Thank you all！ According to this discussion, I have sorted out a task list about JSON split. I very much hope that people who are interested in implementing coding can join us. the issue : https://github.com/apache/incubator-dolphinscheduler/issues/4325
Hemin Wen <[email protected]> 于2020年12月4日周五 下午2:24写道： > According to the results of the discussion, the plan has been re-optimized > and related development work: > > ## 1. Currently > The workflow definition of the current DS system includes task definition > data and task relationship data. In the design of the database, task data > and task relationship data are stored in the workflow as a string type > field (process_definition_json) Definition table (t_ds_process_definition). > > With the increase of workflow and tasks, the following problems will arise: > > -Task data, relational data and workflow data are coupled together, which > is not friendly to the scenario of single-task scheduling. The task must be > created in the workflow > > -The task cannot be reused because the task is created in the workflow > > -The maintenance cost is high. If you move the whole body and modify any > task, you need to update the data in the workflow as a whole, and it also > increases the log cost > > -When there are many tasks in the workflow, the efficiency of global search > and statistical analysis is low, such as querying which tasks use which > data source > > -Poor scalability, for example, the realization of blood relationship > function in the future will only lead to more and more bloated workflow > definitions > > -Tasks, relationships, and workflow boundaries are blurred. Condition nodes > and delay nodes are also regarded as a task, which is actually a > combination of relationships and conditions > > Based on the above pain points, we need to redefine the business boundaries > of tasks, relationships, and workflows, and redesign their data structures > based on this > > ## 2. Design Ideas > > ### 2.1 Workflow, relationship, job > > First of all, we set aside the current implementation and clarify the > business boundaries of tasks (the subsequent description is changed to > jobs), relationships, and workflows, and how to decouple > > -Job: the task to be executed by the scheduling system, the job only > contains the data and resources needed to execute the job > -Relationship: the relationship between the job and the job and the > execution conditions, including the execution relationship (after A > completes, execute B) and execution conditions (after A completes and > succeeds, execute B; after A completes and fails, execute C; A completes 30 > After minutes, execute D) > -Workflow: the carrier of a set of relationships, the workflow only saves > the relationships between jobs (DAG is a form of presentation of workflow, > a way to create relationships) > > Combined with the functions supported by the current DS, we can make a > classification > > -Job: Dependency check, sub-process, Shell, stored procedure, Sql, Spark, > Flink, MR, Python, Http, DataX, Sqoop > -Relationship: serial execution, parallel execution, aggregate execution, > conditional branch, delayed execution > -Workflow: the boundary of scheduling execution, including a set of > relationships > > #### 2.1.1 Further refinement > > The job definition data is not much different from the current job > definition data. Both are composed of public fields and custom fields. You > only need to remove the fields related to the relationship. > > The workflow definition data is not much different from the current > workflow definition data, just remove the json field. > > Relational data, we can abstract into two nodes and one path according