Google Cloud Storage Operators¶

Cloud Storage allows world-wide storage and retrieval of any amount of data at any time. You can use Cloud Storage for a range of scenarios including serving website content, storing data for archival and disaster recovery, or distributing large data objects to users via direct download.

Prerequisite Tasks¶

Operators¶

GCSToBigQueryOperator¶

Use the GCSToBigQueryOperator to execute a BigQuery load job.

airflow/providers/google/cloud/example_dags/example_gcs_to_bigquery.pyView Source

load_csv = GCSToBigQueryOperator(
    task_id='gcs_to_bigquery_example',
    bucket='cloud-samples-data',
    source_objects=['bigquery/us-states/us-states.csv'],
    destination_project_dataset_table=f"{DATASET_NAME}.{TABLE_NAME}",
    schema_fields=[
        {'name': 'name', 'type': 'STRING', 'mode': 'NULLABLE'},
        {'name': 'post_abbr', 'type': 'STRING', 'mode': 'NULLABLE'},
    ],
    write_disposition='WRITE_TRUNCATE',
)

GCSTimeSpanFileTransformOperator¶

Use the GCSTimeSpanFileTransformOperator to transform files that were modified in a specific time span (the data interval). The time span is defined by the time span's start and end timestamps. If a DAG does not have a next DAG instance scheduled, the time span end infinite, meaning the operator processes all files older than data_interval_start.

airflow/providers/google/cloud/example_dags/example_gcs_timespan_file_transform.pyView Source

gcs_timespan_transform_files_task = GCSTimeSpanFileTransformOperator(
    task_id="gcs_timespan_transform_files",
    source_bucket=SOURCE_BUCKET,
    source_prefix=SOURCE_PREFIX,
    source_gcp_conn_id=SOURCE_GCP_CONN_ID,
    destination_bucket=DESTINATION_BUCKET,
    destination_prefix=DESTINATION_PREFIX,
    destination_gcp_conn_id=DESTINATION_GCP_CONN_ID,
    transform_script=["python", PATH_TO_TRANSFORM_SCRIPT],
)

GCSBucketCreateAclEntryOperator¶

Creates a new ACL entry on the specified bucket.

For parameter definition, take a look at GCSBucketCreateAclEntryOperator

Using the operator¶

airflow/providers/google/cloud/example_dags/example_gcs.pyView Source

gcs_bucket_create_acl_entry_task = GCSBucketCreateAclEntryOperator(
    bucket=BUCKET_1,
    entity=GCS_ACL_ENTITY,
    role=GCS_ACL_BUCKET_ROLE,
    task_id="gcs_bucket_create_acl_entry_task",
)

Templating¶

template_fields = (
    'bucket',
    'entity',
    'role',
    'user_project',
    'impersonation_chain',
)

More information¶

See Google Cloud Storage Documentation to create a new ACL entry for a bucket.

GCSObjectCreateAclEntryOperator¶

Creates a new ACL entry on the specified object.

For parameter definition, take a look at GCSObjectCreateAclEntryOperator

Using the operator¶

airflow/providers/google/cloud/example_dags/example_gcs.pyView Source

gcs_object_create_acl_entry_task = GCSObjectCreateAclEntryOperator(
    bucket=BUCKET_1,
    object_name=BUCKET_FILE_LOCATION,
    entity=GCS_ACL_ENTITY,
    role=GCS_ACL_OBJECT_ROLE,
    task_id="gcs_object_create_acl_entry_task",
)

Templating¶

template_fields = (
    'bucket',
    'object_name',
    'entity',
    'generation',
    'role',
    'user_project',
    'impersonation_chain',
)

More information¶

See Google Cloud Storage insert documentation to create a ACL entry for ObjectAccess.

Deleting Bucket¶

Deleting Bucket allows you to remove bucket object from the Google Cloud Storage. It is performed through the GCSDeleteBucketOperator operator.

airflow/providers/google/cloud/example_dags/example_gcs.pyView Source

delete_bucket_1 = GCSDeleteBucketOperator(task_id="delete_bucket_1", bucket_name=BUCKET_1)
delete_bucket_2 = GCSDeleteBucketOperator(task_id="delete_bucket_2", bucket_name=BUCKET_2)

You can use Jinja templating with bucket_name, gcp_conn_id, impersonation_chain parameters which allows you to dynamically determine values.

Reference¶

For further information, look at:

Sensors¶

GCSObjectExistenceSensor¶

Use the GCSObjectExistenceSensor to wait (poll) for the existence of a file in Google Cloud Storage.

airflow/providers/google/cloud/example_dags/example_gcs.pyView Source

gcs_object_exists = GCSObjectExistenceSensor(
    bucket=BUCKET_1,
    object=BUCKET_FILE_LOCATION,
    mode='poke',
    task_id="gcs_object_exists_task",
)

GCSObjectsWithPrefixExistenceSensor¶

Use the GCSObjectsWithPrefixExistenceSensor to wait (poll) for the existence of a file with a specified prefix in Google Cloud Storage.

airflow/providers/google/cloud/example_dags/example_gcs.pyView Source

gcs_object_with_prefix_exists = GCSObjectsWithPrefixExistenceSensor(
    bucket=BUCKET_1,
    prefix=PATH_TO_UPLOAD_FILE_PREFIX,
    mode='poke',
    task_id="gcs_object_with_prefix_exists_task",
)

More information¶

Sensors have different modes that determine the behaviour of resources while the task is executing. See Airflow sensors documentation for best practices when using sensors.

Reference¶

For further information, look at: