知识点07：Shell调度测试

• 目标：实现Shell命令的调度测试

• 实施

  • 需求：使用BashOperator调度执行一条Linux命令

  • 代码

    • 创建

      # 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
      

    • 开发

    # 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
    

    • 工作中使用bashOperator

      bash_command='sh xxxx.sh'
      

    • xxxx.sh：根据需求

      • Linux命令
      • hive -f
      • spark-sql -f
      • spark-submit python | jar

  • 提交

    python first_bash_operator.py 
    

  • 查看

    

  • 执行

    

• 小结

  • 实现Shell命令的调度测试

知识点08：依赖调度测试

• 目标：实现AirFlow的依赖调度测试

• 实施

  • 需求：使用BashOperator调度执行多个Task，并构建依赖关系

  • 代码

    • 创建

      cd /root/airflow/dags
      vim second_bash_operator.py
      

    • 开发

      # 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
      

  • 提交

    python second_bash_operator.py 
    

  • 查看

    

• 小结

  • 实现AirFlow的依赖调度测试

知识点09：Python调度测试

• 目标：实现Python代码的调度测试

• 实施

  • 需求：调度Python代码Task的运行

  • 代码

    • 创建

      cd /root/airflow/dags
      vim python_etl_airflow.py
      

    • 开发

      # 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
      

  • 提交

    python python_etl_airflow.py
    

  • 查看

    

• 小结

  • 实现Python代码的调度测试

知识点10：Oracle与MySQL调度方法

• 目标：了解Oracle与MySQL的调度方法

• 实施

  • Oracle调度：参考《oracle任务调度详细操作文档.md》

    • step1：本地安装Oracle客户端

    • step2：安装AirFlow集成Oracle库

    • step3：创建Oracle连接

    • step4：开发测试

      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
      )
      

  • MySQL调度：《MySQL任务调度详细操作文档.md》

    • step1：本地安装MySQL客户端

    • step2：安装AirFlow集成MySQL库

    • step3：创建MySQL连接

    • step4：开发测试

      • 方式一：指定SQL语句

        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
        )
        

        • 方式二：指定SQL文件

          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
          )
          

      • 方式三：指定变量

        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
        )
        

​

• 小结

  • 了解Oracle与MySQL的调度方法

知识点11：大数据组件调度方法

• 目标：了解大数据组件调度方法

• 实施

  • AirFlow支持的类型

    • HiveOperator
    • PrestoOperator
    • SparkSqlOperator

  • 需求：Sqoop、MR、Hive、Spark、Flink

  • 解决：统一使用BashOperator或者PythonOperator，将对应程序封装在脚本中

    • Sqoop

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

    • Hive

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

    • Spark

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

    • Flink

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

• 小结

  • 了解大数据组件调度方法