#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""This module contains a Google Cloud Storage to BigQuery operator."""
from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any, Sequence
from google.api_core.exceptions import BadRequest, Conflict
from google.api_core.retry import Retry
from google.cloud.bigquery import (
DEFAULT_RETRY,
CopyJob,
ExternalConfig,
ExtractJob,
LoadJob,
QueryJob,
SchemaField,
)
from google.cloud.bigquery.table import EncryptionConfiguration, Table, TableReference
from airflow import AirflowException
from airflow.models import BaseOperator
from airflow.providers.google.cloud.hooks.bigquery import BigQueryHook, BigQueryJob
from airflow.providers.google.cloud.hooks.gcs import GCSHook
from airflow.providers.google.cloud.links.bigquery import BigQueryTableLink
from airflow.providers.google.cloud.triggers.bigquery import BigQueryInsertJobTrigger
if TYPE_CHECKING:
from airflow.utils.context import Context
]
[docs]class GCSToBigQueryOperator(BaseOperator):
"""
Loads files from Google Cloud Storage into BigQuery.
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:GCSToBigQueryOperator`
:param bucket: The bucket to load from. (templated)
:param source_objects: String or List of Google Cloud Storage URIs to load from. (templated)
If source_format is 'DATASTORE_BACKUP', the list must only contain a single URI.
:param destination_project_dataset_table: The dotted
``(<project>.|<project>:)<dataset>.<table>`` BigQuery table to load data into.
If ``<project>`` is not included, project will be the project defined in
the connection json. (templated)
:param schema_fields: If set, the schema field list as defined here:
https://cloud.google.com/bigquery/docs/reference/v2/jobs#configuration.load
Should not be set when source_format is 'DATASTORE_BACKUP'.
Parameter must be defined if 'schema_object' is null and autodetect is False.
:param schema_object: If set, a GCS object path pointing to a .json file that
contains the schema for the table. (templated)
Parameter must be defined if 'schema_fields' is null and autodetect is False.
:param schema_object_bucket: [Optional] If set, the GCS bucket where the schema object
template is stored. (templated) (Default: the value of ``bucket``)
:param source_format: File format to export.
: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 create_disposition: The create disposition if the table doesn't exist.
:param skip_leading_rows: The number of rows at the top of a CSV file that BigQuery
will skip when loading the data.
When autodetect is on, the behavior is the following:
skip_leading_rows unspecified - Autodetect tries to detect headers in the first row.
If they are not detected, the row is read as data. Otherwise, data is read starting
from the second row.
skip_leading_rows is 0 - Instructs autodetect that there are no headers and data
should be read starting from the first row.
skip_leading_rows = N > 0 - Autodetect skips N-1 rows and tries to detect headers
in row N. If headers are not detected, row N is just skipped. Otherwise, row N is
used to extract column names for the detected schema.
Default value set to None so that autodetect option can detect schema fields.
:param write_disposition: The write disposition if the table already exists.
:param field_delimiter: The delimiter to use when loading from a 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 ignore_unknown_values: [Optional] Indicates if BigQuery should allow
extra values that are not represented in the table schema.
If true, the extra values are ignored. If false, records with extra columns
are treated as bad records, and if there are too many bad records, an
invalid error is returned in the job result.
: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 encoding: The character encoding of the data. See:
https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query.tableDefinitions.(key).csvOptions.encoding
https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#externalDataConfiguration.csvOptions.encoding
:param max_id_key: If set, the name of a column in the BigQuery table
that's to be loaded. This will be used to select the MAX value from
BigQuery after the load occurs. The results will be returned by the
execute() command, which in turn gets stored in XCom for future
operators to use. This can be helpful with incremental loads--during
future executions, you can pick up from the max ID.
:param delegate_to: The account to impersonate using domain-wide delegation of authority,
if any. For this to work, the service account making the request must have
domain-wide delegation enabled.
:param schema_update_options: Allows the schema of the destination
table to be updated as a side effect of the load job.
:param src_fmt_configs: configure optional fields specific to the source format
:param external_table: Flag to specify if the destination table should be
a BigQuery external table. Default Value is False.
:param time_partitioning: configure optional time partitioning fields i.e.
partition by field, type and expiration as per API specifications.
Note that 'field' is not available in concurrency with
dataset.table$partition.
:param cluster_fields: Request that the result of this load 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.
