_nulls: bool, as_py: bool = True) -> Optional[U]:
"""Returns a max of the values in the column"""
raise NotImplementedError()
def mean(self, *, ignore_nulls: bool, as_py: bool = True) -> Optional[U]:
"""Returns a mean of the values in the column"""
raise NotImplementedError()
def quantile(
self, *, q: float, ignore_nulls: bool, as_py: bool = True
) -> Optional[U]:
"""Returns requested quantile of the given column"""
raise NotImplementedError()
def unique(self) -> BlockColumn:
"""Returns new column holding only distinct values of the current one"""
raise NotImplementedError()
def flatten(self) -> BlockColumn:
"""Flattens nested lists merging them into top-level container"""
raise NotImplementedError()
def sum_of_squared_diffs_from_mean(
self,
*,
ignore_nulls: bool,
mean: Optional[U] = None,
as_py: bool = True,
) -> Optional[U]:
"""Returns a sum of diffs (from mean) squared for the column"""
raise NotImplementedError()
def to_pylist(self) -> List[Any]:
"""Converts block column to a list of Python native objects"""
raise NotImplementedError()
def to_numpy(self, zero_copy_only: bool = False) -> np.ndarray:
"""Converts underlying column to Numpy"""
raise NotImplementedError()
def _as_arrow_compatible(self) -> Union[List[Any], "pyarrow.Array"]:
"""Converts block column into a representation compatible with Arrow"""
raise NotImplementedError()
@staticmethod
def for_column(col: BlockColumn) -> "BlockColumnAccessor":
"""Create a column accessor for the given column"""
_check_pyarrow_version()
import pandas as pd
import pyarrow as pa
if isinstance(col, pa.Array) or isinstance(col, pa.ChunkedArray):
from ray.data._internal.arrow_block import ArrowBlockColumnAccessor
return ArrowBlockColumnAccessor(col)
elif isinstance(col, pd.Series):
from ray.data._internal.pandas_block import PandasBlockColumnAccessor
return PandasBlockColumnAccessor(col)
else:
raise TypeError(
f"Expected either a pandas.Series or pyarrow.Array (ChunkedArray) "
f"(got {type(col)})"
)
def _get_group_boundaries_sorted_numpy(columns: list[np.ndarray]) -> np.ndarray:
# There are 3 categories: general, numerics with NaN, and categorical with None.
# We only needed to check the last element for NaNs/None, as they are assumed to
# be sorted.
general_arrays = []
num_arrays_with_nan = []
cat_arrays_with_none = []
for arr in columns:
if np.issubdtype(arr.dtype, np.number) and np.isnan(arr[-1]):
num_arrays_with_nan.append(arr)
elif not np.issubdtype(arr.dtype, np.number) and arr[-1] is None:
cat_arrays_with_none.append(arr)
else:
general_arrays.append(arr)
# Compute the difference between each pair of elements. Handle the cases
# where neighboring elements are both NaN or None. Output as a list of
# boolean arrays.
diffs = []
if len(general_arrays) > 0:
diffs.append(
np.vstack([arr[1:] != arr[:-1] for arr in general_arrays]).any(axis=0)
)
if len(num_arrays_with_nan) > 0:
# Two neighboring numeric elements belong to the same group when they are
# 1) both finite and equal
# or 2) both np.nan
diffs.append(
np.vstack(
[
(arr[1:] != arr[:-1])
& (np.isfinite(arr[1:]) | np.isfinite(arr[:-1]))
for arr in num_arrays_with_nan
]
).any(axis=0)
)
if len(cat_arrays_with_none) > 0:
# Two neighboring str/object elements belong to the same group when they are
# 1) both finite and equal
# or 2) both None
diffs.append(
np.vstack(
[
(arr[1:] != arr[:-1])
& ~(np.equal(arr[1:], None) & np.equal(arr[:-1], None))
for arr in cat_arrays_with_none
]
).any(axis=0)
)
# A series of vectorized operations to compute the boundaries:
# - column_stack: stack the bool arrays into a single 2D bool array
# - any() and nonzero(): find the indices where any of the column diffs are True
# - add 1 to get the index of the first element of the next group
# - hstack(): include the 0 and last indices to the boundaries
boundaries = np.hstack(
[
[0],
(np.column_stack(diffs).any(axis=1).nonzero()[0] + 1),
[len(columns[0])],
]
).astype(int)
return boundaries