import copy
import logging
from contextlib import ExitStack
from datetime import datetime
from enum import Enum
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterator,
List,
Mapping,
NamedTuple,
Optional,
Sequence,
Set,
TypeVar,
Union,
cast,
)
import pendulum
from typing_extensions import TypeAlias
import dagster._check as check
from dagster._annotations import public
from dagster._core.definitions.instigation_logger import InstigationLogger
from dagster._core.definitions.resource_annotation import get_resource_args
from dagster._core.definitions.scoped_resources_builder import Resources
from dagster._serdes import whitelist_for_serdes
from dagster._utils import ensure_gen
from dagster._utils.merger import merge_dicts
from dagster._utils.schedules import is_valid_cron_schedule
from ..decorator_utils import has_at_least_one_parameter
from ..errors import (
DagsterInvalidDefinitionError,
DagsterInvalidInvocationError,
DagsterInvariantViolationError,
ScheduleExecutionError,
user_code_error_boundary,
)
from ..instance import DagsterInstance
from ..instance.ref import InstanceRef
from ..storage.pipeline_run import DagsterRun
from .graph_definition import GraphDefinition
from .mode import DEFAULT_MODE_NAME
from .pipeline_definition import PipelineDefinition
from .run_request import RunRequest, SkipReason
from .target import DirectTarget, ExecutableDefinition, RepoRelativeTarget
from .unresolved_asset_job_definition import UnresolvedAssetJobDefinition
from .utils import check_valid_name, validate_tags
if TYPE_CHECKING:
from dagster import ResourceDefinition
T = TypeVar("T")
RunConfig: TypeAlias = Mapping[str, Any]
RunRequestIterator: TypeAlias = Iterator[Union[RunRequest, SkipReason]]
ScheduleEvaluationFunctionReturn: TypeAlias = Union[
RunRequest, SkipReason, RunConfig, RunRequestIterator, Sequence[RunRequest]
]
RawScheduleEvaluationFunction: TypeAlias = Callable[..., ScheduleEvaluationFunctionReturn]
ScheduleRunConfigFunction: TypeAlias = Union[
Callable[["ScheduleEvaluationContext"], RunConfig],
Callable[[], RunConfig],
]
ScheduleTagsFunction: TypeAlias = Callable[["ScheduleEvaluationContext"], Mapping[str, str]]
ScheduleShouldExecuteFunction: TypeAlias = Callable[["ScheduleEvaluationContext"], bool]
ScheduleExecutionFunction: TypeAlias = Union[
Callable[["ScheduleEvaluationContext"], Any],
"DecoratedScheduleFunction",
]
@whitelist_for_serdes
class DefaultScheduleStatus(Enum):
RUNNING = "RUNNING"
STOPPED = "STOPPED"
def get_or_create_schedule_context(
fn: Callable, *args: Any, **kwargs: Any
) -> "ScheduleEvaluationContext":
"""Based on the passed resource function and the arguments passed to it, returns the
user-passed ScheduleEvaluationContext or creates one if it is not passed.
Raises an exception if the user passes more than one argument or if the user-provided
function requires a context parameter but none is passed.
"""
from dagster._core.definitions.sensor_definition import get_context_param_name
context_param_name = get_context_param_name(fn)
if len(args) + len(kwargs) > 1:
raise DagsterInvalidInvocationError(
"Schedule invocation received multiple arguments. Only a first "
"positional context parameter should be provided."
)
context: Optional[ScheduleEvaluationContext] = None
if len(args) > 0:
context = check.opt_inst(args[0], ScheduleEvaluationContext)
elif len(kwargs) > 0:
if context_param_name and context_param_name not in kwargs:
raise DagsterInvalidInvocationError(
f"Schedule invocation expected argument '{context_param_name}'."
)
context_param_name = context_param_name or list(kwargs.keys())[0]
context = check.opt_inst(kwargs.get(context_param_name), ScheduleEvaluationContext)
elif context_param_name:
# If the context parameter is present but no value was provided, we error
raise DagsterInvalidInvocationError(
"Schedule evaluation function expected context argument, but no context argument "
"was provided when invoking."
