import os
import threading
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
from ray._private.ray_constants import env_bool, env_integer
from ray.util.annotations import DeveloperAPI
from ray.util.scheduling_strategies import SchedulingStrategyT
if TYPE_CHECKING:
from ray.data._internal.execution.interfaces import ExecutionOptions
# The context singleton on this process.
_default_context: "Optional[DataContext]" = None
_context_lock = threading.Lock()
# An estimate of what fraction of the object store a Dataset can use without too high
# a risk of triggering spilling. This is used to generate user warnings only.
ESTIMATED_SAFE_MEMORY_FRACTION = 0.25
# We chose 128MiB for default: With streaming execution and num_cpus many concurrent
# tasks, the memory footprint will be about 2 * num_cpus * target_max_block_size ~= RAM
# * DEFAULT_OBJECT_STORE_MEMORY_LIMIT_FRACTION * 0.3 (default object store memory
# fraction set by Ray core), assuming typical memory:core ratio of 4:1.
DEFAULT_TARGET_MAX_BLOCK_SIZE = 128 * 1024 * 1024
# We set a higher target block size because we have to materialize
# all input blocks anyway, so there is no performance advantage to having
# smaller blocks. Setting a larger block size allows avoiding overhead from an
# excessive number of partitions.
# We choose 1GiB as 4x less than the typical memory:core ratio (4:1).
DEFAULT_SHUFFLE_TARGET_MAX_BLOCK_SIZE = 1024 * 1024 * 1024
# We will attempt to slice blocks whose size exceeds this factor *
# target_max_block_size. We will warn the user if slicing fails and we produce
# blocks larger than this threshold.
MAX_SAFE_BLOCK_SIZE_FACTOR = 1.5
DEFAULT_TARGET_MIN_BLOCK_SIZE = 1 * 1024 * 1024
# This default appears to work well with most file sizes on remote storage systems,
# which is very sensitive to the buffer size.
DEFAULT_STREAMING_READ_BUFFER_SIZE = 32 * 1024 * 1024
DEFAULT_ENABLE_PANDAS_BLOCK = True
DEFAULT_READ_OP_MIN_NUM_BLOCKS = 200
DEFAULT_ACTOR_PREFETCHER_ENABLED = False
DEFAULT_USE_PUSH_BASED_SHUFFLE = bool(
os.environ.get("RAY_DATA_PUSH_BASED_SHUFFLE", None)
)
DEFAULT_SCHEDULING_STRATEGY = "SPREAD"
# This default enables locality-based scheduling in Ray for tasks where arg data
# transfer is a bottleneck.
DEFAULT_SCHEDULING_STRATEGY_LARGE_ARGS = "DEFAULT"
DEFAULT_LARGE_ARGS_THRESHOLD = 50 * 1024 * 1024
DEFAULT_USE_POLARS = False
DEFAULT_EAGER_FREE = bool(int(os.environ.get("RAY_DATA_EAGER_FREE", "1")))
DEFAULT_DECODING_SIZE_ESTIMATION_ENABLED = True
DEFAULT_MIN_PARALLELISM = 200
DEFAULT_ENABLE_TENSOR_EXTENSION_CASTING = True
DEFAULT_AUTO_LOG_STATS = False
DEFAULT_VERBOSE_STATS_LOG = False
DEFAULT_TRACE_ALLOCATIONS = bool(int(os.environ.get("RAY_DATA_TRACE_ALLOCATIONS", "0")))
DEFAULT_LOG_INTERNAL_STACK_TRACE_TO_STDOUT = False
DEFAULT_USE_RAY_TQDM = bool(int(os.environ.get("RAY_TQDM", "1")))
DEFAULT_ENABLE_PROGRESS_BARS = not bool(
env_integer("RAY_DATA_DISABLE_PROGRESS_BARS", 0)
)
DEFAULT_ENABLE_GET_OBJECT_LOCATIONS_FOR_METRICS = False
DEFAULT_WRITE_FILE_RETRY_ON_ERRORS = (
"AWS Error INTERNAL_FAILURE",
"AWS Error NETWORK_CONNECTION",
"AWS Error SLOW_DOWN",
)
DEFAULT_WARN_ON_DRIVER_MEMORY_USAGE_BYTES = 2 * 1024 * 1024 * 1024
DEFAULT_ACTOR_TASK_RETRY_ON_ERRORS = False
DEFAULT_ENABLE_OP_RESOURCE_RESERVATION = env_bool(
"RAY_DATA_ENABLE_OP_RESOURCE_RESERVATION", True
)
DEFAULT_OP_RESOURCE_RESERVATION_RATIO = float(
os.environ.get("RAY_DATA_OP_RESERVATION_RATIO", "0.5")
)
DEFAULT_MAX_ERRORED_BLOCKS = 0
# Use this to prefix important warning messages for the user.
WARN_PREFIX = "⚠️ "
# Use this to prefix important success messages for the user.
OK_PREFIX = "✔️ "
