.venv/lib/python3.11/site-packages/ray/data/context.py

import logging
import os
import threading
import warnings
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union

import ray
from ray._private.ray_constants import env_bool, env_integer
from ray._private.worker import WORKER_MODE
from ray.util.annotations import DeveloperAPI
from ray.util.debug import log_once
from ray.util.scheduling_strategies import SchedulingStrategyT

if TYPE_CHECKING:
    from ray.data._internal.execution.interfaces import ExecutionOptions

logger = logging.getLogger(__name__)

# The context singleton on this process.
_default_context: "Optional[DataContext]" = None
_context_lock = threading.Lock()


# 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

# NOTE: V1 tensor type format only supports tensors of no more than 2Gb in
#       total cumulative size (due to it internally utilizing int32 offsets)
#
#       V2 in turn relies on int64 offsets, therefore having a limit of ~9Eb (exabytes)
DEFAULT_USE_ARROW_TENSOR_V2 = env_bool("RAY_DATA_USE_ARROW_TENSOR_V2", 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 = env_bool(
    "RAY_DATA_LOG_INTERNAL_STACK_TRACE_TO_STDOUT", False
)

DEFAULT_RAY_DATA_RAISE_ORIGINAL_MAP_EXCEPTION = env_bool(
    "RAY_DATA_RAISE_ORIGINAL_MAP_EXCEPTION", False
)

DEFAULT_USE_RAY_TQDM = bool(int(os.environ.get("RAY_TQDM", "1")))

# Globally enable or disable all progress bars.
# If this is False, both the global and operator-level progress bars are disabled.
DEFAULT_ENABLE_PROGRESS_BARS = not bool(
    env_integer("RAY_DATA_DISABLE_PROGRESS_BARS", 0)
)
DEFAULT_ENABLE_PROGRESS_BAR_NAME_TRUNCATION = env_bool(
    "RAY_DATA_ENABLE_PROGRESS_BAR_NAME_TRUNCATION", True
)

DEFAULT_ENABLE_GET_OBJECT_LOCATIONS_FOR_METRICS = False


# `write_file_retry_on_errors` is deprecated in favor of `retried_io_errors`. You
# shouldn't need to modify `DEFAULT_WRITE_FILE_RETRY_ON_ERRORS`.
DEFAULT_WRITE_FILE_RETRY_ON_ERRORS = (
    "AWS Error INTERNAL_FAILURE",
    "AWS Error NETWORK_CONNECTION",
    "AWS Error SLOW_DOWN",
    "AWS Error UNKNOWN (HTTP status 503)",
)

DEFAULT_RETRIED_IO_ERRORS = (
    "AWS Error INTERNAL_FAILURE",
    "AWS Error NETWORK_CONNECTION",
    "AWS Error SLOW_DOWN",
    "AWS Error UNKNOWN (HTTP status 503)",
    "AWS Error SERVICE_UNAVAILABLE",
)

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

# Default value for whether or not to try to create directories for write
# calls if the URI is an S3 URI.
DEFAULT_S3_TRY_CREATE_DIR = False

DEFAULT_WAIT_FOR_MIN_ACTORS_S = env_integer(
    "RAY_DATA_DEFAULT_WAIT_FOR_MIN_ACTORS_S", 60 * 10
)


def _execution_options_factory() -> "ExecutionOptions":
    # Lazily import to avoid circular dependencies.
    from ray.data._internal.execution.interfaces import ExecutionOptions

    return ExecutionOptions()


