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"""This module contains Google BigQuery operators."""
from __future__ import annotations
import enum
import json
import warnings
from functools import cached_property
from typing import TYPE_CHECKING, Any, Iterable, Sequence, SupportsAbs
import attr
from google.api_core.exceptions import Conflict
from google.cloud.bigquery import DEFAULT_RETRY, CopyJob, ExtractJob, LoadJob, QueryJob
from google.cloud.bigquery.table import RowIterator
from airflow.configuration import conf
from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning, AirflowSkipException
from airflow.models import BaseOperator, BaseOperatorLink
from airflow.models.xcom import XCom
from airflow.providers.common.sql.operators.sql import (
SQLCheckOperator,
SQLColumnCheckOperator,
SQLIntervalCheckOperator,
SQLTableCheckOperator,
SQLValueCheckOperator,
_parse_boolean,
)
from airflow.providers.google.cloud.hooks.bigquery import BigQueryHook, BigQueryJob
from airflow.providers.google.cloud.hooks.gcs import GCSHook, _parse_gcs_url
from airflow.providers.google.cloud.links.bigquery import BigQueryDatasetLink, BigQueryTableLink
from airflow.providers.google.cloud.operators.cloud_base import GoogleCloudBaseOperator
from airflow.providers.google.cloud.triggers.bigquery import (
BigQueryCheckTrigger,
BigQueryGetDataTrigger,
BigQueryInsertJobTrigger,
BigQueryIntervalCheckTrigger,
BigQueryValueCheckTrigger,
)
from airflow.providers.google.cloud.utils.bigquery import convert_job_id
if TYPE_CHECKING:
from google.api_core.retry import Retry
from google.cloud.bigquery import UnknownJob
from airflow.models.taskinstancekey import TaskInstanceKey
from airflow.utils.context import Context
[docs]BIGQUERY_JOB_DETAILS_LINK_FMT = "https://console.cloud.google.com/bigquery?j={job_id}"
[docs]class BigQueryUIColors(enum.Enum):
"""Hex colors for BigQuery operators."""
[docs]class IfExistAction(enum.Enum):
"""Action to take if the resource exist."""
[docs]class BigQueryConsoleLink(BaseOperatorLink):
"""Helper class for constructing BigQuery link."""
[docs] name = "BigQuery Console"
[docs] def get_link(
self,
operator: BaseOperator,
*,
ti_key: TaskInstanceKey,
):
job_id_path = XCom.get_value(key="job_id_path", ti_key=ti_key)
return BIGQUERY_JOB_DETAILS_LINK_FMT.format(job_id=job_id_path) if job_id_path else ""
@attr.s(auto_attribs=True)
[docs]class BigQueryConsoleIndexableLink(BaseOperatorLink):
"""Helper class for constructing BigQuery link."""
@property
[docs] def name(self) -> str:
return f"BigQuery Console #{self.index + 1}"
[docs] def get_link(
self,
operator: BaseOperator,
*,
ti_key: TaskInstanceKey,
):
job_ids = XCom.get_value(key="job_id_path", ti_key=ti_key)
if not job_ids:
return None
if len(job_ids) < self.index:
return None
job_id = job_ids[self.index]
return BIGQUERY_JOB_DETAILS_LINK_FMT.format(job_id=job_id)
class _BigQueryDbHookMixin:
def get_db_hook(self: BigQueryCheckOperator) -> BigQueryHook: # type:ignore[misc]
"""Get BigQuery DB Hook."""
return BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
use_legacy_sql=self.use_legacy_sql,
location=self.location,
impersonation_chain=self.impersonation_chain,
labels=self.labels,
)
class _BigQueryOpenLineageMixin:
def get_openlineage_facets_on_complete(self, task_instance):
"""
Retrieve OpenLineage data for a COMPLETE BigQuery job.
This method retrieves statistics for the specified job_ids using the BigQueryDatasetsProvider.
It calls BigQuery API, retrieving input and output dataset info from it, as well as run-level
usage statistics.
Run facets should contain:
- ExternalQueryRunFacet
- BigQueryJobRunFacet
Job facets should contain:
- SqlJobFacet if operator has self.sql
Input datasets should contain facets:
- DataSourceDatasetFacet
- SchemaDatasetFacet
Output datasets should contain facets:
- DataSourceDatasetFacet
- SchemaDatasetFacet
- OutputStatisticsOutputDatasetFacet
"""
from openlineage.client.facet import SqlJobFacet
from openlineage.common.provider.bigquery import BigQueryDatasetsProvider
from airflow.providers.openlineage.extractors import OperatorLineage
from airflow.providers.openlineage.utils.utils import normalize_sql
if not self.job_id:
return OperatorLineage()
client = self.hook.get_client(project_id=self.hook.project_id)
job_ids = self.job_id
if isinstance(self.job_id, str):
job_ids = [self.job_id]
inputs, outputs, run_facets = {}, {}, {}
for job_id in job_ids:
stats = BigQueryDatasetsProvider(client=client).get_facets(job_id=job_id)
for input in stats.inputs:
input = input.to_openlineage_dataset()
inputs[input.name] = input
if stats.output:
output = stats.output.to_openlineage_dataset()
outputs[output.name] = output
for key, value in stats.run_facets.items():
run_facets[key] = value
job_facets = {}
if hasattr(self, "sql"):
job_facets["sql"] = SqlJobFacet(query=normalize_sql(self.sql))
return OperatorLineage(
inputs=list(inputs.values()),
outputs=list(outputs.values()),
run_facets=run_facets,
job_facets=job_facets,
)
[docs]class BigQueryCheckOperator(_BigQueryDbHookMixin, SQLCheckOperator):
"""Performs checks against BigQuery.
This operator expects a SQL query that returns a single row. Each value on
that row is evaluated using a Python ``bool`` cast. If any of the values
is falsy, the check errors out.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryCheckOperator`
Note that Python bool casting evals the following as *False*:
* ``False``
* ``0``
* Empty string (``""``)
* Empty list (``[]``)
* Empty dictionary or set (``{}``)
Given a query like ``SELECT COUNT(*) FROM foo``, it will fail only if
the count equals to zero. You can craft much more complex query that could,
for instance, check that the table has the same number of rows as the source
table upstream, or that the count of today's partition is greater than
yesterday's partition, or that a set of metrics are less than three standard
deviation for the 7-day average.
This operator can be used as a data quality check in your pipeline.
Depending on where you put it in your DAG, you have the choice to stop the
critical path, preventing from publishing dubious data, or on the side and
receive email alerts without stopping the progress of the DAG.
:param sql: SQL to execute.
:param gcp_conn_id: Connection ID for Google Cloud.
:param use_legacy_sql: Whether to use legacy SQL (true) or standard SQL (false).
:param location: The geographic location of the job. See details at:
https://cloud.google.com/bigquery/docs/locations#specifying_your_location
: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 labels: a dictionary containing labels for the table, passed to BigQuery.
:param deferrable: Run operator in the deferrable mode.
:param poll_interval: (Deferrable mode only) polling period in seconds to
check for the status of job.
"""
[docs] template_fields: Sequence[str] = (
"sql",
"gcp_conn_id",
"impersonation_chain",
"labels",
)
[docs] template_ext: Sequence[str] = (".sql",)
[docs] ui_color = BigQueryUIColors.CHECK.value
[docs] conn_id_field = "gcp_conn_id"
def __init__(
self,
*,
sql: str,
gcp_conn_id: str = "google_cloud_default",
use_legacy_sql: bool = True,
location: str | None = None,
impersonation_chain: str | Sequence[str] | None = None,
labels: dict | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
poll_interval: float = 4.0,
**kwargs,
) -> None:
super().__init__(sql=sql, **kwargs)
self.gcp_conn_id = gcp_conn_id
self.sql = sql
self.use_legacy_sql = use_legacy_sql
self.location = location
self.impersonation_chain = impersonation_chain
self.labels = labels
self.deferrable = deferrable
self.poll_interval = poll_interval
def _submit_job(
self,
hook: BigQueryHook,
job_id: str,
) -> BigQueryJob:
"""Submit a new job and get the job id for polling the status using Trigger."""
configuration = {"query": {"query": self.sql, "useLegacySql": self.use_legacy_sql}}
return hook.insert_job(
configuration=configuration,
project_id=hook.project_id,
location=self.location,
job_id=job_id,
nowait=True,
)
[docs] def execute(self, context: Context):
if not self.deferrable:
super().execute(context=context)
else:
hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
)
job = self._submit_job(hook, job_id="")
context["ti"].xcom_push(key="job_id", value=job.job_id)
if job.running():
self.defer(
timeout=self.execution_timeout,
trigger=BigQueryCheckTrigger(
conn_id=self.gcp_conn_id,
job_id=job.job_id,
project_id=hook.project_id,
poll_interval=self.poll_interval,
),
method_name="execute_complete",
)
self.log.info("Current state of job %s is %s", job.job_id, job.state)
[docs] def execute_complete(self, context: Context, event: dict[str, Any]) -> None:
"""Callback for when the trigger fires.
This returns immediately. It relies on trigger to throw an exception,
otherwise it assumes execution was successful.
"""
if event["status"] == "error":
raise AirflowException(event["message"])
records = event["records"]
if not records:
raise AirflowException("The query returned empty results")
elif not all(records):
self._raise_exception( # type: ignore[attr-defined]
f"Test failed.\nQuery:\n{self.sql}\nResults:\n{records!s}"
)
self.log.info("Record: %s", event["records"])
self.log.info("Success.")
[docs]class BigQueryValueCheckOperator(_BigQueryDbHookMixin, SQLValueCheckOperator):
"""Perform a simple value check using sql code.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryValueCheckOperator`
:param sql: SQL to execute.
:param use_legacy_sql: Whether to use legacy SQL (true)
or standard SQL (false).
