Source code for dagster._core.definitions.partition

import copy
import inspect
import json
from abc import ABC, abstractmethod
from datetime import datetime, time, timedelta
from enum import Enum
from typing import (
    Any,
    Callable,
    Generic,
    Iterable,
    List,
    Mapping,
    NamedTuple,
    Optional,
    Sequence,
    TypeVar,
    Union,
    cast,
)

import pendulum
from dateutil.relativedelta import relativedelta
from typing_extensions import TypeAlias

import dagster._check as check
from dagster._annotations import PublicAttr, public
from dagster._core.definitions.partition_key_range import PartitionKeyRange
from dagster._core.definitions.target import ExecutableDefinition
from dagster._serdes import whitelist_for_serdes
from dagster._seven.compat.pendulum import PendulumDateTime, to_timezone
from dagster._utils import frozenlist, merge_dicts
from dagster._utils.schedules import schedule_execution_time_iterator

from ..decorator_utils import get_function_params
from ..errors import (
    DagsterInvalidDefinitionError,
    DagsterInvalidInvocationError,
    DagsterInvariantViolationError,
    DagsterUnknownPartitionError,
    ScheduleExecutionError,
    user_code_error_boundary,
)
from ..storage.pipeline_run import PipelineRun
from .config import ConfigMapping
from .mode import DEFAULT_MODE_NAME
from .run_request import RunRequest, SkipReason
from .schedule_definition import (
    DefaultScheduleStatus,
    ScheduleDefinition,
    ScheduleEvaluationContext,
    ScheduleExecutionFunction,
    ScheduleRunConfigFunction,
    ScheduleShouldExecuteFunction,
    ScheduleTagsFunction,
)
from .utils import check_valid_name, validate_tags

DEFAULT_DATE_FORMAT = "%Y-%m-%d"

RawPartitionFunction: TypeAlias = Union[
    Callable[[Optional[datetime]], Sequence[Union[str, "Partition[Any]"]]],
    Callable[[], Sequence[Union[str, "Partition"]]],
]

PartitionFunction: TypeAlias = Callable[[Optional[datetime]], Sequence["Partition[Any]"]]
PartitionTagsFunction: TypeAlias = Callable[["Partition"], Mapping[str, str]]
PartitionScheduleFunction: TypeAlias = Callable[[datetime], Mapping[str, Any]]
PartitionSelectorFunction: TypeAlias = Callable[
    [ScheduleEvaluationContext, "PartitionSetDefinition"],
    Union["Partition", Sequence["Partition"], SkipReason],
]

T = TypeVar("T")


class Partition(Generic[T]):
    """
    A Partition represents a single slice of the entire set of a job's possible work. It consists
    of a value, which is an object that represents that partition, and an optional name, which is
    used to label the partition in a human-readable way.

    Args:
        value (Any): The object for this partition
        name (str): Name for this partition
    """

    def __init__(self, value: T, name: Optional[str] = None):
        self._value = value
        self._name = cast(str, check.opt_str_param(name, "name", str(value)))

    @property
    def value(self) -> T:
        return self._value

    @property
    def name(self) -> str:
        return self._name

    def __eq__(self, other) -> bool:
        return (
            isinstance(other, Partition) and self.value == other.value and self.name == other.name
        )


def schedule_partition_range(
    start: datetime,
    end: Optional[datetime],
    cron_schedule: str,
    fmt: str,
    timezone: Optional[str],
    execution_time_to_partition_fn: Callable,
    current_time: Optional[datetime],
) -> Sequence[Partition[datetime]]:
    if end and start > end:
        raise DagsterInvariantViolationError(
            'Selected date range start "{start}" is after date range end "{end}'.format(
                start=start.strftime(fmt),
                end=end.strftime(fmt),
            )
        )

    tz = timezone if timezone else "UTC"

    _current_time = current_time if current_time else pendulum.now(tz)

    # Coerce to the definition timezone
    _start = (
        to_timezone(start, tz)
        if isinstance(start, PendulumDateTime)
        else pendulum.instance(start, tz=tz)
    )
    _current_time = (
        to_timezone(_current_time, tz)
        if isinstance(_current_time, PendulumDateTime)
        else pendulum.instance(_current_time, tz=tz)
    )

    # The end partition time should be before the last partition that
    # executes before the current time
    end_partition_time = execution_time_to_partition_fn(_current_time)