to > classification. The node is the job. The path includes the conditional > rules that need to be met from the pre-node to the post-node. The > conditional rules include: unconditional, judgment condition, and delay > condition. > > ### 2.2 Version Management > > We clarify the business boundaries. After decoupling, they become a > reference relationship. The workflow and the relationship are one-to-many, > and the relationship and the job are one-to-many. The definition data also > needs to save the version record, which can support the restoration of > historical data in the future. > > So the design idea here is: > > To define data, you need to add a version field > > The definition table needs to add the corresponding log table > > When creating definition data, double write to the definition table and log > table. When modifying the definition data, save the modified version to the > log table > > There is no need to save version information in the reference data of the > definition table (quote the latest version) > > ### 2.3 Example data > > The current DB design already has workflow instance tables and task > instance tables, and DS currently supports data changes in instance tables. > The instance table cannot only save the code and version information of the > definition table, but also needs to maintain detailed definition data. > Therefore, it is necessary to split the workflow instance table into a > workflow instance table and a job relationship table, and the task instance > table is generally unchanged. The fields of the three instance tables are > basically the same as those of the definition table. > > ### 2.4 Business Logo Design > > Here is also involved in the import and export of workflow and job > definition data. According to the previous community discussion, business > identification needs to be introduced. Each data in the workflow definition > table and job definition table will have a business identification, > relationship definition data, and dependent jobs Establish a reference > relationship with the sub-workflow job through the business identifier. The > specific realization of the business logo is the voting result of the plan > to be designed. > > Related Issues: > https://github.com/apache/incubator-dolphinscheduler/issues/3820 > > Design plan: > > ## 3. Design plan > > ### 3.1 Table model design > > #### 3.1.1 Workflow definition table: t_ds_process_definithon > > | Column Name | Description | > | ---- | ---- | > | id | Self-incrementing ID | > | code | Code (the original name field) | > | version | Version | > | description | description | > | project_code | Project code | > | release_state | Release state | > | user_id | Owning user ID | > | global_params | Global parameters | > | flag | Whether the process is available: 0 is not available, 1 is > available | > | receivers | recipients | > | receivers_cc | CC | > | timeout | Timeout time | > | tenant_id | tenant ID | > | locations | Node coordinate information | > | create_time | Creation time | > | update_time | Modification time | > > #### 3.1.2 Workflow job relationship table: t_ds_process_task_relation > > Note: The last node has unconditional data and post data. Here you can > imagine the two ends of a line, the left is the front node, the middle is > the condition, and the right is the post node. > > | Column Name | Description | > | ----------------------- | ------------------------- ------------- | > | id | Self-incrementing ID | > | project_code | Project code | > | process_definition_code | Workflow coding | > | pre_project_code | Pre-quoted project code | > | pre_task_code | Pre-reference job code | > | condition_type | Condition type 0: None 1: