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"""This module contains Google BigQuery to Google Cloud Storage operator."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Sequence
from google.api_core.exceptions import Conflict
from google.cloud.bigquery import DEFAULT_RETRY, UnknownJob
from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from airflow.providers.google.cloud.hooks.bigquery import BigQueryHook, BigQueryJob
from airflow.providers.google.cloud.links.bigquery import BigQueryTableLink
from airflow.providers.google.cloud.triggers.bigquery import BigQueryInsertJobTrigger
from airflow.utils.helpers import merge_dicts
if TYPE_CHECKING:
from google.api_core.retry import Retry
from airflow.utils.context import Context
[docs]class BigQueryToGCSOperator(BaseOperator):
"""
Transfers a BigQuery table to a Google Cloud Storage bucket.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryToGCSOperator`
.. seealso::
For more details about these parameters:
https://cloud.google.com/bigquery/docs/reference/v2/jobs
:param source_project_dataset_table: The dotted
``(<project>.|<project>:)<dataset>.<table>`` BigQuery table to use as the
source data. If ``<project>`` is not included, project will be the project
defined in the connection json. (templated)
:param destination_cloud_storage_uris: The destination Google Cloud
Storage URI (e.g. gs://some-bucket/some-file.txt). (templated) Follows
convention defined here:
https://cloud.google.com/bigquery/exporting-data-from-bigquery#exportingmultiple
:param project_id: Google Cloud Project where the job is running
:param compression: Type of compression to use.
:param export_format: File format to export.
:param field_delimiter: The delimiter to use when extracting to a CSV.
:param print_header: Whether to print a header for a CSV file extract.
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
:param labels: a dictionary containing labels for the job/query,
passed to BigQuery
:param location: The location used for the operation.
: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 result_retry: How to retry the `result` call that retrieves rows
:param result_timeout: The number of seconds to wait for `result` method before using `result_retry`
:param job_id: The ID of the job. It will be suffixed with hash of job configuration
unless ``force_rerun`` is True.
The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), or
dashes (-). The maximum length is 1,024 characters. If not provided then uuid will
be generated.
:param force_rerun: If True then operator will use hash of uuid as job id suffix
:param reattach_states: Set of BigQuery job's states in case of which we should reattach
to the job. Should be other than final states.
:param deferrable: Run operator in the deferrable mode
"""
[docs] template_fields: Sequence[str] = (
"source_project_dataset_table",
"destination_cloud_storage_uris",
"export_format",
"labels",
"impersonation_chain",
"job_id",
)
[docs] template_ext: Sequence[str] = ()
def __init__(
self,
*,
source_project_dataset_table: str,
destination_cloud_storage_uris: list[str],
project_id: str | None = None,
compression: str = "NONE",
export_format: str = "CSV",
field_delimiter: str = ",",
print_header: bool = True,
gcp_conn_id: str = "google_cloud_default",
labels: dict | None = None,
location: str | None = None,
impersonation_chain: str | Sequence[str] | None = None,
result_retry: Retry = DEFAULT_RETRY,
result_timeout: float | None = None,
job_id: str | None = None,
force_rerun: bool = False,
reattach_states: set[str] | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
**kwargs,
) -> None:
super().__init__(**kwargs)
self.project_id = project_id
self.source_project_dataset_table = source_project_dataset_table
self.destination_cloud_storage_uris = destination_cloud_storage_uris
self.compression = compression
self.export_format = export_format
self.field_delimiter = field_delimiter
self.print_header = print_header
self.gcp_conn_id = gcp_conn_id
self.labels = labels
self.location = location
self.impersonation_chain = impersonation_chain
self.result_retry = result_retry
self.result_timeout = result_timeout
self.job_id = job_id
self.force_rerun = force_rerun
self.reattach_states: set[str] = reattach_states or set()
self.hook: BigQueryHook | None = None
self.deferrable = deferrable
self._job_id: str = ""
@staticmethod
def _handle_job_error(job: BigQueryJob | UnknownJob) -> None:
if job.error_result:
raise AirflowException(f"BigQuery job {job.job_id} failed: {job.error_result}")
def _prepare_configuration(self):
source_project, source_dataset, source_table = self.hook.split_tablename(
table_input=self.source_project_dataset_table,
default_project_id=self.hook.project_id,
var_name="source_project_dataset_table",
)
configuration: dict[str, Any] = {
"extract": {
"sourceTable": {
"projectId": source_project,
"datasetId": source_dataset,
"tableId": source_table,
},
"compression": self.compression,
"destinationUris": self.destination_cloud_storage_uris,
"destinationFormat": self.export_format,
}
}
if self.labels:
configuration["labels"] = self.labels
if self.export_format == "CSV":
