本地部署
安裝
-
在官網(wǎng)安裝Flink锁保，并解壓到
/usr/local/flinksudo tar -zxf flink-1.6.2-bin-hadoop27-scala_2.11.tgz -C /usr/local cd /usr/local- 54388226982
-
修改文件名字匣砖，并設(shè)置權(quán)限
sudo mv ./flink-*/ ./flink sudo chown -R hadoop:hadoop ./flink
修改配置文件
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Flink對于本地模式是開箱即用的，如果要修改Java運行環(huán)境带膀，可修改conf/flink-conf.yaml中的env.java.home，設(shè)置為本地java的絕對路徑
添加環(huán)境變量
vim ~/.bashrc
export FLINK_HOME=/usr/local/flink
export PATH=$FLINK_HOME/bin:$PATH
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啟動Flink
start-cluster.sh
- 可以通過觀察logs目錄下的日志來檢測系統(tǒng)是否正在運行了
tail log/flink--jobmanager-.log
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- JobManager同時會在8081端口上啟動一個web前端，通過http://localhost:8081來訪問
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可以發(fā)現(xiàn)flink已經(jīng)正常啟動
運行示例
使用Maven創(chuàng)建Flink項目捡偏，在pom.xml中添加以下依賴：
<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>
批處理運行WordCount
官方示例
可以直接在/usr/local/flink/examples/batch中運行WordCount程序，并且這里還有更多示例：
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運行：
flink run WordCount.jar
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代碼
WordCountData
提供原始數(shù)據(jù)
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
主函數(shù)
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峡迷，則默認(rèn)使用WordCountData中提供的數(shù)據(jù)
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);
//如果沒有指定輸出银伟，則默認(rèn)打印到控制臺
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，這里需要使用-c指定main函數(shù)：
flink run -c WordCount WordCount.jar
流處理運行WordCount
官方示例
可以直接在/usr/local/flink/examples/streaming中運行WordCount程序绘搞，并且這里還有更多示例：
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代碼
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包彤避，然后啟動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
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在netcat中輸入字符流：
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可以在WebUI中查看運行結(jié)果：
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