    # The partition set has an explicit end time that represents the end of the partition range
    if end:
        _end = (
            to_timezone(end, tz)
            if isinstance(end, PendulumDateTime)
            else pendulum.instance(end, tz=tz)
        )

        # If the explicit end time is before the last partition time,
        # update the end partition time
        end_partition_time = min(_end, end_partition_time)

    end_timestamp = end_partition_time.timestamp()

    partitions: List[Partition[datetime]] = []
    for next_time in schedule_execution_time_iterator(_start.timestamp(), cron_schedule, tz):

        partition_time = execution_time_to_partition_fn(next_time)

        if partition_time.timestamp() > end_timestamp:
            break

        if partition_time.timestamp() < _start.timestamp():
            continue

        partitions.append(Partition(value=partition_time, name=partition_time.strftime(fmt)))

    return partitions


@whitelist_for_serdes
class ScheduleType(Enum):
    HOURLY = "HOURLY"
    DAILY = "DAILY"
    WEEKLY = "WEEKLY"
    MONTHLY = "MONTHLY"

    @property
    def ordinal(self):
        return {"HOURLY": 1, "DAILY": 2, "WEEKLY": 3, "MONTHLY": 4}[self.value]

    @property
    def delta(self):
        if self == ScheduleType.HOURLY:
            return timedelta(hours=1)
        elif self == ScheduleType.DAILY:
            return timedelta(days=1)
        elif self == ScheduleType.WEEKLY:
            return timedelta(weeks=1)
        elif self == ScheduleType.MONTHLY:
            return relativedelta(months=1)
        else:
            check.failed(f"Unexpected ScheduleType {self}")

