原文链接：https://ci.apache.org/projects/flink/flink-docs-release-1.3/quickstart/setup_quickstart.html

Setup: Download and Start Flink

Flink可以运行在Linux、Mac OS X以及Windows中，Flink运行的唯一条件就是安装Java

7.X以上的版本的jdk。Windows用户请查看一下Flink on Windows文档，这个文档描述了如何在window运行单机的Flink。Flink on Windows:https://ci.apache.org/projects/flink/flink-docs-release-1.3/setup/flink_on_windows.html

你可以通过下面的命令行来查看安装的Java版本是否正确:

java -version

如果你安装的是Java 8的话，会返回下面的信息:

java version"1.8.0_111"

Java(TM)SE Runtime Environment(build 1.8.0_111-b14)

Java HotSpot(TM)64-Bit Server VM(build 25.111-b14, mixed mode)

Downloadand Compile

从Flink的代码库中clone代码，如下:

$git clone https://github.com/apache/flink.git

$cdflink

$mvn clean package -DskipTests# this will take up to 10 minutes

$cdbuild-target# this is where Flink is installed to

Starta Local Flink Cluster

$./bin/start-local.sh# Start Flink

通过http://localhost:8081来检查JobManager的Web前台，确保每一个进程都起来了。在这个Web前台中应该只有一个TaskManager实例。

还可以通过检查日志目录中的日志文件来判断系统是否正常运行

$tail log/flink-*-jobmanager-*.log

INFO ... - Starting JobManager

INFO ... - Starting JobManager web frontend

INFO ... - Web frontend listening at 127.0.0.1:8081

INFO ... - Registered TaskManager at 127.0.0.1(akka://flink/user/taskmanager)

Readthe Code

你可以在GitHub中查看到这个SocketWindowWordCount实例完整的Java代码和Scala代码。

Scala:

object SocketWindowWordCount {  

  def main(args: Array[String]) : Unit = {        // the port to connect to 

       val port: Int = try {            

            ParameterTool.fromArgs(args).getInt("port")        

       } catch {           

             case e: Exception => { 

               System.err.println("No port specified. Please run 'SocketWindowWordCount --port'")

               return

        }

}

// get the execution environment

val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment

// get input data by connecting to the socket

val text = env.socketTextStream("localhost", port, '\n')

// parse the data, group it, window it, and aggregate the counts

val windowCounts = text.flatMap { w => w.split("\\s") }

                                .map { w => WordWithCount(w, 1) }

                               .keyBy("word")

                               .timeWindow(Time.seconds(5), Time.seconds(1))

                              .sum("count")

// print the results with a single thread, rather than in parallel

windowCounts.print().setParallelism(1)

env.execute("Socket Window WordCount")

}

// Data type for words with count

case class WordWithCount(word: String, count: Long)

}

Runthe Example

现在我们将去执行这个Flink程序，这个程序将去读取socket中产生的文本，并且每隔5秒打印一下前5秒内产生的不同的单次产生的次数。

首先，我们通过netcat来打开一个本地的服务:

$nc -l 9000

提交Flink程序

$./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000

Cluster configuration: Standalone cluster with JobManager at /127.0.0.1:6123

Using address 127.0.0.1:6123 to connect to JobManager.

JobManager web interface address http://127.0.0.1:8081

Starting execution of program

Submitting job with JobID: 574a10c8debda3dccd0c78a3bde55e1b. Waitingforjob completion.

Connected to JobManager at Actor[akka.tcp://flink@127.0.0.1:6123/user/jobmanager#297388688]

11/04/2016 14:04:50Job execution switched to status RUNNING.

11/04/2016 14:04:50Source: Socket Stream -> Flat Map(1/1)switched to SCHEDULED

11/04/2016 14:04:50Source: Socket Stream -> Flat Map(1/1)switched to DEPLOYING

11/04/2016 14:04:50Fast TumblingProcessingTimeWindows(5000)of WindowedStream.main(SocketWindowWordCount.java:79)-> Sink: Unnamed(1/1)switched to SCHEDULED

11/04/2016 14:04:51Fast TumblingProcessingTimeWindows(5000)of WindowedStream.main(SocketWindowWordCount.java:79)-> Sink: Unnamed(1/1)switched to DEPLOYING

11/04/2016 14:04:51Fast TumblingProcessingTimeWindows(5000)of WindowedStream.main(SocketWindowWordCount.java:79)-> Sink: Unnamed(1/1)switched to RUNNING

11/04/2016 14:04:51Source: Socket Stream -> Flat Map(1/1)switched to RUNNING

程序将与socket连接并等待输入，你可以通过web前台来查看作业是否如预期执行。

单词在一个间隔5秒的window(窗口)中执行并且打印到stdout中。监控JobManager的输出文件并写些文档到nc中。

$nc -l 9000

lorem ipsum

ipsum ipsum ipsum

bye

只要单词源源不断的流入的话，.out文件将在时间窗口的最后截止时间打印出单词的计数:例如：

$tail -f log/flink-*-jobmanager-*.out

lorem : 1

bye : 1

ipsum : 4

运行结束后可以停掉Flink:

$./bin/stop-local.sh