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"""
This module contains Google BigQuery operators.
"""
import json
from typing import Iterable
from airflow.contrib.hooks.bigquery_hook import BigQueryHook
from airflow.contrib.hooks.gcs_hook import GoogleCloudStorageHook, _parse_gcs_url
from airflow.exceptions import AirflowException
from airflow.models.baseoperator import BaseOperator, BaseOperatorLink
from airflow.models.taskinstance import TaskInstance
from airflow.utils.decorators import apply_defaults
[docs]class BigQueryConsoleLink(BaseOperatorLink):
"""
Helper class for constructing BigQuery link.
"""
[docs] name = 'BigQuery Console'
[docs] def get_link(self, operator, dttm):
ti = TaskInstance(task=operator, execution_date=dttm)
job_id = ti.xcom_pull(task_ids=operator.task_id, key='job_id')
return 'https://console.cloud.google.com/bigquery?j={job_id}'.format(
job_id=job_id) if job_id else ''
# pylint: disable=too-many-instance-attributes
[docs]class BigQueryOperator(BaseOperator):
"""
Executes BigQuery SQL queries in a specific BigQuery database
:param bql: (Deprecated. Use `sql` parameter instead) the sql code to be
executed (templated)
:type bql: Can receive a str representing a sql statement,
a list of str (sql statements), or reference to a template file.
Template reference are recognized by str ending in '.sql'.
:param sql: the sql code to be executed (templated)
:type sql: Can receive a str representing a sql statement,
a list of str (sql statements), or reference to a template file.
Template reference 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)
:type destination_dataset_table: str
:param write_disposition: Specifies the action that occurs if the destination table
already exists. (default: 'WRITE_EMPTY')
:type write_disposition: str
:param create_disposition: Specifies whether the job is allowed to create new tables.
(default: 'CREATE_IF_NEEDED')
:type create_disposition: str
:param allow_large_results: Whether to allow large results.
:type allow_large_results: bool
: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.
:type flatten_results: bool
:param bigquery_conn_id: reference to a specific BigQuery hook.
:type bigquery_conn_id: str
:param delegate_to: The account to impersonate, if any.
For this to work, the service account making the request must have domain-wide
delegation enabled.
:type delegate_to: str
:param udf_config: The User Defined Function configuration for the query.
See https://cloud.google.com/bigquery/user-defined-functions for details.
:type udf_config: list
:param use_legacy_sql: Whether to use legacy SQL (true) or standard SQL (false).
:type use_legacy_sql: bool
: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.
:type maximum_billing_tier: int
: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.
:type maximum_bytes_billed: float
: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.
:type api_resource_configs: dict
:param schema_update_options: Allows the schema of the destination
table to be updated as a side effect of the load job.
:type schema_update_options: tuple
: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' } }].
:type query_params: list
:param labels: a dictionary containing labels for the job/query,
passed to BigQuery
:type labels: dict
:param priority: Specifies a priority for the query.
Possible values include INTERACTIVE and BATCH.
The default value is INTERACTIVE.
:type priority: str
:param time_partitioning: configure optional time partitioning fields i.e.
partition by field, type and expiration as per API specifications.
:type time_partitioning: dict
:param cluster_fields: Request that the result of this query be stored sorted
by one or more columns. This is only available in conjunction with
time_partitioning. The order of columns given determines the sort order.