# Default batch size for batch transformations.
DEFAULT_BATCH_SIZE = 1024
# Default value of the max number of blocks that can be buffered at the
# streaming generator of each `DataOpTask`.
# Note, if this value is too large, we'll need to allocate more memory
# buffer for the pending task outputs, which may lead to bad performance
# as we may not have enough memory buffer for the operator outputs.
# If the value is too small, the task may be frequently blocked due to
# streaming generator backpressure.
DEFAULT_MAX_NUM_BLOCKS_IN_STREAMING_GEN_BUFFER = 2
def _execution_options_factory() -> "ExecutionOptions":
# Lazily import to avoid circular dependencies.
from ray.data._internal.execution.interfaces import ExecutionOptions
return ExecutionOptions()
[docs]@DeveloperAPI
@dataclass
class DataContext:
"""Global settings for Ray Data.
Configure this class to enable advanced features and tune performance.
.. warning::
Apply changes before creating a :class:`~ray.data.Dataset`. Changes made after
won't take effect.
.. note::
This object is automatically propagated to workers. Access it from the driver
and remote workers with :meth:`DataContext.get_current()`.
Examples:
>>> from ray.data import DataContext
>>> DataContext.get_current().enable_progress_bars = False
Args:
target_max_block_size: The max target block size in bytes for reads and
transformations.
target_shuffle_max_block_size: The max target block size in bytes for shuffle
ops like ``random_shuffle``, ``sort``, and ``repartition``.
target_min_block_size: Ray Data avoids creating blocks smaller than this
size in bytes on read. This takes precedence over
``read_op_min_num_blocks``.
streaming_read_buffer_size: Buffer size when doing streaming reads from local or
remote storage.
enable_pandas_block: Whether pandas block format is enabled.
actor_prefetcher_enabled: Whether to use actor based block prefetcher.
use_push_based_shuffle: Whether to use push-based shuffle.
pipeline_push_based_shuffle_reduce_tasks:
scheduling_strategy: The global scheduling strategy. For tasks with large args,
``scheduling_strategy_large_args`` takes precedence.
scheduling_strategy_large_args: Scheduling strategy for tasks with large args.
large_args_threshold: Size in bytes after which point task arguments are
considered large. Choose a value so that the data transfer overhead is
significant in comparison to task scheduling (i.e., low tens of ms).
use_polars: Whether to use Polars for tabular dataset sorts, groupbys, and
aggregations.
eager_free: Whether to eagerly free memory.
decoding_size_estimation: Whether to estimate in-memory decoding data size for
data source.
min_parallelism: This setting is deprecated. Use ``read_op_min_num_blocks``
instead.
read_op_min_num_blocks: Minimum number of read output blocks for a dataset.
enable_tensor_extension_casting: Whether to automatically cast NumPy ndarray
columns in Pandas DataFrames to tensor extension columns.
enable_auto_log_stats: Whether to automatically log stats after execution. If
disabled, you can still manually print stats with ``Dataset.stats()``.
verbose_stats_logs: Whether stats logs should be verbose. This includes fields
such as `extra_metrics` in the stats output, which are excluded by default.
trace_allocations: Whether to trace allocations / eager free. This adds
significant performance overheads and should only be used for debugging.
execution_options: The
:class:`~ray.data._internal.execution.interfaces.execution_options.ExecutionOptions`
to use.
use_ray_tqdm: Whether to enable distributed tqdm.
enable_progress_bars: Whether to enable progress bars.
enable_get_object_locations_for_metrics: Whether to enable
``get_object_locations`` for metrics.
write_file_retry_on_errors: A list of substrings of error messages that should
trigger a retry when writing files. This is useful for handling transient
errors when writing to remote storage systems.
warn_on_driver_memory_usage_bytes: If driver memory exceeds this threshold,
Ray Data warns you. For now, this only applies to shuffle ops because most
other ops are unlikely to use as much driver memory.
actor_task_retry_on_errors: The application-level errors that actor task should
retry. This follows same format as :ref:`retry_exceptions <task-retries>` in
Ray Core. Default to `False` to not retry on any errors. Set to `True` to
retry all errors, or set to a list of errors to retry.
enable_op_resource_reservation: Whether to reserve resources for each operator.
op_resource_reservation_ratio: The ratio of the total resources to reserve for
each operator.
max_errored_blocks: Max number of blocks that are allowed to have errors,
unlimited if negative. This option allows application-level exceptions in
block processing tasks. These exceptions may be caused by UDFs (e.g., due to
corrupted data samples) or IO errors. Data in the failed blocks are dropped.
This option can be useful to prevent a long-running job from failing due to
a small number of bad blocks.
log_internal_stack_trace_to_stdout: Whether to include internal Ray Data/Ray
Core code stack frames when logging to stdout. The full stack trace is
always written to the Ray Data log file.
print_on_execution_start: If ``True``, print execution information when
execution starts.