    def __gt__(self, other):
        return self.ordinal > other.ordinal

    def __lt__(self, other):
        return self.ordinal < other.ordinal


[docs]class PartitionsDefinition(ABC, Generic[T]): @abstractmethod def get_partitions(self, current_time: Optional[datetime] = None) -> Sequence[Partition[T]]: ... def __str__(self) -> str: joined_keys = ", ".join([f"'{key}'" for key in self.get_partition_keys()]) return joined_keys @public def get_partition_keys(self, current_time: Optional[datetime] = None) -> Sequence[str]: return [partition.name for partition in self.get_partitions(current_time)] def get_last_partition_key(self, current_time: Optional[datetime] = None) -> str: return self.get_partitions(current_time)[-1].name def get_first_partition_key(self, current_time: Optional[datetime] = None) -> str: return self.get_partitions(current_time)[0].name def get_default_partition_mapping(self): from dagster._core.definitions.partition_mapping import IdentityPartitionMapping return IdentityPartitionMapping() def get_partition_keys_in_range(self, partition_key_range: PartitionKeyRange) -> Sequence[str]: partition_keys = self.get_partition_keys() keys_exist = { partition_key_range.start: partition_key_range.start in partition_keys, partition_key_range.end: partition_key_range.end in partition_keys, } if not all(keys_exist.values()): raise DagsterInvalidInvocationError( f"""Partition range {partition_key_range.start} to {partition_key_range.end} is not a valid range. Nonexistent partition keys: {list(key for key in keys_exist if keys_exist[key] is False)}""" ) return partition_keys[ partition_keys.index(partition_key_range.start) : partition_keys.index( partition_key_range.end ) + 1 ] def empty_subset(self) -> "PartitionsSubset": return DefaultPartitionsSubset(self, []) def deserialize_subset(self, serialized: str) -> "PartitionsSubset": return DefaultPartitionsSubset.from_serialized(self, serialized)
[docs]class StaticPartitionsDefinition( PartitionsDefinition[str], ): # pylint: disable=unsubscriptable-object def __init__(self, partition_keys: Sequence[str]): check.sequence_param(partition_keys, "partition_keys", of_type=str) # Dagit selects partition ranges following the format '2022-01-13...2022-01-14' # "..." is an invalid substring in partition keys if any(["..." in partition_key for partition_key in partition_keys]): raise DagsterInvalidDefinitionError("'...' is an invalid substring in a partition key") self._partitions = [Partition(key) for key in partition_keys] def get_partitions( self, current_time: Optional[datetime] = None # pylint: disable=unused-argument ) -> Sequence[Partition[str]]: return self._partitions def __hash__(self): return hash(self.__repr__()) def __eq__(self, other) -> bool: return ( isinstance(other, StaticPartitionsDefinition) and self._partitions == other.get_partitions() ) def __repr__(self) -> str: return f"{type(self).__name__}(partition_keys={[p.name for p in self._partitions]})"
class ScheduleTimeBasedPartitionsDefinition( PartitionsDefinition[datetime], # pylint: disable=unsubscriptable-object NamedTuple( "_ScheduleTimeBasedPartitionsDefinition", [ ("schedule_type", ScheduleType), ("start", datetime), ("execution_time", time), ("execution_day", Optional[int]), ("end", Optional[datetime]), ("fmt", str), ("timezone", str), ("offset", int), ], ), ): """Computes the partitions backwards from the scheduled execution times""" def __new__( # pylint: disable=arguments-differ cls, schedule_type: ScheduleType, start: datetime, execution_time: Optional[time] = None, execution_day: Optional[int] = None, end: Optional[datetime] = None, fmt: Optional[str] = None, timezone: Optional[str] = None, offset: Optional[int] = None, ): if end is not None: check.invariant( start <= end, f'Selected date range start "{start}" ' f'is after date range end "{end}"'.format( start=start.strftime(fmt) if fmt is not None else start, end=cast(datetime, end).strftime(fmt) if fmt is not None else end, ), ) if schedule_type in [ScheduleType.HOURLY, ScheduleType.DAILY]: check.invariant( not execution_day, f'Execution day should not be provided for schedule type "{schedule_type}"', ) elif schedule_type is ScheduleType.WEEKLY: execution_day = execution_day if execution_day is not None else 0 check.invariant( execution_day is not None and 0 <= execution_day <= 6, f'Execution day "{execution_day}" must be between 0 and 6 for ' f'schedule type "{schedule_type}"', ) elif schedule_type is ScheduleType.MONTHLY: execution_day = execution_day if execution_day is not None else 1 check.invariant( execution_day is not None and 1 <= execution_day <= 31, f'Execution day "{execution_day}" must be between 1 and 31 for ' f'schedule type "{schedule_type}"', ) return super(ScheduleTimeBasedPartitionsDefinition, cls).