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大数据-玩转数据-Flink网页埋点PV统计

news 2025/8/6 20:39:31

一、说明

衡量网站流量一个最简单的指标，就是网站的页面浏览量（Page View，PV）。用户每次打开一个页面便记录1次PV，多次打开同一页面则浏览量累计。
一般来说，PV与来访者的数量成正比，但是PV并不直接决定页面的真实来访者数量，如同一个来访者通过不断的刷新页面，也可以制造出非常高的PV。接下来我们就用Flink算子来实现PV的统计。

二、测试数据准备

把数据文件 UserBehavior 复制到project的input目录下
用于封装数据的JavaBean类

package com.atguigu.flink.java.chapter_6;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;/*** @Author lizhenchao@atguigu.cn* @Date 2020/12/10 19:32*/
@Data
@NoArgsConstructor
@AllArgsConstructor
public class UserBehavior {private Long userId;private Long itemId;private Integer categoryId;private String behavior;private Long timestamp;
}

三、代码

pv实现思路1: WordCount

package com.lyh.flink06;import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;public class PVcount {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.readTextFile("input/UserBehavior.csv").map(line -> { // 对数据切割, 然后封装到POJO中String[] split = line.split(",");return new UserBehavior(Long.valueOf(split[0]),Long.valueOf(split[1]),Integer.valueOf(split[2]),String.valueOf(split[3]),Long.valueOf(split[4]));}).filter(behavior -> "pv".equals(behavior.getBehavior())) //过滤出pv行为.map(behavior -> Tuple2.of("pv", 1L)).returns(Types.TUPLE(Types.STRING, Types.LONG)) // 使用Tuple类型, 方便后面求和.keyBy(value -> value.f0)  // keyBy: 按照key分组.sum(1) // 求和.print();env.execute();}
}

pv实现思路2: process

package com.lyh.flink06;import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.util.Collector;public class PVprocess {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(2);env.readTextFile("input/UserBehavior.csv").map(line -> {String[] split = line.split(",");return new UserBehavior(Long.valueOf(split[0]),Long.valueOf(split[1]),Integer.valueOf(split[2]),String.valueOf(split[3]),Long.valueOf(split[4]));}).filter(behavior -> "pv".equals(behavior.getBehavior())).keyBy(UserBehavior::getBehavior).process(new KeyedProcessFunction<String, UserBehavior, Long>() {long count = 0;@Overridepublic void processElement(UserBehavior userBehavior,Context ctx,Collector<Long> out) throws Exception {count++;out.collect(count);}}).print();env.execute();}
}