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Source code for dagster._core.definitions.freshness_policy

import datetime
from typing import AbstractSet, NamedTuple, Optional

import dagster._check as check
from dagster._annotations import deprecated
from dagster._core.definitions.events import AssetKey
from dagster._core.errors import DagsterInvalidDefinitionError
from dagster._serdes import whitelist_for_serdes
from dagster._time import get_timezone
from dagster._utils.schedules import is_valid_cron_schedule, reverse_cron_string_iterator


class FreshnessConstraint(NamedTuple):
    asset_keys: AbstractSet[AssetKey]
    required_data_time: datetime.datetime
    required_by_time: datetime.datetime


class FreshnessMinutes(NamedTuple):
    overdue_minutes: float
    lag_minutes: float


[docs] @deprecated( breaking_version="1.9", additional_warn_text="For monitoring freshness, use freshness checks instead. If using lazy " "auto-materialize, use AutomationCondition.cron() and AutomationCondition.any_downstream_conditions().", ) @whitelist_for_serdes class FreshnessPolicy( NamedTuple( "_FreshnessPolicy", [ ("maximum_lag_minutes", float), ("cron_schedule", Optional[str]), ("cron_schedule_timezone", Optional[str]), ], ) ): """A FreshnessPolicy specifies how up-to-date you want a given asset to be. Attaching a FreshnessPolicy to an asset definition encodes an expectation on the upstream data that you expect to be incorporated into the current state of that asset at certain points in time. How this is calculated differs depending on if the asset is unpartitioned or time-partitioned (other partitioning schemes are not supported). For time-partitioned assets, the current data time for the asset is simple to calculate. The upstream data that is incorporated into the asset is exactly the set of materialized partitions for that asset. Thus, the current data time for the asset is simply the time up to which all partitions have been materialized. For unpartitioned assets, the current data time is based on the upstream materialization records that were read to generate the current state of the asset. More specifically, imagine you have two assets, where A depends on B. If `B` has a FreshnessPolicy defined, this means that at time T, the most recent materialization of `B` should have come after a materialization of `A` which was no more than `maximum_lag_minutes` ago. This calculation is recursive: any given asset is expected to incorporate up-to-date data from all of its upstream assets. It is assumed that all asset definitions with no upstream asset definitions consume from some always-updating source. That is, if you materialize that asset at time T, it will incorporate all data up to time T. If `cron_schedule` is not defined, the given asset will be expected to incorporate upstream data from no more than `maximum_lag_minutes` ago at all points in time. For example, "The events table should always have data from at most 1 hour ago". If `cron_schedule` is defined, the given asset will be expected to incorporate upstream data from no more than `maximum_lag_minutes` ago at each cron schedule tick. For example, "By 9AM, the signups table should contain all of yesterday's data". The freshness status of assets with policies defined will be visible in the UI. If you are using an asset reconciliation sensor, this sensor will kick off runs to help keep your assets up to date with respect to their FreshnessPolicy. Args: maximum_lag_minutes (float): An upper bound for how old the data contained within this asset may be. cron_schedule (Optional[str]): A cron schedule string (e.g. ``"0 1 * * *"``) specifying a series of times by which the `maximum_lag_minutes` constraint must be satisfied. If no cron schedule is provided, then this constraint must be satisfied at all times. cron_schedule_timezone (Optional[str]): Timezone in which the cron schedule should be evaluated. If not specified, defaults to UTC. 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". .. code-block:: python # At any point in time, this asset must incorporate all upstream data from at least 30 minutes ago. @asset(freshness_policy=FreshnessPolicy(maximum_lag_minutes=30)) def fresh_asset(): ... # At any point in time, this asset must incorporate all upstream data from at least 30 minutes ago. @asset(freshness_policy=FreshnessPolicy(maximum_lag_minutes=30)) def cron_up_to_date_asset(): ... """ def __new__( cls, *, maximum_lag_minutes: float, cron_schedule: Optional[str] = None, cron_schedule_timezone: Optional[str] = None, ): if cron_schedule is not None: if not is_valid_cron_schedule(cron_schedule): raise DagsterInvalidDefinitionError(f"Invalid cron schedule '{cron_schedule}'.") check.param_invariant( is_valid_cron_schedule(cron_schedule), "cron_schedule", f"Invalid cron schedule '{cron_schedule}'.", ) if cron_schedule_timezone is not None: check.param_invariant( cron_schedule is not None, "cron_schedule_timezone", "Cannot specify cron_schedule_timezone without a cron_schedule.", ) try: # Verify that the timezone can be loaded get_timezone(cron_schedule_timezone) except Exception as e: raise DagsterInvalidDefinitionError( "Invalid cron schedule timezone '{cron_schedule_timezone}'. " ) from e return super(FreshnessPolicy, cls).__new__( cls, maximum_lag_minutes=float( check.numeric_param(maximum_lag_minutes, "maximum_lag_minutes") ), cron_schedule=check.opt_str_param(cron_schedule, "cron_schedule"), cron_schedule_timezone=check.opt_str_param( cron_schedule_timezone, "cron_schedule_timezone" ), ) @classmethod def _create(cls, *args): """Pickle requires a method with positional arguments to construct instances of a class. Since the constructor for this class has keyword arguments only, we define this method to be used by pickle. """ return cls(maximum_lag_minutes=args[0], cron_schedule=args[1]) def __reduce__(self): return (self._create, (self.maximum_lag_minutes, self.cron_schedule)) @property def maximum_lag_delta(self) -> datetime.timedelta: return datetime.timedelta(minutes=self.maximum_lag_minutes) def get_evaluation_tick( self, evaluation_time: datetime.datetime, ) -> Optional[datetime.datetime]: if self.cron_schedule: # most recent cron schedule tick schedule_ticks = reverse_cron_string_iterator( end_timestamp=evaluation_time.timestamp(), cron_string=self.cron_schedule, execution_timezone=self.cron_schedule_timezone, ) return next(schedule_ticks) else: return evaluation_time def minutes_overdue( self, data_time: Optional[datetime.datetime], evaluation_time: datetime.datetime, ) -> Optional[FreshnessMinutes]: """Returns a number of minutes past the specified freshness policy that this asset currently is. If the asset is missing upstream data, or is not materialized at all, then it is unknown how overdue it is, and this will return None. Args: data_time (Optional[datetime]): The timestamp of the data that was used to create the current version of this asset. evaluation_time (datetime): The time at which we're evaluating the overdueness of this asset. Generally, this is the current time. """ if data_time is None: return None evaluation_tick = self.get_evaluation_tick(evaluation_time) if evaluation_tick is None: return None required_time = evaluation_tick - self.maximum_lag_delta return FreshnessMinutes( lag_minutes=max(0.0, (evaluation_tick - data_time).total_seconds() / 60), overdue_minutes=max(0.0, (required_time - data_time).total_seconds() / 60), )