def create_dag(
args: Mapping[str, Any],
parent_dag_name: Optional[str] = None,
) -> models.DAG:
"""Generates a DAG that pushes data from Google Cloud Storage to GA.
Args:
args: Arguments to provide to the Airflow DAG object as defaults.
parent_dag_name: If this is provided, this is a SubDAG.
Returns:
The DAG object.
"""
dag = airflow_utils.initialize_airflow_dag(
dag_id=dag_utils.get_dag_id(_DAG_NAME, parent_dag_name),
schedule=None,
retries=blockbuster_constants.DEFAULT_DAG_RETRY,
retry_delay=blockbuster_constants.DEFAULT_DAG_RETRY_DELAY_MINS,
start_days_ago=blockbuster_constants.DEFAULT_START_DAYS_AGO, **args)
storage_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_STORAGE_CONFIG)
bucket_name, bucket_path = dag_utils.extract_bucket_parts(
storage_vars['gcs_output_path'])
_cleanup_storage_task(dag, bucket_name, bucket_path)
return dag
def create_dag(
args: Mapping[str, Any],
parent_dag_name: Optional[str] = None,
) -> models.DAG:
"""Generates a DAG that trains a new model using the training dataset.
Args:
args: Arguments to provide to the Airflow DAG object as defaults.
parent_dag_name: If this is provided, this is a SubDAG.
Returns:
The DAG object.
"""
dag = airflow_utils.initialize_airflow_dag(
dag_id=dag_utils.get_dag_id(_DAG_NAME, parent_dag_name),
schedule=None,
retries=blockbuster_constants.DEFAULT_DAG_RETRY,
retry_delay=blockbuster_constants.DEFAULT_DAG_RETRY_DELAY_MINS,
start_days_ago=blockbuster_constants.DEFAULT_START_DAYS_AGO,
**args)
bb_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_GLOBAL_CONFIG)
training_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_TRAINING_CONFIG)
create_model_task = _add_create_model_task(dag, bb_vars, training_vars)
update_airflow_variable_task = _add_update_airflow_variable_task(dag)
helpers.chain(create_model_task, update_airflow_variable_task)
return dag
def create_dag(
args: Mapping[Text, Any],
parent_dag_name: Optional[Text] = None,
) -> models.DAG:
"""Generates a DAG that loads data into an AutoML Dataset.
Args:
args: Arguments to provide to the AutoML operators as defaults.
parent_dag_name: If this value is provided, the newly created dag object is
made a subdag of the parent dag.
Returns:
The DAG object.
"""
# Load params from Variables.
bb_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_GLOBAL_CONFIG)
storage_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_STORAGE_CONFIG)
# Create dag.
dag = airflow_utils.initialize_airflow_dag(
dag_utils.get_dag_id(_DAG_NAME, parent_dag_name),
None, # schedule
blockbuster_constants.DEFAULT_DAG_RETRY,
blockbuster_constants.DEFAULT_DAG_RETRY_DELAY_MINS,
blockbuster_constants.DEFAULT_START_DAYS_AGO,
local_macros={
'get_column_spec': _get_column_spec,
'target': 'predictionLabel',
'extract_object_id':
automl_hook.AutoMLTablesHook.extract_object_id,
},
**args)
dataset_creation_task = _add_dataset_creation_task(dag, bb_vars)
dataset_id = (
"{{ task_instance.xcom_pull('create_dataset_task', key='dataset_id') }}"
)
import_data_task = _add_import_data_task(dag, dataset_id, bb_vars,
storage_vars)
list_table_specs_task = _add_list_table_specs_task(dag, dataset_id,
bb_vars)
list_column_specs_task = _add_list_column_specs_task(
dag, dataset_id, bb_vars)
update_dataset_task = _add_update_dataset_task(dag, bb_vars)
update_airflow_variable_task = _add_update_airflow_variable_task(dag)
helpers.chain(dataset_creation_task, import_data_task,
list_table_specs_task, list_column_specs_task,
update_dataset_task, update_airflow_variable_task)
return dag
def create_dag(
args: Dict[str, Union[Dict[str, Any], dt.datetime]],
output_type: blockbuster_constants.PreprocessingType,
parent_dag_name: Optional[str] = None,
) -> models.DAG:
"""Generates a DAG that create source table from GA tables.