)
return context or build_schedule_context()
[docs]class ScheduleEvaluationContext:
"""The context object available as the first argument various functions defined on a :py:class:`dagster.ScheduleDefinition`.
A `ScheduleEvaluationContext` object is passed as the first argument to ``run_config_fn``, ``tags_fn``,
and ``should_execute``.
Users should not instantiate this object directly. To construct a `ScheduleEvaluationContext` for testing purposes, use :py:func:`dagster.build_schedule_context`.
Attributes:
instance_ref (Optional[InstanceRef]): The serialized instance configured to run the schedule
scheduled_execution_time (datetime):
The time in which the execution was scheduled to happen. May differ slightly
from both the actual execution time and the time at which the run config is computed.
Not available in all schedulers - currently only set in deployments using
DagsterDaemonScheduler.
Example:
.. code-block:: python
from dagster import schedule, ScheduleEvaluationContext
@schedule
def the_schedule(context: ScheduleEvaluationContext):
...
"""
__slots__ = [
"_instance_ref",
"_scheduled_execution_time",
"_exit_stack",
"_instance",
"_log_key",
"_logger",
"_repository_name",
"_schedule_name",
"_resources_cm",
"_resources",
"_resources_contain_cm",
"_cm_scope_entered",
]
def __init__(
self,
instance_ref: Optional[InstanceRef],
scheduled_execution_time: Optional[datetime],
repository_name: Optional[str] = None,
schedule_name: Optional[str] = None,
resources: Optional[Mapping[str, "ResourceDefinition"]] = None,
):
from dagster._core.definitions.scoped_resources_builder import (
IContainsGenerator,
)
from dagster._core.execution.build_resources import build_resources
self._exit_stack = ExitStack()
self._instance = None
self._instance_ref = check.opt_inst_param(instance_ref, "instance_ref", InstanceRef)
self._scheduled_execution_time = check.opt_inst_param(
scheduled_execution_time, "scheduled_execution_time", datetime
)
self._log_key = (
[
repository_name,
schedule_name,
scheduled_execution_time.strftime("%Y%m%d_%H%M%S"),
]
if repository_name and schedule_name and scheduled_execution_time
else None
)
self._logger = None
self._repository_name = repository_name
self._schedule_name = schedule_name
self._resources_cm = build_resources(resources or {})
self._resources = self._resources_cm.__enter__()
self._resources_contain_cm = isinstance(self._resources, IContainsGenerator)
self._cm_scope_entered = False
def __enter__(self) -> "ScheduleEvaluationContext":
self._cm_scope_entered = True
return self
def __exit__(self, *exc) -> None:
self._resources_cm.__exit__(*exc) # pylint: disable=no-member
self._exit_stack.close()
self._logger = None
def __del__(self) -> None:
if self._resources_contain_cm and not self._cm_scope_entered:
self._resources_cm.__exit__(None, None, None) # pylint: disable=no-member
@property
def resources(self) -> Resources:
if self._resources_contain_cm and not self._cm_scope_entered:
raise DagsterInvariantViolationError(
"At least one provided resource is a generator, but attempting to access "
"resources outside of context manager scope. You can use the following syntax to "
"open a context manager: `with build_sensor_context(...) as context:`"
)
return self._resources
@public
@property
def instance(self) -> "DagsterInstance":
# self._instance_ref should only ever be None when this ScheduleEvaluationContext was
# constructed under test.
if not self._instance_ref:
raise DagsterInvariantViolationError(
"Attempted to initialize dagster instance, but no instance reference was provided."
)
if not self._instance:
self._instance = self._exit_stack.enter_context(
DagsterInstance.from_ref(self._instance_ref)
)
return cast(DagsterInstance, self._instance)
@property
def instance_ref(self) -> Optional[InstanceRef]:
return self._instance_ref
@public
@property
def scheduled_execution_time(self) -> datetime:
if self._scheduled_execution_time is None:
check.failed(
"Attempting to access scheduled_execution_time, but no scheduled_execution_time was"
" set on this context"
)
return self._scheduled_execution_time
@property
def log(self) -> logging.Logger:
if self._logger:
return self._logger
if not self._instance_ref:
self._logger = self._exit_stack.enter_context(
InstigationLogger(
self._log_key,
repository_name=self._repository_name,
name=self._schedule_name,
)
)
self._logger = self._exit_stack.enter_context(
InstigationLogger(
self._log_key,
self.instance,
repository_name=self._repository_name,
name=self._schedule_name,
)
)
return cast(InstigationLogger, self._logger)
def has_captured_logs(self):
return self._logger and self._logger.has_captured_logs()
@property
def log_key(self) -> Optional[List[str]]:
return self._log_key
class DecoratedScheduleFunction(NamedTuple):
"""Wrapper around the decorated schedule function. Keeps track of both to better support the
optimal return value for direct invocation of the evaluation function.
