初识Flink
【摘要】 Apache Flink是一个用于分布式流和批处理数据处理的开源平台。Flink的核心是流数据流引擎，为数据流上的分布式计算提供数据分发，通信和容错。Flink在流引擎之上构建批处理，覆盖本机迭代支持，托管内存和程序优化。
一、Flink 的下载安装启动
设置：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...
Apache Flink是一个用于分布式流和批处理数据处理的开源平台。Flink的核心是流数据流引擎，为数据流上的分布式计算提供数据分发，通信和容错。Flink在流引擎之上构建批处理，覆盖本机迭代支持，托管内存和程序优化。
一、Flink 的下载安装启动
设置：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，唯一的要求是安装一个有效的Java 8.x. Windows用户，请查看Windows上的Flink指南，该指南介绍了如何在Windows上运行Flink以进行本地设置。
您可以通过发出以下命令来检查Java的正确安装：
java -version
如果你有Java 8，输出将如下所示：
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java version "1.8.0_111"
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Java(TM) SE Runtime Environment (build 1.8.0_111-b14)
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Java HotSpot(TM) 64-Bit Server VM (build 25.111-b14, mixed mode)
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$ cd ~/Downloads # Go to download directory
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$ tar xzf flink-*.tgz # Unpack the downloaded archive
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$ cd flink-1.7.0
二、启动本地Flink群集
$ ./bin/start-cluster.sh # Start Flink
检查web前端ui页面在http://localhost:8081，并确保一切都正常运行。Web前端应报告单个可用的TaskManager实例。
您还可以通过检查logs目录中的日志文件来验证系统是否正在运行：
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$ tail log/flink-*-standalonesession-*.log
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INFO ... - Rest endpoint listening at localhost:8081
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INFO ... - http://localhost:8081 was granted leadership ...
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INFO ... - Web frontend listening at http://localhost:8081.
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INFO ... - Starting RPC endpoint for StandaloneResourceManager at akka://flink/user/resourcemanager .
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INFO ... - Starting RPC endpoint for StandaloneDispatcher at akka://flink/user/dispatcher .
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INFO ... - ResourceManager akka.tcp://flink@localhost:6123/user/resourcemanager was granted leadership ...
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INFO ... - Starting the SlotManager.
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INFO ... - Dispatcher akka.tcp://flink@localhost:6123/user/dispatcher was granted leadership ...
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INFO ... - Recovering all persisted jobs.
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INFO ... - Registering TaskManager ... under ... at the SlotManager.
三、阅读代码
您可以在Scala中找到此SocketWindowWordCount示例的完整源代码，并在GitHub上找到Java。
- Scala的
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object SocketWindowWordCount {
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def main(args: Array[String]) : Unit = {
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// the port to connect to
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val port: Int = try {
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ParameterTool.fromArgs(args).getInt("port")
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} catch {
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case e: Exception => {
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System.err.println("No port specified. Please run 'SocketWindowWordCount --port <port>'")
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return
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}
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}
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// get the execution environment
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val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
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// get input data by connecting to the socket
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val text = env.socketTextStream("localhost", port, '\n')
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// parse the data, group it, window it, and aggregate the counts
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val windowCounts = text
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.flatMap { w => w.split("\\s") }
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.map { w => WordWithCount(w, 1) }
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.keyBy("word")
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.timeWindow(Time.seconds(5), Time.seconds(1))
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.sum("count")
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// print the results with a single thread, rather than in parallel
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windowCounts.print().setParallelism(1)
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env.execute("Socket Window WordCount")
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}
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// Data type for words with count
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case class WordWithCount(word: String, count: Long)
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}
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public class SocketWindowWordCount {
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public static void main(String[] args) throws Exception {
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// the port to connect to
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final int port;
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try {
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final ParameterTool params = ParameterTool.fromArgs(args);
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port = params.getInt("port");
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} catch (Exception e) {
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System.err.println("No port specified. Please run 'SocketWindowWordCount --port <port>'");
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return;
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}
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// get the execution environment
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final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
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// get input data by connecting to the socket
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DataStream<String> text = env.socketTextStream("localhost", port, "\n");
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// parse the data, group it, window it, and aggregate the counts
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DataStream<WordWithCount> windowCounts = text
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.flatMap(new FlatMapFunction<String, WordWithCount>() {
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@Override
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public void flatMap(String value, Collector<WordWithCount> out) {
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for (String word : value.split("\\s")) {
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out.collect(new WordWithCount(word, 1L));
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}
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}
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})
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.keyBy("word")
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.timeWindow(Time.seconds(5), Time.seconds(1))
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.reduce(new ReduceFunction<WordWithCount>() {
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@Override
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public WordWithCount reduce(WordWithCount a, WordWithCount b) {
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return new WordWithCount(a.word, a.count + b.count);
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}
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});
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// print the results with a single thread, rather than in parallel
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windowCounts.print().setParallelism(1);
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env.execute("Socket Window WordCount");
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}
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// Data type for words with count
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public static class WordWithCount {
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public String word;
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public long count;
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public WordWithCount() {}
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public WordWithCount(String word, long count) {
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this.word = word;
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this.count = count;
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}
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@Override
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public String toString() {
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return word + " : " + count;
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}
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}
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}
四、运行示例
现在，我们将运行此Flink应用程序。它将从套接字读取文本，并且每5秒打印一次前5秒内每个不同单词的出现次数，即处理时间的翻滚窗口，只要文字漂浮在其中。
- 首先，我们使用netcat来启动本地服务器
$ nc -l 9000
- 提交Flink计划：
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$ ./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000
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Starting execution of program
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：
- 单词在5秒的时间窗口（处理时间，翻滚窗口）中计算并打印到
stdout。监视TaskManager的输出文件并写入一些文本nc（输入在点击后逐行发送到Flink）：
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$ nc -l 9000
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lorem ipsum
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ipsum ipsum ipsum
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bye
该.out文件将在每个时间窗口结束时，只要打印算作字浮在，例如：
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$ tail -f log/flink-*-taskexecutor-*.out
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lorem : 1
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bye : 1
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ipsum : 4
要停止Flink你可以执行如下命令：
$ ./bin/stop-cluster.sh
文章来源: blog.csdn.net，作者：血煞风雨城2018，版权归原作者所有，如需转载，请联系作者。
原文链接：blog.csdn.net/qq_31905135/article/details/86649409
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