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"""This module contains Google Cloud Storage sensors."""
from __future__ import annotations
import os
import textwrap
import warnings
from datetime import datetime, timedelta
from typing import TYPE_CHECKING, Any, Callable, Sequence
from google.api_core.retry import Retry
from google.cloud.storage.retry import DEFAULT_RETRY
from airflow.configuration import conf
from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning
from airflow.providers.google.cloud.hooks.gcs import GCSHook
from airflow.providers.google.cloud.triggers.gcs import (
GCSBlobTrigger,
GCSCheckBlobUpdateTimeTrigger,
GCSPrefixBlobTrigger,
GCSUploadSessionTrigger,
)
from airflow.sensors.base import BaseSensorOperator, poke_mode_only
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class GCSObjectExistenceSensor(BaseSensorOperator):
"""
Checks for the existence of a file in Google Cloud Storage.
:param bucket: The Google Cloud Storage bucket where the object is.
:param object: The name of the object to check in the Google cloud
storage bucket.
:param google_cloud_conn_id: The connection ID to use when
connecting to Google Cloud Storage.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
:param retry: (Optional) How to retry the RPC
"""
[docs] template_fields: Sequence[str] = (
"bucket",
"object",
"impersonation_chain",
)
def __init__(
self,
*,
bucket: str,
object: str,
google_cloud_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
retry: Retry = DEFAULT_RETRY,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
) -> None:
super().__init__(**kwargs)
self.bucket = bucket
self.object = object
self.google_cloud_conn_id = google_cloud_conn_id
self.impersonation_chain = impersonation_chain
self.retry = retry
self.deferrable = deferrable
[docs] def poke(self, context: Context) -> bool:
self.log.info("Sensor checks existence of : %s, %s", self.bucket, self.object)
hook = GCSHook(
gcp_conn_id=self.google_cloud_conn_id,
impersonation_chain=self.impersonation_chain,
)
return hook.exists(self.bucket, self.object, self.retry)
[docs] def execute(self, context: Context) -> None:
"""Airflow runs this method on the worker and defers using the trigger."""
if not self.deferrable:
super().execute(context)
else:
if not self.poke(context=context):
self.defer(
timeout=timedelta(seconds=self.timeout),
trigger=GCSBlobTrigger(
bucket=self.bucket,
object_name=self.object,
poke_interval=self.poke_interval,
google_cloud_conn_id=self.google_cloud_conn_id,
hook_params={
"impersonation_chain": self.impersonation_chain,
},
),
method_name="execute_complete",
)
[docs] def execute_complete(self, context: Context, event: dict[str, str]) -> str:
"""
Callback for when the trigger fires - returns immediately.
Relies on trigger to throw an exception, otherwise it assumes execution was successful.
"""
if event["status"] == "error":
raise AirflowException(event["message"])
self.log.info("File %s was found in bucket %s.", self.object, self.bucket)
return event["message"]
[docs]class GCSObjectExistenceAsyncSensor(GCSObjectExistenceSensor):
"""
Checks for the existence of a file in Google Cloud Storage.
This class is deprecated and will be removed in a future release.
Please use :class:`airflow.providers.google.cloud.sensors.gcs.GCSObjectExistenceSensor`
and set *deferrable* attribute to *True* instead.
:param bucket: The Google Cloud Storage bucket where the object is.
:param object: The name of the object to check in the Google cloud storage bucket.
:param google_cloud_conn_id: The connection ID to use when connecting to Google Cloud Storage.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
def __init__(self, **kwargs: Any) -> None:
warnings.warn(
"Class `GCSObjectExistenceAsyncSensor` is deprecated and will be removed in a future release. "
"Please use `GCSObjectExistenceSensor` and set `deferrable` attribute to `True` instead",
AirflowProviderDeprecationWarning,
)
super().__init__(deferrable=True, **kwargs)
[docs]def ts_function(context):
"""
Default callback for the GoogleCloudStorageObjectUpdatedSensor.
The default behaviour is check for the object being updated after the data interval's end,
or execution_date + interval on Airflow versions prior to 2.2 (before AIP-39 implementation).
"""
try:
return context["data_interval_end"]
except KeyError:
return context["dag"].following_schedule(context["execution_date"])
[docs]class GCSObjectUpdateSensor(BaseSensorOperator):
"""
Checks if an object is updated in Google Cloud Storage.
:param bucket: The Google Cloud Storage bucket where the object is.
:param object: The name of the object to download in the Google cloud
storage bucket.
:param ts_func: Callback for defining the update condition. The default callback
returns execution_date + schedule_interval. The callback takes the context
as parameter.