__new__( cls, check.inst_param(schedule_type, "schedule_type", ScheduleType), check.inst_param(start, "start", datetime), check.opt_inst_param(execution_time, "execution_time", time, time(0, 0)), check.opt_int_param( execution_day, "execution_day", ), check.opt_inst_param(end, "end", datetime), check.opt_str_param(fmt, "fmt", default=DEFAULT_DATE_FORMAT), check.opt_str_param(timezone, "timezone", default="UTC"), check.opt_int_param(offset, "offset", default=1), ) def get_partitions( self, current_time: Optional[datetime] = None ) -> Sequence[Partition[datetime]]: check.opt_inst_param(current_time, "current_time", datetime) return schedule_partition_range( start=self.start, end=self.end, cron_schedule=self.get_cron_schedule(), fmt=self.fmt, timezone=self.timezone, execution_time_to_partition_fn=self.get_execution_time_to_partition_fn(), current_time=current_time, ) def get_cron_schedule(self) -> str: return cron_schedule_from_schedule_type_and_offsets( schedule_type=self.schedule_type, minute_offset=self.execution_time.minute, hour_offset=self.execution_time.hour, day_offset=self.execution_day, ) def get_execution_time_to_partition_fn(self) -> Callable[[datetime], datetime]: if self.schedule_type is ScheduleType.HOURLY: # Using subtract(minutes=d.minute) here instead of .replace(minute=0) because on # pendulum 1, replace(minute=0) sometimes changes the timezone: # >>> a = create_pendulum_time(2021, 11, 7, 0, 0, tz="US/Central") # # >>> a.add(hours=1) # <Pendulum [2021-11-07T01:00:00-05:00]> # >>> a.add(hours=1).replace(minute=0) # <Pendulum [2021-11-07T01:00:00-06:00]> return lambda d: pendulum.instance(d).subtract(hours=self.offset, minutes=d.minute) elif self.schedule_type is ScheduleType.DAILY: return ( lambda d: pendulum.instance(d).replace(hour=0, minute=0).subtract(days=self.offset) ) elif self.schedule_type is ScheduleType.WEEKLY: execution_day = cast(int, self.execution_day) day_difference = (execution_day - (self.start.weekday() + 1)) % 7 return ( lambda d: pendulum.instance(d) .replace(hour=0, minute=0) .subtract( weeks=self.offset, days=day_difference, ) ) elif self.schedule_type is ScheduleType.MONTHLY: execution_day = cast(int, self.execution_day) return ( lambda d: pendulum.instance(d) .replace(hour=0, minute=0) .subtract(months=self.offset, days=execution_day - 1) ) else: check.assert_never(self.schedule_type) class DynamicPartitionsDefinition( PartitionsDefinition, NamedTuple( "_DynamicPartitionsDefinition", [ ( "partition_fn", PublicAttr[ Callable[[Optional[datetime]], Union[Sequence[Partition], Sequence[str]]] ], ) ], ), ): def __new__( # pylint: disable=arguments-differ cls, partition_fn: Callable[[Optional[datetime]], Union[Sequence[Partition], Sequence[str]]] ): return super(DynamicPartitionsDefinition, cls).__new__( cls, check.callable_param(partition_fn, "partition_fn") ) def get_partitions(self, current_time: Optional[datetime] = None) -> Sequence[Partition]: partitions = self.partition_fn(current_time) if all(isinstance(partition, Partition) for partition in partitions): return cast(Sequence[Partition], partitions) else: return [Partition(p) for p in partitions] class PartitionSetDefinition(Generic[T]): """ Defines a partition set, representing the set of slices making up an axis of a pipeline Args: name (str): Name for this partition set pipeline_name (str): The name of the pipeline definition partition_fn (Optional[Callable[void, Sequence[Partition]]]): User-provided function to define the set of valid partition objects. solid_selection (Optional[Sequence[str]]): A list of solid subselection (including single solid names) to execute with this partition. e.g. ``['*some_solid+', 'other_solid']`` mode (Optional[str]): The mode to apply when executing this partition. (default: 'default') run_config_fn_for_partition (Callable[[Partition], Any]): A function that takes a :py:class:`~dagster.Partition` and returns the run configuration that parameterizes the execution for this partition. tags_fn_for_partition (Callable[[Partition], Optional[dict[str, str]]]): A function that takes a :py:class:`~dagster.Partition` and returns a list of key value pairs that will be added to the generated run for this partition. partitions_def (Optional[PartitionsDefinition]): A set of parameters used to construct the set of valid partition objects. """ _name: str _pipeline_name: Optional[str] _job_name: Optional[str] _solid_selection: Optional[Sequence[str]] _mode: Optional[str] _user_defined_run_config_fn_for_partition: Callable[[Partition], Mapping[str, Any]] _user_defined_tags_fn_for_partition: Callable[[Partition], Optional[Mapping[str, str]]] _partitions_def: PartitionsDefinition def __init__( self, name: str, pipeline_name: Optional[str] = None, partition_fn: Optional[RawPartitionFunction] = None, solid_selection: Optional[Sequence[str]] = None, mode: Optional[str] = None, run_config_fn_for_partition: Callable[ [Partition[T]], Mapping[str, Any] ] = lambda _partition: {}, tags_fn_for_partition: Callable[ [Partition[T]], Optional[Mapping[str, str]] ] = lambda _partition: {}, partitions_def: Optional[ PartitionsDefinition[T] # pylint: disable=unsubscriptable-object ] = None, job_name: Optional[str] = None, ): check.invariant( not (partition_fn and partitions_def), "Only one of `partition_fn` or `partitions_def` must be supplied.", ) check.invariant( (pipeline_name or job_name) and not (pipeline_name and job_name), "Exactly one one of `job_name` and `pipeline_name` must be supplied.", ) self._name = check_valid_name(name) self._pipeline_name = check.opt_str_param(pipeline_name, "pipeline_name") self._job_name = check.opt_str_param(job_name, "job_name") self._solid_selection = check.opt_nullable_sequence_param( solid_selection, "solid_selection", of_type=str ) self._mode = check.opt_str_param(mode, "mode", DEFAULT_MODE_NAME) # Type ignores workaround for mypy bug "cannot assign to a method" self._user_defined_run_config_fn_for_partition = check.callable_param( # type: ignore run_config_fn_for_partition, "run_config_fn_for_partition" ) self._user_defined_tags_fn_for_partition = check.callable_param( # type: ignore tags_fn_for_partition, "tags_fn_for_partition" ) if partitions_def is not None: self._partitions_def = check.inst_param( partitions_def, "partitions_def", PartitionsDefinition ) elif partition_fn is not None: _wrapped = self._wrap_partition_fn(partition_fn) self._partitions_def = DynamicPartitionsDefinition(partition_fn=_wrapped) else: check.failed( "One of `partition_fn` or `partitions_def` must be supplied.", ) def _wrap_partition_fn(self, partition_fn: RawPartitionFunction) -> PartitionFunction: partition_fn_param_count = len(inspect.signature(partition_fn).parameters) def wrap_partition(x: Union[str, Partition]) -> Partition: if isinstance(x, Partition): return x if isinstance(x, str): return Partition(x) raise DagsterInvalidDefinitionError( "Expected <Partition> | <str>, received {type}".format(type=type(x)) ) def wrapper(current_time: Optional[datetime] = None) -> Sequence[Partition]: if not current_time: current_time = pendulum.now("UTC") check.callable_param(partition_fn, "partition_fn") # type: ignore if partition_fn_param_count == 1: obj_list = cast( Callable[..., Sequence[Union[Partition[T], str]]], partition_fn, )(current_time) else: obj_list = partition_fn() # type: ignore return [wrap_partition(obj) for obj in obj_list] return wrapper @property def name(self) -> str: return self._name @property def pipeline_name(self) -> Optional[str]: return self._pipeline_name @property def job_name(self) -> Optional[str]: return self._job_name @property def pipeline_or_job_name(self) -> str: # one is guaranteed to be set return cast(str, self._pipeline_name or self._job_name) @property def solid_selection(self) -> Optional[Sequence[str]]: return self._solid_selection @property def mode(self) -> Optional[str]: return self._mode def run_config_for_partition(self, partition: Partition[T]) -> Mapping[str, Any]: return copy.deepcopy(self._user_defined_run_config_fn_for_partition(partition)) # type: ignore def tags_for_partition(self, partition: Partition[T]) -> Mapping[str, str]: user_tags = validate_tags( self._user_defined_tags_fn_for_partition(partition), allow_reserved_tags=False # type: ignore ) tags = merge_dicts(user_tags, PipelineRun.tags_for_partition_set(self, partition)) return tags def get_partitions(self, current_time: Optional[datetime] = None) -> Sequence[Partition[T]]: """Return the set of known partitions. Arguments: current_time (Optional[datetime]): The evaluation time for the partition function, which is passed through to the ``partition_fn`` (if it accepts a parameter). Defaults to the current time in UTC. """ return self._partitions_def.get_partitions(current_time) def get_partition(self, name: str) -> Partition[T]: for partition in self.get_partitions(): if partition.name == name: return partition raise DagsterUnknownPartitionError(f"Could not find a partition with key `{name}`") def get_partition_names(self, current_time: Optional[datetime] = None) -> Sequence[str]: return [part.name for part in self.get_partitions(current_time)] def create_schedule_definition( self, schedule_name: str, cron_schedule: str, partition_selector: PartitionSelectorFunction, should_execute: Optional[Callable[..., bool]] = None, environment_vars: Optional[Mapping[str, str]] = None, execution_timezone: Optional[str] = None, description: Optional[str] = None, decorated_fn: Optional[PartitionScheduleFunction] = None, job: Optional[ExecutableDefinition] = None, default_status=DefaultScheduleStatus.STOPPED, ) -> "PartitionScheduleDefinition": """Create a ScheduleDefinition from a PartitionSetDefinition. Arguments: schedule_name (str): The name of the schedule. cron_schedule (str): A valid cron string for the schedule partition_selector (Callable[[ScheduleEvaluationContext, PartitionSetDefinition], Union[Partition, Sequence[Partition]]]): Function that determines the partition to use at a given execution time. Can return either a single Partition or a list of Partitions. For time-based partition sets, will likely be either `identity_partition_selector` or a selector returned by `create_offset_partition_selector`. should_execute (Optional[function]): Function that runs at schedule execution time that determines whether a schedule should execute. Defaults to a function that always returns ``True``. environment_vars (Optional[dict]): The environment variables to set for the schedule. execution_timezone (Optional[str]): Timezone in which the schedule should run. Supported strings for timezones are the ones provided by the `IANA time zone database <https://www.iana.org/time-zones>` - e.g. "America/Los_Angeles". description (Optional[str]): A human-readable description of the schedule. default_status (DefaultScheduleStatus): Whether the schedule starts as running or not. The default status can be overridden from Dagit or via the GraphQL API. Returns: PartitionScheduleDefinition: The generated PartitionScheduleDefinition for the partition selector """ check.str_param(schedule_name, "schedule_name") check.str_param(cron_schedule, "cron_schedule") check.opt_callable_param(should_execute, "should_execute") check.opt_mapping_param(environment_vars, "environment_vars", key_type=str, value_type=str) check.callable_param(partition_selector, "partition_selector") check.opt_str_param(execution_timezone, "execution_timezone") check.opt_str_param(description, "description") check.inst_param(default_status, "default_status", DefaultScheduleStatus) def _execution_fn(context): check.inst_param(context, "context", ScheduleEvaluationContext) with user_code_error_boundary( ScheduleExecutionError, lambda: f"Error occurred during the execution of partition_selector for schedule {schedule_name}", ): selector_result = partition_selector(context, self) if isinstance(selector_result, SkipReason): yield selector_result return selected_partitions = ( selector_result if isinstance(selector_result, (frozenlist, list)) else [selector_result] ) check.is_list(selected_partitions, of_type=Partition) if not selected_partitions: yield SkipReason("Partition selector returned an empty list of partitions.") return partition_names = self.get_partition_names(context.scheduled_execution_time) missing_partition_names = [ partition.name for partition in selected_partitions if partition.name not in partition_names ] if missing_partition_names: yield SkipReason( "Partition selector returned partition" + ("s" if len(missing_partition_names) > 1 else "") + f" not in the partition set: {', '.join(missing_partition_names)}." ) return with user_code_error_boundary( ScheduleExecutionError, lambda: f"Error occurred during the execution of should_execute for schedule {schedule_name}", ): if should_execute and not should_execute(context): yield SkipReason( "should_execute function for {schedule_name} returned false.".format( schedule_name=schedule_name ) ) return for selected_partition in selected_partitions: with user_code_error_boundary( ScheduleExecutionError, lambda: f"Error occurred during the execution of run_config_fn for schedule {schedule_name}", ): run_config = self.run_config_for_partition(selected_partition) with user_code_error_boundary( ScheduleExecutionError, lambda: f"Error occurred during the execution of tags_fn for schedule {schedule_name}", ): tags = self.tags_for_partition(selected_partition) yield RunRequest( run_key=selected_partition.name if len(selected_partitions) > 0 else None, run_config=run_config, tags=tags, ) return PartitionScheduleDefinition( name=schedule_name, cron_schedule=cron_schedule, pipeline_name=self._pipeline_name, tags_fn=None, should_execute=None, environment_vars=environment_vars, partition_set=self, execution_timezone=execution_timezone, execution_fn=_execution_fn, description=description, decorated_fn=decorated_fn, job=job, default_status=default_status, )
[docs]class PartitionScheduleDefinition(ScheduleDefinition): __slots__ = ["_partition_set"] def __init__( self, name: str, cron_schedule: str, pipeline_name: Optional[str], tags_fn: Optional[ScheduleTagsFunction], should_execute: Optional[ScheduleShouldExecuteFunction], partition_set: PartitionSetDefinition, environment_vars: Optional[Mapping[str, str]] = None, run_config_fn: Optional[ScheduleRunConfigFunction] = None, execution_timezone: Optional[str] = None, execution_fn: Optional[ScheduleExecutionFunction] = None, description: Optional[str] = None, decorated_fn: Optional[PartitionScheduleFunction] = None, job: Optional[ExecutableDefinition] = None, default_status: DefaultScheduleStatus = DefaultScheduleStatus.STOPPED, ): super(PartitionScheduleDefinition, self).