Flink项目系列3-实时流量统计
文章目录
- 一.项目概述
-
- 1.1 模块创建和数据准备
- 1.2 基于服务器 log 的热门页面浏览量统计
- 二.pom文件配置
- 三.代码
-
- 3.1 POJO类
- 3.2 热门页面
- 3.3 页面访问量
- 3.4 页面独立访问量
- 3.5 布隆过滤器实现独立访问量
- 参考:
一.项目概述
1.1 模块创建和数据准备
新建一个NetworkFlowAnalysis的package。
将 apache 服务器的日志文件 apache.log 复制到资源文件目录 src/main/resources
下，我们将从这里读取数据。
当然， 我们也可以仍然用 UserBehavior.csv 作为数据源， 这时我们分析的就不 是每一次对服务器的访问请求了，而是具体的页面浏览（“pv”） 操作。
1.2 基于服务器 log 的热门页面浏览量统计
我们现在要实现的模块是 “ 实时流量统计”。对于一个电商平台而言，用户登 录的入口流量、不同页面的访问流量都是值得分析的重要数据，而这些数据，可以 简单地从 web 服务器的日志中提取出来。
我们在这里先实现“ 热门页面浏览数” 的统计， 也就是读取服务器日志中的每 一行 log， 统计在一段时间内用户访问每一个 url 的次数，然后排序输出显示。
具体做法为： 每隔 5 秒， 输出最近 10 分钟内访问量最多的前 N 个 URL。 可以 看出，这个需求与之前“实时热门商品统计” 非常类似，所以我们完全可以借鉴此 前的代码。
在 NetworkFlowAnalysis 下创建 NetworkFlow 类，在 beans 下 定 义 POJO 类 ApacheLogEvent，这是输入的日志数据流；另外还有 UrlViewCount，这是窗口操作 统计的输出数据类型。在 main 函数中创建 StreamExecutionEnvironment 并做配置， 然后从 apache.log 文件中读取数据， 并包装成 ApacheLogEvent 类型。
需要注意的是， 原始日志中的时间是“ dd/MM/yyyy:HH:mm:ss” 的形式， 需要 定义一个 DateTimeFormat 将其转换为我们需要的时间戳格式：
.map( line -> {
String[] fields = line.split(" "); SimpleDateFormat simpleDateFormat = new
SimpleDateFormat("dd/MM/yyyy:HH:mm:ss");
Long timestamp = simpleDateFormat.parse(fields[3]).getTime();
return new ApacheLogEvent(fields[0], fields[1], timestamp, fields[5], fields[6]);
} )
二.pom文件配置
pom文件如下:
org.apache.flink
flink-java
1.10.1
provided
org.apache.flink
flink-streaming-java_2.11
1.10.1
provided
org.apache.flink
flink-connector-kafka_2.11
1.10.1
org.apache.flink
flink-core
1.10.1
org.apache.flink
flink-clients_2.11
1.10.1
org.apache.flink
flink-connector-redis_2.11
1.1.5
mysql
mysql-connector-java
8.0.19
org.apache.flink
flink-statebackend-rocksdb_2.11
1.10.1
org.apache.flink
flink-table-planner-blink_2.11
1.10.1
org.apache.flink
flink-table-planner_2.11
1.10.1
org.apache.flink
flink-table-api-java-bridge_2.11
1.10.1
org.apache.flink
flink-streaming-scala_2.11
1.10.1
org.apache.flink
flink-table-common
1.10.1
org.apache.flink
flink-csv
1.10.1
三.代码
3.1 POJO类
ApacheLogEvent
package com.zqs.flink.project.networkflowanalysis.beans;
public class ApacheLogEvent {
private String ip;
private String userId;
private Long timestamp;
private String method;
private String url;
public ApacheLogEvent(){
}
public ApacheLogEvent(String ip, String userId, Long timestamp, String method, String url) {
this.ip = ip;
this.userId = userId;
this.timestamp = timestamp;
this.method = method;
this.url = url;
}
public String getIp() {
return ip;
}
public String getUserId() {
return userId;
}
public Long getTimestamp() {
return timestamp;
}
public String getMethod() {
return method;
}
public String getUrl() {
return url;
}
public void setIp(String ip) {
this.ip = ip;
}
public void setUserId(String userId) {
this.userId = userId;
}
public void setTimestamp(Long timestamp) {
this.timestamp = timestamp;
}
public void setMethod(String method) {
this.method = method;
}
public void setUrl(String url) {
this.url = url;
}
@Override
public String toString() {
return "ApacheLogEvent{" +
"ip='" + ip + '\'' +
", userId='" + userId + '\'' +
", timestamp=" + timestamp +
", method='" + method + '\'' +
