flink提供了丰富的客户端操作来提交任务和与任务进行交互。下面主要从Flink命令行、Scala Shell、SQL Client、Restful API和 Web五个方面进行整理。
在Flink安装目录的bin目录下可以看到flink，start-scala-shell.sh和sql-client.sh等文件，这些都是客户端操作的入口。
flink 常见操作：可以通过 -help 查看帮助
run 运行任务
-d：以分离模式运行作业-c：如果没有在jar包中指定入口类，则需要在这里通过这个参数指定；-m：指定需要连接的jobmanager(主节点)地址，使用这个参数可以指定一个不同于配置文件中的jobmanager，可以说是yarn集群名称；-p：指定程序的并行度。可以覆盖配置文件中的默认值；-s：保存点savepoint的路径以还原作业来自（例如hdfs:///flink/savepoint-1537)；
[root@hadoop1 flink-1.10.1]# bin/flink run -d examples/streaming/TopSpeedWindowing.jar
Executing TopSpeedWindowing example with default input data set.
Use --input to specify file input.
Printing result to stdout. Use --output to specify output path.
Job has been submitted with JobID dce7b69ad15e8756766967c46122736f
就可以看到我们提交的JobManager，默认是一个并发。
点进去就可以看到详细的信息
点击左侧TaskManager —Stdout能看到具体输出的日志信息。
或者查看TaskManager节点的log目录下的*.out文件，也能看到具体的输出信息。
list 查看任务列表
-m：jobmanager<arg>作业管理器（主）的地址连接。
[root@hadoop1 flink-1.10.1]# bin/flink list -m 127.0.0.1:8081
Waiting for response...
------------------ Running/Restarting Jobs -------------------
09.07.2020 16:44:09 : dce7b69ad15e8756766967c46122736f : CarTopSpeedWindowingExample (RUNNING)
--------------------------------------------------------------
No scheduled jobs.
Stop 停止任务
需要指定jobmanager的ip:prot和jobId。如下报错可知，一个job能够被stop要求所有的source都是可以stoppable的，即实现了 StoppableFunction接口。
[root@hadoop1 flink-1.10.1]# bin/flink stop -m 127.0.0.1:8081 dce7b69ad15e8756766967c46122736f
Suspending job "dce7b69ad15e8756766967c46122736f" with a savepoint.
------------------------------------------------------------
The program finished with the following exception:
org.apache.flink.util.FlinkException: Could not stop with a savepoint job "dce7b69ad15e8756766967c46122736f".
at org.apache.flink.client.cli.CliFrontend.lambda$stop$5(CliFrontend.java:458)
StoppableFunction接口如下，属于优雅停止任务。
/**
* @Description 需要 stoppabel 的函数必须实现此接口，例如流式任务 source*
* stop() 方法在任务收到 stop信号的时候调用
* source 在接收到这个信号后，必须停止发送新的数据优雅的停止。
* @Date 2020/7/9 17:26
*/
@PublicEvolving
public interface StoppableFunction {
/**
* 停止 source，与 cancel() 不同的是，这是一个让 source优雅停止的请求。
* 等待中的数据可以继续发送出去，不需要立即停止
*/
void stop();
}
Cancel 取消任务
如果在conf/flink-conf.yaml里面配置state.savepoints.dir，会保存savepoint，否则不会保存savepoint。（重启）
state.savepoints.dir: file:///tmp/savepoint
执行 Cancel命令 取消任务
[root@hadoop1 flink-1.10.1]# bin/flink cancel -m 127.0.0.1:8081 -s e8ce0d111262c52bf8228d5722742d47
DEPRECATION WARNING: Cancelling a job with savepoint is deprecated. Use "stop" instead.
Cancelling job e8ce0d111262c52bf8228d5722742d47 with savepoint to default savepoint directory.
Cancelled job e8ce0d111262c52bf8228d5722742d47. Savepoint stored in file:/tmp/savepoint/savepoint-e8ce0d-f7fa96a085d8.
也可以在停止的时候显示指定savepoint目录
1 [root@hadoop1 flink-1.10.1]# bin/flink cancel -m 127.0.0.1:8081 -s /tmp/savepoint f58bb4c49ee5580ab5f27fdb24083353
DEPRECATION WARNING: Cancelling a job with savepoint is deprecated. Use "stop" instead.
Cancelling job f58bb4c49ee5580ab5f27fdb24083353 with savepoint to /tmp/savepoint.
Cancelled job f58bb4c49ee5580ab5f27fdb24083353. Savepoint stored in file:/tmp/savepoint/savepoint-f58bb4-127b7e84910e.