# Only set fieldDelimiter and printHeader fields if using CSV.
# Google does not like it if you set these fields for other export
# formats.
configuration["extract"]["fieldDelimiter"] = self.field_delimiter
configuration["extract"]["printHeader"] = self.print_header
return configuration
def _submit_job(
self,
hook: BigQueryHook,
job_id: str,
configuration: dict,
) -> BigQueryJob:
# Submit a new job without waiting for it to complete.
return hook.insert_job(
configuration=configuration,
project_id=self.project_id or hook.project_id,
location=self.location,
job_id=job_id,
timeout=self.result_timeout,
retry=self.result_retry,
nowait=self.deferrable,
)
[docs] def execute(self, context: Context):
self.log.info(
"Executing extract of %s into: %s",
self.source_project_dataset_table,
self.destination_cloud_storage_uris,
)
hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
location=self.location,
impersonation_chain=self.impersonation_chain,
)
self.hook = hook
configuration = self._prepare_configuration()
job_id = hook.generate_job_id(
job_id=self.job_id,
dag_id=self.dag_id,
task_id=self.task_id,
logical_date=context["logical_date"],
configuration=configuration,
force_rerun=self.force_rerun,
)
try:
self.log.info("Executing: %s", configuration)
job: BigQueryJob | UnknownJob = self._submit_job(
hook=hook, job_id=job_id, configuration=configuration
)
except Conflict:
# If the job already exists retrieve it
job = hook.get_job(
project_id=self.project_id,
location=self.location,
job_id=job_id,
)
if job.state in self.reattach_states:
# We are reattaching to a job
job.result(timeout=self.result_timeout, retry=self.result_retry)
self._handle_job_error(job)
else:
# Same job configuration so we need force_rerun
raise AirflowException(
f"Job with id: {job_id} already exists and is in {job.state} state. If you "
f"want to force rerun it consider setting `force_rerun=True`."
f"Or, if you want to reattach in this scenario add {job.state} to `reattach_states`"
)
self._job_id = job.job_id
conf = job.to_api_repr()["configuration"]["extract"]["sourceTable"]
dataset_id, project_id, table_id = conf["datasetId"], conf["projectId"], conf["tableId"]
BigQueryTableLink.persist(
context=context,
task_instance=self,
dataset_id=dataset_id,
project_id=project_id,
table_id=table_id,
)
if self.deferrable:
self.defer(
timeout=self.execution_timeout,
trigger=BigQueryInsertJobTrigger(
conn_id=self.gcp_conn_id,
job_id=self._job_id,
project_id=self.project_id or self.hook.project_id,
impersonation_chain=self.impersonation_chain,
),
method_name="execute_complete",
)
else:
job.result(timeout=self.result_timeout, retry=self.result_retry)
[docs] def execute_complete(self, context: Context, event: dict[str, Any]):
"""
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(
"%s completed with response %s ",
self.task_id,
event["message"],
)
[docs] def get_openlineage_facets_on_complete(self, task_instance):
"""Implementing on_complete as we will include final BQ job id."""
from pathlib import Path
from openlineage.client.facet import (
ExternalQueryRunFacet,
SymlinksDatasetFacet,
SymlinksDatasetFacetIdentifiers,
)
from openlineage.client.run import Dataset
from airflow.providers.google.cloud.hooks.gcs import _parse_gcs_url
from airflow.providers.google.cloud.utils.openlineage import (
get_facets_from_bq_table,
get_identity_column_lineage_facet,
)
from airflow.providers.openlineage.extractors import OperatorLineage
table_object = self.hook.get_client(self.hook.project_id).get_table(self.source_project_dataset_table)
input_dataset = Dataset(
namespace="bigquery",
name=str(table_object.reference),
facets=get_facets_from_bq_table(table_object),
)
output_dataset_facets = {
"schema": input_dataset.facets["schema"],
"columnLineage": get_identity_column_lineage_facet(
field_names=[field.name for field in table_object.schema], input_datasets=[input_dataset]
),
}
output_datasets = []
for uri in sorted(self.destination_cloud_storage_uris):
bucket, blob = _parse_gcs_url(uri)
additional_facets = {}
if "*" in blob:
# If wildcard ("*") is used in gcs path, we want the name of dataset to be directory name,
# but we create a symlink to the full object path with wildcard.
additional_facets = {
"symlink": SymlinksDatasetFacet(
identifiers=[
SymlinksDatasetFacetIdentifiers(
namespace=f"gs://{bucket}", name=blob, type="file"
)
]
),
}
blob = Path(blob).parent.as_posix()
if blob == ".":
# blob path does not have leading slash, but we need root dataset name to be "/"
blob = "/"
dataset = Dataset(
namespace=f"gs://{bucket}",
name=blob,
facets=merge_dicts(output_dataset_facets, additional_facets),
)
output_datasets.append(dataset)
run_facets = {}
if self._job_id:
run_facets = {
"externalQuery": ExternalQueryRunFacet(externalQueryId=self._job_id, source="bigquery"),
}
return OperatorLineage(inputs=[input_dataset], outputs=output_datasets, run_facets=run_facets)