:type cluster_fields: list[str]
: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
:type location: str
"""
[docs] template_fields = ('bql', 'sql', 'destination_dataset_table', 'labels')
[docs] template_ext = ('.sql', )
)
# pylint: disable=too-many-arguments
@apply_defaults
def __init__(self,
bql=None,
sql=None,
destination_dataset_table=None,
write_disposition='WRITE_EMPTY',
allow_large_results=False,
flatten_results=None,
bigquery_conn_id='bigquery_default',
delegate_to=None,
udf_config=None,
use_legacy_sql=True,
maximum_billing_tier=None,
maximum_bytes_billed=None,
create_disposition='CREATE_IF_NEEDED',
schema_update_options=(),
query_params=None,
labels=None,
priority='INTERACTIVE',
time_partitioning=None,
api_resource_configs=None,
cluster_fields=None,
location=None,
*args,
**kwargs):
super(BigQueryOperator, self).__init__(*args, **kwargs)
self.bql = bql
self.sql = sql if sql else bql
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.bigquery_conn_id = bigquery_conn_id
self.delegate_to = delegate_to
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.bq_cursor = None
self.priority = priority
self.time_partitioning = time_partitioning
self.api_resource_configs = api_resource_configs
self.cluster_fields = cluster_fields
self.location = location
# TODO remove `bql` in Airflow 2.0
if self.bql:
import warnings
warnings.warn('Deprecated parameter `bql` used in Task id: {}. '
'Use `sql` parameter instead to pass the sql to be '
'executed. `bql` parameter is deprecated and '
'will be removed in a future version of '
'Airflow.'.format(self.task_id),
category=DeprecationWarning)
if self.sql is None:
raise TypeError('{} missing 1 required positional '
'argument: `sql`'.format(self.task_id))
[docs] def execute(self, context):
if self.bq_cursor is None:
self.log.info('Executing: %s', self.sql)
hook = BigQueryHook(
bigquery_conn_id=self.bigquery_conn_id,
use_legacy_sql=self.use_legacy_sql,
delegate_to=self.delegate_to,
location=self.location,
)
conn = hook.get_conn()
self.bq_cursor = conn.cursor()
if isinstance(self.sql, str):
job_id = self.bq_cursor.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,
)
elif isinstance(self.sql, Iterable):
job_id = [
self.bq_cursor.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,
)
for s in self.sql]
else:
raise AirflowException(
"argument 'sql' of type {} is neither a string nor an iterable".format(type(str)))
context['task_instance'].xcom_push(key='job_id', value=job_id)
[docs] def on_kill(self):
super(BigQueryOperator, self).on_kill()
if self.bq_cursor is not None:
self.log.info('Cancelling running query')
self.bq_cursor.cancel_query()
[docs]class BigQueryCreateEmptyTableOperator(BaseOperator):
"""
Creates a new, empty 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.
:param project_id: The project to create the table into. (templated)
:type project_id: str
:param dataset_id: The dataset to create the table into. (templated)
:type dataset_id: str
:param table_id: The Name of the table to be created. (templated)
:type table_id: str
: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"}]
:type schema_fields: list
:param gcs_schema_object: Full path to the JSON file containing
schema (templated). For
example: ``gs://test-bucket/dir1/dir2/employee_schema.json``
:type gcs_schema_object: str
: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
:type time_partitioning: dict
:param bigquery_conn_id: Reference to a specific BigQuery hook.
:type bigquery_conn_id: str
:param google_cloud_storage_conn_id: Reference to a specific Google
cloud storage hook.
:type google_cloud_storage_conn_id: str
:param delegate_to: The account to impersonate, if any. For this to
work, the service account making the request must have domain-wide
delegation enabled.
:type delegate_to: str
: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',
bigquery_conn_id='airflow-service-account',
google_cloud_storage_conn_id='airflow-service-account'
)
**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"}],
bigquery_conn_id='airflow-service-account',
google_cloud_storage_conn_id='airflow-service-account'
)
:type labels: dict
"""
[docs] template_fields = ('dataset_id', 'table_id', 'project_id',
'gcs_schema_object', 'labels')
# pylint: disable=too-many-arguments
@apply_defaults
def __init__(self,
dataset_id,
table_id,
project_id=None,
schema_fields=None,
gcs_schema_object=None,
time_partitioning=None,
bigquery_conn_id='bigquery_default',
google_cloud_storage_conn_id='google_cloud_default',
delegate_to=None,
labels=None,
*args, **kwargs):
super(BigQueryCreateEmptyTableOperator, self).__init__(*args, **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.bigquery_conn_id = bigquery_conn_id
self.google_cloud_storage_conn_id = google_cloud_storage_conn_id
self.delegate_to = delegate_to
self.time_partitioning = {} if time_partitioning is None else time_partitioning
self.labels = labels
[docs] def execute(self, context):
bq_hook = BigQueryHook(bigquery_conn_id=self.bigquery_conn_id,
delegate_to=self.delegate_to)
if not self.schema_fields and self.gcs_schema_object:
gcs_bucket, gcs_object = _parse_gcs_url(self.gcs_schema_object)
gcs_hook = GoogleCloudStorageHook(
google_cloud_storage_conn_id=self.google_cloud_storage_conn_id,
delegate_to=self.delegate_to)
schema_fields = json.loads(gcs_hook.download(
gcs_bucket,
gcs_object).decode("utf-8"))
else:
schema_fields = self.schema_fields
conn = bq_hook.get_conn()
cursor = conn.cursor()
cursor.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,
labels=self.labels
)
# pylint: disable=too-many-instance-attributes
[docs]class BigQueryCreateExternalTableOperator(BaseOperator):
"""
Creates a new external table in the dataset with the data in 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.