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
:param location: The geographic location of the job. See details at:
https://cloud.google.com/bigquery/docs/locations#specifying_your_location
: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 labels: a dictionary containing labels for the table, passed to BigQuery.
:param deferrable: Run operator in the deferrable mode.
:param poll_interval: (Deferrable mode only) polling period in seconds to
check for the status of job.
"""
[docs] template_fields: Sequence[str] = (
"sql",
"gcp_conn_id",
"pass_value",
"impersonation_chain",
"labels",
)
[docs] template_ext: Sequence[str] = (".sql",)
[docs] ui_color = BigQueryUIColors.CHECK.value
[docs] conn_id_field = "gcp_conn_id"
def __init__(
self,
*,
sql: str,
pass_value: Any,
tolerance: Any = None,
gcp_conn_id: str = "google_cloud_default",
use_legacy_sql: bool = True,
location: str | None = None,
impersonation_chain: str | Sequence[str] | None = None,
labels: dict | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
poll_interval: float = 4.0,
**kwargs,
) -> None:
super().__init__(sql=sql, pass_value=pass_value, tolerance=tolerance, **kwargs)
self.location = location
self.gcp_conn_id = gcp_conn_id
self.use_legacy_sql = use_legacy_sql
self.impersonation_chain = impersonation_chain
self.labels = labels
self.deferrable = deferrable
self.poll_interval = poll_interval
def _submit_job(
self,
hook: BigQueryHook,
job_id: str,
) -> BigQueryJob:
"""Submit a new job and get the job id for polling the status using Triggerer."""
configuration = {
"query": {
"query": self.sql,
"useLegacySql": self.use_legacy_sql,
},
}
return hook.insert_job(
configuration=configuration,
project_id=hook.project_id,
location=self.location,
job_id=job_id,
nowait=True,
)
[docs] def execute(self, context: Context) -> None: # type: ignore[override]
if not self.deferrable:
super().execute(context=context)
else:
hook = BigQueryHook(gcp_conn_id=self.gcp_conn_id)
job = self._submit_job(hook, job_id="")
context["ti"].xcom_push(key="job_id", value=job.job_id)
if job.running():
self.defer(
timeout=self.execution_timeout,
trigger=BigQueryValueCheckTrigger(
conn_id=self.gcp_conn_id,
job_id=job.job_id,
project_id=hook.project_id,
sql=self.sql,
pass_value=self.pass_value,
tolerance=self.tol,
poll_interval=self.poll_interval,
),
method_name="execute_complete",
)
self._handle_job_error(job)
# job.result() returns a RowIterator. Mypy expects an instance of SupportsNext[Any] for
# the next() call which the RowIterator does not resemble to. Hence, ignore the arg-type error.
records = next(job.result()) # type: ignore[arg-type]
self.check_value(records)
self.log.info("Current state of job %s is %s", job.job_id, job.state)
@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}")
[docs] def execute_complete(self, context: Context, event: dict[str, Any]) -> None:
"""Callback for when the trigger fires.
This returns immediately. It 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]class BigQueryIntervalCheckOperator(_BigQueryDbHookMixin, SQLIntervalCheckOperator):
"""
Check that the values of metrics given as SQL expressions are within a tolerance of the older ones.
This method constructs a query like so ::
SELECT {metrics_threshold_dict_key} FROM {table}
WHERE {date_filter_column}=<date>
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryIntervalCheckOperator`
:param table: the table name
:param days_back: number of days between ds and the ds we want to check
against. Defaults to 7 days
:param metrics_thresholds: a dictionary of ratios indexed by metrics, for
example 'COUNT(*)': 1.5 would require a 50 percent or less difference
between the current day, and the prior days_back.
:param use_legacy_sql: Whether to use legacy SQL (true)
or standard SQL (false).
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
:param location: The geographic location of the job. See details at:
https://cloud.google.com/bigquery/docs/locations#specifying_your_location
: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 labels: a dictionary containing labels for the table, passed to BigQuery
:param deferrable: Run operator in the deferrable mode
:param poll_interval: (Deferrable mode only) polling period in seconds to check for the status of job.
Defaults to 4 seconds.
:param project_id: a string represents the BigQuery projectId
"""
[docs] template_fields: Sequence[str] = (
"table",
"gcp_conn_id",
"sql1",
"sql2",
"impersonation_chain",
"labels",
)
[docs] ui_color = BigQueryUIColors.CHECK.value
[docs] conn_id_field = "gcp_conn_id"
def __init__(
self,
*,
table: str,
metrics_thresholds: dict,
date_filter_column: str = "ds",
days_back: SupportsAbs[int] = -7,
gcp_conn_id: str = "google_cloud_default",
use_legacy_sql: bool = True,
location: str | None = None,
impersonation_chain: str | Sequence[str] | None = None,
labels: dict | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
poll_interval: float = 4.0,
project_id: str | None = None,
**kwargs,
) -> None:
super().__init__(
table=table,
metrics_thresholds=metrics_thresholds,
date_filter_column=date_filter_column,
days_back=days_back,
**kwargs,
)
self.gcp_conn_id = gcp_conn_id
self.use_legacy_sql = use_legacy_sql
self.location = location
self.impersonation_chain = impersonation_chain
self.labels = labels
self.project_id = project_id
self.deferrable = deferrable
self.poll_interval = poll_interval
def _submit_job(
self,
hook: BigQueryHook,
sql: str,
job_id: str,
) -> BigQueryJob:
"""Submit a new job and get the job id for polling the status using Triggerer."""
configuration = {"query": {"query": sql, "useLegacySql": self.use_legacy_sql}}
return hook.insert_job(
configuration=configuration,
project_id=self.project_id or hook.project_id,
location=self.location,
job_id=job_id,
nowait=True,
)
[docs] def execute(self, context: Context):
if not self.deferrable:
super().execute(context)
else:
hook = BigQueryHook(gcp_conn_id=self.gcp_conn_id)
self.log.info("Using ratio formula: %s", self.ratio_formula)
self.log.info("Executing SQL check: %s", self.sql1)
job_1 = self._submit_job(hook, sql=self.sql1, job_id="")
context["ti"].xcom_push(key="job_id", value=job_1.job_id)
self.log.info("Executing SQL check: %s", self.sql2)
job_2 = self._submit_job(hook, sql=self.sql2, job_id="")
self.defer(
timeout=self.execution_timeout,
trigger=BigQueryIntervalCheckTrigger(
conn_id=self.gcp_conn_id,
first_job_id=job_1.job_id,
second_job_id=job_2.job_id,
project_id=hook.project_id,
table=self.table,
metrics_thresholds=self.metrics_thresholds,
date_filter_column=self.date_filter_column,
days_back=self.days_back,
ratio_formula=self.ratio_formula,
ignore_zero=self.ignore_zero,
poll_interval=self.poll_interval,
),
method_name="execute_complete",
)
[docs] def execute_complete(self, context: Context, event: dict[str, Any]) -> None:
"""Callback for when the trigger fires.
This returns immediately. It 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]class BigQueryColumnCheckOperator(_BigQueryDbHookMixin, SQLColumnCheckOperator):
"""
Subclasses the SQLColumnCheckOperator in order to provide a job id for OpenLineage to parse.
See base class docstring for usage.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryColumnCheckOperator`
:param table: the table name
:param column_mapping: a dictionary relating columns to their checks
:param partition_clause: a string SQL statement added to a WHERE clause
to partition data
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
:param use_legacy_sql: Whether to use legacy SQL (true)
or standard SQL (false).
:param location: The geographic location of the job. See details at:
https://cloud.google.com/bigquery/docs/locations#specifying_your_location
: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 labels: a dictionary containing labels for the table, passed to BigQuery
"""
[docs] template_fields: Sequence[str] = tuple(set(SQLColumnCheckOperator.template_fields) | {"gcp_conn_id"})
[docs] conn_id_field = "gcp_conn_id"
def __init__(
self,
*,
table: str,
column_mapping: dict,
partition_clause: str | None = None,
database: str | None = None,
accept_none: bool = True,
gcp_conn_id: str = "google_cloud_default",
use_legacy_sql: bool = True,
location: str | None = None,
impersonation_chain: str | Sequence[str] | None = None,
labels: dict | None = None,
**kwargs,
) -> None:
super().__init__(
table=table,
column_mapping=column_mapping,
partition_clause=partition_clause,
database=database,
accept_none=accept_none,
**kwargs,
)
self.table = table
self.column_mapping = column_mapping
self.partition_clause = partition_clause
self.database = database
self.accept_none = accept_none
self.gcp_conn_id = gcp_conn_id
self.use_legacy_sql = use_legacy_sql
self.location = location
self.impersonation_chain = impersonation_chain
self.labels = labels
def _submit_job(
self,
hook: BigQueryHook,
job_id: str,
) -> BigQueryJob:
"""Submit a new job and get the job id for polling the status using Trigger."""
configuration = {"query": {"query": self.sql, "useLegacySql": self.use_legacy_sql}}
return hook.insert_job(
configuration=configuration,
project_id=hook.project_id,
location=self.location,
job_id=job_id,
nowait=False,
)
[docs] def execute(self, context=None):
"""Perform checks on the given columns."""
hook = self.get_db_hook()
failed_tests = []
job = self._submit_job(hook, job_id="")
context["ti"].xcom_push(key="job_id", value=job.job_id)
records = job.result().to_dataframe()
if records.empty:
raise AirflowException(f"The following query returned zero rows: {self.sql}")
records.columns = records.columns.str.lower()
self.log.info("Record: %s", records)
for row in records.iterrows():
column = row[1].get("col_name")
check = row[1].get("check_type")
result = row[1].get("check_result")
tolerance = self.column_mapping[column][check].get("tolerance")
self.column_mapping[column][check]["result"] = result
self.column_mapping[column][check]["success"] = self._get_match(
self.column_mapping[column][check], result, tolerance
)
failed_tests.extend(
f"Column: {col}\n\tCheck: {check},\n\tCheck Values: {check_values}\n"
for col, checks in self.column_mapping.items()
for check, check_values in checks.items()
if not check_values["success"]
)
if failed_tests:
exception_string = (
f"Test failed.\nResults:\n{records!s}\n"
f"The following tests have failed:"
f"\n{''.join(failed_tests)}"
)
self._raise_exception(exception_string)
self.log.info("All tests have passed")
[docs]class BigQueryTableCheckOperator(_BigQueryDbHookMixin, SQLTableCheckOperator):
"""
Subclasses the SQLTableCheckOperator in order to provide a job id for OpenLineage to parse.
