【Flink】DataStream API 教程

设置一个maven项目

使用maven 创建一个flink项目，使用下面命令：

$ mvn archetype:generate \
    -DarchetypeGroupId=org.apache.flink \
    -DarchetypeArtifactId=flink-quickstart-java \
    -DarchetypeVersion=1.9.0 \
    -DgroupId=wiki-edits \
    -DartifactId=wiki-edits \
    -Dversion=0.1 \
    -Dpackage=wikiedits \
    -DinteractiveMode=false

可以根据需要编辑groupid artifactId 和 package，目录项目结构如下：

$ tree wiki-edits
wiki-edits/
├── pom.xml
└── src
    └── main
        ├── java
        │   └── wikiedits
        │       ├── BatchJob.java
        │       └── StreamingJob.java
        └── resources
            └── log4j.propertie

这是项目已经创建了一些样例代码，我们可以直接删除这些样例代码结构在src/main/java

$ rm wiki-edits/src/main/java/wikiedits/*.java

最后我们需要添加一些我们程序需要的依赖，在pom.xml中添加:

<dependencies>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-java</artifactId>
        <version>${flink.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-streaming-java_2.11</artifactId>
        <version>${flink.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-clients_2.11</artifactId>
        <version>${flink.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-connector-wikiedits_2.11</artifactId>
        <version>${flink.version}</version>
    </dependency>
</dependencies>

写一个flink程序

打开IDE，添加一个文件src/main/java/wikiedits/WikipediaAnalysis.java:

package wikiedits;

public class WikipediaAnalysis {

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

    }
}

这只是一个基础的main函数，接着我们的第一步就是创建一个环境变量StreamExecutionEnvironment (如果是批处理的话就创建一个ExecutionEnvironment)，这个可以用来读取外部文件资源和用来执行程序，所以我们现在main函数中添加这个方法。

StreamExecutionEnvironment see = StreamExecutionEnvironment.getExecutionEnvironment();

接下来我们再创一个source，来接受Wikipedia 的IRC log

DataStream<WikipediaEditEvent> edits = see.addSource(new WikipediaEditsSource());

这里创建了一个 WikipediaEditEvent 的datastream来帮助我们进一步处理程序。第一步我们需要指明userName为分组key。

KeyedStream<WikipediaEditEvent, String> keyedEdits = edits
    .keyBy(new KeySelector<WikipediaEditEvent, String>() {
        @Override
        public String getKey(WikipediaEditEvent event) {
            return event.getUser();
        }
    });

接着我们需要指定我们想要的结果在一个窗口的输出的时间大小，和做一些聚合操作。本示例展示的是在时间窗口内每个用户增加或者删除字节的数量，一个窗口在一个流里面执行计算，在无限流的数据流中我们需要设置窗口，在示例中窗口设置为5s。

DataStream<Tuple2<String, Long>> result = keyedEdits
    .timeWindow(Time.seconds(5))
    .aggregate(new AggregateFunction<WikipediaEditEvent, Tuple2<String, Long>, Tuple2<String, Long>>() {
        @Override
        public Tuple2<String, Long> createAccumulator() {
            return new Tuple2<>("", 0L);
        }

        @Override
        public Tuple2<String, Long> add(WikipediaEditEvent value, Tuple2<String, Long> accumulator) {
            accumulator.f0 = value.getUser();
            accumulator.f1 += value.getByteDiff();
            return accumulator;
        }

        @Override
        public Tuple2<String, Long> getResult(Tuple2<String, Long> accumulator) {
            return accumulator;
        }

        @Override
        public Tuple2<String, Long> merge(Tuple2<String, Long> a, Tuple2<String, Long> b) {
            return new Tuple2<>(a.f0, a.f1 + b.f1);
        }
    });

最后打印结果并提交执行

result.print();

see.execute();

所有的操作，例如：建立一个source，transformations，sink，都是在内部建立有向图，
只有我们执行execute()的时候，这些操作图才会在我们本机或者集群上执行。

完整的代码如下：

package wikiedits;

import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.connectors.wikiedits.WikipediaEditEvent;
import org.apache.flink.streaming.connectors.wikiedits.WikipediaEditsSource;

public class WikipediaAnalysis {

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

    StreamExecutionEnvironment see = StreamExecutionEnvironment.getExecutionEnvironment();

    DataStream<WikipediaEditEvent> edits = see.addSource(new WikipediaEditsSource());

    KeyedStream<WikipediaEditEvent, String> keyedEdits = edits
      .keyBy(new KeySelector<WikipediaEditEvent, String>() {
        @Override
        public String getKey(WikipediaEditEvent event) {
          return event.getUser();
        }
      });