Not applicable for external tables.
:param autodetect: [Optional] Indicates if we should automatically infer the
options and schema for CSV and JSON sources. (Default: ``True``).
Parameter must be set to True if 'schema_fields' and 'schema_object' are undefined.
It is suggested to set to True if table are create outside of Airflow.
If autodetect is None and no schema is provided (neither via schema_fields
nor a schema_object), assume the table already exists.
:param encryption_configuration: [Optional] Custom encryption configuration (e.g., Cloud KMS keys).
**Example**: ::
encryption_configuration = {
"kmsKeyName": "projects/testp/locations/us/keyRings/test-kr/cryptoKeys/test-key"
}
:param location: [Optional] 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 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: [Optional] Labels for the BiqQuery table.
:param description: [Optional] Description for the BigQuery table. This will only be used if the
destination table is newly created. If the table already exists and a value different than the
current description is provided, the job will fail.
:param deferrable: Run operator in the deferrable mode
"""
[docs] template_fields: Sequence[str] = (
"bucket",
"source_objects",
"schema_object",
"schema_object_bucket",
"destination_project_dataset_table",
"impersonation_chain",
)
[docs] template_ext: Sequence[str] = (".sql",)
def __init__(
self,
*,
bucket,
source_objects,
destination_project_dataset_table,
schema_fields=None,
schema_object=None,
schema_object_bucket=None,
source_format="CSV",
compression="NONE",
create_disposition="CREATE_IF_NEEDED",
skip_leading_rows=None,
write_disposition="WRITE_EMPTY",
field_delimiter=",",
max_bad_records=0,
quote_character=None,
ignore_unknown_values=False,
allow_quoted_newlines=False,
allow_jagged_rows=False,
encoding="UTF-8",
max_id_key=None,
gcp_conn_id="google_cloud_default",
delegate_to=None,
schema_update_options=(),
src_fmt_configs=None,
external_table=False,
time_partitioning=None,
cluster_fields=None,
autodetect=True,
encryption_configuration=None,
location=None,
impersonation_chain: str | Sequence[str] | None = None,
labels=None,
description=None,
deferrable: bool = False,
result_retry: Retry = DEFAULT_RETRY,
result_timeout: float | None = None,
cancel_on_kill: bool = True,
job_id: str | None = None,
force_rerun: bool = True,
reattach_states: set[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.hook: BigQueryHook | None = None
self.configuration: dict[str, Any] = {}
# GCS config
if src_fmt_configs is None:
src_fmt_configs = {}
if time_partitioning is None:
time_partitioning = {}
self.bucket = bucket
self.source_objects = source_objects
self.schema_object = schema_object
if schema_object_bucket is None:
schema_object_bucket = bucket
self.schema_object_bucket = schema_object_bucket
# BQ config
self.destination_project_dataset_table = destination_project_dataset_table
self.schema_fields = schema_fields
if source_format.upper() not in ALLOWED_FORMATS:
raise ValueError(
f"{source_format} is not a valid source format. "
f"Please use one of the following types: {ALLOWED_FORMATS}."