See base class for usage.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryTableCheckOperator`
:param table: the table name
:param checks: a dictionary of check names and boolean SQL statements
:param partition_clause: a string SQL statement added to a WHERE clause
to partition data
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
:param use_legacy_sql: Whether to use legacy SQL (true)
or standard SQL (false).
:param location: The geographic location of the job. See details at:
https://cloud.google.com/bigquery/docs/locations#specifying_your_location
: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 labels: a dictionary containing labels for the table, passed to BigQuery
"""
[docs] template_fields: Sequence[str] = tuple(set(SQLTableCheckOperator.template_fields) | {"gcp_conn_id"})
[docs] conn_id_field = "gcp_conn_id"
def __init__(
self,
*,
table: str,
checks: dict,
partition_clause: str | None = None,
gcp_conn_id: str = "google_cloud_default",
use_legacy_sql: bool = True,
location: str | None = None,
impersonation_chain: str | Sequence[str] | None = None,
labels: dict | None = None,
**kwargs,
) -> None:
super().__init__(table=table, checks=checks, partition_clause=partition_clause, **kwargs)
self.table = table
self.checks = checks
self.partition_clause = partition_clause
self.gcp_conn_id = gcp_conn_id
self.use_legacy_sql = use_legacy_sql
self.location = location
self.impersonation_chain = impersonation_chain
self.labels = labels
def _submit_job(
self,
hook: BigQueryHook,
job_id: str,
) -> BigQueryJob:
"""Submit a new job and get the job id for polling the status using Trigger."""
configuration = {"query": {"query": self.sql, "useLegacySql": self.use_legacy_sql}}
return hook.insert_job(
configuration=configuration,
project_id=hook.project_id,
location=self.location,
job_id=job_id,
nowait=False,
)
[docs] def execute(self, context=None):
"""Execute the given checks on the table."""
hook = self.get_db_hook()
job = self._submit_job(hook, job_id="")
context["ti"].xcom_push(key="job_id", value=job.job_id)
records = job.result().to_dataframe()
if records.empty:
raise AirflowException(f"The following query returned zero rows: {self.sql}")
records.columns = records.columns.str.lower()
self.log.info("Record:\n%s", records)
for row in records.iterrows():
check = row[1].get("check_name")
result = row[1].get("check_result")
self.checks[check]["success"] = _parse_boolean(str(result))
failed_tests = [
f"\tCheck: {check},\n\tCheck Values: {check_values}\n"
for check, check_values in self.checks.items()
if not check_values["success"]
]
if failed_tests:
exception_string = (
f"Test failed.\nQuery:\n{self.sql}\nResults:\n{records!s}\n"
f"The following tests have failed:\n{', '.join(failed_tests)}"
)
self._raise_exception(exception_string)
self.log.info("All tests have passed")
[docs]class BigQueryGetDataOperator(GoogleCloudBaseOperator):
"""
Fetches the data from a BigQuery table (alternatively fetch data for selected columns) and returns data.
Data is returned in either of the following two formats, based on "as_dict" value:
1. False (Default) - A Python list of lists, with the number of nested lists equal to the number of rows
fetched. Each nested list represents a row, where the elements within it correspond to the column values
for that particular row.
**Example Result**: ``[['Tony', 10], ['Mike', 20]``
2. True - A Python list of dictionaries, where each dictionary represents a row. In each dictionary,
the keys are the column names and the values are the corresponding values for those columns.
**Example Result**: ``[{'name': 'Tony', 'age': 10}, {'name': 'Mike', 'age': 20}]``
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryGetDataOperator`
.. note::
If you pass fields to ``selected_fields`` which are in different order than the
order of columns already in
BQ table, the data will still be in the order of BQ table.
For example if the BQ table has 3 columns as
``[A,B,C]`` and you pass 'B,A' in the ``selected_fields``
the data would still be of the form ``'A,B'``.
**Example**::
get_data = BigQueryGetDataOperator(
task_id='get_data_from_bq',
dataset_id='test_dataset',
table_id='Transaction_partitions',
project_id='internal-gcp-project',
max_results=100,
selected_fields='DATE',
gcp_conn_id='airflow-conn-id'
)
:param dataset_id: The dataset ID of the requested table. (templated)
:param table_id: The table ID of the requested table. (templated)
:param table_project_id: (Optional) The project ID of the requested table.
If None, it will be derived from the hook's project ID. (templated)
:param job_project_id: (Optional) Google Cloud Project where the job is running.
If None, it will be derived from the hook's project ID. (templated)
:param project_id: (Deprecated) (Optional) The name of the project where the data
will be returned from. If None, it will be derived from the hook's project ID. (templated)
:param max_results: The maximum number of records (rows) to be fetched
from the table. (templated)
:param selected_fields: List of fields to return (comma-separated). If
unspecified, all fields are returned.
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
: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 deferrable: Run operator in the deferrable mode
:param poll_interval: (Deferrable mode only) polling period in seconds to check for the status of job.
Defaults to 4 seconds.
:param as_dict: if True returns the result as a list of dictionaries, otherwise as list of lists
(default: False).
:param use_legacy_sql: Whether to use legacy SQL (true) or standard SQL (false).
"""
[docs] template_fields: Sequence[str] = (
"dataset_id",
"table_id",
"table_project_id",
"job_project_id",
"project_id",
"max_results",
"selected_fields",
"impersonation_chain",
)
[docs] ui_color = BigQueryUIColors.QUERY.value
def __init__(
self,
*,
dataset_id: str,
table_id: str,
table_project_id: str | None = None,
job_project_id: str | None = None,
project_id: str | None = None,
max_results: int = 100,
selected_fields: str | None = None,
gcp_conn_id: str = "google_cloud_default",
location: str | None = None,
impersonation_chain: str | Sequence[str] | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
poll_interval: float = 4.0,
as_dict: bool = False,
use_legacy_sql: bool = True,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.table_project_id = table_project_id
self.dataset_id = dataset_id
self.table_id = table_id
self.job_project_id = job_project_id
self.max_results = int(max_results)
self.selected_fields = selected_fields
self.gcp_conn_id = gcp_conn_id
self.location = location
self.impersonation_chain = impersonation_chain
self.project_id = project_id
self.deferrable = deferrable
self.poll_interval = poll_interval
self.as_dict = as_dict
self.use_legacy_sql = use_legacy_sql
def _submit_job(
self,
hook: BigQueryHook,
job_id: str,
) -> BigQueryJob:
get_query = self.generate_query(hook=hook)
configuration = {"query": {"query": get_query, "useLegacySql": self.use_legacy_sql}}
"""Submit a new job and get the job id for polling the status using Triggerer."""
return hook.insert_job(
configuration=configuration,
location=self.location,
project_id=self.job_project_id or hook.project_id,
job_id=job_id,
nowait=True,
)
[docs] def generate_query(self, hook: BigQueryHook) -> str:
"""Generate a SELECT query if for the given dataset and table ID."""
query = "select "
if self.selected_fields:
query += self.selected_fields
else:
query += "*"
query += (
f" from `{self.table_project_id or hook.project_id}.{self.dataset_id}"
f".{self.table_id}` limit {self.max_results}"
)
return query
[docs] def execute(self, context: Context):
if self.project_id:
self.log.warning(
"The project_id parameter is deprecated, and will be removed in a future release."
" Please use table_project_id instead.",
)
if not self.table_project_id:
self.table_project_id = self.project_id
else:
self.log.info("Ignoring project_id parameter, as table_project_id is found.")
hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
use_legacy_sql=self.use_legacy_sql,
)
if not self.deferrable:
self.log.info(
"Fetching Data from %s.%s.%s max results: %s",
self.table_project_id or hook.project_id,
self.dataset_id,
self.table_id,
self.max_results,
)
if not self.selected_fields:
schema: dict[str, list] = hook.get_schema(
dataset_id=self.dataset_id,
table_id=self.table_id,
project_id=self.table_project_id or hook.project_id,
)
if "fields" in schema:
self.selected_fields = ",".join([field["name"] for field in schema["fields"]])
rows = hook.list_rows(
dataset_id=self.dataset_id,
table_id=self.table_id,
max_results=self.max_results,
selected_fields=self.selected_fields,
location=self.location,
project_id=self.table_project_id or hook.project_id,
)
if isinstance(rows, RowIterator):
raise TypeError(
"BigQueryHook.list_rows() returns iterator when return_iterator is False (default)"
)
self.log.info("Total extracted rows: %s", len(rows))
if self.as_dict:
table_data = [dict(row) for row in rows]
else:
table_data = [row.values() for row in rows]
return table_data
job = self._submit_job(hook, job_id="")
context["ti"].xcom_push(key="job_id", value=job.job_id)
self.defer(
timeout=self.execution_timeout,
trigger=BigQueryGetDataTrigger(
conn_id=self.gcp_conn_id,
job_id=job.job_id,
dataset_id=self.dataset_id,
table_id=self.table_id,
project_id=self.job_project_id or hook.project_id,
poll_interval=self.poll_interval,
as_dict=self.as_dict,
),
method_name="execute_complete",
)
[docs] def execute_complete(self, context: Context, event: dict[str, Any]) -> Any:
"""Callback for when the trigger fires.
This returns immediately. It relies on trigger to throw an exception,
otherwise it assumes execution was successful.
