本地部署

安裝

1. 在官網安裝Flink，並解壓到/usr/local/flink

   • sudo tar -zxf flink-1.6.2-bin-hadoop27-scala_2.11.tgz -C /usr/local
     cd /usr/local
     

   • 

2. 修改檔名字，並設定許可權

   • sudo mv ./flink-*/ ./flink
     sudo chown -R hadoop:hadoop ./flink
     

修改配置檔案

• Flink對於本地模式是開箱即用的，如果要修改Java執行環境，可修改conf/flink-conf.yaml
  中的env.java.home，設定為本地java的絕對路徑

新增環境變數

vim ~/.bashrc
export FLNK_HOME=/usr/local/flink
export PATH=$FLINK_HOME/bin:$PATH

啟動Flink

start-cluster.sh

• 可以通過觀察logs目錄下的日誌來檢測系統是否正在運行了

tail log/flink--jobmanager-.log

• JobManager同時會在8081埠上啟動一個web前端，通過http://localhost:8081來訪問

可以發現flink已經正常啟動

執行示例

使用Maven建立Flink專案，在pom.xml中新增以下依賴：

    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.6.2</version>
        </dependency>
        <
 
dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.11</artifactId>
            <version>1.6.2</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.11</artifactId>
            <version>1.6.2</version>
        </dependency>
    </dependencies>

批處理執行WordCount

官方示例

可以直接在/usr/local/flink/examples/batch中執行WordCount程式，並且這裡還有更多示例：

執行：

flink run WordCount.jar 

程式碼

WordCountData

提供原始資料

import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;

public class WordCountData {
    public static final String[] WORDS=new String[]{"To be, or not to be,--that is the question:--", "Whether \'tis nobler in the mind to suffer", "The slings and arrows of outrageous fortune", "Or to take arms against a sea of troubles,", "And by opposing end them?--To die,--to sleep,--", "No more; and by a sleep to say we end", "The heartache, and the thousand natural shocks", "That flesh is heir to,--\'tis a consummation", "Devoutly to be wish\'d. To die,--to sleep;--", "To sleep! perchance to dream:--ay, there\'s the rub;", "For in that sleep of death what dreams may come,", "When we have shuffled off this mortal coil,", "Must give us pause: there\'s the respect", "That makes calamity of so long life;", "For who would bear the whips and scorns of time,", "The oppressor\'s wrong, the proud man\'s contumely,", "The pangs of despis\'d love, the law\'s delay,", "The insolence of office, and the spurns", "That patient merit of the unworthy takes,", "When he himself might his quietus make", "With a bare bodkin? who would these fardels bear,", "To grunt and sweat under a weary life,", "But that the dread of something after death,--", "The undiscover\'d country, from whose bourn", "No traveller returns,--puzzles the will,", "And makes us rather bear those ills we have", "Than fly to others that we know not of?", "Thus conscience does make cowards of us all;", "And thus the native hue of resolution", "Is sicklied o\'er with the pale cast of thought;", "And enterprises of great pith and moment,", "With this regard, their currents turn awry,", "And lose the name of action.--Soft you now!", "The fair Ophelia!--Nymph, in thy orisons", "Be all my sins remember\'d."};
    public WordCountData() {
    }
    public static DataSet<String> getDefaultTextLineDataset(ExecutionEnvironment env){
        return env.fromElements(WORDS);
    }
}

WordCountTokenizer

切分句子

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;


public class WordCountTokenizer implements FlatMapFunction<String, Tuple2<String,Integer>>{

    public WordCountTokenizer(){}


    public void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception {
        String[] tokens = value.toLowerCase().split("\\W+");
        int len = tokens.length;

        for(int i = 0; i<len;i++){
            String tmp = tokens[i];
            if(tmp.length()>0){
                out.collect(new Tuple2<String, Integer>(tmp,Integer.valueOf(1)));
            }
        }
    }
}

WordCount

主函式

import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.AggregateOperator;
import org.apache.flink.api.java.utils.ParameterTool;


public class WordCount {

    public WordCount(){}

    public static void main(String[] args) throws Exception {
        ParameterTool params = ParameterTool.fromArgs(args);
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
        env.getConfig().setGlobalJobParameters(params);
        Object text;
        //如果沒有指定輸入路徑，則預設使用WordCountData中提供的資料
        if(params.has("input")){
            text = env.readTextFile(params.get("input"));
        }else{
            System.out.println("Executing WordCount example with default input data set.");
            System.out.println("Use -- input to specify file input.");
            text = WordCountData.getDefaultTextLineDataset(env);
        }

        AggregateOperator counts = ((DataSet)text).flatMap(new WordCountTokenizer()).groupBy(new int[]{0}).sum(1);
        //如果沒有指定輸出，則預設列印到控制檯
        if(params.has("output")){
            counts.writeAsCsv(params.get("output"),"\n", " ");
            env.execute();
        }else{
            System.out.println("Printing result to stdout. Use --output to specify output path.");
            counts.print();
        }

    }
}

首先打包成JAR包，這裡需要使用-c指定main函式：

flink run -c WordCount WordCount.jar

流處理執行WordCount

官方示例

可以直接在/usr/local/flink/examples/streaming中執行WordCount程式，並且這裡還有更多示例：

程式碼

SocketWindowWordCount

import org.apache.flink.api.common.functions.FlatMapFunction;
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 java.sql.Time;
import java.util.stream.Collector;

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;
        }
    }
}

首先打包成JAR包，然後啟動netcat：

nc -l 9000

將終端啟動netcat作為輸入流：

提交Jar包：

flink run -c SocketWindowWordCount WordCountSteaming.jar --port 9000

這樣終端會一直等待netcat的輸入流

在netcat中輸入字元流：

可以在WebUI中檢視執行結果：