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Posted to commits@airflow.apache.org by GitBox <gi...@apache.org> on 2020/10/06 17:06:36 UTC
[GitHub] [airflow] casassg commented on a change in pull request #11308: Airflow tutorial to use functional DAGs
casassg commented on a change in pull request #11308:
URL: https://github.com/apache/airflow/pull/11308#discussion_r500449668
##########
File path: airflow/example_dags/tutorial_functional_etl_dag.py
##########
@@ -0,0 +1,119 @@
+#
+# 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.
+
+"""
+### Functional DAG Tutorial Documentation
+
+This is a simple ETL data pipeline example which demonstrates the use of Functional DAGs
+using three simple tasks for Extract, Transform, and Load.
+
+Documentation that goes along with the Airflow Functional DAG tutorial located
+[here](https://airflow.apache.org/tutorial_functional_dag.html)
+"""
+# [START tutorial]
+# [START import_module]
+from datetime import timedelta
+import json
+
+# The DAG object; we'll need this to instantiate a DAG
+from airflow import DAG
+# Operators; we need this to operate!
+from airflow.operators.python_operator import PythonOperator
+from airflow.utils.dates import days_ago
+from airflow.decorators import task
+
+# [END import_module]
+
+# [START default_args]
+# These args will get passed on to each operator
+# You can override them on a per-task basis during operator initialization
+default_args = {
+ 'owner': 'airflow',
+ 'depends_on_past': False,
+ 'email': ['airflow@example.com'],
+ 'email_on_failure': False,
+ 'email_on_retry': False,
+ 'retries': 1,
+ 'retry_delay': timedelta(minutes=5),
+}
+# [END default_args]
+
+# [START instantiate_dag]
+with DAG(
+ 'tutorial_functional_etl_dag',
+ default_args=default_args,
+ description='Functional ETL DAG tutorial',
+ schedule_interval=timedelta(days=1),
+ start_date=days_ago(2),
+ tags=['example'],
+) as dag:
+# [END instantiate_dag]
+# [START documentation]
+ dag.doc_md = __doc__
+# [END documentation]
+
+# [START extract]
+ @dag.task()
+ def extract():
+ data_string = u'{"1001": 301.27, "1002": 433.21, "1003": 502.22}'
+ return data_string
+# [END extract]
+ extract.doc_md = """\
+ #### Extract task
+ A simple Extract task to get data ready for the rest of the data pipeline.
+ In this case, getting data is simulated by reading from a hardcoded JSON string.
+ """
+
+# [START transform]
+ @dag.task()
+ def transform(order_data_string: str):
+ order_data = json.loads(order_data_string)
+
+ total_order_value = 0
+ for value in order_data.values():
+ total_order_value += value
+
+ total_value = {"total_order_value": total_order_value}
+ total_value_json_string = json.dumps(total_value)
+ return total_value_json_string
Review comment:
I would return directly the dictionary and use `multiple_outputs=True` instead. See: https://github.com/apache/airflow/blob/master/tests/operators/test_python.py#L357
##########
File path: airflow/example_dags/tutorial_functional_etl_dag.py
##########
@@ -0,0 +1,119 @@
+#
+# 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.
+
+"""
+### Functional DAG Tutorial Documentation
+
+This is a simple ETL data pipeline example which demonstrates the use of Functional DAGs
+using three simple tasks for Extract, Transform, and Load.
+
+Documentation that goes along with the Airflow Functional DAG tutorial located
+[here](https://airflow.apache.org/tutorial_functional_dag.html)
+"""
+# [START tutorial]
+# [START import_module]
+from datetime import timedelta
+import json
+
+# The DAG object; we'll need this to instantiate a DAG
+from airflow import DAG
+# Operators; we need this to operate!
+from airflow.operators.python_operator import PythonOperator
+from airflow.utils.dates import days_ago
+from airflow.decorators import task
+
+# [END import_module]
+
+# [START default_args]
+# These args will get passed on to each operator
+# You can override them on a per-task basis during operator initialization
+default_args = {
+ 'owner': 'airflow',
+ 'depends_on_past': False,
+ 'email': ['airflow@example.com'],
+ 'email_on_failure': False,
+ 'email_on_retry': False,
+ 'retries': 1,
+ 'retry_delay': timedelta(minutes=5),
+}
+# [END default_args]
+
+# [START instantiate_dag]
+with DAG(
+ 'tutorial_functional_etl_dag',
+ default_args=default_args,
+ description='Functional ETL DAG tutorial',
+ schedule_interval=timedelta(days=1),
+ start_date=days_ago(2),
+ tags=['example'],
+) as dag:
+# [END instantiate_dag]
+# [START documentation]
+ dag.doc_md = __doc__
+# [END documentation]
+
+# [START extract]
+ @dag.task()
+ def extract():
+ data_string = u'{"1001": 301.27, "1002": 433.21, "1003": 502.22}'
+ return data_string
+# [END extract]
+ extract.doc_md = """\
+ #### Extract task
+ A simple Extract task to get data ready for the rest of the data pipeline.
+ In this case, getting data is simulated by reading from a hardcoded JSON string.
+ """
+
+# [START transform]
+ @dag.task()
+ def transform(order_data_string: str):
+ order_data = json.loads(order_data_string)
+
+ total_order_value = 0
+ for value in order_data.values():
+ total_order_value += value
+
+ total_value = {"total_order_value": total_order_value}
+ total_value_json_string = json.dumps(total_value)
+ return total_value_json_string
+# [END transform]
+ transform.doc_md = """\
+ #### Transform task
+ A simple Transform task which takes in the collection of order data and computes
+ the total order value.