"""
if event["status"] == "error":
raise AirflowException(event["message"])
self.log.info("Total extracted rows: %s", len(event["records"]))
return event["records"]
[docs]class BigQueryExecuteQueryOperator(GoogleCloudBaseOperator):
"""Executes BigQuery SQL queries in a specific BigQuery database.
This operator is deprecated. Please use
:class:`airflow.providers.google.cloud.operators.bigquery.BigQueryInsertJobOperator`
instead.
This operator does not assert idempotency.
:param sql: the SQL code to be executed as a single string, or
a list of str (sql statements), or a reference to a template file.
Template references are recognized by str ending in '.sql'
:param destination_dataset_table: A dotted
``(<project>.|<project>:)<dataset>.<table>`` that, if set, will store the results
of the query. (templated)
:param write_disposition: Specifies the action that occurs if the destination table
already exists. (default: 'WRITE_EMPTY')
:param create_disposition: Specifies whether the job is allowed to create new tables.
(default: 'CREATE_IF_NEEDED')
:param allow_large_results: Whether to allow large results.
:param flatten_results: If true and query uses legacy SQL dialect, flattens
all nested and repeated fields in the query results. ``allow_large_results``
must be ``true`` if this is set to ``false``. For standard SQL queries, this
flag is ignored and results are never flattened.
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
:param udf_config: The User Defined Function configuration for the query.
See https://cloud.google.com/bigquery/user-defined-functions for details.
:param use_legacy_sql: Whether to use legacy SQL (true) or standard SQL (false).
:param maximum_billing_tier: Positive integer that serves as a multiplier
of the basic price.
Defaults to None, in which case it uses the value set in the project.
:param maximum_bytes_billed: Limits the bytes billed for this job.
Queries that will have bytes billed beyond this limit will fail
(without incurring a charge). If unspecified, this will be
set to your project default.
:param api_resource_configs: a dictionary that contain params
'configuration' applied for Google BigQuery Jobs API:
https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs
for example, {'query': {'useQueryCache': False}}. You could use it
if you need to provide some params that are not supported by BigQueryOperator
like args.
:param schema_update_options: Allows the schema of the destination
table to be updated as a side effect of the load job.
:param query_params: a list of dictionary containing query parameter types and
values, passed to BigQuery. The structure of dictionary should look like
'queryParameters' in Google BigQuery Jobs API:
https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs.
For example, [{ 'name': 'corpus', 'parameterType': { 'type': 'STRING' },
'parameterValue': { 'value': 'romeoandjuliet' } }]. (templated)
:param labels: a dictionary containing labels for the job/query,
passed to BigQuery
:param priority: Specifies a priority for the query.
Possible values include INTERACTIVE and BATCH.
The default value is INTERACTIVE.
:param time_partitioning: configure optional time partitioning fields i.e.
partition by field, type and expiration as per API specifications.
:param cluster_fields: Request that the result of this query be stored sorted
by one or more columns. BigQuery supports clustering for both partitioned and
non-partitioned tables. The order of columns given determines the sort order.
:param location: The geographic location of the job. Required except for
US and EU. See details at
https://cloud.google.com/bigquery/docs/locations#specifying_your_location
:param encryption_configuration: [Optional] Custom encryption configuration (e.g., Cloud KMS keys).
.. code-block:: python
encryption_configuration = {
"kmsKeyName": "projects/testp/locations/us/keyRings/test-kr/cryptoKeys/test-key",
}
: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).
"""
[docs] template_fields: Sequence[str] = (
"sql",
"destination_dataset_table",
"labels",
"query_params",
"impersonation_chain",
)
[docs] template_ext: Sequence[str] = (".sql",)
[docs] template_fields_renderers = {"sql": "sql"}
[docs] ui_color = BigQueryUIColors.QUERY.value
@property
def __init__(
self,
*,
sql: str | Iterable[str],
destination_dataset_table: str | None = None,
write_disposition: str = "WRITE_EMPTY",
allow_large_results: bool = False,
flatten_results: bool | None = None,
gcp_conn_id: str = "google_cloud_default",
udf_config: list | None = None,
use_legacy_sql: bool = True,
maximum_billing_tier: int | None = None,
maximum_bytes_billed: float | None = None,
create_disposition: str = "CREATE_IF_NEEDED",
schema_update_options: list | tuple | set | None = None,
query_params: list | None = None,
labels: dict | None = None,
priority: str = "INTERACTIVE",
time_partitioning: dict | None = None,
api_resource_configs: dict | None = None,
cluster_fields: list[str] | None = None,
location: str | None = None,
encryption_configuration: dict | None = None,
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
warnings.warn(
"This operator is deprecated. Please use `BigQueryInsertJobOperator`.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
self.sql = sql
self.destination_dataset_table = destination_dataset_table
self.write_disposition = write_disposition
self.create_disposition = create_disposition
self.allow_large_results = allow_large_results
self.flatten_results = flatten_results
self.gcp_conn_id = gcp_conn_id
self.udf_config = udf_config
self.use_legacy_sql = use_legacy_sql
self.maximum_billing_tier = maximum_billing_tier
self.maximum_bytes_billed = maximum_bytes_billed
self.schema_update_options = schema_update_options
self.query_params = query_params
self.labels = labels
self.priority = priority
self.time_partitioning = time_partitioning
self.api_resource_configs = api_resource_configs
self.cluster_fields = cluster_fields
self.location = location
self.encryption_configuration = encryption_configuration
self.hook: BigQueryHook | None = None
self.impersonation_chain = impersonation_chain
self.job_id: str | list[str] | None = None
[docs] def execute(self, context: Context):
if self.hook is None:
self.log.info("Executing: %s", self.sql)
self.hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
use_legacy_sql=self.use_legacy_sql,
location=self.location,
impersonation_chain=self.impersonation_chain,
)
if isinstance(self.sql, str):
self.job_id = self.hook.run_query(
sql=self.sql,
destination_dataset_table=self.destination_dataset_table,
write_disposition=self.write_disposition,
allow_large_results=self.allow_large_results,
flatten_results=self.flatten_results,
udf_config=self.udf_config,
maximum_billing_tier=self.maximum_billing_tier,
maximum_bytes_billed=self.maximum_bytes_billed,
create_disposition=self.create_disposition,
query_params=self.query_params,
labels=self.labels,
schema_update_options=self.schema_update_options,
priority=self.priority,
time_partitioning=self.time_partitioning,
api_resource_configs=self.api_resource_configs,
cluster_fields=self.cluster_fields,
encryption_configuration=self.encryption_configuration,
)
elif isinstance(self.sql, Iterable):
self.job_id = [
self.hook.run_query(
sql=s,
destination_dataset_table=self.destination_dataset_table,
write_disposition=self.write_disposition,
allow_large_results=self.allow_large_results,
flatten_results=self.flatten_results,
udf_config=self.udf_config,
maximum_billing_tier=self.maximum_billing_tier,
maximum_bytes_billed=self.maximum_bytes_billed,
create_disposition=self.create_disposition,
query_params=self.query_params,
labels=self.labels,
schema_update_options=self.schema_update_options,
priority=self.priority,
time_partitioning=self.time_partitioning,
api_resource_configs=self.api_resource_configs,
cluster_fields=self.cluster_fields,
encryption_configuration=self.encryption_configuration,
)
for s in self.sql
]
else:
raise AirflowException(f"argument 'sql' of type {type(str)} is neither a string nor an iterable")
project_id = self.hook.project_id
if project_id:
job_id_path = convert_job_id(job_id=self.job_id, project_id=project_id, location=self.location)
context["task_instance"].xcom_push(key="job_id_path", value=job_id_path)
return self.job_id
[docs] def on_kill(self) -> None:
super().on_kill()
if self.hook is not None:
self.log.info("Cancelling running query")
self.hook.cancel_job(self.hook.running_job_id)
[docs]class BigQueryCreateEmptyTableOperator(GoogleCloudBaseOperator):
"""Creates a new table in the specified BigQuery dataset, optionally with schema.
The schema to be used for the BigQuery table may be specified in one of
two ways. You may either directly pass the schema fields in, or you may
point the operator to a Google Cloud Storage object name. The object in
Google Cloud Storage must be a JSON file with the schema fields in it.
You can also create a table without schema.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryCreateEmptyTableOperator`
:param project_id: The project to create the table into. (templated)
:param dataset_id: The dataset to create the table into. (templated)
:param table_id: The Name of the table to be created. (templated)
:param table_resource: Table resource as described in documentation:
https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#Table
If provided all other parameters are ignored. (templated)
:param schema_fields: If set, the schema field list as defined here:
https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.load.schema
**Example**::
schema_fields=[{"name": "emp_name", "type": "STRING", "mode": "REQUIRED"},
{"name": "salary", "type": "INTEGER", "mode": "NULLABLE"}]
:param gcs_schema_object: Full path to the JSON file containing
schema (templated). For
example: ``gs://test-bucket/dir1/dir2/employee_schema.json``
:param time_partitioning: configure optional time partitioning fields i.e.
partition by field, type and expiration as per API specifications.
.. seealso::
https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#timePartitioning
:param gcp_conn_id: [Optional] The connection ID used to connect to Google Cloud and
interact with the Bigquery service.
:param google_cloud_storage_conn_id: [Optional] The connection ID used to connect to Google Cloud.
and interact with the Google Cloud Storage service.