Judgment 2: Delayed | > | condition_params | Condition parameters (json) | > | post_project_code | Post reference project code | > | post_task_code | Post reference job code | > | create_time | Creation time | > | update_time | Modification time | > > #### 3.1.3 Job definition table: t_ds_task_definithon > > | Column Name | Description | > | ----------------------- | -------------- | > | id | Self-incrementing ID | > | code | Code (the original name field) | > | version | Version | > | description | description | > | project_code | Project code | > | task_type | Job type | > | task_params | Job custom parameters | > | run_flag | Run flag | > | task_priority | Job priority | > | worker_group | worker group | > | fail_retry_times | Number of failed retries | > | fail_retry_interval | Failure retry interval | > | timeout_flag | Timeout flag | > | timeout_notify_strategy | Timeout notification strategy | > | timeout_duration | Timeout duration | > | create_time | Creation time | > | update_time | Modification time | > > #### 3.1.4 Workflow definition log table: t_ds_process_definithon_log > > Add operation type (add, modify, delete), operator, and operation time > based on the workflow definition table > > #### 3.1.5 Workflow job relationship log table: > t_ds_process_task_relation_log > > Add workflow version, operation type (add, modify, delete), operator, > operation time based on the job relationship table > > #### 3.1.6 Job definition log table: t_ds_task_definithon_log > > Add operation type (add, modify, delete), operator, and operation time > based on the job definition table > > ### 3.2 Master-Worker scheduling design > > When the Master schedules the workflow, it queries the workflow details and > all job relationship data according to the project code and workflow code > (job data is not loaded here), generates a DAG, traverses the DAG job, and > sends the project code and job code to the Worker. Project code, job code > query detailed job data and execute the job > > ## 4. Related work split > > ### 4.1 Frontend > > Added job management related functions, including: job list, job creation, > update, delete, view details operations > > To create a workflow page, you need to pass workflow information, job > relationship information, and job information to the back-end API layer to > save/update > > Workflow page, when dragging and dropping task nodes, it also supports > reference project-job (default current search job under current project) > and create job operation > > ### 4.2 API layer > > Added job data related processing interface, including version processing > (query, create, modify, delete, online and offline...) > > Refactored workflow data related processing interface, including version > processing (query, create, modify, delete, import, export, online and > offline...) > > Refactored the processing interface of workflow instance data (query, > modify, Gantt chart) > > Refactoring job instance query interface > > Refactored workflow instance, job instance related statistical interface > (UI system homepage, project homepage statistical data, related monitoring > data) > > ### 3.3 Master > > Rebuild Master according to the <3.2 Master-Worker Scheduling Design> > scheme > > ### 3.4 Worker > > Refactor Worker according to the <3.2 Master-Worker Scheduling Design> > scheme > > > ------------------------------------------------------------------------------------------------------------------------------------------ > > 根据讨论结果，重新优化了方案，还有相关开发工作 > > ## 1.现状 > > 当前DS系统的工作流定义包含了任务定义数据和任务之间关系数据，并且在数据库的设计上，任务数据和任务关系数据是以一个字符串类型字段（process_definition_json）的方式，保存在工作流定义表（t_ds_process_definition）中。 > > 随着工作流和任务的增加，会产生如下问题： > > - 任务数据、关系数据和工作流数据耦合在一起，对单任务调度的场景不友好，任务必须创建在工作流内 > > - 任务无法复用，因为任务是创建在工作流内的 > > - 维护成本高，牵一发动全身，修改任何一个任务，都需要整体更新工作流内数据，同时也增加了日志成本 > > - 工作流内任务较多时，全局搜索和统计分析效率低，例如查询哪些任务用到了哪个数据源 > > - 扩展性差，例如未来要实现血缘功能，只会导致工作流定义越来越臃肿 > > - 任务、关系、工作流边界模糊，条件节点、延迟节点也被当做一种任务，实际是关系与条件的组合 > > 基于以上痛点，我们需要重新定义任务、关系、工作流的业务边界，基于此重新设计它们的数据结构 > > ## 2.设计思路 > > ### 2.1 工作流、关系、作业 > > 首先，我们抛开当前的实现，明确任务（后续描述更改为作业）、关系、工作流的业务边界，如何去解耦 > > - 作业：调度系统要执行的任务，作业内只包含执行作业所需要的数据和资源 > - > > 关系：作业与作业之间的关系以及执行条件，包含执行关系（A完成后，执行B）和执行条件（A完成并成功后，执行B；A完成并失败后，执行C；A完成30分钟后，执行D） > - 工作流：一组关系的载体，工作流只保存作业间的关系（DAG是工作流的一种展示形式，创建关系的一种方式） > > 结合当前DS支持的功能，我们可以做一个分类 > > - 作业：依赖检查、子流程、Shell、存储过程、Sql、Spark、Flink、MR、Python、Http、DataX、Sqoop > - 关系：串行执行、并行执行、聚合执行、条件分支、延迟执行 > - 工作流：调度执行的边界，包含一组关系 > > #### 2.1.1 进一步细化 > > 作业定义数据，和当前的作业定义数据差别不大，都是由公共字段和自定义字段组成，只需要去掉关系相关的字段就可以了。 > > 工作流定义数据，和当前的工作流定义数据差别也不大，去掉json字段就可以了。 > > > 关系数据，我们根据分类可以抽象为两个节点和一个路径。节点就是作业，路径包含前置节点到后置节点需要满足的条件规则是什么，条件规则包含：无条件、判断条件、延迟条件。 > > ### 2.2 版本管理 > > > 我们明确业务边界，解耦后它们之间就变成了引用关系，工作流和关系之间是一对多，关系和作业之间是一对多。定义数据还需要保存版本记录，后续可以支持恢复历史数据。 > > 所以这里的设计思路是： > > 定义数据需要增加版本字段 > > 定义表需要增加对应的日志表 > > 创建定义数据时，双写到定义表和日志表，修改定义数据时，保存修改后的版本到日志表 > > 定义表的引用数据中不需要保存版本信息（引用最新版本） > > ### 2.3 实例数据 > > > 当前DB设计已经有工作流实例表、任务实例表，而且DS当前支持实例表的数据变更。实例表不能仅保存定义表的编码和版本信息，还需要维护详细的定义数据。所以需要将工作流实例表拆分为工作流实例表和作业关系表，任务实例表总体不变。三张实例表的字段和定义表字段基本一致。 > > ### 2.4 业务标识设计 > > > 这里还涉及到工作流、作业定义数据导入导出问题，根据之前社区讨论的方案，需要引入业务标识，工作流定义表和作业定义表每条数据都会有一个业务标识，关系定义数据、依赖作业和子工作流作业内部通过业务标识建立引用关系。业务标识的具体实现待设计方案的投票结果。 > > 相关Issue：https://github.com/apache/incubator-dolphinscheduler/issues/3820 > > 设计方案： > > ## 3.设计方案 > > ### 3.1 表模型设计 > > #### 3.1.1 工作流定义表：t_ds_process_definithon > > | 列名 | 描述 | > | ---- | ---- | > | id | 自增ID | > | code | 编码（原name字段） | > | version | 版本 | > | description | 描述 | > | project_code | 项目编码 | > | release_state | 发布状态 | > | user_id | 所属用户ID | > | global_params | 全局参数 | > | flag | 流程是否可用：0 不可用，1 可用 | > | receivers | 收件人 | > | receivers_cc | 抄送人 | > | timeout | 超时时间 | > | tenant_id | 租户ID | > | locations | 节点坐标信息 | > | create_time | 创建时间 | > | update_time | 修改时间 | > > #### 3.1.2 工作流作业关系表：t_ds_process_task_relation > > 注：最后一个节点无条件数据和后置数据，这里可以想象一条线的两端，左边是前置节点，中间是条件，右边是后置节点 > > | 列名 | 描述 | > | ----------------------- | -------------------------------------- | > | id | 自增ID | > | project_code | 项目编码 | > | process_definition_code | 工作流编码 | > | pre_project_code | 前置引用项目编码 | > | pre_task_code | 前置引用作业编码 | > | condition_type | 条件类型 0：无 1：判断 2：延迟 | > | condition_params | 条件参数（json） | > | post_project_code | 后置引用项目编码 | > | post_task_code | 后置引用作业编码 | > | create_time | 创建时间 | > | update_time | 修改时间 | > > #### 3.1.3 作业定义表：t_ds_task_definithon > > | 列名 | 描述 | > | ----------------------- | -------------- | > | id | 自增ID | > | code | 编码（原name字段） | > | version | 版本 | > | description | 描述 | > | project_code | 项目编码 | > | task_type | 作业类型 | > | task_params | 作业自定义参数 | > | run_flag | 运行标志 | > | task_priority | 作业优先级 | > | worker_group | worker分组 | > | fail_retry_times | 失败重试次数 | > | fail_retry_interval | 失败重试间隔 | > | timeout_flag | 超时标志 | > | timeout_notify_strategy | 超时通知策略 | > | timeout_duration | 超时时长 | > | create_time | 创建时间 | > | update_time | 修改时间 | > > #### 3.1.4 工作流定义日志表：t_ds_process_definithon_log > > 工作流定义表基础上增加操作类型（新增、修改、删除）、操作人、操作时间 > > #### 3.1.5 工作流作业关系日志表：t_ds_process_task_relation_log > > 作业关系表基础上增加工作流版本、操作类型（新增、修改、删除）、操作人、操作时间 > > #### 3.1.6 作业定义日志表：t_ds_task_definithon_log > > 作业定义表基础上增加操作类型（新增、修改、删除）、操作人、操作时间 > > ### 3.2 Master-Worker调度设计 > > > Master调度工作流时，根据项目编码、工作流编码查询工作流详细信息和所有作业关系数据（这里不加载作业数据），生成DAG，遍历DAG作业，发送项目编码、作业编码到Worker，Worker根据项目编码、作业编码查询作业详细数据并执行作业 > > ## 4.