"""
decorated_fn: RawScheduleEvaluationFunction
wrapped_fn: Callable[[ScheduleEvaluationContext], RunRequestIterator]
has_context_arg: bool
[docs]def build_schedule_context(
instance: Optional[DagsterInstance] = None,
scheduled_execution_time: Optional[datetime] = None,
resources: Optional[Mapping[str, "ResourceDefinition"]] = None,
) -> ScheduleEvaluationContext:
"""Builds schedule execution context using the provided parameters.
The instance provided to ``build_schedule_context`` must be persistent;
DagsterInstance.ephemeral() will result in an error.
Args:
instance (Optional[DagsterInstance]): The dagster instance configured to run the schedule.
scheduled_execution_time (datetime): The time in which the execution was scheduled to
happen. May differ slightly from both the actual execution time and the time at which
the run config is computed.
Examples:
.. code-block:: python
context = build_schedule_context(instance)
daily_schedule.evaluate_tick(context)
"""
check.opt_inst_param(instance, "instance", DagsterInstance)
return ScheduleEvaluationContext(
instance_ref=instance.get_ref() if instance and instance.is_persistent else None,
scheduled_execution_time=check.opt_inst_param(
scheduled_execution_time, "scheduled_execution_time", datetime
),
resources=resources,
)
@whitelist_for_serdes
class ScheduleExecutionData(
NamedTuple(
"_ScheduleExecutionData",
[
("run_requests", Optional[Sequence[RunRequest]]),
("skip_message", Optional[str]),
("captured_log_key", Optional[Sequence[str]]),
],
)
):
def __new__(
cls,
run_requests: Optional[Sequence[RunRequest]] = None,
skip_message: Optional[str] = None,
captured_log_key: Optional[Sequence[str]] = None,
):
check.opt_sequence_param(run_requests, "run_requests", RunRequest)
check.opt_str_param(skip_message, "skip_message")
check.opt_list_param(captured_log_key, "captured_log_key", str)
check.invariant(
not (run_requests and skip_message), "Found both skip data and run request data"
)
return super(ScheduleExecutionData, cls).__new__(
cls,
run_requests=run_requests,
skip_message=skip_message,
captured_log_key=captured_log_key,
)
def validate_and_get_schedule_resource_dict(
resources: Resources, schedule_name: str, required_resource_keys: Set[str]
) -> Dict[str, Any]:
"""Validates that the context has all the required resources and returns a dictionary of
resource key to resource object.
"""
for k in required_resource_keys:
if not hasattr(resources, k):
raise DagsterInvalidDefinitionError(
f"Resource with key '{k}' required by schedule '{schedule_name}' was not provided."
)
return {k: getattr(resources, k) for k in required_resource_keys}
[docs]class ScheduleDefinition:
"""Define a schedule that targets a job.
Args:
name (Optional[str]): The name of the schedule to create. Defaults to the job name plus
"_schedule".
cron_schedule (Union[str, Sequence[str]]): A valid cron string or sequence of cron strings
specifying when the schedule will run, e.g., ``'45 23 * * 6'`` for a schedule that runs
at 11:45 PM every Saturday. If a sequence is provided, then the schedule will run for
the union of all execution times for the provided cron strings, e.g.,
``['45 23 * * 6', '30 9 * * 0]`` for a schedule that runs at 11:45 PM every Saturday and
9:30 AM every Sunday.
execution_fn (Callable[ScheduleEvaluationContext]): The core evaluation function for the
schedule, which is run at an interval to determine whether a run should be launched or
not. Takes a :py:class:`~dagster.ScheduleEvaluationContext`.