:param labels: a dictionary containing labels for the table, passed to BigQuery
**Example (with schema JSON in GCS)**::
CreateTable = BigQueryCreateEmptyTableOperator(
task_id='BigQueryCreateEmptyTableOperator_task',
dataset_id='ODS',
table_id='Employees',
project_id='internal-gcp-project',
gcs_schema_object='gs://schema-bucket/employee_schema.json',
gcp_conn_id='airflow-conn-id',
google_cloud_storage_conn_id='airflow-conn-id'
)
**Corresponding Schema file** (``employee_schema.json``)::
[
{
"mode": "NULLABLE",
"name": "emp_name",
"type": "STRING"
},
{
"mode": "REQUIRED",
"name": "salary",
"type": "INTEGER"
}
]
**Example (with schema in the DAG)**::
CreateTable = BigQueryCreateEmptyTableOperator(
task_id='BigQueryCreateEmptyTableOperator_task',
dataset_id='ODS',
table_id='Employees',
project_id='internal-gcp-project',
schema_fields=[{"name": "emp_name", "type": "STRING", "mode": "REQUIRED"},
{"name": "salary", "type": "INTEGER", "mode": "NULLABLE"}],
gcp_conn_id='airflow-conn-id-account',
google_cloud_storage_conn_id='airflow-conn-id'
)
:param view: [Optional] A dictionary containing definition for the view.
If set, it will create a view instead of a table:
.. seealso::
https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#ViewDefinition
:param materialized_view: [Optional] The materialized view definition.
:param encryption_configuration: [Optional] Custom encryption configuration (e.g., Cloud KMS keys).
.. code-block:: python
encryption_configuration = {
"kmsKeyName": "projects/testp/locations/us/keyRings/test-kr/cryptoKeys/test-key",
}
:param location: The location used for the operation.
:param cluster_fields: [Optional] The fields used for clustering.
BigQuery supports clustering for both partitioned and
non-partitioned tables.
.. seealso::
https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#clustering.fields
: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 if_exists: What should Airflow do if the table exists. If set to `log`, the TI will be passed to
success and an error message will be logged. Set to `ignore` to ignore the error, set to `fail` to
fail the TI, and set to `skip` to skip it.
:param exists_ok: Deprecated - use `if_exists="ignore"` instead.
"""
[docs] template_fields: Sequence[str] = (
"dataset_id",
"table_id",
"table_resource",
"project_id",
"gcs_schema_object",
"labels",
"view",
"materialized_view",
"impersonation_chain",
)
[docs] template_fields_renderers = {"table_resource": "json", "materialized_view": "json"}
[docs] ui_color = BigQueryUIColors.TABLE.value
def __init__(
self,
*,
dataset_id: str,
table_id: str,
table_resource: dict[str, Any] | None = None,
project_id: str | None = None,
schema_fields: list | None = None,
gcs_schema_object: str | None = None,
time_partitioning: dict | None = None,
gcp_conn_id: str = "google_cloud_default",
google_cloud_storage_conn_id: str = "google_cloud_default",
labels: dict | None = None,
view: dict | None = None,
materialized_view: dict | None = None,
encryption_configuration: dict | None = None,
location: str | None = None,
cluster_fields: list[str] | None = None,
impersonation_chain: str | Sequence[str] | None = None,
if_exists: str = "log",
bigquery_conn_id: str | None = None,
exists_ok: bool | None = None,
**kwargs,
) -> None:
if bigquery_conn_id:
warnings.warn(
"The bigquery_conn_id parameter has been deprecated. Use the gcp_conn_id parameter instead.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
gcp_conn_id = bigquery_conn_id
super().__init__(**kwargs)
self.project_id = project_id
self.dataset_id = dataset_id
self.table_id = table_id
self.schema_fields = schema_fields
self.gcs_schema_object = gcs_schema_object
self.gcp_conn_id = gcp_conn_id
self.google_cloud_storage_conn_id = google_cloud_storage_conn_id
self.time_partitioning = {} if time_partitioning is None else time_partitioning
self.labels = labels
self.view = view
self.materialized_view = materialized_view
self.encryption_configuration = encryption_configuration
self.location = location
self.cluster_fields = cluster_fields
self.table_resource = table_resource
self.impersonation_chain = impersonation_chain
if exists_ok is not None:
warnings.warn(
"`exists_ok` parameter is deprecated, please use `if_exists`",
AirflowProviderDeprecationWarning,
)
self.if_exists = IfExistAction.IGNORE if exists_ok else IfExistAction.LOG
else:
self.if_exists = IfExistAction(if_exists)
[docs] def execute(self, context: Context) -> None:
bq_hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
location=self.location,
impersonation_chain=self.impersonation_chain,
)
if not self.schema_fields and self.gcs_schema_object:
gcs_bucket, gcs_object = _parse_gcs_url(self.gcs_schema_object)
gcs_hook = GCSHook(
gcp_conn_id=self.google_cloud_storage_conn_id,
impersonation_chain=self.impersonation_chain,
)
schema_fields_string = gcs_hook.download_as_byte_array(gcs_bucket, gcs_object).decode("utf-8")
schema_fields = json.loads(schema_fields_string)
else:
schema_fields = self.schema_fields
try:
self.log.info("Creating table")
table = bq_hook.create_empty_table(
project_id=self.project_id,
dataset_id=self.dataset_id,
table_id=self.table_id,
schema_fields=schema_fields,
time_partitioning=self.time_partitioning,
cluster_fields=self.cluster_fields,
labels=self.labels,
view=self.view,
materialized_view=self.materialized_view,
encryption_configuration=self.encryption_configuration,
table_resource=self.table_resource,
exists_ok=self.if_exists == IfExistAction.IGNORE,
)
persist_kwargs = {
"context": context,
"task_instance": self,
"project_id": table.to_api_repr()["tableReference"]["projectId"],
"dataset_id": table.to_api_repr()["tableReference"]["datasetId"],
"table_id": table.to_api_repr()["tableReference"]["tableId"],
}
self.log.info(
"Table %s.%s.%s created successfully", table.project, table.dataset_id, table.table_id
)
except Conflict:
error_msg = f"Table {self.dataset_id}.{self.table_id} already exists."
if self.if_exists == IfExistAction.LOG:
self.log.info(error_msg)
persist_kwargs = {
"context": context,
"task_instance": self,
"project_id": self.project_id or bq_hook.project_id,
"dataset_id": self.dataset_id,
"table_id": self.table_id,
}
elif self.if_exists == IfExistAction.FAIL:
raise AirflowException(error_msg)
else:
raise AirflowSkipException(error_msg)
BigQueryTableLink.persist(**persist_kwargs)
[docs]class BigQueryCreateExternalTableOperator(GoogleCloudBaseOperator):
"""Create a new external table with data from Google Cloud Storage.
The schema to be used for the BigQuery table may be specified in one of
two ways. You may either directly pass the schema fields in, or you may
point the operator to a Google Cloud Storage object name. The object in
Google Cloud Storage must be a JSON file with the schema fields in it.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryCreateExternalTableOperator`
:param bucket: The bucket to point the external table to. (templated)
:param source_objects: List of Google Cloud Storage URIs to point
table to. If source_format is 'DATASTORE_BACKUP', the list must only contain a single URI.
:param destination_project_dataset_table: The dotted ``(<project>.)<dataset>.<table>``
BigQuery table to load data into (templated). If ``<project>`` is not included,
project will be the project defined in the connection json.
:param schema_fields: If set, the schema field list as defined here:
https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.load.schema
**Example**::
schema_fields=[{"name": "emp_name", "type": "STRING", "mode": "REQUIRED"},
{"name": "salary", "type": "INTEGER", "mode": "NULLABLE"}]
Should not be set when source_format is 'DATASTORE_BACKUP'.
:param table_resource: Table resource as described in documentation:
https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#Table
If provided all other parameters are ignored. External schema from object will be resolved.
:param schema_object: If set, a GCS object path pointing to a .json file that
contains the schema for the table. (templated)
:param gcs_schema_bucket: GCS bucket name where the schema JSON is stored (templated).
The default value is self.bucket.
:param source_format: File format of the data.
:param autodetect: Try to detect schema and format options automatically.
The schema_fields and schema_object options will be honored when specified explicitly.
https://cloud.google.com/bigquery/docs/schema-detect#schema_auto-detection_for_external_data_sources
:param compression: [Optional] The compression type of the data source.
Possible values include GZIP and NONE.
The default value is NONE.
This setting is ignored for Google Cloud Bigtable,
Google Cloud Datastore backups and Avro formats.
:param skip_leading_rows: Number of rows to skip when loading from a CSV.
:param field_delimiter: The delimiter to use for the CSV.
:param max_bad_records: The maximum number of bad records that BigQuery can
ignore when running the job.
:param quote_character: The value that is used to quote data sections in a CSV file.
:param allow_quoted_newlines: Whether to allow quoted newlines (true) or not (false).
:param allow_jagged_rows: Accept rows that are missing trailing optional columns.
The missing values are treated as nulls. If false, records with missing trailing
columns are treated as bad records, and if there are too many bad records, an
invalid error is returned in the job result. Only applicable to CSV, ignored
for other formats.
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud and
interact with the Bigquery service.
:param google_cloud_storage_conn_id: (Optional) The connection ID used to connect to Google Cloud
and interact with the Google Cloud Storage service.
:param src_fmt_configs: configure optional fields specific to the source format
:param labels: a dictionary containing labels for the table, passed to BigQuery
:param encryption_configuration: [Optional] Custom encryption configuration (e.g., Cloud KMS keys).
.. code-block:: python
encryption_configuration = {
"kmsKeyName": "projects/testp/locations/us/keyRings/test-kr/cryptoKeys/test-key",
}
: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).