相关工作拆分 > > ### 4.1 前端 > > 增加作业管理相关功能，包括：作业列表，作业的创建、更新、删除、查看详情操作 > > 创建工作流页面，需要将工作流信息、作业关系信息、作业信息传给后端API层保存/更新 > > 工作流页面，拖拽任务节点时，同时支持引用项目-作业（默认当前搜索当前项目下作业）和创建作业操作 > > ### 4.2 API层 > > 增加作业数据相关处理接口，包含版本处理（查询、新建、修改、删除、上下线...） > > 重构工作流数据相关处理接口，包含版本处理（查询、新建、修改、删除、导入、导出、上下线...） > > 重构工作流实例数据相关处理接口（查询、修改、甘特图） > > 重构作业实例查询接口 > > 重构工作流实例、作业实例相关统计接口（UI系统首页、项目首页统计数据、相关监控数据） > > ### 3.3 Master > > 根据《3.2 Master-Worker调度设计》方案重构Master > > ### 3.4 Worker > > 根据《3.2 Master-Worker调度设计》方案重构Worker > > -------------------- > DolphinScheduler(Incubator) Commtter > Hemin Wen 温合民 > [email protected] > -------------------- > > > Hemin Wen <[email protected]> 于2020年11月25日周三 上午10:01写道： > > > Hi! > > > > About json splitting of workflow definition, The following is the design > > plan for splitting three tables. > > > > Everyone can discuss together. > > > > > > > -------------------------------------------------------------------------------------------------------------- > > > > ## 1. Currently > > The workflow definition of the current DS system includes task definition > > data and task relationship data. In the design of the database, task data > > and task relationship data are stored in the workflow as a string type > > field (process_definition_json) Definition table > (t_ds_process_definition). > > > > With the increase of workflow and tasks, the following problems will > arise: > > > > -Task data, relational data and workflow data are coupled together, which > > is not friendly to the scenario of single-task scheduling. The task must > be > > created in the workflow > > > > -The task cannot be reused because the task is created in the workflow > > > > -The maintenance cost is high. If you move the whole body and modify any > > task, you need to update the data in the workflow as a whole, and it also > > increases the log cost > > > > -When there are many tasks in the workflow, the efficiency of global > > search and statistical analysis is low, such as querying which tasks use > > which data source > > > > -Poor scalability, for example, the realization of blood relationship > > function in the future will only lead to more and more bloated workflow > > definitions > > > > -Tasks, relationships, and workflow boundaries are blurred. Condition > > nodes and delay nodes are also regarded as a task, which is actually a > > combination of relationships and conditions > > > > Based on the above pain points, we need to redefine the business > > boundaries of tasks, relationships, and workflows, and redesign their > data > > structures based on this > > > > ## 2. Design Ideas > > > > ### 2.1 Workflow, relation, job > > > > First of all, we set aside the current implementation and clarify the > > business boundaries of tasks (the subsequent description is changed to > > jobs), relationships, and workflows, and how to decouple > > > > -Job: the task that the scheduling system really needs to execute, the > job > > only contains the data and resources needed to execute the job > > -relation: the relationship between the job and the job and the execution > > conditions, including the execution relationship (after A completes, > > execute B) and execution conditions (after A completes and succeeds, > > execute B; after A completes and fails, execute C; A completes 30 After > > minutes, execute D) > > -Workflow: the carrier of a set of relationships, the workflow only saves > > the relationships between jobs (DAG is a display form of workflow, a way > to > > create relationships) > > > > Combined with the functions supported by the current DS, we can make a > > classification > > > > -Job: Dependency check, sub-process, Shell, stored procedure, Sql, Spark, > > Flink, MR, Python, Http, DataX, Sqoop > > -Relationship: serial execution, parallel execution, aggregate execution, > > conditional branch, delayed execution > > -Workflow: the boundary of scheduling execution, including a set of > > relationships > > > > #### 2.1.1 Further refinement > > > > The job definition data is not much different from the current job > > definition data. Both are composed of public fields and custom fields. > You > > only need to remove the fields related to the relationship. > > > > The workflow definition data is not much different from the current > > workflow definition data, just remove the json field. > > > > Relational data, we can abstract into two nodes and one path according to > > classification. The node is the job, and the path includes the > conditional > > rules that need to be met from the pre-node to the post-node. The > > conditional rules include: unconditional, judgment condition, and delay > > condition. > > > > ### 2.2 Version Management > > > > We clarify the business boundaries. After decoupling, they become a > > reference relationship. The workflow and the relationship are > one-to-many, > > and the relationship and the job are one-to-many. Not only is the > > definition of data, we also need to consider instance data. Every time a > > workflow is scheduled and executed, a workflow instance will be > generated. > > Jobs and workflows can be changed, and the workflow instance must support > > viewing, rerun, recovery failure, etc. . This requires the introduction > of > > version management of the definition data. Every time workflow, > > relationship, and job changes need to save old version data and generate > > new version data. > > > > So the design idea here is: > > > > To define data, you need to add a version field > > > > The definition table needs to add the corresponding log table > > > > When creating definition data, double write to the definition table and > > log table. When modifying the definition data, save the modified version > to > > the log table > > > > There is no need to save version information in the reference data of the > > definition table (refer to the latest version), and the version > information > > at the time of execution is saved in the instance data > > > > ### 2.3 Coding Design > > > > This also involves the import and export of workflow and job definition > > data. According to the previous community discussion, a coding scheme > needs > > to be introduced. Each piece of data in workflow, relationship, and job > > will have a unique code. Related Issues: https://github > > .com/apache/incubator-dolphinscheduler/issues/3820 > > > > Resource: RESOURCE_xxx > > > > Task: TASK_xxx > > > > Relation: RELATION_xxx > > > > Workflow: PROCESS_xxx > > > > Project: PROJECT_xxx > > > > ## 3. Design plan > > > > ### 3.1 Table model design > > > > #### 3.1.1 Job definition table: t_ds_task_definithon > > > > | Column Name | Description | > > | ----------------------- | -------------- | > > | id | Self-incrementing ID | > > | union_code | unique code | > > | version | Version | > > | name | Job name | > > | description | description | > > | task_type | Job type | > > | task_params | Job custom parameters | > > | run_flag | Run flag | > > | task_priority | Job priority | > > | worker_group | worker group | > > | fail_retry_times | Number of failed retries | > > | fail_retry_interval | Failure retry interval | > > | timeout_flag | Timeout flag | > > | timeout_notify_strategy | Timeout notification strategy | > > | timeout_duration | Timeout duration | > > | create_time | Creation time | > > | update_time | Modification time | > > > > #### 3.1.2 Task relation table: t_ds_task_relation > > > > | Column Name | Description | > > | ----------------------- | ------------------------- ------------- | > > | id | Self-incrementing ID | > > | union_code | unique code | > > | version | Version | > > | process_definition_code | Workflow coding | > > | node_code | Node code (workflow code/job code) | > > | post_node_code | Post node code (workflow code/job code) | > > | condition_type | Condition type 0: None 1: Judgment condition 2: Delay > > condition | > > | condition_params | Condition parameters | > > | create_time | Creation time | > > | update_time | Modification time | > > > > #### 3.1.3 Workflow definition table: t_ds_process_definithon > > > > | Column Name | Description | > > | ---- | ---- | > > | id | Self-incrementing ID | > > | union_code | unique code | > > | version | Version | > > | name | Workflow name | > > | project_code | Project code | > > | release_state | Release state | > > | user_id | Owning user ID | > > | description | description | > > | global_params | Global parameters | > > | flag | Whether the process is available: 0 is not available, 1 is > > available | > > | receivers | recipients | > > | receivers_cc | CC | > > | timeout | Timeout time | > > | tenant_id | tenant ID | > > | create_time | Creation time | > > | update_time | Modification time | > > > > #### 3.1.4 Job definition log table: t_ds_task_definithon_log > > > > Add operation type (add, modify, delete), operator, and operation time > > based on the job definition table > > > > #### 3.1.5 Job relation log table: t_ds_task_relation_log > > > > Add operation type (add, modify, delete), operator, and operation time > > based on the job relationship table > > > > #### 3.1.6 Workflow definition log table: t_ds_process_definithon_log > > > > Add operation type (add, modify, delete), operator, and operation time > > based on the workflow definition table > > > > ### 3.2 Frontend > > > > *The design here is just a personal idea, and the front-end help is > needed > > to design the interaction* > > > > Need to add job management related functions, including: job list, job > > creation, update, delete, view details operations > > > > To create a workflow page, you need to split json into workflow > definition > > data and job relationship data to the back-end API layer to save/update > > > > Workflow page, when dragging task nodes, add reference job options > > > > The conditional branch nodes and delay nodes need to be resolved into the > > conditional rule data in the relationship; conversely, the conditional > rule > > data returned by the backend needs to be displayed as the corresponding > > node when querying the workflow > > > > ### 3.3 Master > > > > When the Master schedules the workflow, you need to modify <Build dag > from > > json> to <Build dag from relational data>. When executing a workflow, > first > > load the relational data in full (no job data is loaded here), generate > > DAG, and traverse DAG execution , And then get the job data that needs to > > be executed > > > > Other execution processes are consistent with existing processes > > > > > > > -------------------------------------------------------------------------------------------------------------- > > > > ## 1.现状 > > > > > 当前DS系统的工作流定义包含了任务定义数据和任务之间关系数据，并且在数据库的设计上，任务数据和任务关系数据是以一个字符串类型字段（process_definition_json）的方式，保存在工作流定义表（t_ds_process_definition）中。 > > > > 随着工作流和任务的增加，会产生如下问题： > > > > - 任务数据、关系数据和工作流数据耦合在一起，对单任务调度的场景不友好，任务必须创建在工作流内 > > > > - 任务无法复用，因为任务是创建在工作流内的 > > > > - 维护成本高，牵一发动全身，修改任何一个任务，都需要整体更新工作流内数据，同时也增加了日志成本 > > > > - 工作流内任务较多时，全局搜索和统计分析效率低，例如查询哪些任务用到了哪个数据源 > > > > - 扩展性差，例如未来要实现血缘功能，只会导致工作流定义越来越臃肿 > > > > - 任务、关系、工作流边界模糊，条件节点、延迟节点也被当做一种任务，实际是关系与条件的组合 > > > > 基于以上痛点，我们需要重新定义任务、关系、工作流的业务边界，基于此重新设计它们的数据结构 > > > > ## 2.