:param bucket: The bucket to point the external table to. (templated)
:type bucket: str
:param source_objects: List of Google cloud storage URIs to point
table to. (templated)
If source_format is 'DATASTORE_BACKUP', the list must only contain a single URI.
:type source_objects: list
: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.
:type destination_project_dataset_table: str
: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'.
:type schema_fields: list
:param schema_object: If set, a GCS object path pointing to a .json file that
contains the schema for the table. (templated)
:type schema_object: str
:param source_format: File format of the data.
:type source_format: str
: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.
:type compression: str
:param skip_leading_rows: Number of rows to skip when loading from a CSV.
:type skip_leading_rows: int
:param field_delimiter: The delimiter to use for the CSV.
:type field_delimiter: str
:param max_bad_records: The maximum number of bad records that BigQuery can
ignore when running the job.
:type max_bad_records: int
:param quote_character: The value that is used to quote data sections in a CSV file.
:type quote_character: str
:param allow_quoted_newlines: Whether to allow quoted newlines (true) or not (false).
:type allow_quoted_newlines: bool
: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.
:type allow_jagged_rows: bool
:param bigquery_conn_id: Reference to a specific BigQuery hook.
:type bigquery_conn_id: str
:param google_cloud_storage_conn_id: Reference to a specific Google
cloud storage hook.
:type google_cloud_storage_conn_id: str
:param delegate_to: The account to impersonate, if any. For this to
work, the service account making the request must have domain-wide
delegation enabled.
:type delegate_to: str
:param src_fmt_configs: configure optional fields specific to the source format
:type src_fmt_configs: dict
:param labels: a dictionary containing labels for the table, passed to BigQuery
:type labels: dict
"""
[docs] template_fields = ('bucket', 'source_objects',
'schema_object', 'destination_project_dataset_table', 'labels')
# pylint: disable=too-many-arguments
@apply_defaults
def __init__(self,
bucket,
source_objects,
destination_project_dataset_table,
schema_fields=None,
schema_object=None,
source_format='CSV',
compression='NONE',
skip_leading_rows=0,
field_delimiter=',',
max_bad_records=0,
quote_character=None,
allow_quoted_newlines=False,
allow_jagged_rows=False,
bigquery_conn_id='bigquery_default',
google_cloud_storage_conn_id='google_cloud_default',
delegate_to=None,
src_fmt_configs=None,
labels=None,
*args, **kwargs):
super(BigQueryCreateExternalTableOperator, self).__init__(*args, **kwargs)
# GCS config
self.bucket = bucket
self.source_objects = source_objects
self.schema_object = schema_object
# BQ config
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.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.bigquery_conn_id = bigquery_conn_id
self.google_cloud_storage_conn_id = google_cloud_storage_conn_id
self.delegate_to = delegate_to
self.src_fmt_configs = src_fmt_configs if src_fmt_configs is not None else dict()
self.labels = labels
[docs] def execute(self, context):
bq_hook = BigQueryHook(bigquery_conn_id=self.bigquery_conn_id,
delegate_to=self.delegate_to)
if not self.schema_fields and self.schema_object \
and self.source_format != 'DATASTORE_BACKUP':
gcs_hook = GoogleCloudStorageHook(
google_cloud_storage_conn_id=self.google_cloud_storage_conn_id,
delegate_to=self.delegate_to)
schema_fields = json.loads(gcs_hook.download(
self.bucket,
self.schema_object).decode("utf-8"))
else:
schema_fields = self.schema_fields
source_uris = ['gs://{}/{}'.format(self.bucket, source_object)
for source_object in self.source_objects]
conn = bq_hook.get_conn()
cursor = conn.cursor()
cursor.create_external_table(
external_project_dataset_table=self.destination_project_dataset_table,
schema_fields=schema_fields,
source_uris=source_uris,
source_format=self.source_format,
compression=self.compression,
skip_leading_rows=self.skip_leading_rows,
field_delimiter=self.field_delimiter,
max_bad_records=self.max_bad_records,
quote_character=self.quote_character,
allow_quoted_newlines=self.allow_quoted_newlines,
allow_jagged_rows=self.allow_jagged_rows,
src_fmt_configs=self.src_fmt_configs,
labels=self.labels
)
[docs]class BigQueryDeleteDatasetOperator(BaseOperator):
"""
This operator deletes an existing dataset from your Project in Big query.