    DataStream<Tuple2<String, Long>> result = keyedEdits
      .timeWindow(Time.seconds(5))
      .aggregate(new AggregateFunction<WikipediaEditEvent, Tuple2<String, Long>, Tuple2<String, Long>>() {
        @Override
        public Tuple2<String, Long> createAccumulator() {
          return new Tuple2<>("", 0L);
        }

        @Override
        public Tuple2<String, Long> add(WikipediaEditEvent value, Tuple2<String, Long> accumulator) {
          accumulator.f0 = value.getUser();
          accumulator.f1 += value.getByteDiff();
          return accumulator;
        }

        @Override
        public Tuple2<String, Long> getResult(Tuple2<String, Long> accumulator) {
          return accumulator;
        }

        @Override
        public Tuple2<String, Long> merge(Tuple2<String, Long> a, Tuple2<String, Long> b) {
          return new Tuple2<>(a.f0, a.f1 + b.f1);
        }
      });

    result.print();

    see.execute();
  }
}

可以是用maven执行完成的程序，在命令行中：

$ mvn clean package
$ mvn exec:java -Dexec.mainClass=wikiedits.WikipediaAnalysis

输出如下

1> (Fenix down,114)
6> (AnomieBOT,155)
8> (BD2412bot,-3690)
7> (IgnorantArmies,49)
3> (Ckh3111,69)
5> (Slade360,0)
7> (Narutolovehinata5,2195)
6> (Vuyisa2001,79)
4> (Ms Sarah Welch,269)
4> (KasparBot,-245)

每行前面的数字代表哪个并执行器接受并执行了任务。

练习：在集群上运行并写入kafka

请先在本机搭建本地集群环境并且安装kafka

我们需要添加kafka-connector 并且sink到kafka中，第一步，我们需要在pom.xml添加相关依赖

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-kafka-0.11_2.11</artifactId>
    <version>${flink.version}</version>
</dependency>

接下来，我们需要修改我们的程序。将print sink替换成kafka sink，程序如下:

result
    .map(new MapFunction<Tuple2<String,Long>, String>() {
        @Override
        public String map(Tuple2<String, Long> tuple) {
            return tuple.toString();
        }
    })
    .addSink(new FlinkKafkaProducer011<>("localhost:9092", "wiki-result", new SimpleStringSchema()));

添加import依赖如下：

import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer011;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.common.functions.MapFunction;

使用maven编译出jar包：

$ mvn clean package

生成的jar文件地址在 target/wiki-edits-0.1.jar

现在我们需要启动flink集群

$ cd my/flink/directory
$ bin/start-cluster.sh

同时也需要创建一个kafka topic，保证我们的程序能够写入金去：

$ cd my/kafka/directory
$ bin/kafka-topics.sh --create --zookeeper localhost:2181 \
--replication-factor 1 --partitions 1 --topic wiki-results

现在我们可以准备在我们本地flink集群中运行jar文件

$ cd my/flink/directory
$ bin/flink run -c wikiedits.WikipediaAnalysis path/to/wikiedits-0.1.jar

输出的日志如下:

03/08/2016 15:09:27 Job execution switched to status RUNNING.
03/08/2016 15:09:27 Source: Custom Source(1/1) switched to SCHEDULED
03/08/2016 15:09:27 Source: Custom Source(1/1) switched to DEPLOYING
03/08/2016 15:09:27 Window(TumblingProcessingTimeWindows(5000), ProcessingTimeTrigger, AggregateFunction$3, PassThroughWindowFunction) -> Sink: Print to Std. Out (1/1) switched from CREATED to SCHEDULED
03/08/2016 15:09:27 Window(TumblingProcessingTimeWindows(5000), ProcessingTimeTrigger, AggregateFunction$3, PassThroughWindowFunction) -> Sink: Print to Std. Out (1/1) switched from SCHEDULED to DEPLOYING
03/08/2016 15:09:27 Window(TumblingProcessingTimeWindows(5000), ProcessingTimeTrigger, AggregateFunction$3, PassThroughWindowFunction) -> Sink: Print to Std. Out (1/1) switched from DEPLOYING to RUNNING
03/08/2016 15:09:27 Source: Custom Source(1/1) switched to RUNNING

可以登录http://localhost:8081查看任务运行的情况，我们可以看到有两个operation，出于性能考虑，window之后的操作会被折叠成一个，被称为chaining

可以使用kafka custom命令观察kafka的数据

bin/kafka-console-consumer.sh  --zookeeper localhost:2181 --topic wiki-result