This function must return a generator, which must yield either a single SkipReason
or one or more RunRequest objects.
run_config (Optional[Mapping]): The config that parameterizes this execution,
as a dict.
run_config_fn (Optional[Callable[[ScheduleEvaluationContext], [Mapping]]]): A function that
takes a ScheduleEvaluationContext object and returns the run configuration that
parameterizes this execution, as a dict. You may set only one of ``run_config``,
``run_config_fn``, and ``execution_fn``.
tags (Optional[Mapping[str, str]]): A dictionary of tags (string key-value pairs) to attach
to the scheduled runs.
tags_fn (Optional[Callable[[ScheduleEvaluationContext], Optional[Mapping[str, str]]]]): A
function that generates tags to attach to the schedules runs. Takes a
:py:class:`~dagster.ScheduleEvaluationContext` and returns a dictionary of tags (string
key-value pairs). You may set only one of ``tags``, ``tags_fn``, and ``execution_fn``.
should_execute (Optional[Callable[[ScheduleEvaluationContext], bool]]): A function that runs
at schedule execution time to determine whether a schedule should execute or skip. Takes
a :py:class:`~dagster.ScheduleEvaluationContext` and returns a boolean (``True`` if the
schedule should execute). Defaults to a function that always returns ``True``.
environment_vars (Optional[dict[str, str]]): 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.
job (Optional[Union[GraphDefinition, JobDefinition]]): The job that should execute when this
schedule runs.
default_status (DefaultScheduleStatus): Whether the schedule starts as running or not. The default
status can be overridden from Dagit or via the GraphQL API.
required_resource_keys (Optional[Set[str]]): The set of resource keys required by the schedule.
"""
def with_updated_job(self, new_job: ExecutableDefinition) -> "ScheduleDefinition":
"""Returns a copy of this schedule with the job replaced.
Args:
job (ExecutableDefinition): The job that should execute when this
schedule runs.
"""
return ScheduleDefinition(
name=self.name,
cron_schedule=self._cron_schedule,
job_name=self.job_name,
environment_vars=self.environment_vars,
execution_timezone=self.execution_timezone,
execution_fn=self._execution_fn,
description=self.description,
job=new_job,
default_status=self.default_status,
)
def __init__(
self,
name: Optional[str] = None,
*,
cron_schedule: Optional[Union[str, Sequence[str]]] = None,
job_name: Optional[str] = None,
run_config: Optional[Any] = None,
run_config_fn: Optional[ScheduleRunConfigFunction] = None,
tags: Optional[Mapping[str, str]] = None,
tags_fn: Optional[ScheduleTagsFunction] = None,
should_execute: Optional[ScheduleShouldExecuteFunction] = None,
environment_vars: Optional[Mapping[str, str]] = None,
execution_timezone: Optional[str] = None,
execution_fn: Optional[ScheduleExecutionFunction] = None,
description: Optional[str] = None,
job: Optional[ExecutableDefinition] = None,
default_status: DefaultScheduleStatus = DefaultScheduleStatus.STOPPED,
required_resource_keys: Optional[Set[str]] = None,
):
self._cron_schedule = check.inst_param(cron_schedule, "cron_schedule", (str, Sequence))
if not isinstance(self._cron_schedule, str):
check.sequence_param(self._cron_schedule, "cron_schedule", of_type=str) # type: ignore
if not is_valid_cron_schedule(self._cron_schedule): # type: ignore
raise DagsterInvalidDefinitionError(
f"Found invalid cron schedule '{self._cron_schedule}' for schedule '{name}''. "
"Dagster recognizes standard cron expressions consisting of 5 fields."
)
if job is not None:
self._target: Union[DirectTarget, RepoRelativeTarget] = DirectTarget(job)
else:
self._target = RepoRelativeTarget(
pipeline_name=check.str_param(job_name, "job_name"),
mode=DEFAULT_MODE_NAME,
solid_selection=None,
)
if name:
self._name = check_valid_name(name)
elif job_name:
self._name = job_name + "_schedule"
elif job:
self._name = job.name + "_schedule"
self._description = check.opt_str_param(description, "description")
self._environment_vars = check.opt_mapping_param(
environment_vars, "environment_vars", key_type=str, value_type=str
)
self._execution_timezone = check.opt_str_param(execution_timezone, "execution_timezone")
if execution_fn and (run_config_fn or tags_fn or should_execute or tags or run_config):
raise DagsterInvalidDefinitionError(
"Attempted to provide both execution_fn and individual run_config/tags arguments "
"to ScheduleDefinition. Must provide only one of the two."