取消和停止（流作业）的区别如下：
● cancel()调用， 立即调用作业算子的cancel()方法，以尽快取消它们。如果算子在接到cancel()调用后没有停止，Flink将开始定期中断算子线程的执行，直到所有算子停止为止。
● stop()调用 ，是更优雅的停止正在运行流作业的方式。stop()仅适用于source实现了StoppableFunction接口的作业。当用户请求停止作业时，作业的所有source都将接收stop()方法调用。直到所有source正常关闭时，作业才会正常结束。这种方式，使 作业正常处理完所有作业。
触发 savepoint
当需要生成savepoint文件时，需要手动触发savepoint。如下，需要指定正在运行的 JobID 和生成文件的存放目录。同时，我们也可以看到它会返回给用户存放的savepoint的文件名称等信息。
[root@hadoop1 flink-1.10.1]# bin/flink run -d examples/streaming/TopSpeedWindowing.jar
Executing TopSpeedWindowing example with default input data set.
Use --input to specify file input.
Printing result to stdout. Use --output to specify output path.
Job has been submitted with JobID 216c427d63e3754eb757d2***268a448d
[root@hadoop1 flink-1.10.1]# bin/flink savepoint -m 127.0.0.1:8081 216c427d63e3754eb757d2***268a448d /tmp/savepoint/
Triggering savepoint for job 216c427d63e3754eb757d2***268a448d.
Waiting for response...
Savepoint ***pleted. Path: file:/tmp/savepoint/savepoint-216c42-154a34cf6bfd
You can resume your program from this savepoint with the run ***mand.
savepoint和checkpoint的区别：
● checkpoint是增量做的，每次的时间较短，数据量较小，只要在程序里面启用后会自动触发，用户无须感知；savepoint是全量做的，每次的时间较长，数据量较大，需要用户主动去触发。
● checkpoint是作业failover的时候自动使用，不需要用户指定。savepoint一般用于程序的版本更新，bug修复，A/B Test等场景，需要用户指定。
从指定 savepoint 中启动
[root@hadoop1 flink-1.10.1]# bin/flink run -d -s /tmp/savepoint/savepoint-f58bb4-127b7e84910e/ examples/streaming/TopSpeedWindowing.jar
Executing TopSpeedWindowing example with default input data set.
Use --input to specify file input.
Printing result to stdout. Use --output to specify output path.
Job has been submitted with JobID 1a5c5ce279e0e4bd8609f541b37652e2
查看JobManager的日志能够看到Reset the checkpoint ID为我们指定的savepoint文件中的ID
modify 修改任务并行度
这里修改master的conf/flink-conf.yaml将task slot数修改为4。并通过xsync分发到 两个slave节点上。
taskmanager.numberOfTaskSlots: 4
修改参数后需要重启集群生效：关闭/启动集群
[root@hadoop1 flink-1.10.1]# bin/stop-cluster.sh && bin/start-cluster.sh
Stopping taskexecutor daemon (pid: 8236) on host hadoop2.
Stopping taskexecutor daemon (pid: 8141) on host hadoop3.
Stopping standalonesession daemon (pid: 22633) on host hadoop1.
Starting cluster.
Starting standalonesession daemon on host hadoop1.
Starting taskexecutor daemon on host hadoop2.
Starting taskexecutor daemon on host hadoop3.
启动任务
[root@hadoop1 flink-1.10.1]# bin/flink run -d examples/streaming/TopSpeedWindowing.jar
Executing TopSpeedWindowing example with default input data set.
Use --input to specify file input.
Printing result to stdout. Use --output to specify output path.
Job has been submitted with JobID 2e833a438da7d8052f14d5433910515a
从页面上能看到Task Slots总计变为了8，运行的Slot为1，剩余Slot数量为7。
这时候默认的并行度是1
Flink1.0版本命令行flink modify已经没有这个行为了，被移除了。。。Flink1.7上是可以运行的。
[root@hadoop1 flink-1.10.1]# bin/flink modify -p 4 ***22***3d09f5d65651d637be6fb0a1c3
"modify" is not a valid action.
