Flink1.4 安装与启动

1. 下载

Flink 可以运行在 Linux, Mac OS X和Windows上。为了运行Flink, 唯一的要求是必须在Java 7.x (或者更高版本)上安装。Windows 用户, 请查看 Flink在Windows上的安装指南。

你可以使用以下命令检查Java当前运行的版本:

java -version

如果你安装的是Java 8,输出结果类似于如下:

java version "1.8.0_91"
Java(TM) SE Runtime Environment (build 1.8.0_91-b14)
Java HotSpot(TM) 64-Bit Server VM (build 25.91-b14, mixed mode)

从下载页下载一个二进制的包,你可以选择任何你喜欢的Hadoop/Scala组合方式。如果你只是打算使用本地文件系统,那么可以使用任何版本的Hadoop。进入下载目录,解压下载的压缩包:

xiaosi@yoona:~$ tar -zxvf flink-1.3.2-bin-hadoop27-scala_2.11.tgz -C opt/
flink-1.3.2/
flink-1.3.2/opt/
flink-1.3.2/opt/flink-cep_2.11-1.3.2.jar
flink-1.3.2/opt/flink-metrics-datadog-1.3.2.jar
flink-1.3.2/opt/flink-metrics-statsd-1.3.2.jar
flink-1.3.2/opt/flink-gelly_2.11-1.3.2.jar
flink-1.3.2/opt/flink-metrics-dropwizard-1.3.2.jar
flink-1.3.2/opt/flink-gelly-scala_2.11-1.3.2.jar
flink-1.3.2/opt/flink-metrics-ganglia-1.3.2.jar
flink-1.3.2/opt/flink-cep-scala_2.11-1.3.2.jar
flink-1.3.2/opt/flink-table_2.11-1.3.2.jar
flink-1.3.2/opt/flink-ml_2.11-1.3.2.jar
flink-1.3.2/opt/flink-metrics-graphite-1.3.2.jar
flink-1.3.2/lib/
...

2. 启动本地集群

使用如下命令启动Flink:

xiaosi@yoona:~/opt/flink-1.3.2$ ./bin/start-local.sh
Starting jobmanager daemon on host yoona.

通过访问 http://localhost:8081 检查JobManager网页,确保所有组件都启动并已运行。网页会显示一个有效的TaskManager实例。

img

你也可以通过检查日志目录里的日志文件来验证系统是否已经运行:

xiaosi@yoona:~/opt/flink-1.3.2/log$ cat flink-xiaosi-jobmanager-0-yoona.log | less
2017-10-16 14:42:10,972 INFO org.apache.flink.runtime.jobmanager.JobManager - Starting JobManager (Version: 1.3.2, Rev:0399bee, Date:03.08.2017 @ 10:23:11 UTC)
...
2017-10-16 14:42:11,109 INFO org.apache.flink.runtime.jobmanager.JobManager - Starting JobManager without high-availability
2017-10-16 14:42:11,111 INFO org.apache.flink.runtime.jobmanager.JobManager - Starting JobManager on localhost:6123 with execution mode LOCAL
...
2017-10-16 14:42:11,915 INFO org.apache.flink.runtime.jobmanager.JobManager - Starting JobManager web frontend
...
2017-10-16 14:42:13,941 INFO org.apache.flink.runtime.instance.InstanceManager - Registered TaskManager at localhost (akka://flink/user/taskmanager) as 0df4d4ebd25ffec4878906726c29f88c. Current number of registered hosts is 1. Current number of alive task slots is 1.
...

3. Example Code

你可以在GitHub上找到SocketWindowWordCount例子的完整代码,有JavaScala两个版本。

Scala:

package org.apache.flink.streaming.scala.examples.socket

import org.apache.flink.api.java.utils.ParameterTool
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.windowing.time.Time

/**
* Implements a streaming windowed version of the "WordCount" program.
*
* This program connects to a server socket and reads strings from the socket.
* The easiest way to try this out is to open a text sever (at port 12345)
* using the ''netcat'' tool via
* {{{
* nc -l 12345
* }}}
* and run this example with the hostname and the port as arguments..
*/
object SocketWindowWordCount {

/** Main program method */
def main(args: Array[String]) : Unit = {

// the host and the port to connect to
var hostname: String = "localhost"
var port: Int = 0

try {
val params = ParameterTool.fromArgs(args)
hostname = if (params.has("hostname")) params.get("hostname") else "localhost"
port = params.getInt("port")
} catch {
case e: Exception => {
System.err.println("No port specified. Please run 'SocketWindowWordCount " +
"--hostname <hostname> --port <port>', where hostname (localhost by default) and port " +
"is the address of the text server")
System.err.println("To start a simple text server, run 'netcat -l <port>' " +
"and type the input text into the command line")
return
}
}

// get the execution environment
val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment

// get input data by connecting to the socket
val text: DataStream[String] = env.socketTextStream(hostname, 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))
.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版本:

package org.apache.flink.streaming.examples.socket;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;