+ """
+
+# [START load]
+ @dag.task()
+ def load(total_value_string: str):
+ total_order_value = json.loads(total_value_string)
+
+ print(total_order_value)
+# [END load]
+ load.doc_md = """\
+ #### Load task
+ A simple Load task which takes in the result of the Transform task and instead of
+ saving it to end user review, just prints it out.
+ """
+
+ order_data = extract()
+ order_summary = transform(order_data)
+ load(order_summary)
Review comment:
```suggestion
load(order_summary["total_order_value"])
```
##########
File path: airflow/example_dags/tutorial_functional_etl_dag.py
##########
@@ -0,0 +1,119 @@
+#
+# 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.
+
+"""
+### Functional DAG Tutorial Documentation
+
+This is a simple ETL data pipeline example which demonstrates the use of Functional DAGs
+using three simple tasks for Extract, Transform, and Load.
+
+Documentation that goes along with the Airflow Functional DAG tutorial located
+[here](https://airflow.apache.org/tutorial_functional_dag.html)
+"""
+# [START tutorial]
+# [START import_module]
+from datetime import timedelta
+import json
+
+# The DAG object; we'll need this to instantiate a DAG
+from airflow import DAG
+# Operators; we need this to operate!
+from airflow.operators.python_operator import PythonOperator
+from airflow.utils.dates import days_ago
+from airflow.decorators import task
+
+# [END import_module]
+
+# [START default_args]
+# These args will get passed on to each operator
+# You can override them on a per-task basis during operator initialization
+default_args = {
+ 'owner': 'airflow',
+ 'depends_on_past': False,
+ 'email': ['airflow@example.com'],
+ 'email_on_failure': False,
+ 'email_on_retry': False,
+ 'retries': 1,
+ 'retry_delay': timedelta(minutes=5),
+}
+# [END default_args]
+
+# [START instantiate_dag]
+with DAG(
+ 'tutorial_functional_etl_dag',
+ default_args=default_args,
+ description='Functional ETL DAG tutorial',
+ schedule_interval=timedelta(days=1),
+ start_date=days_ago(2),
+ tags=['example'],
+) as dag:
+# [END instantiate_dag]
+# [START documentation]
+ dag.doc_md = __doc__
+# [END documentation]
+
+# [START extract]
+ @dag.task()
+ def extract():
+ data_string = u'{"1001": 301.27, "1002": 433.21, "1003": 502.22}'
+ return data_string
+# [END extract]
+ extract.doc_md = """\
+ #### Extract task
+ A simple Extract task to get data ready for the rest of the data pipeline.
+ In this case, getting data is simulated by reading from a hardcoded JSON string.
+ """
+
+# [START transform]
+ @dag.task()
+ def transform(order_data_string: str):
+ order_data = json.loads(order_data_string)
+
+ total_order_value = 0
+ for value in order_data.values():
+ total_order_value += value
+
+ total_value = {"total_order_value": total_order_value}
Review comment:
```suggestion
return {"total_order_value": total_order_value}
```
##########
File path: airflow/example_dags/tutorial_functional_etl_dag.py
##########
@@ -0,0 +1,119 @@
+#
+# 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.
+
+"""
+### Functional DAG Tutorial Documentation
+
+This is a simple ETL data pipeline example which demonstrates the use of Functional DAGs
+using three simple tasks for Extract, Transform, and Load.
+
+Documentation that goes along with the Airflow Functional DAG tutorial located
+[here](https://airflow.apache.org/tutorial_functional_dag.html)
+"""
+# [START tutorial]
+# [START import_module]
+from datetime import timedelta
+import json
+
+# The DAG object; we'll need this to instantiate a DAG
+from airflow import DAG
+# Operators; we need this to operate!
+from airflow.operators.python_operator import PythonOperator
+from airflow.utils.dates import days_ago
+from airflow.decorators import task
+
+# [END import_module]
+
+# [START default_args]
+# These args will get passed on to each operator
+# You can override them on a per-task basis during operator initialization
+default_args = {
+ 'owner': 'airflow',
+ 'depends_on_past': False,
+ 'email': ['airflow@example.com'],
+ 'email_on_failure': False,
+ 'email_on_retry': False,
+ 'retries': 1,
+ 'retry_delay': timedelta(minutes=5),
+}
+# [END default_args]
+
+# [START instantiate_dag]
+with DAG(
+ 'tutorial_functional_etl_dag',
+ default_args=default_args,
+ description='Functional ETL DAG tutorial',
+ schedule_interval=timedelta(days=1),
+ start_date=days_ago(2),
+ tags=['example'],
+) as dag:
+# [END instantiate_dag]
+# [START documentation]
+ dag.doc_md = __doc__
+# [END documentation]
+
+# [START extract]
+ @dag.task()
+ def extract():
+ data_string = u'{"1001": 301.27, "1002": 433.21, "1003": 502.22}'
+ return data_string
+# [END extract]
+ extract.doc_md = """\
+ #### Extract task
+ A simple Extract task to get data ready for the rest of the data pipeline.
+ In this case, getting data is simulated by reading from a hardcoded JSON string.
+ """
+
+# [START transform]
+ @dag.task()
Review comment:
```suggestion
@dag.task(multiple_outputs=True)
```
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