助力工业物联网，工业大数据之服务域：Shell调度测试【三十三】

# 默认的Airflow自动检测工作流程序的文件的目录
mkdir -p /root/airflow/dags
cd /root/airflow/dags
vim first_bash_operator.py

# import
from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.utils.dates import days_ago
from datetime import timedelta
# define args
default_args = {
    'owner': 'airflow',
    'email': ['airflow@example.com'],
    'email_on_failure': True,
    'email_on_retry': True,
    'retries': 1,
    'retry_delay': timedelta(minutes=1),
}
# define dag
dag = DAG(
    'first_airflow_dag',
    default_args=default_args,
    description='first airflow task DAG',
    schedule_interval=timedelta(days=1),
    start_date=days_ago(1),
    tags=['itcast_bash'],
)
# define task1
run_bash_task = BashOperator(
    task_id='first_bashoperator_task',
    bash_command='echo "hello airflow"',
    dag=dag,
)
# run the task
run_bash_task

bash_command='sh xxxx.sh'

python first_bash_operator.py 

cd /root/airflow/dags
vim second_bash_operator.py

# import
from datetime import timedelta
from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.utils.dates import days_ago
# define args
default_args = {
    'owner': 'airflow',
    'email': ['airflow@example.com'],
    'email_on_failure': True,
    'email_on_retry': True,
    'retries': 1,
    'retry_delay': timedelta(minutes=1),
}
# define dag
dag = DAG(
    'second_airflow_dag',
    default_args=default_args,
    description='first airflow task DAG',
    schedule_interval=timedelta(days=1),
    start_date=days_ago(1),
    tags=['itcast_bash'],
)
# define task1
say_hello_task = BashOperator(
    task_id='say_hello_task',
    bash_command='echo "start task"',
    dag=dag,
)
# define task2
print_date_format_task2 = BashOperator(
    task_id='print_date_format_task2',
    bash_command='date +"%F %T"',
    dag=dag,
)
# define task3
print_date_format_task3 = BashOperator(
    task_id='print_date_format_task3',
    bash_command='date +"%F %T"',
    dag=dag,
)
# define task4
end_task4 = BashOperator(
    task_id='end_task',
    bash_command='echo "end task"',
    dag=dag,
)
say_hello_task >> [print_date_format_task2,print_date_format_task3] >> end_task4

python second_bash_operator.py 

cd /root/airflow/dags
vim python_etl_airflow.py

# import package
from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.utils.dates import days_ago
import json
# define args
default_args = {
    'owner': 'airflow',
}
# define the dag
with DAG(
    'python_etl_dag',
    default_args=default_args,
    description='DATA ETL DAG',
    schedule_interval=None,
    start_date=days_ago(2),
    tags=['itcast'],
) as dag:
    # function1
    def extract(**kwargs):
        ti = kwargs['ti']
        data_string = '{"1001": 301.27, "1002": 433.21, "1003": 502.22, "1004": 606.65, "1005": 777.03}'
        ti.xcom_push('order_data', data_string)
    # function2
    def transform(**kwargs):
        ti = kwargs['ti']
        extract_data_string = ti.xcom_pull(task_ids='extract', key='order_data')
        order_data = json.loads(extract_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)
        ti.xcom_push('total_order_value', total_value_json_string)
    # function3
    def load(**kwargs):
        ti = kwargs['ti']
        total_value_string = ti.xcom_pull(task_ids='transform', key='total_order_value')
        total_order_value = json.loads(total_value_string)
        print(total_order_value)
    # task1
    extract_task = PythonOperator(
        task_id='extract',
        python_callable=extract,
    )
    extract_task.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.
This data is then put into xcom, so that it can be processed by the next task.
"""
  # task2
    transform_task = PythonOperator(
        task_id='transform',
        python_callable=transform,
    )
    transform_task.doc_md = """\
#### Transform task
A simple Transform task which takes in the collection of order data from xcom
and computes the total order value.
This computed value is then put into xcom, so that it can be processed by the next task.
"""
  # task3
    load_task = PythonOperator(
        task_id='load',
        python_callable=load,
    )
    load_task.doc_md = """\
#### Load task
A simple Load task which takes in the result of the Transform task, by reading it
from xcom and instead of saving it to end user review, just prints it out.
"""
# run
extract_task >> transform_task >> load_task

python python_etl_airflow.py

query_oracle_task = OracleOperator(
    task_id = 'oracle_operator_task',
    sql = 'select * from ciss4.ciss_base_areas',
    oracle_conn_id = 'oracle-airflow-connection',
    autocommit = True,
    dag=dag
)

query_table_mysql_task = MySqlOperator(
    task_id='query_table_mysql', 
    mysql_conn_id='mysql_airflow_connection', 
    sql=r"""select * from test.test_airflow_mysql_task;""",
    dag=dag
)

query_table_mysql_task = MySqlOperator(
    task_id='query_table_mysql_second', 
    mysql_conn_id='mysql-airflow-connection', 
    sql='test_airflow_mysql_task.sql',
    dag=dag
)

insert_sql = r"""
INSERT INTO `test`.`test_airflow_mysql_task`(`task_name`) VALUES ( 'test airflow mysql task3');
INSERT INTO `test`.`test_airflow_mysql_task`(`task_name`) VALUES ( 'test airflow mysql task4');
INSERT INTO `test`.`test_airflow_mysql_task`(`task_name`) VALUES ( 'test airflow mysql task5');
"""
insert_table_mysql_task = MySqlOperator(
    task_id='mysql_operator_insert_task', 
    mysql_conn_id='mysql-airflow-connection', 
    sql=insert_sql,
    dag=dag
)

run_sqoop_task = BashOperator(
    task_id='sqoop_task',
    bash_command='sqoop --options-file xxxx.sqoop',
    dag=dag,
)

run_hive_task = BashOperator(
    task_id='hive_task',
    bash_command='hive -f xxxx.sql',
    dag=dag,
)

run_spark_task = BashOperator(
    task_id='spark_task',
    bash_command='spark-sql -f xxxx.sql',
    dag=dag,
)

run_flink_task = BashOperator(
    task_id='flink_task',
    bash_command='flink run /opt/flink-1.12.2/examples/batch/WordCount.jar',
    dag=dag,
)