)
elif execution_fn:
self._execution_fn: Optional[
Union[Callable[..., Any], DecoratedScheduleFunction]
] = None
if isinstance(execution_fn, DecoratedScheduleFunction):
self._execution_fn = execution_fn
else:
self._execution_fn = check.opt_callable_param(execution_fn, "execution_fn")
self._run_config_fn = None
else:
if run_config_fn and run_config:
raise DagsterInvalidDefinitionError(
"Attempted to provide both run_config_fn and run_config as arguments"
" to ScheduleDefinition. Must provide only one of the two."
)
def _default_run_config_fn(context: ScheduleEvaluationContext) -> RunConfig:
return check.opt_dict_param(run_config, "run_config")
self._run_config_fn = check.opt_callable_param(
run_config_fn, "run_config_fn", default=_default_run_config_fn
)
if tags_fn and tags:
raise DagsterInvalidDefinitionError(
"Attempted to provide both tags_fn and tags as arguments"
" to ScheduleDefinition. Must provide only one of the two."
)
elif tags:
tags = validate_tags(tags, allow_reserved_tags=False)
tags_fn = lambda _context: tags
else:
tags_fn = check.opt_callable_param(
tags_fn, "tags_fn", default=lambda _context: cast(Mapping[str, str], {})
)
self._tags_fn = tags_fn
self._tags = tags
_should_execute: ScheduleShouldExecuteFunction = check.opt_callable_param(
should_execute, "should_execute", default=lambda _context: True
)
# Several type-ignores are present in this function to work around bugs in mypy
# inference.
def _execution_fn(context: ScheduleEvaluationContext) -> RunRequestIterator:
with user_code_error_boundary(
ScheduleExecutionError,
lambda: f"Error occurred during the execution of should_execute for schedule {name}",
):
if not _should_execute(context):
yield SkipReason(
"should_execute function for {schedule_name} returned false.".format(
schedule_name=name
)
)
return
with user_code_error_boundary(
ScheduleExecutionError,
lambda: f"Error occurred during the execution of run_config_fn for schedule {name}",
):
_run_config_fn = check.not_none(self._run_config_fn)
evaluated_run_config = copy.deepcopy(
_run_config_fn(context)
if has_at_least_one_parameter(_run_config_fn)
else _run_config_fn() # type: ignore # (strict type guard)
)
with user_code_error_boundary(
ScheduleExecutionError,
lambda: f"Error occurred during the execution of tags_fn for schedule {name}",
):
evaluated_tags = validate_tags(tags_fn(context), allow_reserved_tags=False)
yield RunRequest(
run_key=None,
run_config=evaluated_run_config,
tags=evaluated_tags,
)
self._execution_fn = _execution_fn
if self._execution_timezone:
try:
# Verify that the timezone can be loaded
pendulum.tz.timezone(self._execution_timezone) # type: ignore
except Exception as e:
raise DagsterInvalidDefinitionError(
f"Invalid execution timezone {self._execution_timezone} for {name}"
) from e
self._default_status = check.inst_param(
default_status, "default_status", DefaultScheduleStatus
)
resource_arg_names: Set[str] = (
{arg.name for arg in get_resource_args(self._execution_fn.decorated_fn)}
if isinstance(self._execution_fn, DecoratedScheduleFunction)
else set()
)
check.param_invariant(
len(required_resource_keys or []) == 0 or len(resource_arg_names) == 0,
(
"Cannot specify resource requirements in both @sensor decorator and as arguments to"
" the decorated function"
),
)
self._required_resource_keys = (
check.opt_set_param(required_resource_keys, "required_resource_keys", of_type=str)
or resource_arg_names
)
def __call__(self, *args, **kwargs) -> ScheduleEvaluationFunctionReturn:
from dagster._core.definitions.sensor_definition import get_context_param_name
from .decorators.schedule_decorator import DecoratedScheduleFunction
if not isinstance(self._execution_fn, DecoratedScheduleFunction):
raise DagsterInvalidInvocationError(
"Schedule invocation is only supported for schedules created via the schedule "
"decorators."