设计思路 > > > > ### 2.1 工作流、关系、作业 > > > > 首先，我们抛开当前的实现，明确任务（后续描述更改为作业）、关系、工作流的业务边界，如何去解耦 > > > > - 作业：调度系统要执行的任务，作业内只包含执行作业所需要的数据和资源 > > - > > > 关系：作业与作业之间的关系以及执行条件，包含执行关系（A完成后，执行B）和执行条件（A完成并成功后，执行B；A完成并失败后，执行C；A完成30分钟后，执行D） > > - 工作流：一组关系的载体，工作流只保存作业间的关系（DAG是工作流的一种展示形式，创建关系的一种方式） > > > > 结合当前DS支持的功能，我们可以做一个分类 > > > > - 作业：依赖检查、子流程、Shell、存储过程、Sql、Spark、Flink、MR、Python、Http、DataX、Sqoop > > - 关系：串行执行、并行执行、聚合执行、条件分支、延迟执行 > > - 工作流：调度执行的边界，包含一组关系 > > > > #### 2.1.1 进一步细化 > > > > 作业定义数据，和当前的作业定义数据差别不大，都是由公共字段和自定义字段组成，只需要去掉关系相关的字段就可以了。 > > > > 工作流定义数据，和当前的工作流定义数据差别也不大，去掉json字段就可以了。 > > > > > > > 关系数据，我们根据分类可以抽象为两个节点和一个路径。节点就是作业，路径包含前置节点到后置节点需要满足的条件规则是什么，条件规则包含：无条件、判断条件、延迟条件。 > > > > ### 2.2 版本管理 > > > > > > > 我们明确业务边界，解耦后它们之间就变成了引用关系，工作流和关系之间是一对多，关系和作业之间是一对多。不仅是定义数据，我们还要考虑实例数据，每次工作流的调度执行都会产生工作流实例，作业和工作流都是可以变更的，而工作流实例又要支持查看、重跑、恢复失败等。这就需要引入定义数据的版本管理了。每一次工作流、关系、作业变更都需要保存旧版本数据，生成新版本数据。 > > > > 所以这里的设计思路是： > > > > 定义数据需要增加版本字段 > > > > 定义表需要增加对应的日志表 > > > > 创建定义数据时，双写到定义表和日志表，修改定义数据时，保存修改后的版本到日志表 > > > > 定义表的引用数据中不需要保存版本信息（引用最新版本），实例数据中保存执行时的版本信息 > > > > ### 2.3 编码设计 > > > > > 这里还涉及到工作流、作业定义数据导入导出问题，根据之前社区讨论的方案，需要引入编码方案，工作流、关系、作业每条数据都会有一个唯一编码，相关Issue： > > https://github.com/apache/incubator-dolphinscheduler/issues/3820 > > > > 资源：RESOURCE_xxx > > > > 作业：TASK_xxx > > > > 关系：RELATION_xxx > > > > 工作流：PROCESS_xxx > > > > 项目：PROJECT_xxx > > > > ## 3.设计方案 > > > > ### 3.1 表模型设计 > > > > #### 3.1.1 作业定义表：t_ds_task_definithon > > > > | 列名 | 描述 | > > | ----------------------- | -------------- | > > | id | 自增ID | > > | union_code | 唯一编码 | > > | version | 版本 | > > | name | 作业名称 | > > | description | 描述 | > > | task_type | 作业类型 | > > | task_params | 作业自定义参数 | > > | run_flag | 运行标志 | > > | task_priority | 作业优先级 | > > | worker_group | worker分组 | > > | fail_retry_times | 失败重试次数 | > > | fail_retry_interval | 失败重试间隔 | > > | timeout_flag | 超时标志 | > > | timeout_notify_strategy | 超时通知策略 | > > | timeout_duration | 超时时长 | > > | create_time | 创建时间 | > > | update_time | 修改时间 | > > > > #### 3.1.2 作业关系表：t_ds_task_relation > > > > | 列名 | 描述 | > > | ----------------------- | -------------------------------------- | > > | id | 自增ID | > > | union_code | 唯一编码 | > > | version | 版本 | > > | process_definition_code | 工作流编码 | > > | node_code | 节点编码（工作流编码/作业编码） | > > | post_node_code | 后置节点编码（工作流编码/作业编码） | > > | condition_type | 条件类型 0：无 1：判断条件 2：延迟条件 | > > | condition_params | 条件参数 | > > | create_time | 创建时间 | > > | update_time | 修改时间 | > > > > #### 3.1.3 工作流定义表：t_ds_process_definithon > > > > | 列名 | 描述 | > > | ---- | ---- | > > | id | 自增ID | > > | union_code | 唯一编码 | > > | version | 版本 | > > | name | 工作流名称 | > > | project_code | 项目编码 | > > | release_state | 发布状态 | > > | user_id | 所属用户ID | > > | description | 描述 | > > | global_params | 全局参数 | > > | flag | 流程是否可用：0 不可用，1 可用 | > > | receivers | 收件人 | > > | receivers_cc | 抄送人 | > > | timeout | 超时时间 | > > | tenant_id | 租户ID | > > | create_time | 创建时间 | > > | update_time | 修改时间 | > > > > #### 3.1.4 作业定义日志表：t_ds_task_definithon_log > > > > 作业定义表基础上增加操作类型（新增、修改、删除）、操作人、操作时间 > > > > #### 3.1.5 作业关系日志表：t_ds_task_relation_log > > > > 作业关系表基础上增加操作类型（新增、修改、删除）、操作人、操作时间 > > > > #### 3.1.6 工作流定义日志表：t_ds_process_definithon_log > > > > 工作流定义表基础上增加操作类型（新增、修改、删除）、操作人、操作时间 > > > > ### 3.2 前端 > > > > *这里的设计只是个人想法，交互上还需要前端帮助设计下* > > > > 需要增加作业管理相关功能，包括：作业列表，作业的创建、更新、删除、查看详情操作 > > > > 创建工作流页面，需要将json拆分为工作流定义数据、作业关系数据传给后端API层保存/更新 > > > > 工作流页面，拖拽任务节点时，增加引用作业选项 > > > > 条件分支节点、延迟节点需要解析为关系中的条件规则数据；反之，查询工作流时需要将后端返回的条件规则数据展示为对应的节点 > > > > ### 3.3 Master > > > > > > > Master调度工作流时，需要将<从json构建dag>修改为<从关系数据构建dag>，执行一个工作流时先全量加载关系数据（这里不加载作业数据），生成DAG，遍历DAG执行时，再获取需要执行的作业数据 > > > > 其他执行流程和现有流程一致 > > > > -------------------- > > DolphinScheduler(Incubator) Commtter > > Hemin Wen 温合民 > > [email protected] > > -------------------- > > > -- DolphinScheduler(Incubator) PPMC BaoLiang 鲍亮 [email protected]