"""
[docs] template_fields: Sequence[str] = (
"bucket",
"source_objects",
"schema_object",
"gcs_schema_bucket",
"destination_project_dataset_table",
"labels",
"table_resource",
"impersonation_chain",
)
[docs] template_fields_renderers = {"table_resource": "json"}
[docs] ui_color = BigQueryUIColors.TABLE.value
def __init__(
self,
*,
bucket: str | None = None,
source_objects: list[str] | None = None,
destination_project_dataset_table: str | None = None,
table_resource: dict[str, Any] | None = None,
schema_fields: list | None = None,
schema_object: str | None = None,
gcs_schema_bucket: str | None = None,
source_format: str | None = None,
autodetect: bool = False,
compression: str | None = None,
skip_leading_rows: int | None = None,
field_delimiter: str | None = None,
max_bad_records: int = 0,
quote_character: str | None = None,
allow_quoted_newlines: bool = False,
allow_jagged_rows: bool = False,
gcp_conn_id: str = "google_cloud_default",
google_cloud_storage_conn_id: str = "google_cloud_default",
src_fmt_configs: dict | None = None,
labels: dict | None = None,
encryption_configuration: dict | None = None,
location: str | None = None,
impersonation_chain: str | Sequence[str] | None = None,
bigquery_conn_id: str | None = None,
**kwargs,
) -> None:
if bigquery_conn_id:
warnings.warn(
"The bigquery_conn_id parameter has been deprecated. Use the gcp_conn_id parameter instead.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
gcp_conn_id = bigquery_conn_id
super().__init__(**kwargs)
# BQ config
kwargs_passed = any(
[
destination_project_dataset_table,
schema_fields,
source_format,
compression,
skip_leading_rows,
field_delimiter,
max_bad_records,
autodetect,
quote_character,
allow_quoted_newlines,
allow_jagged_rows,
src_fmt_configs,
labels,
encryption_configuration,
]
)
if not table_resource:
warnings.warn(
"Passing table parameters via keywords arguments will be deprecated. "
"Please provide table definition using `table_resource` parameter.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
if not bucket:
raise ValueError("`bucket` is required when not using `table_resource`.")
if not gcs_schema_bucket:
gcs_schema_bucket = bucket
if not source_objects:
raise ValueError("`source_objects` is required when not using `table_resource`.")
if not source_format:
source_format = "CSV"
if not compression:
compression = "NONE"
if not skip_leading_rows:
skip_leading_rows = 0
if not field_delimiter:
field_delimiter = ","
if not destination_project_dataset_table:
raise ValueError(
"`destination_project_dataset_table` is required when not using `table_resource`."
)
self.bucket = bucket
self.source_objects = source_objects
self.schema_object = schema_object
self.gcs_schema_bucket = gcs_schema_bucket
self.destination_project_dataset_table = destination_project_dataset_table
self.schema_fields = schema_fields
self.source_format = source_format
self.compression = compression
self.skip_leading_rows = skip_leading_rows
self.field_delimiter = field_delimiter
self.table_resource = None
else:
self.table_resource = table_resource
self.bucket = ""
self.source_objects = []
self.schema_object = None
self.gcs_schema_bucket = ""
self.destination_project_dataset_table = ""
if table_resource and kwargs_passed:
raise ValueError("You provided both `table_resource` and exclusive keywords arguments.")
self.max_bad_records = max_bad_records
self.quote_character = quote_character
self.allow_quoted_newlines = allow_quoted_newlines
self.allow_jagged_rows = allow_jagged_rows
self.gcp_conn_id = gcp_conn_id
self.google_cloud_storage_conn_id = google_cloud_storage_conn_id
self.autodetect = autodetect
self.src_fmt_configs = src_fmt_configs or {}
self.labels = labels
self.encryption_configuration = encryption_configuration
self.location = location
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context) -> None:
bq_hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
location=self.location,
impersonation_chain=self.impersonation_chain,
)
if self.table_resource:
table = bq_hook.create_empty_table(
table_resource=self.table_resource,
)
BigQueryTableLink.persist(
context=context,
task_instance=self,
dataset_id=table.to_api_repr()["tableReference"]["datasetId"],
project_id=table.to_api_repr()["tableReference"]["projectId"],
table_id=table.to_api_repr()["tableReference"]["tableId"],
)
return
if not self.schema_fields and self.schema_object and self.source_format != "DATASTORE_BACKUP":
gcs_hook = GCSHook(
gcp_conn_id=self.google_cloud_storage_conn_id,
impersonation_chain=self.impersonation_chain,
)
schema_fields = json.loads(
gcs_hook.download(self.gcs_schema_bucket, self.schema_object).decode("utf-8")
)
else:
schema_fields = self.schema_fields
source_uris = [f"gs://{self.bucket}/{source_object}" for source_object in self.source_objects]
project_id, dataset_id, table_id = bq_hook.split_tablename(
table_input=self.destination_project_dataset_table,
default_project_id=bq_hook.project_id or "",
)
external_data_configuration = {
"source_uris": source_uris,
"source_format": self.source_format,
"autodetect": self.autodetect,
"compression": self.compression,
"maxBadRecords": self.max_bad_records,
}
if self.source_format == "CSV":
external_data_configuration["csvOptions"] = {
"fieldDelimiter": self.field_delimiter,
"skipLeadingRows": self.skip_leading_rows,
"quote": self.quote_character,
"allowQuotedNewlines": self.allow_quoted_newlines,
"allowJaggedRows": self.allow_jagged_rows,
}
table_resource = {
"tableReference": {
"projectId": project_id,
"datasetId": dataset_id,
"tableId": table_id,
},
"labels": self.labels,
"schema": {"fields": schema_fields},
"externalDataConfiguration": external_data_configuration,
"location": self.location,
"encryptionConfiguration": self.encryption_configuration,
}
table = bq_hook.create_empty_table(
table_resource=table_resource,
)
BigQueryTableLink.persist(
context=context,
task_instance=self,
dataset_id=table.to_api_repr()["tableReference"]["datasetId"],
project_id=table.to_api_repr()["tableReference"]["projectId"],
table_id=table.to_api_repr()["tableReference"]["tableId"],
)
[docs]class BigQueryDeleteDatasetOperator(GoogleCloudBaseOperator):
"""Delete an existing dataset from your Project in BigQuery.
https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets/delete
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryDeleteDatasetOperator`
:param project_id: The project id of the dataset.
:param dataset_id: The dataset to be deleted.
:param delete_contents: (Optional) Whether to force the deletion even if the dataset is not empty.
Will delete all tables (if any) in the dataset if set to True.
Will raise HttpError 400: "{dataset_id} is still in use" if set to False and dataset is not empty.
The default value is False.
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
: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).
**Example**::
delete_temp_data = BigQueryDeleteDatasetOperator(
dataset_id='temp-dataset',
project_id='temp-project',
delete_contents=True, # Force the deletion of the dataset as well as its tables (if any).
gcp_conn_id='_my_gcp_conn_',
task_id='Deletetemp',
dag=dag)
"""
[docs] template_fields: Sequence[str] = (
"dataset_id",
"project_id",
"impersonation_chain",
)
[docs] ui_color = BigQueryUIColors.DATASET.value
def __init__(
self,
*,
dataset_id: str,
project_id: str | None = None,
delete_contents: bool = False,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
self.dataset_id = dataset_id
self.project_id = project_id
self.delete_contents = delete_contents
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
super().__init__(**kwargs)
[docs] def execute(self, context: Context) -> None:
self.log.info("Dataset id: %s Project id: %s", self.dataset_id, self.project_id)
bq_hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
bq_hook.delete_dataset(
project_id=self.project_id, dataset_id=self.dataset_id, delete_contents=self.delete_contents
)
[docs]class BigQueryCreateEmptyDatasetOperator(GoogleCloudBaseOperator):
"""Create a new dataset for your Project in BigQuery.
https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryCreateEmptyDatasetOperator`
:param project_id: The name of the project where we want to create the dataset.
:param dataset_id: The id of dataset. Don't need to provide, if datasetId in dataset_reference.
:param location: The geographic location where the dataset should reside.
:param dataset_reference: Dataset reference that could be provided with request body.
More info:
https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
: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 if_exists: What should Airflow do if the dataset exists. If set to `log`, the TI will be passed to
success and an error message will be logged. Set to `ignore` to ignore the error, set to `fail` to
fail the TI, and set to `skip` to skip it.
**Example**::
create_new_dataset = BigQueryCreateEmptyDatasetOperator(
dataset_id='new-dataset',
project_id='my-project',
dataset_reference={"friendlyName": "New Dataset"}
gcp_conn_id='_my_gcp_conn_',
task_id='newDatasetCreator',
dag=dag)
:param exists_ok: Deprecated - use `if_exists="ignore"` instead.
"""
[docs] template_fields: Sequence[str] = (
"dataset_id",
"project_id",
"dataset_reference",
"impersonation_chain",
)
[docs] template_fields_renderers = {"dataset_reference": "json"}
[docs] ui_color = BigQueryUIColors.DATASET.value
def __init__(
self,
*,
dataset_id: str | None = None,
project_id: str | None = None,
dataset_reference: dict | None = None,
location: str | None = None,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
if_exists: str = "log",
exists_ok: bool | None = None,
**kwargs,
) -> None:
self.dataset_id = dataset_id
self.project_id = project_id
self.location = location
self.gcp_conn_id = gcp_conn_id
self.dataset_reference = dataset_reference or {}
self.impersonation_chain = impersonation_chain
if exists_ok is not None:
warnings.warn(
"`exists_ok` parameter is deprecated, please use `if_exists`",
AirflowProviderDeprecationWarning,
)
self.if_exists = IfExistAction.IGNORE if exists_ok else IfExistAction.LOG
else:
self.if_exists = IfExistAction(if_exists)
super().__init__(**kwargs)
[docs] def execute(self, context: Context) -> None:
bq_hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
location=self.location,
impersonation_chain=self.impersonation_chain,
)
try:
dataset = bq_hook.create_empty_dataset(
project_id=self.project_id,
dataset_id=self.dataset_id,
dataset_reference=self.dataset_reference,
location=self.location,
exists_ok=self.if_exists == IfExistAction.IGNORE,
)
persist_kwargs = {
"context": context,
"task_instance": self,
"project_id": dataset["datasetReference"]["projectId"],
"dataset_id": dataset["datasetReference"]["datasetId"],
}
except Conflict:
dataset_id = self.dataset_reference.get("datasetReference", {}).get("datasetId", self.dataset_id)
project_id = self.dataset_reference.get("datasetReference", {}).get(
"projectId", self.project_id or bq_hook.project_id
)
persist_kwargs = {
"context": context,
"task_instance": self,
"project_id": project_id,
"dataset_id": dataset_id,
}
error_msg = f"Dataset {dataset_id} already exists."
if self.if_exists == IfExistAction.LOG:
self.log.info(error_msg)
elif self.if_exists == IfExistAction.FAIL:
raise AirflowException(error_msg)
else:
raise AirflowSkipException(error_msg)
BigQueryDatasetLink.persist(**persist_kwargs)
[docs]class BigQueryGetDatasetOperator(GoogleCloudBaseOperator):
"""Get the dataset specified by ID.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryGetDatasetOperator`
:param dataset_id: The id of dataset. Don't need to provide,
if datasetId in dataset_reference.