__init__( name=check_valid_name(name), cron_schedule=cron_schedule, job_name=pipeline_name, run_config_fn=run_config_fn, tags_fn=tags_fn, should_execute=should_execute, environment_vars=environment_vars, execution_timezone=execution_timezone, execution_fn=execution_fn, description=description, job=job, default_status=default_status, ) self._partition_set = check.inst_param( partition_set, "partition_set", PartitionSetDefinition ) self._decorated_fn = check.opt_callable_param(decorated_fn, "decorated_fn") def __call__(self, *args, **kwargs) -> Mapping[str, Any]: if not self._decorated_fn: raise DagsterInvalidInvocationError( "Only partition schedules created using one of the partition schedule decorators " "can be directly invoked." ) if len(args) == 0 and len(kwargs) == 0: raise DagsterInvalidInvocationError( "Schedule decorated function has date argument, but no date argument was " "provided when invoking." ) if len(args) + len(kwargs) > 1: raise DagsterInvalidInvocationError( "Schedule invocation received multiple arguments. Only a first " "positional date parameter should be provided when invoking." ) date_param_name = get_function_params(self._decorated_fn)[0].name if args: date = check.opt_inst_param(args[0], date_param_name, datetime) else: if date_param_name not in kwargs: raise DagsterInvalidInvocationError( f"Schedule invocation expected argument '{date_param_name}'." ) date = check.opt_inst_param(kwargs[date_param_name], date_param_name, datetime) return self._decorated_fn(date) def get_partition_set(self) -> PartitionSetDefinition: return self._partition_set
[docs]class PartitionedConfig(Generic[T]): """Defines a way of configuring a job where the job can be run on one of a discrete set of partitions, and each partition corresponds to run configuration for the job. Setting PartitionedConfig as the config for a job allows you to launch backfills for that job and view the run history across partitions. """ def __init__( self, partitions_def: PartitionsDefinition[T], # pylint: disable=unsubscriptable-object run_config_for_partition_fn: Callable[[Partition[T]], Mapping[str, Any]], decorated_fn: Optional[Callable[..., Mapping[str, Any]]] = None, tags_for_partition_fn: Optional[Callable[[Partition[T]], Mapping[str, str]]] = None, ): self._partitions = check.inst_param(partitions_def, "partitions_def", PartitionsDefinition) self._run_config_for_partition_fn = check.callable_param( run_config_for_partition_fn, "run_config_for_partition_fn" ) self._decorated_fn = decorated_fn self._tags_for_partition_fn = check.opt_callable_param( tags_for_partition_fn, "tags_for_partition_fn" ) @public # type: ignore @property def partitions_def(self) -> PartitionsDefinition[T]: # pylint: disable=unsubscriptable-object return self._partitions @public # type: ignore @property def run_config_for_partition_fn(self) -> Callable[[Partition[T]], Mapping[str, Any]]: return self._run_config_for_partition_fn @public # type: ignore @property def tags_for_partition_fn(self) -> Optional[Callable[[Partition[T]], Mapping[str, str]]]: return self._tags_for_partition_fn def get_partition_keys(self, current_time: Optional[datetime] = None) -> Sequence[str]: return [partition.name for partition in self.partitions_def.get_partitions(current_time)] def get_run_config_for_partition_key(self, partition_key: str) -> Mapping[str, Any]: """Generates the run config corresponding to a partition key. Args: partition_key (str): the key for a partition that should be used to generate a run config. """ partitions = self.partitions_def.get_partitions() partition = [p for p in partitions if p.name == partition_key] if len(partition) == 0: raise DagsterInvalidInvocationError(f"No partition for partition key {partition_key}.") return self.run_config_for_partition_fn(partition[0]) @classmethod def from_flexible_config( cls, config: Optional[Union[ConfigMapping, Mapping[str, object], "PartitionedConfig"]], partitions_def: PartitionsDefinition, ) -> "PartitionedConfig": check.invariant( not isinstance(config, ConfigMapping), "Can't supply a ConfigMapping for 'config' when 'partitions_def' is supplied.", ) if isinstance(config, PartitionedConfig): check.invariant( config.partitions_def == partitions_def, "Can't supply a PartitionedConfig for 'config' with a different " "PartitionsDefinition than supplied for 'partitions_def'.", ) return config else: hardcoded_config = config if config else {} return cls(partitions_def, lambda _: cast(Mapping, hardcoded_config)) def __call__(self, *args, **kwargs): if self._decorated_fn is None: raise DagsterInvalidInvocationError( "Only PartitionedConfig objects created using one of the partitioned config " "decorators can be directly invoked." ) else: return self._decorated_fn(*args, **kwargs)