:param google_cloud_conn_id: The connection ID to use when
connecting to Google Cloud Storage.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
:param deferrable: Run sensor in deferrable mode
"""
[docs] template_fields: Sequence[str] = (
"bucket",
"object",
"impersonation_chain",
)
def __init__(
self,
bucket: str,
object: str,
ts_func: Callable = ts_function,
google_cloud_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
) -> None:
super().__init__(**kwargs)
self.bucket = bucket
self.object = object
self.ts_func = ts_func
self.google_cloud_conn_id = google_cloud_conn_id
self.impersonation_chain = impersonation_chain
self.deferrable = deferrable
[docs] def poke(self, context: Context) -> bool:
self.log.info("Sensor checks existence of : %s, %s", self.bucket, self.object)
hook = GCSHook(
gcp_conn_id=self.google_cloud_conn_id,
impersonation_chain=self.impersonation_chain,
)
return hook.is_updated_after(self.bucket, self.object, self.ts_func(context))
[docs] def execute(self, context: Context) -> None:
"""Airflow runs this method on the worker and defers using the trigger."""
if self.deferrable is False:
super().execute(context)
else:
if not self.poke(context=context):
self.defer(
timeout=timedelta(seconds=self.timeout),
trigger=GCSCheckBlobUpdateTimeTrigger(
bucket=self.bucket,
object_name=self.object,
target_date=self.ts_func(context),
poke_interval=self.poke_interval,
google_cloud_conn_id=self.google_cloud_conn_id,
hook_params={
"impersonation_chain": self.impersonation_chain,
},
),
method_name="execute_complete",
)
[docs] def execute_complete(self, context: dict[str, Any], event: dict[str, str] | None = None) -> str:
"""Callback for when the trigger fires."""
if event:
if event["status"] == "success":
self.log.info(
"Checking last updated time for object %s in bucket : %s", self.object, self.bucket
)
return event["message"]
raise AirflowException(event["message"])
raise AirflowException("No event received in trigger callback")
[docs]class GCSObjectsWithPrefixExistenceSensor(BaseSensorOperator):
"""
Checks for the existence of GCS objects at a given prefix, passing matches via XCom.
When files matching the given prefix are found, the poke method's criteria will be
fulfilled and the matching objects will be returned from the operator and passed
through XCom for downstream tasks.
:param bucket: The Google Cloud Storage bucket where the object is.
:param prefix: The name of the prefix to check in the Google cloud
storage bucket.
:param google_cloud_conn_id: The connection ID to use when
connecting to Google Cloud Storage.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
:param deferrable: Run sensor in deferrable mode
"""
[docs] template_fields: Sequence[str] = (
"bucket",
"prefix",
"impersonation_chain",
)
def __init__(
self,
bucket: str,
prefix: str,
google_cloud_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
) -> None:
super().__init__(**kwargs)
self.bucket = bucket
self.prefix = prefix
self.google_cloud_conn_id = google_cloud_conn_id
self._matches: list[str] = []
self.impersonation_chain = impersonation_chain
self.deferrable = deferrable
[docs] def poke(self, context: Context) -> bool:
self.log.info("Checking for existence of object: %s, %s", self.bucket, self.prefix)
hook = GCSHook(
gcp_conn_id=self.google_cloud_conn_id,
impersonation_chain=self.impersonation_chain,
)
self._matches = hook.list(self.bucket, prefix=self.prefix)
return bool(self._matches)
[docs] def execute(self, context: Context):
"""Overridden to allow matches to be passed."""
self.log.info("Checking for existence of object: %s, %s", self.bucket, self.prefix)
if not self.deferrable:
super().execute(context)
return self._matches
else:
if not self.poke(context=context):
self.defer(
timeout=timedelta(seconds=self.timeout),
trigger=GCSPrefixBlobTrigger(
bucket=self.bucket,
prefix=self.prefix,
poke_interval=self.poke_interval,
google_cloud_conn_id=self.google_cloud_conn_id,
hook_params={
"impersonation_chain": self.impersonation_chain,
},
),
method_name="execute_complete",
)
[docs] def execute_complete(self, context: dict[str, Any], event: dict[str, str | list[str]]) -> str | list[str]:
"""Callback for the trigger; returns immediately and relies on trigger to throw a success event."""
self.log.info("Resuming from trigger and checking status")
if event["status"] == "success":
return event["matches"]
raise AirflowException(event["message"])
[docs]def get_time():
"""This is just a wrapper of datetime.datetime.now to simplify mocking in the unittests."""
return datetime.now()
@poke_mode_only
[docs]class GCSUploadSessionCompleteSensor(BaseSensorOperator):
"""
Return True if the inactivity period has passed with no increase in the number of objects in the bucket.
Checks for changes in the number of objects at prefix in Google Cloud Storage
bucket and returns True if the inactivity period has passed with no
increase in the number of objects. Note, this sensor will not behave correctly
in reschedule mode, as the state of the listed objects in the GCS bucket will
be lost between rescheduled invocations.
:param bucket: The Google Cloud Storage bucket where the objects are.
expected.
:param prefix: The name of the prefix to check in the Google cloud
storage bucket.
:param inactivity_period: The total seconds of inactivity to designate
an upload session is over. Note, this mechanism is not real time and
this operator may not return until a poke_interval after this period
has passed with no additional objects sensed.
