Spring Cloud Data Flow用Shell来操做，方便创建CICD

1 前言

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以前咱们用两篇文章讲解了Spring Cloud Data Flow，例子都是用UI操做的，但咱们在Linux系统上常常是没法提供界面来操做，集成在Jenkins上也没法使用UI。好在官方提供了Data Flow Shell工具，能够在命令行模式下进行操做，很是方便。

相关文章可参考：

Spring Cloud Data Flow初体验，以Local模式运行

把Spring Cloud Data Flow部署在Kubernetes上，再跑个任务试试

Spring Cloud Data Flow Server提供了可操做的REST API，因此这个Shell工具的本质仍是经过调用REST API来交互的。

2 经常使用操做

2.1 启动

首先要确保咱们已经安装有Java环境和下载了可执行的jar包：spring-cloud-dataflow-shell-2.5.3.RELEASE.jar

而后启动以下：

$ java -jar spring-cloud-dataflow-shell-2.5.3.RELEASE.jar

默认是链接了http://localhost:9393的Server，能够经过--dataflow.uri=地址来指定。若是须要认证信息，须要加上--dataflow.username=用户 --dataflow.password=密码。

好比咱们链接以前安装在Kubernetes上的Server以下：

$ java -jar spring-cloud-dataflow-shell-2.5.3.RELEASE.jar --dataflow.uri=http://localhost:30093

2.2 Application操做

介绍一下Application相关操做：

列出全部目前注册的app：

dataflow:>app list
╔═══╤══════╤═════════╤════╤════════════════════╗
║app│source│processor│sink│        task        ║
╠═══╪══════╪═════════╪════╪════════════════════╣
║   │      │         │    │composed-task-runner║
║   │      │         │    │timestamp-batch     ║
║   │      │         │    │timestamp           ║
╚═══╧══════╧═════════╧════╧════════════════════╝

查看某个app的信息：

dataflow:>app info --type task timestamp

清除app注册信息：

dataflow:>app unregister --type task timestamp
Successfully unregistered application 'timestamp' with type 'task'.

清除全部app注册信息：

dataflow:>app all unregister
Successfully unregistered applications.
dataflow:>app list 
No registered apps.
You can register new apps with the 'app register' and 'app import' commands.

注册一个app：

dataflow:>app register --name timestamp-pkslow --type task --uri docker:springcloudtask/timestamp-task:2.1.1.RELEASE
Successfully registered application 'task:timestamp-pkslow'
dataflow:>app list
╔═══╤══════╤═════════╤════╤════════════════╗
║app│source│processor│sink│      task      ║
╠═══╪══════╪═════════╪════╪════════════════╣
║   │      │         │    │timestamp-pkslow║
╚═══╧══════╧═════════╧════╧════════════════╝

批量导入app，能够从一个URL或一个properties文件导入：

dataflow:>app import https://dataflow.spring.io/task-docker-latest
Successfully registered 3 applications from [task.composed-task-runner, task.timestamp.metadata, task.composed-task-runner.metadata, task.timestamp-batch.metadata, task.timestamp-batch, task.timestamp]

须要注意的是，在注册或导入app时，若是重复的话，默认是没法导入的，不会覆盖。若是想要覆盖，能够加参数--force。

dataflow:>app register --name timestamp-pkslow --type task --uri docker:springcloudtask/timestamp-task:2.1.1.RELEASE
Command failed org.springframework.cloud.dataflow.rest.client.DataFlowClientException: The 'task:timestamp-pkslow' application is already registered as docker:springcloudtask/timestamp-task:2.1.1.RELEASE
The 'task:timestamp-pkslow' application is already registered as docker:springcloudtask/timestamp-task:2.1.1.RELEASE

dataflow:>app register --name timestamp-pkslow --type task --uri docker:springcloudtask/timestamp-task:2.1.1.RELEASE --force
Successfully registered application 'task:timestamp-pkslow'

2.3 Task操做

列出task：

dataflow:>task list
╔════════════════╤════════════════════════════════╤═══════════╤═══════════╗
║   Task Name    │        Task Definition         │description│Task Status║
╠════════════════╪════════════════════════════════╪═══════════╪═══════════╣
║timestamp-pkslow│timestamp                       │           │COMPLETE   ║
║timestamp-two   │<t1: timestamp || t2: timestamp>│           │ERROR      ║
║timestamp-two-t1│timestamp                       │           │COMPLETE   ║
║timestamp-two-t2│timestamp                       │           │COMPLETE   ║
╚════════════════╧════════════════════════════════╧═══════════╧═══════════╝

删除一个task，这里咱们删除的是一个组合task，因此会把子task也一并删除了：

dataflow:>task destroy timestamp-two
Destroyed task 'timestamp-two'

删除全部task，会有风险提示：

dataflow:>task all destroy 
Really destroy all tasks? [y, n]: y
All tasks destroyed

dataflow:>task list
╔═════════╤═══════════════╤═══════════╤═══════════╗
║Task Name│Task Definition│description│Task Status║
╚═════════╧═══════════════╧═══════════╧═══════════╝

建立一个task：

dataflow:>task create timestamp-pkslow-t1 --definition "timestamp --format=\"yyyy\"" --description "pkslow timestamp task"
Created new task 'timestamp-pkslow-t1'

启动一个task并查看状态，启动时须要记录执行ID，而后经过执行ID来查询状态：

dataflow:>task launch timestamp-pkslow-t1
Launched task 'timestamp-pkslow-t1' with execution id 8
dataflow:>task execution status 8

