Apache Flink 定义
Apache Flink是一个框架和分布式处理引擎，用于对无界和有界数据流进行状态计算。Flink设计为在所有常见的集群环境中运行，以内存速度和任何规模执行计算。
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：
java version "1.8.0_201"
Java(TM) SE Runtime Environment (build 1.8.0_201-b09)
Java HotSpot(TM) 64-Bit Server VM (build 25.201-b09, mixed mode)
下载和解压缩
- 从下载页面下载二进制文件。您可以选择任何您喜欢的Hadoop / Scala组合。如果您打算只使用本地文件系统，任何Hadoop版本都可以正常工作。
- 转到下载目录。
- 解压缩下载的存档。
$ cd ~/Downloads # Go to download directory
$ tar xzf flink-*.tgz # Unpack the downloaded archive
$ cd flink-1.8.0
对于MacOS X用户，可以通过Homebrew安装Flink 。
$ brew install apache-flink ...
$ flink --version Version: 1.8.0, Commit ID: 4caec0d
启动本地Flink群集
$ ./bin/start-cluster.sh # Start Flink
检查分派器的web前端在HTTP：//本地主机：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。
jobmanager-1.png
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：
$ tail log/flink-*-standalonesession-*.log
INFO ... - Rest endpoint listening at localhost:8081
INFO ... - http://localhost:8081 was granted leadership ...
INFO ... - Web frontend listening at http://localhost:8081.
INFO ... - Starting RPC endpoint for StandaloneResourceManager at akka://flink/user/resourcemanager .
INFO ... - Starting RPC endpoint for StandaloneDispatcher at akka://flink/user/dispatcher .
INFO ... - ResourceManager akka.tcp://flink@localhost:6123/user/resourcemanager was granted leadership ...
INFO ... - Starting the SlotManager.
INFO ... - Dispatcher akka.tcp://flink@localhost:6123/user/dispatcher was granted leadership ...
INFO ... - Recovering all persisted jobs.
INFO ... - Registering TaskManager ... under ... at the SlotManager.
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
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)
}
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 '");
return;
}
// get the execution environment
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// get input data by connecting to the socket
DataStream text = env.socketTextStream("localhost", port, "\n");
// parse the data, group it, window it, and aggregate the counts
DataStream windowCounts = text
.flatMap(new FlatMapFunction() {
@Override
public void flatMap(String value, Collector 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() {
@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：
$ ./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000 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）：
$ nc -l 9000
lorem ipsum
ipsum ipsum ipsum
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：
$ tail -f log/flink-*-taskexecutor-*.out
lorem : 1
bye : 1
ipsum : 4
要停止Flink 所要做的操作：
$ ./bin/stop-cluster.sh