:param project_id: The name of the project where we want to create the dataset.
Don't need to provide, if projectId in dataset_reference.
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
: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).
"""
[docs] template_fields: Sequence[str] = (
"dataset_id",
"project_id",
"impersonation_chain",
)
[docs] ui_color = BigQueryUIColors.DATASET.value
def __init__(
self,
*,
dataset_id: str,
project_id: str | None = None,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
self.dataset_id = dataset_id
self.project_id = project_id
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
super().__init__(**kwargs)
[docs] def execute(self, context: Context):
bq_hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Start getting dataset: %s:%s", self.project_id, self.dataset_id)
dataset = bq_hook.get_dataset(dataset_id=self.dataset_id, project_id=self.project_id)
dataset_api_repr = dataset.to_api_repr()
BigQueryDatasetLink.persist(
context=context,
task_instance=self,
dataset_id=dataset_api_repr["datasetReference"]["datasetId"],
project_id=dataset_api_repr["datasetReference"]["projectId"],
)
return dataset_api_repr
[docs]class BigQueryGetDatasetTablesOperator(GoogleCloudBaseOperator):
"""Retrieve the list of tables in the specified dataset.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryGetDatasetTablesOperator`
:param dataset_id: the dataset ID of the requested dataset.
:param project_id: (Optional) the project of the requested dataset. If None,
self.project_id will be used.
:param max_results: (Optional) the maximum number of tables to return.
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
: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).
"""
[docs] template_fields: Sequence[str] = (
"dataset_id",
"project_id",
"impersonation_chain",
)
[docs] ui_color = BigQueryUIColors.DATASET.value
def __init__(
self,
*,
dataset_id: str,
project_id: str | None = None,
max_results: int | None = None,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
self.dataset_id = dataset_id
self.project_id = project_id
self.max_results = max_results
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
super().__init__(**kwargs)
[docs] def execute(self, context: Context):
bq_hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
return bq_hook.get_dataset_tables(
dataset_id=self.dataset_id,
project_id=self.project_id,
max_results=self.max_results,
)
[docs]class BigQueryPatchDatasetOperator(GoogleCloudBaseOperator):
"""Patch a dataset for your Project in BigQuery.
This operator is deprecated. Please use
:class:`airflow.providers.google.cloud.operators.bigquery.BigQueryUpdateTableOperator`
instead.
Only replaces fields that are provided in the submitted dataset resource.
:param dataset_id: The id of dataset. Don't need to provide,
if datasetId in dataset_reference.
:param dataset_resource: Dataset resource that will be provided with request body.
https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource
:param project_id: The name of the project where we want to create the dataset.
Don't need to provide, if projectId in dataset_reference.
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
: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).
"""
[docs] template_fields: Sequence[str] = (
"dataset_id",
"project_id",
"impersonation_chain",
)
[docs] template_fields_renderers = {"dataset_resource": "json"}
[docs] ui_color = BigQueryUIColors.DATASET.value
def __init__(
self,
*,
dataset_id: str,
dataset_resource: dict,
project_id: str | None = None,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
warnings.warn(
"This operator is deprecated. Please use BigQueryUpdateDatasetOperator.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
self.dataset_id = dataset_id
self.project_id = project_id
self.gcp_conn_id = gcp_conn_id
self.dataset_resource = dataset_resource
self.impersonation_chain = impersonation_chain
super().__init__(**kwargs)
[docs] def execute(self, context: Context):
bq_hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
return bq_hook.patch_dataset(
dataset_id=self.dataset_id,
dataset_resource=self.dataset_resource,
project_id=self.project_id,
)
[docs]class BigQueryUpdateTableOperator(GoogleCloudBaseOperator):
"""Update a table for your Project in BigQuery.
Use ``fields`` to specify which fields of table to update. If a field
is listed in ``fields`` and is ``None`` in table, it will be deleted.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryUpdateTableOperator`
:param dataset_id: The id of dataset. Don't need to provide,
if datasetId in table_reference.
:param table_id: The id of table. Don't need to provide,
if tableId in table_reference.
:param table_resource: Dataset resource that will be provided with request body.
https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#resource
:param fields: The fields of ``table`` to change, spelled as the Table
properties (e.g. "friendly_name").
:param project_id: The name of the project where we want to create the table.
Don't need to provide, if projectId in table_reference.
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
: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).
"""
[docs] template_fields: Sequence[str] = (
"dataset_id",
"table_id",
"project_id",
"impersonation_chain",
)
[docs] template_fields_renderers = {"table_resource": "json"}
[docs] ui_color = BigQueryUIColors.TABLE.value
def __init__(
self,
*,
table_resource: dict[str, Any],
fields: list[str] | None = None,
dataset_id: str | None = None,
table_id: str | None = None,
project_id: str | None = None,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
self.dataset_id = dataset_id
self.table_id = table_id
self.project_id = project_id
self.fields = fields
self.gcp_conn_id = gcp_conn_id
self.table_resource = table_resource
self.impersonation_chain = impersonation_chain
super().__init__(**kwargs)
[docs] def execute(self, context: Context):
bq_hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
table = bq_hook.update_table(
table_resource=self.table_resource,
fields=self.fields,
dataset_id=self.dataset_id,
table_id=self.table_id,
project_id=self.project_id,
)
BigQueryTableLink.persist(
context=context,
task_instance=self,
dataset_id=table["tableReference"]["datasetId"],
project_id=table["tableReference"]["projectId"],
table_id=table["tableReference"]["tableId"],
)
return table
[docs]class BigQueryUpdateDatasetOperator(GoogleCloudBaseOperator):
"""Update a dataset for your Project in BigQuery.
Use ``fields`` to specify which fields of dataset to update. If a field
is listed in ``fields`` and is ``None`` in dataset, it will be deleted.
If no ``fields`` are provided then all fields of provided ``dataset_resource``
will be used.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryUpdateDatasetOperator`
:param dataset_id: The id of dataset. Don't need to provide,
if datasetId in dataset_reference.
:param dataset_resource: Dataset resource that will be provided with request body.
https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets#resource
:param fields: The properties of dataset to change (e.g. "friendly_name").
:param project_id: The name of the project where we want to create the dataset.
Don't need to provide, if projectId in dataset_reference.
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
: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).
"""
[docs] template_fields: Sequence[str] = (
"dataset_id",
"project_id",
"impersonation_chain",
)
[docs] template_fields_renderers = {"dataset_resource": "json"}
[docs] ui_color = BigQueryUIColors.DATASET.value
def __init__(
self,
*,
dataset_resource: dict[str, Any],
fields: list[str] | None = None,
dataset_id: str | None = None,
project_id: str | None = None,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
self.dataset_id = dataset_id
self.project_id = project_id
self.fields = fields
self.gcp_conn_id = gcp_conn_id
self.dataset_resource = dataset_resource
self.impersonation_chain = impersonation_chain
super().__init__(**kwargs)
[docs] def execute(self, context: Context):
bq_hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
fields = self.fields or list(self.dataset_resource.keys())
dataset = bq_hook.update_dataset(
dataset_resource=self.dataset_resource,
project_id=self.project_id,
dataset_id=self.dataset_id,
fields=fields,
)
dataset_api_repr = dataset.to_api_repr()
BigQueryDatasetLink.persist(
context=context,
task_instance=self,
dataset_id=dataset_api_repr["datasetReference"]["datasetId"],
project_id=dataset_api_repr["datasetReference"]["projectId"],
)
return dataset_api_repr
[docs]class BigQueryDeleteTableOperator(GoogleCloudBaseOperator):
"""Delete a BigQuery table.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryDeleteTableOperator`
:param deletion_dataset_table: A dotted
``(<project>.|<project>:)<dataset>.<table>`` that indicates which table
will be deleted. (templated)
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
:param ignore_if_missing: if True, then return success even if the
requested table does not exist.
: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).
"""
[docs] template_fields: Sequence[str] = (
"deletion_dataset_table",
"impersonation_chain",
)
[docs] ui_color = BigQueryUIColors.TABLE.value
def __init__(
self,
*,
deletion_dataset_table: str,
gcp_conn_id: str = "google_cloud_default",
ignore_if_missing: bool = False,
location: str | None = None,
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.deletion_dataset_table = deletion_dataset_table
self.gcp_conn_id = gcp_conn_id
self.ignore_if_missing = ignore_if_missing
self.location = location
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context) -> None:
self.log.info("Deleting: %s", self.deletion_dataset_table)
hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
location=self.location,
impersonation_chain=self.impersonation_chain,
)
hook.delete_table(table_id=self.deletion_dataset_table, not_found_ok=self.ignore_if_missing)
[docs]class BigQueryUpsertTableOperator(GoogleCloudBaseOperator):
"""Upsert to a BigQuery table.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryUpsertTableOperator`
:param dataset_id: A dotted
``(<project>.|<project>:)<dataset>`` that indicates which dataset
will be updated. (templated)
:param table_resource: a table resource. see
https://cloud.google.com/bigquery/docs/reference/v2/tables#resource
:param project_id: The name of the project where we want to update the dataset.
Don't need to provide, if projectId in dataset_reference.
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
: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).
