本地安裝教程
只需幾個簡單的步驟即可啓動並運行Flink示例程序。
設置:下載並啓動Flink
Flink可在Linux,Mac OS X和Windows上運行。爲了能夠運行Flink,唯一的要求是安裝一個有效的Java 8.x環境。 Windows用戶,請查看Windows上的Flink指南,該指南介紹瞭如何在Windows上運行Flink以進行本地設置。
您可以通過發出以下命令來檢查Java正確安裝:
java -version
如果你有Java 8,輸出將如下所示:
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)
下載和解壓縮
- 從下載頁面下載二進制文件。您可以選擇任何您喜歡的Hadoop /Scala組合。如果您打算只使用本地文件系統,任何Hadoop版本都可以正常工作。
- 轉到下載目錄。
- 解壓縮下載的存檔。
$ cd ~/Downloads # Go to download directory
$ tar xzf flink-*.tgz # Unpack the downloaded archive
$ cd flink-1.8.0
對於MacOS X用戶,可以通過Homebrew安裝Flink 。
$ brew install apache-flink
...
$ flink --version
Version: 1.2.0, Commit ID: 1c659cf
啓動本地Flink羣集
$ ./bin/start-cluster.sh # Start Flink
檢查分派器的web前端HTTP://本地主機:8081,並確保一切都正常運行。Web前端應報告單個可用的TaskManager實例。
您還可以通過檢查logs目錄中的日誌文件來驗證系統是否正在運行:
$ 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.
閱讀代碼
您可以在Scala中找到SocketWindowWordCount示例的完整源代碼,並在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 <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;
}
}
}
運行示例
現在,我們將運行Flink應用程序。它將從socket中讀取文本,並且每5秒打印一次前5秒內每個不同單詞的出現次數,即處理時間的翻滾窗口,只要文字出現在其中。
首先,我們使用netcat來啓動本地服務器
$ nc -l 9000
提交Flink應用:
$ ./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000
Starting execution of program
程序連接到socket並等待輸入。您可以檢查Web界面以驗證作業是否按預期運行:
-
單詞在5秒的時間窗口(處理時間,翻滾窗口)中計算並打印到stdout。監控TaskManager的輸出文件並寫入一些文本到nc中(輸入在點擊後逐行發送到Flink):
$ nc -l 9000
lorem ipsum
ipsum ipsum ipsum
bye
該.out文件將在每個時間窗口結束時,只要有輸入就會打印結果,例如:
$ tail -f log/flink-*-taskexecutor-*.out
lorem : 1
bye : 1
ipsum : 4
停止Flink:
$ ./bin/stop-cluster.sh
下一步
查看更多示例以更好地瞭解Flink的編程API。完成後,請繼續閱讀streaming指南。