/**
* Implements a streaming windowed version of the "WordCount" program.
*
* <p>This program connects to a server socket and reads strings from the socket.
* The easiest way to try this out is to open a text server (at port 12345)
* using the <i>netcat</i> tool via
* <pre>
* nc -l 12345
* </pre>
* and run this example with the hostname and the port as arguments.
*/
@SuppressWarnings("serial")
public class SocketWindowWordCount {

public static void main(String[] args) throws Exception {

// the host and the port to connect to
final String hostname;
final int port;
try {
final ParameterTool params = ParameterTool.fromArgs(args);
hostname = params.has("hostname") ? params.get("hostname") : "localhost";
port = params.getInt("port");
} catch (Exception e) {
System.err.println("No port specified. Please run 'SocketWindowWordCount " +
"--hostname <hostname> --port <port>', where hostname (localhost by default) " +
"and port is the address of the text server");
System.err.println("To start a simple text server, run 'netcat -l <port>' and " +
"type the input text into the command line");
return;
}

// get the execution environment
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

// get input data by connecting to the socket
DataStream<String> text = env.socketTextStream(hostname, 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))

.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Example

现在, 我们可以运行Flink 应用程序。 这个例子将会从一个socket中读取一段文本,并且每隔5秒打印之前5秒内每个单词出现的个数。例如:

a tumbling window of processing time, as long as words are floating in.

(1) 首先,我们可以通过netcat命令来启动本地服务:

nc -l 9000

(2) 提交Flink程序:

xiaosi@yoona:~/opt/flink-1.3.2$ ./bin/flink run examples/streaming/SocketWindowWordCount.jar --port 9000
Cluster configuration: Standalone cluster with JobManager at localhost/127.0.0.1:6123
Using address localhost:6123 to connect to JobManager.
JobManager web interface address http://localhost:8081
Starting execution of program
Submitting job with JobID: a963626a1e09f7aeb0dc34412adfb801. Waiting for job completion.
Connected to JobManager at Actor[akka.tcp://flink@localhost:6123/user/jobmanager#941160871] with leader session id 00000000-0000-0000-0000-000000000000.
10/16/2017 15:12:26 Job execution switched to status RUNNING.
10/16/2017 15:12:26 Source: Socket Stream -> Flat Map(1/1) switched to SCHEDULED
10/16/2017 15:12:26 TriggerWindow(TumblingProcessingTimeWindows(5000), ReducingStateDescriptor{serializer=org.apache.flink.api.java.typeutils.runtime.PojoSerializer@37ff898e, reduceFunction=org.apache.flink.streaming.examples.socket.SocketWindowWordCount$1@4d15107f}, ProcessingTimeTrigger(), WindowedStream.reduce(WindowedStream.java:300)) -> Sink: Unnamed(1/1) switched to SCHEDULED
10/16/2017 15:12:26 Source: Socket Stream -> Flat Map(1/1) switched to DEPLOYING
10/16/2017 15:12:26 TriggerWindow(TumblingProcessingTimeWindows(5000), ReducingStateDescriptor{serializer=org.apache.flink.api.java.typeutils.runtime.PojoSerializer@37ff898e, reduceFunction=org.apache.flink.streaming.examples.socket.SocketWindowWordCount$1@4d15107f}, ProcessingTimeTrigger(), WindowedStream.reduce(WindowedStream.java:300)) -> Sink: Unnamed(1/1) switched to DEPLOYING
10/16/2017 15:12:26 Source: Socket Stream -> Flat Map(1/1) switched to RUNNING
10/16/2017 15:12:26 TriggerWindow(TumblingProcessingTimeWindows(5000), ReducingStateDescriptor{serializer=org.apache.flink.api.java.typeutils.runtime.PojoSerializer@37ff898e, reduceFunction=org.apache.flink.streaming.examples.socket.SocketWindowWordCount$1@4d15107f}, ProcessingTimeTrigger(), WindowedStream.reduce(WindowedStream.java:300)) -> Sink: Unnamed(1/1) switched to RUNNING

应用程序连接socket并等待输入,你可以通过web界面来验证任务期望的运行结果:

单词的数量在5秒的时间窗口中进行累加(使用处理时间和tumbling窗口),并打印在stdout。监控JobManager的输出文件,并在nc写一些文本(回车一行就发送一行输入给Flink) :

xiaosi@yoona:~/opt/flink-1.3.2$  nc -l 9000
lorem ipsum
ipsum ipsum ipsum
bye

.out文件将在每个时间窗口截止之际打印每个单词的个数:

xiaosi@yoona:~/opt/flink-1.3.2$  tail -f log/flink-*-jobmanager-*.out
lorem : 1
bye : 1
ipsum : 4

使用以下命令来停止Flink:

./bin/stop-local.sh

阅读更多的例子来熟悉Flink的编程API。 当你完成这些,可以继续阅读streaming指南

赏几毛白!