只需几个简单的步骤即可启动并运行Flink示例程序。
1. 下载并启动Flink
Flink可在Linux，Mac OS X和Windows上运行。 为了能够运行Flink，唯一的要求是安装Java 7.x（或更高版本）。
从下载页面下载Flink (http://flink.apache.org/downloads.html )。 可以使用本地文件系统，任何Hadoop版本都可以正常工作。切换到下载目录，解压缩下载的文件。
$ cd~ / Downloads＃转到下载目录 $ tar xzf flink - * .tgz＃解压缩下载的档案 $ cd flink-1.2
2. 启动本地Flink群集
$ ./bin/start-local.sh
检查JobManager的web前端http：// localhost：8081并确保一切正常运行。 Web前端会显示单个可用的TaskManager实例。
可以通过检查logs目录中的日志文件来验证系统是否正在运行：
$ 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)
3.示例源码
可以在Scala和Java上的GitHub上找到此SocketWindowWordCount示例的完整源代码。
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;
}
}
}
4. 运行示例
现在，将运行此Flink应用程序。 它将从套接字（socket）读取文本，并且每5秒打印一次在前5秒内每个不同单词的出现次数，即使处理时间窗口，只要有文字输入。
首先，使用netcat来启动本地服务器：
nc -l 9000
提交Flint客户端程序；
$ ./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
程序连接到套接字 （Socket）并等待输入, 可以检查Web界面以验证作业是否按预期运行：
单词在5秒的时间窗口中被计算（处理时间）并打印到标准输出窗口。 监视JobManager的输出文件并在nc中写入一些文本：
$ nc -l 9000 lorem ipsum ipsum ipsum ipsum bye
只要有文字输入，.out文件就会在每个时间窗口的末尾打印输入文字的计数，例如：
$ tail -f log/flink-*-jobmanager-*.out lorem : 1 bye : 1 ipsum : 4
关停Flink 服务器，可以使用：
$ ./bin/stop-local.sh
Flink 示例演示
