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from __future__ import annotations

from typing import TYPE_CHECKING, Sequence

from airflow.exceptions import AirflowException, AirflowSkipException
from import DataprocMetastoreHook
from import parse_json_from_gcs
from airflow.sensors.base import BaseSensorOperator

    from google.api_core.operation import Operation

    from airflow.utils.context import Context

[docs]class MetastoreHivePartitionSensor(BaseSensorOperator): """ Waits for partitions to show up in Hive. This sensor uses Google Cloud SDK and passes requests via gRPC. :param service_id: Required. Dataproc Metastore service id. :param region: Required. The ID of the Google Cloud region that the service belongs to. :param table: Required. Name of the partitioned table :param partitions: List of table partitions to wait for. A name of a partition should look like "ds=1", or "a=1/b=2" in case of nested partitions. Note that you cannot use logical or comparison operators as in HivePartitionSensor. If not specified then the sensor will wait for at least one partition regardless its name. :param gcp_conn_id: Airflow Google Cloud connection ID. :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. """
[docs] template_fields: Sequence[str] = ( "service_id", "region", "table", "partitions", "impersonation_chain", )
def __init__( self, service_id: str, region: str, table: str, partitions: list[str] | None, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, *args, **kwargs, ): super().__init__(*args, **kwargs) self.service_id = service_id self.region = region self.table = table self.partitions = partitions or [] self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def poke(self, context: Context) -> bool: hook = DataprocMetastoreHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain ) operation: Operation = hook.list_hive_partitions( region=self.region, service_id=self.service_id, table=self.table, partition_names=self.partitions ) metadata = hook.wait_for_operation(timeout=self.timeout, operation=operation) result_manifest_uri: str = metadata.result_manifest_uri"Received result manifest URI: %s", result_manifest_uri)"Extracting result manifest") manifest: dict = parse_json_from_gcs(gcp_conn_id=self.gcp_conn_id, file_uri=result_manifest_uri) if not (manifest and isinstance(manifest, dict)): # TODO: remove this if check when min_airflow_version is set to higher than 2.7.1 message = ( f"Failed to extract result manifest. " f"Expected not empty dict, but this was received: {manifest}" ) if self.soft_fail: raise AirflowSkipException(message) raise AirflowException(message) if manifest.get("status", {}).get("code") != 0: # TODO: remove this if check when min_airflow_version is set to higher than 2.7.1 message = f"Request failed: {manifest.get('message')}" if self.soft_fail: raise AirflowSkipException(message) raise AirflowException(message) # Extract actual query results result_base_uri = result_manifest_uri.rsplit("/", 1)[0] results = (f"{result_base_uri}//{filename}" for filename in manifest.get("filenames", [])) found_partitions = sum( len(parse_json_from_gcs(gcp_conn_id=self.gcp_conn_id, file_uri=uri).get("rows", [])) for uri in results ) # Return True if we got all requested partitions. # If no partitions were given in the request, then we expect to find at least one. return found_partitions >= max(1, len(set(self.partitions)))

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