Args:
args: Arguments to provide to the operators as defaults.
output_type: Which set of variables to load for preprocessing and
prediction.
parent_dag_name: If this is provided, this is a SubDAG.
Returns:
The DAG object.
"""
# Load params from Airflow Variables.
bb_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_GLOBAL_CONFIG)
preprocess_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_PREPROCESS_CONFIG)
prediction_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_PREDICTION_ACTIVATION_CONFIG)
storage_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_STORAGE_CONFIG)
dag_id = dag_utils.get_dag_id(DAG_NAME, parent_dag_name)
dag = airflow_utils.initialize_airflow_dag(
dag_id, None, blockbuster_constants.DEFAULT_DAG_RETRY,
blockbuster_constants.DEFAULT_DAG_RETRY_DELAY_MINS,
blockbuster_constants.DEFAULT_START_DAYS_AGO, **args)
if output_type == blockbuster_constants.PreprocessingType.TRAINING:
table_suffix = 'training'
sql_vars = get_sql_params_for_training_stage(preprocess_vars)
elif output_type == blockbuster_constants.PreprocessingType.PREDICTION:
table_suffix = 'prediction'
sql_vars = get_sql_params_for_prediction_stage(preprocess_vars,
prediction_vars)
sql = pipeline_utils.render_sql_from_template('source_leads', **sql_vars)
bq_working_project = storage_vars['bq_working_project']
bq_working_dataset = storage_vars['bq_working_dataset']
leads_table = get_leads_table(bq_working_project, bq_working_dataset,
table_suffix)
gcp_region = bb_vars['gcp_region']
add_prepare_source_data_task_to_dag(dag, sql, leads_table, gcp_region)
return dag
def create_dag(
args: Mapping[str, Any],
output_type: blockbuster_constants.PreprocessingType,
parent_dag_name: Optional[str] = None,
) -> models.DAG:
"""Generates a DAG that preprocesses data.
Args:
args: Arguments to provide to the Operators as defaults.
output_type: Which set of Variables to load for preprocessing
parent_dag_name: If this is provided, this is a SubDAG.
Returns:
The DAG object.
"""
preprocess_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_PREPROCESS_CONFIG)
prediction_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_PREDICTION_ACTIVATION_CONFIG)
storage_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_STORAGE_CONFIG)
training_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_TRAINING_CONFIG)
feature_options = dag_utils.get_feature_config_val(
blockbuster_constants.BLOCKBUSTER_FEATURE_CONFIG)
dag = airflow_utils.initialize_airflow_dag(
dag_utils.get_dag_id(_DAG_NAME, parent_dag_name), None,
blockbuster_constants.DEFAULT_DAG_RETRY,
blockbuster_constants.DEFAULT_DAG_RETRY_DELAY_MINS,
blockbuster_constants.DEFAULT_START_DAYS_AGO, **args)
mlwp_sliding_window_pipeline_task = _add_mlwp_sliding_window_pipeline_task(
dag, output_type, prediction_vars, preprocess_vars, storage_vars,
training_vars)
mlwp_generate_features_pipeline_task = _add_mlwp_generate_features_pipeline_task(
dag, output_type, feature_options, storage_vars)
prepare_automl_data_in_bq_task = _add_prepare_automl_data_in_bq_task(
dag, output_type, prediction_vars, storage_vars)
helpers.chain(mlwp_sliding_window_pipeline_task,
mlwp_generate_features_pipeline_task,
prepare_automl_data_in_bq_task)
return dag
def create_batch_predict_dag(
args: Dict[str, Union[Dict[str, Any], dt.datetime]],
parent_dag_name: Optional[str] = None,
) -> models.DAG:
"""Generates a DAG/SubDAG that predicts using an existing model.
Args:
args: Arguments to provide to the operators as defaults.
parent_dag_name: If this is provided, this is a SubDAG.
