原文链接 译者：清英
Flink 可以运行在 Linux, Mac OS X和Windows上。为了运行Flink, 唯一的要求是必须在Java 7.x (或者更高版本)上安装。Windows 用户, 请查看 Flink在Windows上的安装指南。
你可以使用以下命令检查Java当前运行的版本：
java -version
如果你有安装Java 8，命令行有如下回显
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)** 下载和解压 **
从下载页下载一个二进制的包，你可以选择任何你喜欢的Hadoop/Scala组合包。如果你计划使用文件系统，那么可以使用任何Hadoop版本。进入下载目录解压下载的压缩包 $ cd ~/Downloads # Go to download directory $ tar xzf flink-*.tgz # Unpack the downloaded archive $ cd flink-1.2.0 Start a Local Flink ClusterMacOS X
对于 MacOS X 用户, Flink 可以通过Homebrew 进行安装。
~~~bash $ brew install apache-flink … $ flink –version Version: 1.2.0, Commit ID: 1c659cf ~~~使用如下命令启动Flink：
$ ./bin/start-local.sh # Start Flink通过访问http://localhost:8081检查JobManager网页,确保所有组件都已运行。网页会显示一个有效的TaskManager实例。
译注：本地需要有localhost 127.0.0.1的域名映射
你也可以通过检查日志目录里的日志文件来验证系统是否已经运行：
$ 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)你可以在GitHub中找到SocketWindowWordCount完整的代码，有JAVA和SCALA两个版本。
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 <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) }Java
public class SocketWindowWordCount { public static void main(String[] args) throws Exception { // the port to connect to final int port; try { final ParameterTool params = ParameterTool.fromArgs(args); port = params.getInt("port"); } catch (Exception e) { System.err.println("No port specified. Please run 'SocketWindowWordCount --port <port>'"); return; } // get the execution environment final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); // get input data by connecting to the socket DataStream<String> text = env.socketTextStream("localhost", port, "\n"); // parse the data, group it, window it, and aggregate the counts DataStream<WordWithCount> windowCounts = text .flatMap(new FlatMapFunction<String, WordWithCount>() { @Override public void flatMap(String value, Collector<WordWithCount> out) { for (String word : value.split("\\s")) { out.collect(new WordWithCount(word, 1L)); } } }) .keyBy("word") .timeWindow(Time.seconds(5), Time.seconds(1)) .reduce(new ReduceFunction<WordWithCount>() { @Override public WordWithCount reduce(WordWithCount a, WordWithCount b) { return new WordWithCount(a.word, a.count + b.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 public static class WordWithCount { public String word; public long count; public WordWithCount() {} public WordWithCount(String word, long count) { this.word = word; this.count = count; } @Override public String toString() { return word + " : " + count; } } }现在, 我们可以运行Flink 应用程序。 这个例子将会从一个socket中读一段文本，并且每隔5秒打印每个单词出现的数量。 例如 a tumbling window of processing time, as long as words are floating in.
第一步, 我们可以通过netcat命令来启动本地服务。 $ 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. Waiting for job completion. Connected to JobManager at Actor[akka.tcp://flink@127.0.0.1:6123/user/jobmanager#297388688] 11/04/2016 14:04:50 Job execution switched to status RUNNING. 11/04/2016 14:04:50 Source: Socket Stream -> Flat Map(1/1) switched to SCHEDULED 11/04/2016 14:04:50 Source: Socket Stream -> Flat Map(1/1) switched to DEPLOYING 11/04/2016 14:04:50 Fast TumblingProcessingTimeWindows(5000) of WindowedStream.main(SocketWindowWordCount.java:79) -> Sink: Unnamed(1/1) switched to SCHEDULED 11/04/2016 14:04:51 Fast TumblingProcessingTimeWindows(5000) of WindowedStream.main(SocketWindowWordCount.java:79) -> Sink: Unnamed(1/1) switched to DEPLOYING 11/04/2016 14:04:51 Fast TumblingProcessingTimeWindows(5000) of WindowedStream.main(SocketWindowWordCount.java:79) -> Sink: Unnamed(1/1) switched to RUNNING 11/04/2016 14:04:51 Source: Socket Stream -> Flat Map(1/1) switched to RUNNING译者注：你也可以提交一个简单的任务examples/batch/WordCount.jar任务，也可以界面提交任务，提交前需要选择一下Entry Class。
程序连接socket并等待输入，你可以通过web界面来验证任务期望的运行结果：
单词的数量在5秒的时间窗口中进行累加（使用处理时间和tumbling窗口），并打印在stdout。监控JobManager的输出文件，并在nc写一些文本(回车一行就发送一行输入给Flink) :
$ nc -l 9000 lorem ipsum ipsum ipsum ipsum bye译者注：mac下使用命令nc -l -p 9000来启动监听端口，如果有问题可以telnet localhost 9000看下监听端口是否已经启动，如果启动有问题可以重装netcat ，使用命令brew install netcat。
.out文件将被打印每个时间窗口单词的总数：
$ tail -f log/flink-*-jobmanager-*.out lorem : 1 bye : 1 ipsum : 4使用以下命令来停止Flink:
$ ./bin/stop-local.shCheck out更多的例子来熟悉Flink的编程API。 当你完成这些，可以继续阅读streaming指南。
原创文章，转载请注明： 转载自并发编程网 – ifeve.com本文链接地址: 《Flink官方文档》Quick Start