[docs]def static_partitioned_config( partition_keys: Sequence[str], tags_for_partition_fn: Optional[Callable[[str], Mapping[str, str]]] = None, ) -> Callable[[Callable[[str], Mapping[str, Any]]], PartitionedConfig]: """Creates a static partitioned config for a job. The provided partition_keys returns a static list of strings identifying the set of partitions, given an optional datetime argument (representing the current time). The list of partitions is static, so while the run config returned by the decorated function may change over time, the list of valid partition keys does not. This has performance advantages over `dynamic_partitioned_config` in terms of loading different partition views in Dagit. The decorated function takes in a partition key and returns a valid run config for a particular target job. Args: partition_keys (Sequence[str]): A list of valid partition keys, which serve as the range of values that can be provided to the decorated run config function. Returns: PartitionedConfig """ check.sequence_param(partition_keys, "partition_keys", str) def inner(fn: Callable[[str], Mapping[str, Any]]) -> PartitionedConfig: check.callable_param(fn, "fn") def _run_config_wrapper(partition: Partition) -> Mapping[str, Any]: return fn(partition.name) def _tag_wrapper(partition: Partition) -> Mapping[str, str]: return tags_for_partition_fn(partition.name) if tags_for_partition_fn else {} return PartitionedConfig( partitions_def=StaticPartitionsDefinition(partition_keys), run_config_for_partition_fn=_run_config_wrapper, decorated_fn=fn, tags_for_partition_fn=_tag_wrapper, ) return inner
[docs]def dynamic_partitioned_config( partition_fn: Callable[[Optional[datetime]], Sequence[str]], tags_for_partition_fn: Optional[Callable[[str], Mapping[str, str]]] = None, ) -> Callable[[Callable[[str], Mapping[str, Any]]], PartitionedConfig]: """Creates a dynamic partitioned config for a job. The provided partition_fn returns a list of strings identifying the set of partitions, given an optional datetime argument (representing the current time). The list of partitions returned may change over time. The decorated function takes in a partition key and returns a valid run config for a particular target job. Args: partition_fn (Callable[[datetime.datetime], Sequence[str]]): A function that generates a list of valid partition keys, which serve as the range of values that can be provided to the decorated run config function. Returns: PartitionedConfig """ check.callable_param(partition_fn, "partition_fn") def inner(fn: Callable[[str], Mapping[str, Any]]) -> PartitionedConfig: def _run_config_wrapper(partition: Partition) -> Mapping[str, Any]: return fn(partition.name) def _tag_wrapper(partition: Partition) -> Mapping[str, str]: return tags_for_partition_fn(partition.name) if tags_for_partition_fn else {} return PartitionedConfig( partitions_def=DynamicPartitionsDefinition(partition_fn), run_config_for_partition_fn=_run_config_wrapper, decorated_fn=fn, tags_for_partition_fn=_tag_wrapper, ) return inner
def cron_schedule_from_schedule_type_and_offsets( schedule_type: ScheduleType, minute_offset: int, hour_offset: int, day_offset: Optional[int], ): if schedule_type is ScheduleType.HOURLY: return f"{minute_offset} * * * *" elif schedule_type is ScheduleType.DAILY: return f"{minute_offset} {hour_offset} * * *" elif schedule_type is ScheduleType.WEEKLY: return f"{minute_offset} {hour_offset} * * {day_offset if day_offset != None else 0}" elif schedule_type is ScheduleType.MONTHLY: return f"{minute_offset} {hour_offset} {day_offset if day_offset != None else 1} * *" else: check.assert_never(schedule_type) class PartitionsSubset(ABC): """Represents a subset of the partitions within a PartitionsDefinition""" @abstractmethod def get_partition_keys_not_in_subset( self, current_time: Optional[datetime] = None ) -> Iterable[str]: raise NotImplementedError() @abstractmethod def with_partition_keys(self, partition_keys: Iterable[str]) -> "PartitionsSubset": raise NotImplementedError() @abstractmethod def serialize(self) -> str: raise NotImplementedError() class DefaultPartitionsSubset(PartitionsSubset): def __init__(self, partitions_def: PartitionsDefinition, subset=None): self._partitions_def = partitions_def self._subset = subset or set() def get_partition_keys_not_in_subset( self, current_time: Optional[datetime] = None ) -> Iterable[str]: return set(self._partitions_def.get_partition_keys()) - self._subset def with_partition_keys(self, partition_keys: Iterable[str]) -> "DefaultPartitionsSubset": return DefaultPartitionsSubset(self._partitions_def, self._subset | set(partition_keys)) def serialize(self) -> str: return json.dumps(self._subset) @staticmethod def from_serialized( partitions_def: PartitionsDefinition, serialized: str ) -> "DefaultPartitionsSubset": return DefaultPartitionsSubset(subset=json.loads(serialized), partitions_def=partitions_def)