"""
[docs] template_fields: Sequence[str] = (
"dataset_id",
"table_resource",
"impersonation_chain",
"project_id",
)
[docs] template_fields_renderers = {"table_resource": "json"}
[docs] ui_color = BigQueryUIColors.TABLE.value
def __init__(
self,
*,
dataset_id: str,
table_resource: dict,
project_id: str | None = None,
gcp_conn_id: str = "google_cloud_default",
location: str | None = None,
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.dataset_id = dataset_id
self.table_resource = table_resource
self.project_id = project_id
self.gcp_conn_id = gcp_conn_id
self.location = location
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context) -> None:
self.log.info("Upserting Dataset: %s with table_resource: %s", self.dataset_id, self.table_resource)
hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
location=self.location,
impersonation_chain=self.impersonation_chain,
)
table = hook.run_table_upsert(
dataset_id=self.dataset_id,
table_resource=self.table_resource,
project_id=self.project_id,
)
BigQueryTableLink.persist(
context=context,
task_instance=self,
dataset_id=table["tableReference"]["datasetId"],
project_id=table["tableReference"]["projectId"],
table_id=table["tableReference"]["tableId"],
)
[docs]class BigQueryUpdateTableSchemaOperator(GoogleCloudBaseOperator):
"""Update BigQuery Table Schema.
Updates fields on a table schema based on contents of the supplied schema_fields_updates
parameter. The supplied schema does not need to be complete, if the field
already exists in the schema you only need to supply keys & values for the
items you want to patch, just ensure the "name" key is set.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryUpdateTableSchemaOperator`
:param schema_fields_updates: a partial schema resource. see
https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#TableSchema
.. code-block:: python
schema_fields_updates = [
{"name": "emp_name", "description": "Some New Description"},
{
"name": "salary",
"policyTags": {"names": ["some_new_policy_tag"]},
},
{
"name": "departments",
"fields": [
{"name": "name", "description": "Some New Description"},
{"name": "type", "description": "Some New Description"},
],
},
]
:param include_policy_tags: (Optional) If set to True policy tags will be included in
the update request which requires special permissions even if unchanged (default False)
see https://cloud.google.com/bigquery/docs/column-level-security#roles
:param dataset_id: A dotted
``(<project>.|<project>:)<dataset>`` that indicates which dataset
will be updated. (templated)
:param table_id: The table ID of the requested table. (templated)
:param project_id: The name of the project where we want to update the dataset.
Don't need to provide, if projectId in dataset_reference.
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
: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).
"""
[docs] template_fields: Sequence[str] = (
"schema_fields_updates",
"dataset_id",
"table_id",
"project_id",
"impersonation_chain",
)
[docs] template_fields_renderers = {"schema_fields_updates": "json"}
[docs] ui_color = BigQueryUIColors.TABLE.value
def __init__(
self,
*,
schema_fields_updates: list[dict[str, Any]],
dataset_id: str,
table_id: str,
include_policy_tags: bool = False,
project_id: str | None = None,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
self.schema_fields_updates = schema_fields_updates
self.include_policy_tags = include_policy_tags
self.table_id = table_id
self.dataset_id = dataset_id
self.project_id = project_id
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
super().__init__(**kwargs)
[docs] def execute(self, context: Context):
bq_hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
table = bq_hook.update_table_schema(
schema_fields_updates=self.schema_fields_updates,
include_policy_tags=self.include_policy_tags,
dataset_id=self.dataset_id,
table_id=self.table_id,
project_id=self.project_id,
)
BigQueryTableLink.persist(
context=context,
task_instance=self,
dataset_id=table["tableReference"]["datasetId"],
project_id=table["tableReference"]["projectId"],
table_id=table["tableReference"]["tableId"],
)
return table
[docs]class BigQueryInsertJobOperator(GoogleCloudBaseOperator, _BigQueryOpenLineageMixin):
"""Execute a BigQuery job.
Waits for the job to complete and returns job id.
This operator work in the following way:
- it calculates a unique hash of the job using job's configuration or uuid if ``force_rerun`` is True
- creates ``job_id`` in form of
``[provided_job_id | airflow_{dag_id}_{task_id}_{exec_date}]_{uniqueness_suffix}``
- submits a BigQuery job using the ``job_id``
- if job with given id already exists then it tries to reattach to the job if its not done and its
state is in ``reattach_states``. If the job is done the operator will raise ``AirflowException``.
Using ``force_rerun`` will submit a new job every time without attaching to already existing ones.
For job definition see here:
https://cloud.google.com/bigquery/docs/reference/v2/jobs
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BigQueryInsertJobOperator`
:param configuration: The configuration parameter maps directly to BigQuery's
configuration field in the job object. For more details see
https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#jobconfiguration
: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 project_id: Google Cloud Project where the job is running
:param location: location the job is running
:param gcp_conn_id: The connection ID used to connect to Google Cloud.
: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 cancel_on_kill: Flag which indicates whether cancel the hook's job or not, when on_kill is called
: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 deferrable: Run operator in the deferrable mode
:param poll_interval: (Deferrable mode only) polling period in seconds to check for the status of job.
Defaults to 4 seconds.
"""
[docs] template_fields: Sequence[str] = (
"configuration",
"job_id",
"impersonation_chain",
"project_id",
)
[docs] template_ext: Sequence[str] = (
".json",
".sql",
)
[docs] template_fields_renderers = {"configuration": "json", "configuration.query.query": "sql"}
[docs] ui_color = BigQueryUIColors.QUERY.value
def __init__(
self,
configuration: dict[str, Any],
project_id: str | None = None,
location: str | None = None,
job_id: str | None = None,
force_rerun: bool = True,
reattach_states: set[str] | None = None,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
cancel_on_kill: bool = True,
result_retry: Retry = DEFAULT_RETRY,
result_timeout: float | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
poll_interval: float = 4.0,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.configuration = configuration
self.location = location
self.job_id = job_id
self.project_id = project_id
self.gcp_conn_id = gcp_conn_id
self.force_rerun = force_rerun
self.reattach_states: set[str] = reattach_states or set()
self.impersonation_chain = impersonation_chain
self.cancel_on_kill = cancel_on_kill
self.result_retry = result_retry
self.result_timeout = result_timeout
self.hook: BigQueryHook | None = None
self.deferrable = deferrable
self.poll_interval = poll_interval
@cached_property
[docs] def sql(self) -> str | None:
try:
return self.configuration["query"]["query"]
except KeyError:
return None
[docs] def prepare_template(self) -> None:
# If .json is passed then we have to read the file
if isinstance(self.configuration, str) and self.configuration.endswith(".json"):
with open(self.configuration) as file:
self.configuration = json.loads(file.read())
def _submit_job(
self,
hook: BigQueryHook,
job_id: str,
) -> BigQueryJob:
# Submit a new job without waiting for it to complete.
return hook.insert_job(
configuration=self.configuration,
project_id=self.project_id,
location=self.location,
job_id=job_id,
timeout=self.result_timeout,
retry=self.result_retry,
nowait=True,
)
@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}")
[docs] def execute(self, context: Any):
hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
self.hook = hook
self.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=self.configuration,
force_rerun=self.force_rerun,
)
try:
self.log.info("Executing: %s'", self.configuration)
job: BigQueryJob | UnknownJob = self._submit_job(hook, self.job_id)
except Conflict:
# If the job already exists retrieve it
job = hook.get_job(
project_id=self.project_id,
location=self.location,
job_id=self.job_id,
)
if job.state in self.reattach_states:
# We are reattaching to a job
job._begin()
self._handle_job_error(job)
else:
# Same job configuration so we need force_rerun
raise AirflowException(
f"Job with id: {self.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`"
)
job_types = {
LoadJob._JOB_TYPE: ["sourceTable", "destinationTable"],
CopyJob._JOB_TYPE: ["sourceTable", "destinationTable"],
ExtractJob._JOB_TYPE: ["sourceTable"],
QueryJob._JOB_TYPE: ["destinationTable"],
}
project_id = self.project_id or hook.project_id
if project_id:
for job_type, tables_prop in job_types.items():
job_configuration = job.to_api_repr()["configuration"]
if job_type in job_configuration:
for table_prop in tables_prop:
if table_prop in job_configuration[job_type]:
table = job_configuration[job_type][table_prop]
persist_kwargs = {
"context": context,
"task_instance": self,
"project_id": project_id,
"table_id": table,
}
if not isinstance(table, str):
persist_kwargs["table_id"] = table["tableId"]
persist_kwargs["dataset_id"] = table["datasetId"]
persist_kwargs["project_id"] = table["projectId"]
BigQueryTableLink.persist(**persist_kwargs)
self.job_id = job.job_id
project_id = self.project_id or self.hook.project_id
if project_id:
job_id_path = convert_job_id(
job_id=self.job_id, project_id=project_id, location=self.location # type: ignore[arg-type]
)
context["ti"].xcom_push(key="job_id_path", value=job_id_path)
# Wait for the job to complete
if not self.deferrable:
job.result(timeout=self.result_timeout, retry=self.result_retry)
self._handle_job_error(job)
return self.job_id
else:
if job.running():
self.defer(
timeout=self.execution_timeout,
trigger=BigQueryInsertJobTrigger(
conn_id=self.gcp_conn_id,
job_id=self.job_id,
project_id=self.project_id,
poll_interval=self.poll_interval,
),
method_name="execute_complete",
)
self.log.info("Current state of job %s is %s", job.job_id, job.state)
self._handle_job_error(job)
[docs] def execute_complete(self, context: Context, event: dict[str, Any]):
"""Callback for when the trigger fires.
This returns immediately. It 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"],
)
return self.job_id
[docs] def on_kill(self) -> None:
if self.job_id and self.cancel_on_kill:
self.hook.cancel_job( # type: ignore[union-attr]
job_id=self.job_id, project_id=self.project_id, location=self.location
)
else:
self.log.info("Skipping to cancel job: %s:%s.%s", self.project_id, self.location, self.job_id)
