# 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. """ A example workflow for task datax. This example will create a workflow named `task_datax`. `task_datax` is true workflow define and run task task_datax. You can create data sources `first_mysql` and `first_mysql` through UI. It creates a task to synchronize datax from the source database to the target database. """ from pydolphinscheduler.core.process_definition import ProcessDefinition from pydolphinscheduler.tasks.datax import CustomDataX, DataX # datax json template JSON_TEMPLATE = { "job": { "content": [ { "reader": { "name": "mysqlreader", "parameter": { "username": "usr", "password": "pwd", "column": [ "id", "name", "code", "description" ], "splitPk": "id", "connection": [ { "table": [ "source_table" ], "jdbcUrl": [ "jdbc:mysql://127.0.0.1:3306/source_db" ] } ] } }, "writer": { "name": "mysqlwriter", "parameter": { "writeMode": "insert", "username": "usr", "password": "pwd", "column": [ "id", "name" ], "connection": [ { "jdbcUrl": "jdbc:mysql://127.0.0.1:3306/target_db", "table": [ "target_table" ] } ] } } } ] } } with ProcessDefinition( name="task_datax_1", tenant="tenant_exists", ) as pd: # This task synchronizes the data in `t_ds_project` # of `first_mysql` database to `target_project` of `second_mysql` database. task1 = DataX( name="task_datax", datasource_name="first_mysql", datatarget_name="second_mysql", sql="select id, name, code, description from source_table", target_table="target_table", ) # you can custom json_template of datax to sync data. This task create job # same as task1 do task2 = CustomDataX(name="task_custom_datax", json=str(JSON_TEMPLATE)) pd.run()