)
else:
self.source_format = source_format.upper()
self.compression = compression
self.create_disposition = create_disposition
self.skip_leading_rows = skip_leading_rows
self.write_disposition = write_disposition
self.field_delimiter = field_delimiter
self.max_bad_records = max_bad_records
self.quote_character = quote_character
self.ignore_unknown_values = ignore_unknown_values
self.allow_quoted_newlines = allow_quoted_newlines
self.allow_jagged_rows = allow_jagged_rows
self.external_table = external_table
self.encoding = encoding
self.max_id_key = max_id_key
self.gcp_conn_id = gcp_conn_id
self.delegate_to = delegate_to
self.schema_update_options = schema_update_options
self.src_fmt_configs = src_fmt_configs
self.time_partitioning = time_partitioning
self.cluster_fields = cluster_fields
self.autodetect = autodetect
self.encryption_configuration = encryption_configuration
self.location = location
self.impersonation_chain = impersonation_chain
self.labels = labels
self.description = description
self.job_id = job_id
self.deferrable = deferrable
self.result_retry = result_retry
self.result_timeout = result_timeout
self.force_rerun = force_rerun
self.reattach_states: set[str] = reattach_states or set()
self.cancel_on_kill = cancel_on_kill
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=hook.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) -> None:
if job.error_result:
raise AirflowException(f"BigQuery job {job.job_id} failed: {job.error_result}")
[docs] def execute(self, context: Context):
hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
location=self.location,
impersonation_chain=self.impersonation_chain,
)
self.hook = hook
self.source_format = self.source_format.upper()
job_id = self.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,
)
self.source_objects = (
self.source_objects if isinstance(self.source_objects, list) else [self.source_objects]
)
self.source_uris = [f"gs://{self.bucket}/{source_object}" for source_object in self.source_objects]
if not self.schema_fields:
# Check for self.autodetect explicitly False. self.autodetect equal to None
# entails we do not want to detect schema from files. Instead, it means we
# rely on an already existing table's schema
if not self.schema_object and self.autodetect is False:
raise AirflowException(
"Table schema was not found. Neither schema object nor schema fields were specified"
)
if self.schema_object and self.source_format != "DATASTORE_BACKUP":
gcs_hook = GCSHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
self.schema_fields = json.loads(
gcs_hook.download(self.schema_object_bucket, self.schema_object).decode("utf-8")
)
self.log.info("Loaded fields from schema object: %s", self.schema_fields)
else:
self.schema_fields = None
if self.external_table:
self.log.info("Creating a new BigQuery table for storing data...")
table_obj_api_repr = self._create_empty_table()
BigQueryTableLink.persist(
context=context,
task_instance=self,
dataset_id=table_obj_api_repr["tableReference"]["datasetId"],
project_id=table_obj_api_repr["tableReference"]["projectId"],
table_id=table_obj_api_repr["tableReference"]["tableId"],
)
if self.max_id_key:
max_id = self._find_max_value_in_column()
return max_id
else:
self.log.info("Using existing BigQuery table for storing data...")
self.configuration = self._use_existing_table()
try:
self.log.info("Executing: %s", self.configuration)
job = self._submit_job(self.hook, job_id)
except Conflict:
# If the job already exists retrieve it
job = self.hook.get_job(
project_id=self.hook.project_id,
location=self.location,
job_id=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: {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"],
}
if self.hook.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": self.hook.project_id,
"table_id": table,
}
if not isinstance(table, str):
persist_kwargs["table_id"] = table["tableId"]
persist_kwargs["dataset_id"] = table["datasetId"]
BigQueryTableLink.persist(**persist_kwargs)
self.job_id = job.job_id
context["ti"].xcom_push(key="job_id", value=self.job_id)
if self.deferrable:
self.defer(
timeout=self.execution_timeout,
trigger=BigQueryInsertJobTrigger(
conn_id=self.gcp_conn_id,
job_id=self.job_id,
project_id=self.hook.project_id,
),
method_name="execute_complete",
)
else:
job.result(timeout=self.result_timeout, retry=self.result_retry)
self._handle_job_error(job)
if self.max_id_key:
return self._find_max_value_in_column()
[docs] def execute_complete(self, context: Context, event: dict[str, Any]):
"""
Callback for when the trigger fires - returns immediately.
Relies on trigger to throw an exception, otherwise it assumes execution was
successful.
"""
if event["status"] == "error":
raise AirflowException(event["message"])
self.log.info(
"%s completed with response %s ",
self.task_id,
event["message"],
)
return self._find_max_value_in_column()
def _find_max_value_in_column(self):
hook = BigQueryHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
location=self.location,
impersonation_chain=self.impersonation_chain,
)
if self.max_id_key:
self.log.info(f"Selecting the MAX value from BigQuery column '{self.max_id_key}'...")
select_command = (
f"SELECT MAX({self.max_id_key}) AS max_value "
f"FROM {self.destination_project_dataset_table}"
)
self.configuration = {
"query": {
"query": select_command,
"useLegacySql": False,
"schemaUpdateOptions": [],
}
}
try:
job_id = hook.insert_job(configuration=self.configuration, project_id=hook.project_id)
rows = list(hook.get_job(job_id=job_id, location=self.location).result())
except BadRequest as e:
if "Unrecognized name:" in e.message:
raise AirflowException(
f"Could not determine MAX value in column {self.max_id_key} "
f"since the default value of 'string_field_n' was set by BQ"
)
else:
raise AirflowException(e.message)
if rows:
for row in rows:
max_id = row[0] if row[0] else 0
self.log.info(
"Loaded BQ data with MAX value of column %s.%s: %s",
self.destination_project_dataset_table,
self.max_id_key,
max_id,
)
return str(max_id)
else:
raise RuntimeError(f"The {select_command} returned no rows!")