查看全部task执行并查看执行日志：

dataflow:>task execution list 


dataflow:>task execution log 8

  .   ____          _            __ _ _
 /\\ / ___'_ __ _ _(_)_ __  __ _ \ \ \ \
( ( )\___ | '_ | '_| | '_ \/ _` | \ \ \ \
 \\/  ___)| |_)| | | | | || (_| |  ) ) ) )
  '  |____| .__|_| |_|_| |_\__, | / / / /
 =========|_|==============|___/=/_/_/_/
 :: Spring Boot ::       (v2.1.13.RELEASE)

2020-08-01 17:20:51.626  INFO 1 --- [       Thread-5] com.zaxxer.hikari.HikariDataSource       : HikariPool-1 - Shutdown initiated...
2020-08-01 17:20:51.633  INFO 1 --- [       Thread-5] com.zaxxer.hikari.HikariDataSource       : HikariPool-1 - Shutdown completed.

2.4 Http请求

能够进行http请求：

dataflow:>http get https://www.pkslow.com

dataflow:>http post --target https://www.pkslow.com --data "data"
> POST (text/plain) https://www.pkslow.com data
> 405 METHOD_NOT_ALLOWED

Error sending data 'data' to 'https://www.pkslow.com'

2.5 读取并执行文件

先准备一个脚本文件，用来放Data Flow Shell命令，文件名为pkslow.shell，内容以下：

version
date
app list

执行与结果以下：

dataflow:>script pkslow.shell
version
2.5.3.RELEASE
date
Sunday, August 2, 2020 1:59:34 AM CST
app list
╔═══╤══════╤═════════╤════╤════════════════════╗
║app│source│processor│sink│        task        ║
╠═══╪══════╪═════════╪════╪════════════════════╣
║   │      │         │    │timestamp-pkslow    ║
║   │      │         │    │composed-task-runner║
║   │      │         │    │timestamp-batch     ║
║   │      │         │    │timestamp           ║
╚═══╧══════╧═════════╧════╧════════════════════╝

Script required 0.045 seconds to execute
dataflow:>

但其实咱们在CI/CD的pipeline中，并不想先启动一个shell命令行，而后再执行一个脚本。咱们想一步到位，直接执行，执行完毕后退出shell命令行。这也是有办法的，能够在启动的时候经过 --spring.shell.commandFile指定文件，若是有多个文件则用逗号,分隔。以下所示：

$ java -jar spring-cloud-dataflow-shell-2.5.3.RELEASE.jar --dataflow.uri=http://localhost:30093 --spring.shell.commandFile=pkslow.shell
Successfully targeted http://localhost:30093
2020-08-02T02:03:49+0800 INFO main o.s.c.d.s.DataflowJLineShellComponent:311 - 2.5.3.RELEASE
2020-08-02T02:03:49+0800 INFO main o.s.c.d.s.DataflowJLineShellComponent:311 - Sunday, August 2, 2020 2:03:49 AM CST
2020-08-02T02:03:49+0800 INFO main o.s.c.d.s.DataflowJLineShellComponent:309 - 
╔═══╤══════╤═════════╤════╤════════════════════╗
║app│source│processor│sink│        task        ║
╠═══╪══════╪═════════╪════╪════════════════════╣
║   │      │         │    │timestamp-pkslow    ║
║   │      │         │    │composed-task-runner║
║   │      │         │    │timestamp-batch     ║
║   │      │         │    │timestamp           ║
╚═══╧══════╧═════════╧════╧════════════════════╝
$

执行完毕后，不会在shell命令行模式里，而是退回linux的终端。这正是咱们所须要的。

咱们来准备一个注册应用——建立任务——执行任务的脚本试试：

version
date
app register --name pkslow-app-1 --type task --uri docker:springcloudtask/timestamp-task:2.1.1.RELEASE
task create pkslow-task-1 --definition "pkslow-app-1"
task launch pkslow-task-1

执行与结果以下：

$ java -jar spring-cloud-dataflow-shell-2.5.3.RELEASE.jar --dataflow.uri=http://localhost:30093 --spring.shell.commandFile=pkslow.shell
Successfully targeted http://localhost:30093
2020-08-02T02:06:41+0800 INFO main o.s.c.d.s.DataflowJLineShellComponent:311 - 2.5.3.RELEASE
2020-08-02T02:06:41+0800 INFO main o.s.c.d.s.DataflowJLineShellComponent:311 - Sunday, August 2, 2020 2:06:41 AM CST
2020-08-02T02:06:41+0800 INFO main o.s.c.d.s.DataflowJLineShellComponent:311 - Successfully registered application 'task:pkslow-app-1'
2020-08-02T02:06:42+0800 INFO main o.s.c.d.s.DataflowJLineShellComponent:311 - Created new task 'pkslow-task-1'
2020-08-02T02:06:51+0800 INFO main o.s.c.d.s.DataflowJLineShellComponent:311 - Launched task 'pkslow-task-1' with execution id 9

这样，咱们就能够实现自动化打包与部署运行了。

3 一些使用技巧

强大的shell工具提供了许多命令，其实不用一一记住，能够经过help命令查看全部命令：

dataflow:>help

若是只对特定的一类命令感兴趣，能够经过help xxx的方式获取帮助：

dataflow:>help version
* version - Displays shell version

dataflow:>help app
* app all unregister - Unregister all applications
* app default - Change the default application version
* app import - Register all applications listed in a properties file
* app info - Get information about an application
* app list - List all registered applications
* app register - Register a new application
* app unregister - Unregister an application

shell还支持tab键补全命令。

4 总结

本文的命令比较多，不想形成冗长，部分执行结果就不贴出来了，原文可到官网参考。

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