https://cloud.google.com/bigquery/docs/reference/rest/v2/datasets/delete
:param project_id: The project id of the dataset.
:type project_id: str
:param dataset_id: The dataset to be deleted.
:type dataset_id: str
**Example**: ::
delete_temp_data = BigQueryDeleteDatasetOperator(dataset_id = 'temp-dataset',
project_id = 'temp-project',
bigquery_conn_id='_my_gcp_conn_',
task_id='Deletetemp',
dag=dag)
"""
[docs] template_fields = ('dataset_id', 'project_id')
@apply_defaults
def __init__(self,
dataset_id,
project_id=None,
bigquery_conn_id='bigquery_default',
delegate_to=None,
*args, **kwargs):
self.dataset_id = dataset_id
self.project_id = project_id
self.bigquery_conn_id = bigquery_conn_id
self.delegate_to = delegate_to
self.log.info('Dataset id: %s', self.dataset_id)
self.log.info('Project id: %s', self.project_id)
super(BigQueryDeleteDatasetOperator, self).__init__(*args, **kwargs)
[docs] def execute(self, context):
bq_hook = BigQueryHook(bigquery_conn_id=self.bigquery_conn_id,
delegate_to=self.delegate_to)
conn = bq_hook.get_conn()
cursor = conn.cursor()
cursor.delete_dataset(
project_id=self.project_id,
dataset_id=self.dataset_id
)
[docs]class BigQueryCreateEmptyDatasetOperator(BaseOperator):
"""
This operator is used to create new dataset for your Project in Big query.
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.
:type project_id: str
:param dataset_id: The id of dataset. Don't need to provide,
if datasetId in dataset_reference.
:type dataset_id: str
: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
:type dataset_reference: dict
**Example**: ::
create_new_dataset = BigQueryCreateEmptyDatasetOperator(
dataset_id = 'new-dataset',
project_id = 'my-project',
dataset_reference = {"friendlyName": "New Dataset"}
bigquery_conn_id='_my_gcp_conn_',
task_id='newDatasetCreator',
dag=dag)
"""
[docs] template_fields = ('dataset_id', 'project_id')
@apply_defaults
def __init__(self,
dataset_id,
project_id=None,
dataset_reference=None,
bigquery_conn_id='bigquery_default',
delegate_to=None,
*args, **kwargs):
self.dataset_id = dataset_id
self.project_id = project_id
self.bigquery_conn_id = bigquery_conn_id
self.dataset_reference = dataset_reference if dataset_reference else {}
self.delegate_to = delegate_to
self.log.info('Dataset id: %s', self.dataset_id)
self.log.info('Project id: %s', self.project_id)
super(BigQueryCreateEmptyDatasetOperator, self).__init__(*args, **kwargs)
[docs] def execute(self, context):
bq_hook = BigQueryHook(bigquery_conn_id=self.bigquery_conn_id,
delegate_to=self.delegate_to)
conn = bq_hook.get_conn()
cursor = conn.cursor()
cursor.create_empty_dataset(
project_id=self.project_id,
dataset_id=self.dataset_id,
dataset_reference=self.dataset_reference)