@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.
        use_arrow_tensor_v2: Config enabling V2 version of ArrowTensorArray supporting
            tensors > 2Gb in size (off by default)
        enable_fallback_to_arrow_object_ext_type: Enables fallback to serialize column
            values not suppported by Arrow natively (like user-defined custom Python
            classes for ex, etc) using `ArrowPythonObjectType` (simply serializing
            these as bytes)
        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_progress_bar_name_truncation: If True, the name of the progress bar
            (often the operator name) will be truncated if it exceeds
            `ProgressBar.MAX_NAME_LENGTH`. Otherwise, the full operator name is shown.
        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.
        raise_original_map_exception: Whether to raise the original exception
            encountered in map UDF instead of wrapping it in a `UserCodeException`.
        print_on_execution_start: If ``True``, print execution information when
            execution starts.
        s3_try_create_dir: If ``True``, try to create directories on S3 when a write
            call is made with a S3 URI.
        wait_for_min_actors_s: The default time to wait for minimum requested
            actors to start before raising a timeout, in seconds.
        retried_io_errors: A list of substrings of error messages that should
            trigger a retry when reading or writing files. This is useful for handling
            transient errors when reading from remote storage systems.
    """

    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
    use_arrow_tensor_v2: bool = DEFAULT_USE_ARROW_TENSOR_V2
    enable_fallback_to_arrow_object_ext_type: Optional[bool] = None
    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
    # By default, enable the progress bar for operator-level progress.
    # In __post_init__(), we disable operator-level progress
    # bars when running in a Ray job.
    enable_operator_progress_bars: bool = True
    enable_progress_bar_name_truncation: bool = (
        DEFAULT_ENABLE_PROGRESS_BAR_NAME_TRUNCATION
    )
    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
    )
    raise_original_map_exception: bool = DEFAULT_RAY_DATA_RAISE_ORIGINAL_MAP_EXCEPTION
    print_on_execution_start: bool = True
    s3_try_create_dir: bool = DEFAULT_S3_TRY_CREATE_DIR
    wait_for_min_actors_s: int = DEFAULT_WAIT_FOR_MIN_ACTORS_S
    retried_io_errors: List[str] = field(
        default_factory=lambda: list(DEFAULT_RETRIED_IO_ERRORS)
    )

    override_object_store_memory_limit_fraction: float = None

    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
        )

        is_ray_job = os.environ.get("RAY_JOB_ID") is not None
        if is_ray_job:
            is_driver = ray.get_runtime_context().worker.mode != WORKER_MODE
            if is_driver and log_once(
                "ray_data_disable_operator_progress_bars_in_ray_jobs"
            ):
                logger.info(
                    "Disabling operator-level progress bars by default in Ray Jobs. "
                    "To enable progress bars for all operators, set "
                    "`ray.data.DataContext.get_current()"
                    ".enable_operator_progress_bars = True`."
                )
            # Disable operator-level progress bars by default in Ray jobs.
            # The global progress bar for the overall Dataset execution will
            # still be enabled, unless the user also sets
            # `ray.data.DataContext.get_current().enable_progress_bars = False`.
            self.enable_operator_progress_bars = False
        else:
            # When not running in Ray job, operator-level progress
            # bars are enabled by default.
            self.enable_operator_progress_bars = True

    def __setattr__(self, name: str, value: Any) -> None:
        if (
            name == "write_file_retry_on_errors"
            and value != DEFAULT_WRITE_FILE_RETRY_ON_ERRORS
        ):
            warnings.warn(
                "`write_file_retry_on_errors` is deprecated. Configure "
                "`retried_io_errors` instead.",
                DeprecationWarning,
            )

        super().__setattr__(name, value)

    @staticmethod
    def get_current() -> "DataContext":
        """Get or create the current DataContext.

        When a Dataset is created, the current DataContext will be sealed.
        Changes to `DataContext.get_current()` will not impact existing Datasets.

        Examples:

            .. testcode::
                import ray

                context = ray.data.DataContext.get_current()

                context.target_max_block_size = 100 * 1024 ** 2
                ds1 = ray.data.range(1)
                context.target_max_block_size = 1 * 1024 ** 2
                ds2 = ray.data.range(1)

                # ds1's target_max_block_size will be 100MB
                ds1.take_all()
                # ds2's target_max_block_size will be 1MB
                ds2.take_all()

        Developer notes: Avoid using `DataContext.get_current()` in data
        internal components, use the DataContext object captured in the
        Dataset and pass it around as arguments.
        """

        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