Info 显示程序的执行计划
[root@hadoop1 flink-1.10.1]# bin/flink info examples/streaming/TopSpeedWindowing.jar
----------------------- Execution Plan -----------------------
{"nodes":[{"id":1,"type":"Source: Custom Source","pact":"Data Source","contents":"Source: Custom Source","parallelism":1},{"id":2,"type":"Timestamps/Watermarks","pact":"Operator","contents":"Timestamps/Watermarks","parallelism":1,"predecessors":[{"id":1,"ship_strategy":"FORWARD","side":"second"}]},{"id":4,"type":"Window(GlobalWindows(), DeltaTrigger, TimeEvictor, ***parableAggregator, PassThroughWindowFunction)","pact":"Operator","contents":"Window(GlobalWindows(), DeltaTrigger, TimeEvictor, ***parableAggregator, PassThroughWindowFunction)","parallelism":1,"predecessors":[{"id":2,"ship_strategy":"HASH","side":"second"}]},{"id":5,"type":"Sink: Print to Std. Out","pact":"Data Sink","contents":"Sink: Print to Std. Out","parallelism":1,"predecessors":[{"id":4,"ship_strategy":"FORWARD","side":"second"}]}]}
--------------------------------------------------------------
拷贝输出的json内容，粘贴到这个网站：http://flink.apache.org/visualizer/可以生成类似如下的执行图。
可以与实际运行的物理执行计划进行对比。
SQL Client Beta
进入 Flink SQL
[root@hadoop1 flink-1.10.1]# bin/sql-client.sh embedded
Select查询，按Q退出如下界面；
Flink SQL> select 'hello word';
SQL Query Result (Table)
Table program finished. Page: Last of 1 Updated: 16:37:04.649
EXPR$0
hello word
Q Quit + Inc Refresh G Goto Page N Next Page O Open Row
R Refresh - Dec Refresh L Last Page P Prev Page
打开http://hadoop1:8081能看到这条select语句产生的查询任务已经结束了。这个查询采用的是读取固定数据集的Custom Source，输出用的是Stream Collect Sink，且只输出一条结果。
explain 查看 SQL 的执行计划。
Flink SQL> explain SELECT name, COUNT(*) AS ***t FROM (VALUES ('Bob'), ('Alice'), ('Greg'), ('Bob')) AS NameTable(name) GROUP BY name;
== Abstract Syntax Tree == //抽象语法树
LogicalAggregate(group=[{0}], ***t=[COUNT()])
+- LogicalValues(type=[RecordType(VARCHAR(5) name)], tuples=[[{ _UTF-16LE'Bob' }, { _UTF-16LE'Alice' }, { _UTF-16LE'Greg' }, { _UTF-16LE'Bob' }]])
== Optimized Logical Plan == //优化后的逻辑执行计划
GroupAggregate(groupBy=[name], select=[name, COUNT(*) AS ***t])
+- Exchange(distribution=[hash[name]])
+- Values(type=[RecordType(VARCHAR(5) name)], tuples=[[{ _UTF-16LE'Bob' }, { _UTF-16LE'Alice' }, { _UTF-16LE'Greg' }, { _UTF-16LE'Bob' }]])
== Physical Execution Plan == //物理执行计划
Stage 13 : Data Source
content : Source: Values(tuples=[[{ _UTF-16LE'Bob' }, { _UTF-16LE'Alice' }, { _UTF-16LE'Greg' }, { _UTF-16LE'Bob' }]])
Stage 15 : Operator
content : GroupAggregate(groupBy=[name], select=[name, COUNT(*) AS ***t])
ship_strategy : HASH
结果展示
SQL Client支持两种模式来维护并展示查询结果：
table mode
在内存中物化查询结果，并以分页table形式展示。用户可以通过以下命令启用table mode：例如如下案例；
Flink SQL> SET execution.result-mode=table;
[INFO] Session property has been set.
Flink SQL> SELECT name, COUNT(*) AS ***t FROM (VALUES ('Bob'), ('Alice'), ('Greg'), ('Bob')) AS NameTable(name) GROUP BY name;
SQL Query Result (Table)
Table program finished. Page: Last of 1 Updated: 16:55:08.589
name ***t
Alice 1
Greg 1
Bob 2
Q Quit + Inc Refresh G Goto Page N Next Page O Open Row
R Refresh - Dec Refresh L Last Page P Prev Page
changelog mode
不会物化查询结果，而是直接对continuous query产生的添加和撤回retractions结果进行展示：如下案例中的-表示撤回消息
Flink SQL> SET execution.result-mode=changelog;
[INFO] Session property has been set.
Flink SQL> SELECT name, COUNT(*) AS ***t FROM (VALUES ('Bob'), ('Alice'), ('Greg'), ('Bob')) AS NameTable(name) GROUP BY name;
SQL Query Result (Changelog)
Table program finished. Updated: 16:58:05.777
+/- name ***t
+ Bob 1
+ Alice 1
+ Greg 1
- Bob 1
+ Bob 2
Q Quit + Inc Refresh O Open Row
R Refresh - Dec Refresh
Environment Files
CREATE TABLE 创建表DDL语句：
Flink SQL> CREATE TABLE pvuv_sink (
> dt VARCHAR,
> pv BIGINT,
> uv BIGINT
> ) ;
[INFO] Table has been created.
SHOW TABLES 查看所有表名
Flink SQL> show tables;
pvuv_sink
DESCRIBE 表名 查看表的详细信息；
Flink SQL> describe pvuv_sink;
root
|-- dt: STRING
|-- pv: BIGINT
|-- uv: BIGINT
插入等操作均与关系型数据库操作语句一样，省略N个操作
Restful API
接下来我们演示如何通过rest api来提交jar包和执行任务。
通过Show Plan可以看到执行图
提交之后的操作，取消的话点击页面的Cancel Job
