在几个简单的步骤中启动和运行Flink示例程序。
设置：下载和启动Flink
Flink运行在Linux, Mac OS X, and Windows。为了能够运行Flink，唯一的要求就是工作在Java 7.x(或更高)上。Windows用户，请查看Windows上运行Flink指南，它描述了在本地Windows上如何运行Flink。
你可以通过以下命令检查Java安装的正确性：
java -version
如果您有Java 8，那么输出将是这样的:
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)
Download and Unpack
- 从下载页下载二进制包。您可以选择您喜欢的任何Hadoop/Scala组合。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-1.4.1
MacOS X
对于MacOS X用户, 可以通过Homebrew安装Flink。
$ brew install apache-flink
...
$ flink --version
Version: 1.2.0, Commit ID: 1c659cf
启动一个本地Flink集群
$ ./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。
1195625042e58743ce16e6722da1fc2f.png
您还可以通过检查日志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)
阅读代码
你可以在GitHub上找到这个SocketWindowWordCount例子的完整的scala和java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;
}
}
}
运行示例
现在，我们将运行这个Flink应用程序。它将从一个套接字读取文本，每5秒打印出在前5秒内每一个不同单词的出现次数，即处理时间的滚动窗口，只要单词是浮动的。
-
首先，我们使用netcat启动本地服务器。
$ 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
程序连接到套接字并等待输入。您可以检查web界面以验证作业是否按预期运行:
jobmanager-2.png
jobmanager-3.png
单词在5秒的时间窗口(处理时间，滚动的窗口)被计算，并打印到stdout。监视任务管理器的输出文件，并在nc中写入一些文本(点击后按行发送到Flink行):
$ nc -l 9000
lorem ipsum
ipsum ipsum ipsum
bye
The .out file will print the counts at the end of each time window as long as words are floating in, e.g.:
$ tail -f log/flink-*-taskmanager-*.out
lorem : 1
bye : 1
ipsum : 4
通过下述命令停止Flink:
$ ./bin/stop-local.sh