)
context_param_name = get_context_param_name(self._execution_fn.decorated_fn)
context = get_or_create_schedule_context(self._execution_fn.decorated_fn, *args, **kwargs)
context_param = {context_param_name: context} if context_param_name else {}
resources = validate_and_get_schedule_resource_dict(
context.resources, self._name, self._required_resource_keys
)
result = self._execution_fn.decorated_fn(**context_param, **resources)
if isinstance(result, dict):
return copy.deepcopy(result)
else:
return result
@public
@property
def name(self) -> str:
return self._name
@public
@property
def job_name(self) -> str:
return self._target.pipeline_name
@public
@property
def description(self) -> Optional[str]:
return self._description
@public
@property
def cron_schedule(self) -> Union[str, Sequence[str]]:
return self._cron_schedule # type: ignore
@public
@property
def environment_vars(self) -> Mapping[str, str]:
return self._environment_vars
@public
@property
def required_resource_keys(self) -> Set[str]:
return self._required_resource_keys
@public
@property
def execution_timezone(self) -> Optional[str]:
return self._execution_timezone
@public
@property
def job(self) -> Union[GraphDefinition, PipelineDefinition, UnresolvedAssetJobDefinition]:
if isinstance(self._target, DirectTarget):
return self._target.target
raise DagsterInvalidDefinitionError("No job was provided to ScheduleDefinition.")
def evaluate_tick(self, context: "ScheduleEvaluationContext") -> ScheduleExecutionData:
"""Evaluate schedule using the provided context.
Args:
context (ScheduleEvaluationContext): The context with which to evaluate this schedule.
Returns:
ScheduleExecutionData: Contains list of run requests, or skip message if present.
"""
check.inst_param(context, "context", ScheduleEvaluationContext)
execution_fn: Callable[..., "ScheduleEvaluationFunctionReturn"]
if isinstance(self._execution_fn, DecoratedScheduleFunction):
execution_fn = self._execution_fn.wrapped_fn
else:
execution_fn = cast(
Callable[..., "ScheduleEvaluationFunctionReturn"],
self._execution_fn,
)
result = list(ensure_gen(execution_fn(context)))
skip_message: Optional[str] = None
run_requests: List[RunRequest] = []
if not result or result == [None]:
run_requests = []
skip_message = "Schedule function returned an empty result"
elif len(result) == 1:
item = check.inst(result[0], (SkipReason, RunRequest))
if isinstance(item, RunRequest):
run_requests = [item]
skip_message = None
elif isinstance(item, SkipReason):
run_requests = []
skip_message = item.skip_message
else:
# NOTE: mypy is not correctly reading this cast-- not sure why
# (pyright reads it fine). Hence the type-ignores below.
result = cast(List[RunRequest], check.is_list(result, of_type=RunRequest))
check.invariant(
not any(not request.run_key for request in result),
(
"Schedules that return multiple RunRequests must specify a run_key in each"
" RunRequest"
),
)
run_requests = result
skip_message = None
# clone all the run requests with the required schedule tags
run_requests_with_schedule_tags = [
request.with_replaced_attrs(
tags=merge_dicts(request.tags, DagsterRun.tags_for_schedule(self))
)
for request in run_requests
]
return ScheduleExecutionData(
run_requests=run_requests_with_schedule_tags,
skip_message=skip_message,
captured_log_key=context.log_key if context.has_captured_logs() else None,
)
def has_loadable_target(self):
return isinstance(self._target, DirectTarget)
@property
def targets_unresolved_asset_job(self) -> bool:
return self.has_loadable_target() and isinstance(
self.load_target(), UnresolvedAssetJobDefinition
)
def load_target(
self,
) -> Union[GraphDefinition, PipelineDefinition, UnresolvedAssetJobDefinition]:
if isinstance(self._target, DirectTarget):
return self._target.load()
check.failed("Target is not loadable")
@public
@property
def default_status(self) -> DefaultScheduleStatus:
return self._default_status