"""
target_max_block_size: int = DEFAULT_TARGET_MAX_BLOCK_SIZE
target_shuffle_max_block_size: int = DEFAULT_SHUFFLE_TARGET_MAX_BLOCK_SIZE
target_min_block_size: int = DEFAULT_TARGET_MIN_BLOCK_SIZE
streaming_read_buffer_size: int = DEFAULT_STREAMING_READ_BUFFER_SIZE
enable_pandas_block: bool = DEFAULT_ENABLE_PANDAS_BLOCK
actor_prefetcher_enabled: bool = DEFAULT_ACTOR_PREFETCHER_ENABLED
use_push_based_shuffle: bool = DEFAULT_USE_PUSH_BASED_SHUFFLE
pipeline_push_based_shuffle_reduce_tasks: bool = True
scheduling_strategy: SchedulingStrategyT = DEFAULT_SCHEDULING_STRATEGY
scheduling_strategy_large_args: SchedulingStrategyT = (
DEFAULT_SCHEDULING_STRATEGY_LARGE_ARGS
)
large_args_threshold: int = DEFAULT_LARGE_ARGS_THRESHOLD
use_polars: bool = DEFAULT_USE_POLARS
eager_free: bool = DEFAULT_EAGER_FREE
decoding_size_estimation: bool = DEFAULT_DECODING_SIZE_ESTIMATION_ENABLED
min_parallelism: int = DEFAULT_MIN_PARALLELISM
read_op_min_num_blocks: int = DEFAULT_READ_OP_MIN_NUM_BLOCKS
enable_tensor_extension_casting: bool = DEFAULT_ENABLE_TENSOR_EXTENSION_CASTING
enable_auto_log_stats: bool = DEFAULT_AUTO_LOG_STATS
verbose_stats_logs: bool = DEFAULT_VERBOSE_STATS_LOG
trace_allocations: bool = DEFAULT_TRACE_ALLOCATIONS
execution_options: "ExecutionOptions" = field(
default_factory=_execution_options_factory
)
use_ray_tqdm: bool = DEFAULT_USE_RAY_TQDM
enable_progress_bars: bool = DEFAULT_ENABLE_PROGRESS_BARS
enable_get_object_locations_for_metrics: bool = (
DEFAULT_ENABLE_GET_OBJECT_LOCATIONS_FOR_METRICS
)
write_file_retry_on_errors: List[str] = DEFAULT_WRITE_FILE_RETRY_ON_ERRORS
warn_on_driver_memory_usage_bytes: int = DEFAULT_WARN_ON_DRIVER_MEMORY_USAGE_BYTES
actor_task_retry_on_errors: Union[
bool, List[BaseException]
] = DEFAULT_ACTOR_TASK_RETRY_ON_ERRORS
op_resource_reservation_enabled: bool = DEFAULT_ENABLE_OP_RESOURCE_RESERVATION
op_resource_reservation_ratio: float = DEFAULT_OP_RESOURCE_RESERVATION_RATIO
max_errored_blocks: int = DEFAULT_MAX_ERRORED_BLOCKS
log_internal_stack_trace_to_stdout: bool = (
DEFAULT_LOG_INTERNAL_STACK_TRACE_TO_STDOUT
)
print_on_execution_start: bool = True
def __post_init__(self):
# The additonal ray remote args that should be added to
# the task-pool-based data tasks.
self._task_pool_data_task_remote_args: Dict[str, Any] = {}
# The extra key-value style configs.
# These configs are managed by individual components or plugins via
# `set_config`, `get_config` and `remove_config`.
# The reason why we use a dict instead of individual fields is to decouple
# the DataContext from the plugin implementations, as well as to avoid
# circular dependencies.
self._kv_configs: Dict[str, Any] = {}
self._max_num_blocks_in_streaming_gen_buffer = (
DEFAULT_MAX_NUM_BLOCKS_IN_STREAMING_GEN_BUFFER
)
[docs] @staticmethod
def get_current() -> "DataContext":
"""Get or create a singleton context.
If the context has not yet been created in this process, it will be
initialized with default settings.
"""
global _default_context
with _context_lock:
if _default_context is None:
_default_context = DataContext()
return _default_context
@staticmethod
def _set_current(context: "DataContext") -> None:
"""Set the current context in a remote worker.
This is used internally by Dataset to propagate the driver context to
remote workers used for parallelization.
"""
global _default_context
_default_context = context
def get_config(self, key: str, default: Any = None) -> Any:
"""Get the value for a key-value style config.
Args:
key: The key of the config.
default: The default value to return if the key is not found.
Returns: The value for the key, or the default value if the key is not found.
"""
return self._kv_configs.get(key, default)
def set_config(self, key: str, value: Any) -> None:
"""Set the value for a key-value style config.
Args:
key: The key of the config.
value: The value of the config.
"""
self._kv_configs[key] = value
def remove_config(self, key: str) -> None:
"""Remove a key-value style config.
Args:
key: The key of the config.
"""
self._kv_configs.pop(key, None)
# Backwards compatibility alias.
DatasetContext = DataContext