Returns:
DAG
Raises:
KeyError: If recent_model_id Airflow Variable hasn't been set.
"""
dag_id = dag_utils.get_dag_id(DAG_NAME, parent_dag_name)
dag = airflow_utils.initialize_airflow_dag(
dag_id=dag_id,
schedule=None,
retries=blockbuster_constants.DEFAULT_DAG_RETRY,
retry_delay=blockbuster_constants.DEFAULT_DAG_RETRY_DELAY_MINS,
start_days_ago=blockbuster_constants.DEFAULT_START_DAYS_AGO,
local_macros={'extract_dataset_id': _extract_dataset_id}, **args)
batch_predict_task = _get_batch_predictions(dag, 'batch_predict_task')
automl_bq_location = (
'{{ extract_dataset_id(' # macro to be expanded in task Jinja template
'task_instance.xcom_pull('
'"batch_predict_task", key="bq_output_dataset")) }}')
batch_predict_sql = _generate_batch_prediction_sql_template(
'batch_predict', automl_bq_location)
get_output_data_task = _store_final_results_to_bq(dag, 'get_output_data',
batch_predict_sql)
bq_to_gcs_task = _transfer_bigquery_to_gcs(dag, 'bq_to_gcs')
dag >> batch_predict_task >> get_output_data_task >> bq_to_gcs_task
return dag
def create_dag(
args: Mapping[str, Any],
output_type: blockbuster_constants.PreprocessingType,
parent_dag_name: Optional[str] = None,
) -> models.DAG:
"""Generates a DAG that analyzes data before preprocessing.
Args:
args: Arguments to provide to the Operators as defaults.
output_type: Which set of Variables to load for preprocessing.
parent_dag_name: If this is provided, this is a SubDAG.
Returns:
The DAG object.
"""
# Load params from Airflow Variables
preprocess_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_PREPROCESS_CONFIG)
prediction_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_PREDICTION_ACTIVATION_CONFIG)
storage_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_STORAGE_CONFIG)
training_vars = airflow_utils.retrieve_airflow_variable_as_dict(
blockbuster_constants.BLOCKBUSTER_TRAINING_CONFIG)
feature_vars = dag_utils.get_feature_config_val(
blockbuster_constants.BLOCKBUSTER_FEATURE_CONFIG)
dag = airflow_utils.initialize_airflow_dag(
dag_utils.get_dag_id(_DAG_NAME, parent_dag_name), None,
blockbuster_constants.DEFAULT_DAG_RETRY,
blockbuster_constants.DEFAULT_DAG_RETRY_DELAY_MINS,
blockbuster_constants.DEFAULT_START_DAYS_AGO, **args)
if output_type == blockbuster_constants.PreprocessingType.TRAINING:
bucket_name, bucket_path = dag_utils.extract_bucket_parts(
f'{storage_vars["gcs_temp_path"]}/training')
else:
bucket_name, bucket_path = dag_utils.extract_bucket_parts(
f'{storage_vars["gcs_temp_path"]}/prediction')
clean_temp_dir_task = gcs_delete_operator.GoogleCloudStorageDeleteOperator(
task_id=_CLEAN_TEMP_DIR_TASK,
bucket=bucket_name,
directory=bucket_path,
dag=dag)
user_session_pipeline_task = add_user_session_task(
dag, _USER_SESSION_TASK_ID, output_type, feature_vars, prediction_vars,
preprocess_vars, storage_vars, training_vars)
if output_type == blockbuster_constants.PreprocessingType.TRAINING:
data_visualization_pipeline_task = add_data_visualization_task(
dag, _DATA_VISUALISATION_TASK_ID, preprocess_vars, storage_vars)
generate_categorical_stats_task = add_categorical_stats_task(
dag, feature_vars, storage_vars)
generate_numeric_stats_task = add_numeric_stats_task(
dag, feature_vars, storage_vars)
helpers.chain(
clean_temp_dir_task, user_session_pipeline_task,
data_visualization_pipeline_task,
[generate_categorical_stats_task, generate_numeric_stats_task])
else:
helpers.chain(clean_temp_dir_task, user_session_pipeline_task)
return dag