def _create_empty_table(self):
project_id, dataset_id, table_id = self.hook.split_tablename(
table_input=self.destination_project_dataset_table,
default_project_id=self.hook.project_id or "",
)
external_config_api_repr = {
"autodetect": self.autodetect,
"sourceFormat": self.source_format,
"sourceUris": self.source_uris,
"compression": self.compression.upper(),
"ignoreUnknownValues": self.ignore_unknown_values,
}
# if following fields are not specified in src_fmt_configs,
# honor the top-level params for backward-compatibility
backward_compatibility_configs = {
"skipLeadingRows": self.skip_leading_rows,
"fieldDelimiter": self.field_delimiter,
"quote": self.quote_character,
"allowQuotedNewlines": self.allow_quoted_newlines,
"allowJaggedRows": self.allow_jagged_rows,
"encoding": self.encoding,
}
src_fmt_to_param_mapping = {"CSV": "csvOptions", "GOOGLE_SHEETS": "googleSheetsOptions"}
src_fmt_to_configs_mapping = {
"csvOptions": [
"allowJaggedRows",
"allowQuotedNewlines",
"fieldDelimiter",
"skipLeadingRows",
"quote",
"encoding",
],
"googleSheetsOptions": ["skipLeadingRows"],
}
if self.source_format in src_fmt_to_param_mapping.keys():
valid_configs = src_fmt_to_configs_mapping[src_fmt_to_param_mapping[self.source_format]]
self.src_fmt_configs = self._validate_src_fmt_configs(
self.source_format, self.src_fmt_configs, valid_configs, backward_compatibility_configs
)
external_config_api_repr[src_fmt_to_param_mapping[self.source_format]] = self.src_fmt_configs
external_config = ExternalConfig.from_api_repr(external_config_api_repr)
if self.schema_fields:
external_config.schema = [SchemaField.from_api_repr(f) for f in self.schema_fields]
if self.max_bad_records:
external_config.max_bad_records = self.max_bad_records
# build table definition
table = Table(
table_ref=TableReference.from_string(self.destination_project_dataset_table, project_id)
)
table.external_data_configuration = external_config
if self.labels:
table.labels = self.labels
if self.description:
table.description = self.description
if self.encryption_configuration:
table.encryption_configuration = EncryptionConfiguration.from_api_repr(
self.encryption_configuration
)
table_obj_api_repr = table.to_api_repr()
self.log.info("Creating external table: %s", self.destination_project_dataset_table)
self.hook.create_empty_table(
table_resource=table_obj_api_repr, project_id=project_id, location=self.location, exists_ok=True
)
self.log.info("External table created successfully: %s", self.destination_project_dataset_table)
return table_obj_api_repr
def _use_existing_table(self):
destination_project, destination_dataset, destination_table = self.hook.split_tablename(
table_input=self.destination_project_dataset_table,
default_project_id=self.hook.project_id or "",
var_name="destination_project_dataset_table",
)
# bigquery also allows you to define how you want a table's schema to change
# as a side effect of a load
# for more details:
# https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.load.schemaUpdateOptions
allowed_schema_update_options = ["ALLOW_FIELD_ADDITION", "ALLOW_FIELD_RELAXATION"]
if not set(allowed_schema_update_options).issuperset(set(self.schema_update_options)):
raise ValueError(
f"{self.schema_update_options} contains invalid schema update options. "
f"Please only use one or more of the following options: {allowed_schema_update_options}"
)
self.configuration = {
"load": {
"autodetect": self.autodetect,
"createDisposition": self.create_disposition,
"destinationTable": {
"projectId": destination_project,
"datasetId": destination_dataset,
"tableId": destination_table,
},
"sourceFormat": self.source_format,
"sourceUris": self.source_uris,
"writeDisposition": self.write_disposition,
"ignoreUnknownValues": self.ignore_unknown_values,
},
}
self.time_partitioning = self._cleanse_time_partitioning(
self.destination_project_dataset_table, self.time_partitioning
)
if self.time_partitioning:
self.configuration["load"].update({"timePartitioning": self.time_partitioning})
if self.cluster_fields:
self.configuration["load"].update({"clustering": {"fields": self.cluster_fields}})
if self.schema_fields:
self.configuration["load"]["schema"] = {"fields": self.schema_fields}
if self.schema_update_options:
if self.write_disposition not in ["WRITE_APPEND", "WRITE_TRUNCATE"]:
raise ValueError(
"schema_update_options is only "
"allowed if write_disposition is "
"'WRITE_APPEND' or 'WRITE_TRUNCATE'."
