import copy
import enum
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
from ray.data._internal.logging import update_dataset_logger_for_worker
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()
@DeveloperAPI(stability="alpha")
class ShuffleStrategy(str, enum.Enum):
"""Shuffle strategy determines shuffling algorithm employed by operations
like aggregate, repartition, etc"""
SORT_SHUFFLE_PULL_BASED = "sort_shuffle_pull_based"
SORT_SHUFFLE_PUSH_BASED = "sort_shuffle_push_based"
HASH_SHUFFLE = "hash_shuffle"
# 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
# We will attempt to slice blocks whose size exceeds this factor *
# target_num_rows_per_block. We will warn the user if slicing fails and we produce
# blocks with more rows than this threshold.
MAX_SAFE_ROWS_PER_BLOCK_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_SHUFFLE_STRATEGY = os.environ.get(
"RAY_DATA_DEFAULT_SHUFFLE_STRATEGY", ShuffleStrategy.SORT_SHUFFLE_PULL_BASED
)
DEFAULT_MAX_HASH_SHUFFLE_AGGREGATORS = env_integer(
"RAY_DATA_MAX_HASH_SHUFFLE_AGGREGATORS", 64
)
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 = env_integer("RAY_DATA_DEFAULT_MIN_PARALLELISM", 200)
DEFAULT_ENABLE_TENSOR_EXTENSION_CASTING = env_bool(
"RAY_DATA_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 = "✔️ "
# The default batch size for batch transformations before it was changed to `None`.
LEGACY_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
)
# Enable per node metrics reporting for Ray Data, disabled by default.
DEFAULT_ENABLE_PER_NODE_METRICS = bool(
int(os.environ.get("RAY_DATA_PER_NODE_METRICS", "0"))
)
def _execution_options_factory() -> "ExecutionOptions":
# Lazily import to avoid circular dependencies.
from ray.data._internal.execution.interfaces import ExecutionOptions
return ExecutionOptions()
def _deduce_default_shuffle_algorithm() -> ShuffleStrategy:
if DEFAULT_USE_PUSH_BASED_SHUFFLE:
logger.warning(
"RAY_DATA_PUSH_BASED_SHUFFLE is deprecated, please use "
"RAY_DATA_DEFAULT_SHUFFLE_STRATEGY to set shuffling strategy"
)
return ShuffleStrategy.SORT_SHUFFLE_PUSH_BASED
else:
vs = [s for s in ShuffleStrategy] # noqa: C416
assert DEFAULT_SHUFFLE_STRATEGY in vs, (
f"RAY_DATA_DEFAULT_SHUFFLE_STRATEGY has to be one of the [{','.join(vs)}] "
f"(got {DEFAULT_SHUFFLE_STRATEGY})"
)
return DEFAULT_SHUFFLE_STRATEGY
[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.
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.
enable_per_node_metrics: Enable per node metrics reporting for Ray Data,
disabled by default.
memory_usage_poll_interval_s: The interval to poll the USS of map tasks. If `None`,
map tasks won't record memory stats.
"""
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
################################################################
# Sort-based shuffling configuration
################################################################
use_push_based_shuffle: bool = DEFAULT_USE_PUSH_BASED_SHUFFLE
_shuffle_strategy: ShuffleStrategy = _deduce_default_shuffle_algorithm()
pipeline_push_based_shuffle_reduce_tasks: bool = True
################################################################
# Hash-based shuffling configuration
################################################################
# Default hash-shuffle parallelism level (will be used when not
# provided explicitly)
default_hash_shuffle_parallelism = DEFAULT_MIN_PARALLELISM
# Max number of aggregating actors that could be provisioned
# to perform aggregations on partitions produced during hash-shuffling
#
# When unset defaults to `DataContext.min_parallelism`
max_hash_shuffle_aggregators: Optional[int] = DEFAULT_MAX_HASH_SHUFFLE_AGGREGATORS
# Max number of *concurrent* hash-shuffle finalization tasks running
# at the same time. This config is helpful to control concurrency of
# finalization tasks to prevent single aggregator running multiple tasks
# concurrently (for ex, to prevent it failing w/ OOM)
#
# When unset defaults to `DataContext.max_hash_shuffle_aggregators`
max_hash_shuffle_finalization_batch_size: Optional[int] = None
join_operator_actor_num_cpus_per_partition_override: float = None
hash_shuffle_operator_actor_num_cpus_per_partition_override: float = None
hash_aggregate_operator_actor_num_cpus_per_partition_override: float = None
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)
)
enable_per_node_metrics: bool = DEFAULT_ENABLE_PER_NODE_METRICS
override_object_store_memory_limit_fraction: float = None
memory_usage_poll_interval_s: Optional[float] = 1
dataset_logger_id: Optional[str] = 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,
)
elif name == "use_push_based_shuffle":
warnings.warn(
"`use_push_based_shuffle` is deprecated, please configure "
"`shuffle_strategy` instead.",
DeprecationWarning,
)
super().__setattr__(name, value)
[docs]
@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
if (
not _default_context
or _default_context.dataset_logger_id != context.dataset_logger_id
):
update_dataset_logger_for_worker(context.dataset_logger_id)
_default_context = context
@property
def shuffle_strategy(self) -> ShuffleStrategy:
if self.use_push_based_shuffle:
logger.warning(
"`use_push_based_shuffle` is deprecated, please configure "
"`shuffle_strategy` instead.",
)
return ShuffleStrategy.SORT_SHUFFLE_PUSH_BASED
return self._shuffle_strategy
@shuffle_strategy.setter
def shuffle_strategy(self, value: ShuffleStrategy) -> None:
self._shuffle_strategy = value
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)
def copy(self) -> "DataContext":
"""Create a copy of the current DataContext."""
return copy.deepcopy(self)
def set_dataset_logger_id(self, dataset_id: str) -> None:
"""Set the current dataset logger id.
This is used internally to propagate the current dataset logger id to remote
workers.
"""
self.dataset_logger_id = dataset_id
# Backwards compatibility alias.
DatasetContext = DataContext