:param min_objects: The minimum number of objects needed for upload session
to be considered valid.
:param previous_objects: The set of object ids found during the last poke.
:param allow_delete: Should this sensor consider objects being deleted
between pokes valid behavior. If true a warning message will be logged
when this happens. If false an error will be raised.
:param google_cloud_conn_id: The connection ID to use when connecting
to Google Cloud Storage.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
:param deferrable: Run sensor in deferrable mode
"""
[docs] template_fields: Sequence[str] = (
"bucket",
"prefix",
"impersonation_chain",
)
def __init__(
self,
bucket: str,
prefix: str,
inactivity_period: float = 60 * 60,
min_objects: int = 1,
previous_objects: set[str] | None = None,
allow_delete: bool = True,
google_cloud_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
) -> None:
super().__init__(**kwargs)
self.bucket = bucket
self.prefix = prefix
if inactivity_period < 0:
raise ValueError("inactivity_period must be non-negative")
self.inactivity_period = inactivity_period
self.min_objects = min_objects
self.previous_objects = previous_objects if previous_objects else set()
self.inactivity_seconds = 0
self.allow_delete = allow_delete
self.google_cloud_conn_id = google_cloud_conn_id
self.last_activity_time = None
self.impersonation_chain = impersonation_chain
self.hook: GCSHook | None = None
self.deferrable = deferrable
def _get_gcs_hook(self) -> GCSHook | None:
if not self.hook:
self.hook = GCSHook(
gcp_conn_id=self.google_cloud_conn_id,
impersonation_chain=self.impersonation_chain,
)
return self.hook
[docs] def is_bucket_updated(self, current_objects: set[str]) -> bool:
"""
Check whether new objects have been added and the inactivity_period has passed, and update the state.
:param current_objects: set of object ids in bucket during last poke.
"""
current_num_objects = len(current_objects)
if current_objects > self.previous_objects:
# When new objects arrived, reset the inactivity_seconds
# and update previous_objects for the next poke.
self.log.info(
"New objects found at %s resetting last_activity_time.",
os.path.join(self.bucket, self.prefix),
)
self.log.debug("New objects: %s", "\n".join(current_objects - self.previous_objects))
self.last_activity_time = get_time()
self.inactivity_seconds = 0
self.previous_objects = current_objects
return False
if self.previous_objects - current_objects:
# During the last poke interval objects were deleted.
if self.allow_delete:
self.previous_objects = current_objects
self.last_activity_time = get_time()
self.log.warning(
textwrap.dedent(
"""\
Objects were deleted during the last
poke interval. Updating the file counter and
resetting last_activity_time.
%s\
"""
),
self.previous_objects - current_objects,
)
return False
raise AirflowException(
"Illegal behavior: objects were deleted in "
f"{os.path.join(self.bucket, self.prefix)} between pokes."
)
if self.last_activity_time:
self.inactivity_seconds = (get_time() - self.last_activity_time).total_seconds()
else:
# Handles the first poke where last inactivity time is None.
self.last_activity_time = get_time()
self.inactivity_seconds = 0
if self.inactivity_seconds >= self.inactivity_period:
path = os.path.join(self.bucket, self.prefix)
if current_num_objects >= self.min_objects:
self.log.info(
textwrap.dedent(
"""\
SUCCESS:
Sensor found %s objects at %s.
Waited at least %s seconds, with no new objects dropped.
"""
),
current_num_objects,
path,
self.inactivity_period,
)
return True
self.log.error("FAILURE: Inactivity Period passed, not enough objects found in %s", path)
return False
return False
[docs] def poke(self, context: Context) -> bool:
return self.is_bucket_updated(
set(self._get_gcs_hook().list(self.bucket, prefix=self.prefix)) # type: ignore[union-attr]
)
[docs] def execute(self, context: Context) -> None:
"""Airflow runs this method on the worker and defers using the trigger."""
hook_params = {"impersonation_chain": self.impersonation_chain}
if not self.deferrable:
return super().execute(context)
if not self.poke(context=context):
self.defer(
timeout=timedelta(seconds=self.timeout),
trigger=GCSUploadSessionTrigger(
bucket=self.bucket,
prefix=self.prefix,
poke_interval=self.poke_interval,
google_cloud_conn_id=self.google_cloud_conn_id,
inactivity_period=self.inactivity_period,
min_objects=self.min_objects,
previous_objects=self.previous_objects,
allow_delete=self.allow_delete,
hook_params=hook_params,
),
method_name="execute_complete",
)
[docs] def execute_complete(self, context: dict[str, Any], event: dict[str, str] | None = None) -> str:
"""
Callback for when the trigger fires - returns immediately.
Relies on trigger to throw an exception, otherwise it assumes execution was successful.
"""
if event:
if event["status"] == "success":
return event["message"]
raise AirflowException(event["message"])
raise AirflowException("No event received in trigger callback")