)
else:
# To provide backward compatibility
self.schema_update_options = list(self.schema_update_options or [])
self.log.info("Adding experimental 'schemaUpdateOptions': %s", self.schema_update_options)
self.configuration["load"]["schemaUpdateOptions"] = self.schema_update_options
if self.max_bad_records:
self.configuration["load"]["maxBadRecords"] = self.max_bad_records
if self.encryption_configuration:
self.configuration["load"]["destinationEncryptionConfiguration"] = self.encryption_configuration
if self.labels or self.description:
self.configuration["load"].update({"destinationTableProperties": {}})
if self.labels:
self.configuration["load"]["destinationTableProperties"]["labels"] = self.labels
if self.description:
self.configuration["load"]["destinationTableProperties"]["description"] = self.description
src_fmt_to_configs_mapping = {
"CSV": [
"allowJaggedRows",
"allowQuotedNewlines",
"autodetect",
"fieldDelimiter",
"skipLeadingRows",
"ignoreUnknownValues",
"nullMarker",
"quote",
"encoding",
],
"DATASTORE_BACKUP": ["projectionFields"],
"NEWLINE_DELIMITED_JSON": ["autodetect", "ignoreUnknownValues"],
"PARQUET": ["autodetect", "ignoreUnknownValues"],
"AVRO": ["useAvroLogicalTypes"],
}
valid_configs = src_fmt_to_configs_mapping[self.source_format]
# if following fields are not specified in src_fmt_configs,
# honor the top-level params for backward-compatibility
backward_compatibility_configs = {
"skipLeadingRows": self.skip_leading_rows,
"fieldDelimiter": self.field_delimiter,
"ignoreUnknownValues": self.ignore_unknown_values,
"quote": self.quote_character,
"allowQuotedNewlines": self.allow_quoted_newlines,
"encoding": self.encoding,
}
self.src_fmt_configs = self._validate_src_fmt_configs(
self.source_format, self.src_fmt_configs, valid_configs, backward_compatibility_configs
)
self.configuration["load"].update(self.src_fmt_configs)
if self.allow_jagged_rows:
self.configuration["load"]["allowJaggedRows"] = self.allow_jagged_rows
return self.configuration
def _validate_src_fmt_configs(
self,
source_format: str,
src_fmt_configs: dict,
valid_configs: list[str],
backward_compatibility_configs: dict | None = None,
) -> dict:
"""
Validates the given src_fmt_configs against a valid configuration for the source format.
Adds the backward compatibility config to the src_fmt_configs.
:param source_format: File format to export.
:param src_fmt_configs: Configure optional fields specific to the source format.
:param valid_configs: Valid configuration specific to the source format
:param backward_compatibility_configs: The top-level params for backward-compatibility
"""
if backward_compatibility_configs is None:
backward_compatibility_configs = {}
for k, v in backward_compatibility_configs.items():
if k not in src_fmt_configs and k in valid_configs:
src_fmt_configs[k] = v
for k, v in src_fmt_configs.items():
if k not in valid_configs:
raise ValueError(f"{k} is not a valid src_fmt_configs for type {source_format}.")
return src_fmt_configs
def _cleanse_time_partitioning(
self, destination_dataset_table: str | None, time_partitioning_in: dict | None
) -> dict: # if it is a partitioned table ($ is in the table name) add partition load option
if time_partitioning_in is None:
time_partitioning_in = {}
time_partitioning_out = {}
if destination_dataset_table and "$" in destination_dataset_table:
time_partitioning_out["type"] = "DAY"
time_partitioning_out.update(time_partitioning_in)
return time_partitioning_out
[docs] def on_kill(self) -> None:
if self.job_id and self.cancel_on_kill:
self.hook.cancel_job(job_id=self.job_id, location=self.location) # type: ignore[union-attr]
else:
self.log.info("Skipping to cancel job: %s.%s", self.location, self.job_id)
