Flink消费kafka消息实战(1)

}

SingleMessage对象的定义：

public class SingleMessage {

private long timeLong;

private String name;

private String bizID;

private String time;

private String message;

public long getTimeLong() {

return timeLong;

}

public void setTimeLong(long timeLong) {

this.timeLong = timeLong;

}

public String getName() {

return name;

}

public void setName(String name) {

this.name = name;

}

public String getBizID() {

return bizID;

}

public void setBizID(String bizID) {

this.bizID = bizID;

}

public String getTime() {

return time;

}

public void setTime(String time) {

this.time = time;

}

public String getMessage() {

return message;

}

public void setMessage(String message) {

this.message = message;

}

实时处理的操作都集中在StreamingJob类，源码的关键位置已经加了注释，就不再赘述了：

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distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
“License”); you may not use this file except in compliance
with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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limitations under the License.

package com.bolingcavalry;

import org.apache.flink.api.common.functions.FlatMapFunction;

import org.apache.flink.api.common.serialization.SimpleStringSchema;

import org.apache.flink.api.java.tuple.Tuple;

import org.apache.flink.api.java.tuple.Tuple2;

import org.apache.flink.streaming.api.TimeCharacteristic;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import org.apache.flink.streaming.api.functions.AssignerWithPunctuatedWatermarks;

import org.apache.flink.streaming.api.functions.windowing.WindowFunction;

import org.apache.flink.streaming.api.watermark.Watermark;

import org.apache.flink.streaming.api.windowing.time.Time;

import org.apache.flink.streaming.api.windowing.windows.TimeWindow;

import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011;

import javax.annotation.Nullable;

import java.util.Properties;

/**

Skeleton for a Flink Streaming Job.
For a tutorial how to write a Flink streaming application, check the
tutorials and examples on the Flink Website.
To package your application into a JAR file for execution, run
‘mvn clean package’ on the command line.
If you change the name of the main class (with the public static void main(String[] args))
method, change the respective entry in the POM.xml file (simply search for ‘mainClass’).

public class StreamingJob {

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

final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

env.enableCheckpointing(5000); // 要设置启动检查点

env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

Properties props = new Properties();

props.setProperty(“bootstrap.servers”, “kafka1:9092”);

props.setProperty(“group.id”, “flink-group”);

//数据源配置，是一个kafka消息的消费者

FlinkKafkaConsumer011 consumer =

new FlinkKafkaConsumer011<>(“topic001”, new SimpleStringSchema(), props);

//增加时间水位设置类

consumer.assignTimestampsAndWatermarks(new AssignerWithPunctuatedWatermarks (){

@Override

public long extractTimestamp(String element, long previousElementTimestamp) {

return JSONHelper.getTimeLongFromRawMessage(element);

}

@Nullable

@Override

public Watermark checkAndGetNextWatermark(String lastElement, long extractedTimestamp) {

if (lastElement != null) {

return new Watermark(JSONHelper.getTimeLongFromRawMessage(lastElement));

}

return null;

}

});

env.addSource(consumer)

//将原始消息转成Tuple2对象，保留用户名称和访问次数(每个消息访问次数为1)

.flatMap((FlatMapFunction<String, Tuple2<String, Long>>) (s, collector) -> {

SingleMessage singleMessage = JSONHelper.parse(s);

if (null != singleMessage) {

collector.collect(new Tuple2<>(singleMessage.getName(), 1L));

}

})

//以用户名为key

.keyBy(0)

//时间窗口为2秒

.timeWindow(Time.seconds(2))

//将每个用户访问次数累加起来

.apply((WindowFunction<Tuple2<String, Long>, Tuple2<String, Long>, Tuple, TimeWindow>) (tuple, window, input, out) -> {

long sum = 0L;

for (Tuple2<String, Long> record: input) {

sum += record.f1;

}

Tuple2<String, Long> result = input.iterator().next();

result.f1 = sum;

out.collect(result);

})

//输出方式是STDOUT

.print();

env.execute(“Flink-Kafka demo”);

}

在pom.xml所在文件夹执行以下命令打包：

mvn clean package -Dmaven.test.skip=true -U

打包成功后，会在target目录下生成文件flinkkafkademo-1.0-SNAPSHOT.jar，将此文件提交到Flinkserver上，如下图：

在这里插入图片描述
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