", url='" + url + '\'' +
'}';
}
}
PageViewCount
package com.zqs.flink.project.networkflowanalysis.beans;
public class PageViewCount {
private String url;
private Long windowEnd;
private Long count;
public PageViewCount(){
}
public PageViewCount(String url, Long windowEnd, Long count) {
this.url = url;
this.windowEnd = windowEnd;
this.count = count;
}
public String getUrl() {
return url;
}
public void setUrl(String url) {
this.url = url;
}
public Long getWindowEnd() {
return windowEnd;
}
public void setWindowEnd(Long windowEnd) {
this.windowEnd = windowEnd;
}
public Long getCount() {
return count;
}
public void setCount(Long count) {
this.count = count;
}
@Override
public String toString() {
return "PageViewCount{" +
"url='" + url + '\'' +
", windowEnd=" + windowEnd +
", count=" + count +
'}';
}
}
UserBehavior
package com.zqs.flink.project.networkflowanalysis.beans;
public class UserBehavior {
// 定义私有属性
private Long userId;
private Long itemId;
private Integer categoryId;
private String behavior;
private Long timestamp;
public UserBehavior() {
}
public UserBehavior(Long userId, Long itemId, Integer categoryId, String behavior, Long timestamp) {
this.userId = userId;
this.itemId = itemId;
this.categoryId = categoryId;
this.behavior = behavior;
this.timestamp = timestamp;
}
public Long getUserId() {
return userId;
}
public void setUserId(Long userId) {
this.userId = userId;
}
public Long getItemId() {
return itemId;
}
public void setItemId(Long itemId) {
this.itemId = itemId;
}
public Integer getCategoryId() {
return categoryId;
}
public void setCategoryId(Integer categoryId) {
this.categoryId = categoryId;
}
public String getBehavior() {
return behavior;
}
public void setBehavior(String behavior) {
this.behavior = behavior;
}
public Long getTimestamp() {
return timestamp;
}
public void setTimestamp(Long timestamp) {
this.timestamp = timestamp;
}
@Override
public String toString() {
return "UserBehavior{" +
"userId=" + userId +
", itemId=" + itemId +
", categoryId=" + categoryId +
", behavior='" + behavior + '\'' +
", timestamp=" + timestamp +
'}';
}
}
3.2 热门页面
代码:
HotPages
package com.zqs.flink.project.networkflowanalysis;
import akka.protobuf.ByteString;
import com.zqs.flink.project.networkflowanalysis.beans.ApacheLogEvent;
import com.zqs.flink.project.networkflowanalysis.beans.PageViewCount;
import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.common.state.ListState;
import org.apache.flink.api.common.state.ListStateDescriptor;
import org.apache.flink.api.common.state.MapState;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.shaded.guava18.com.google.common.collect.Lists;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.functions.windowing.WindowFunction;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;
import org.apache.flink.util.OutputTag;
import java.net.URL;
import java.sql.Timestamp;
import java.text.SimpleDateFormat;
import java.util.ArrayList;
import java.util.Comparator;
import java.util.Map;
import java.util.regex.Pattern;
/**
* @author 只是甲
* @date 2021-10-18
* @remark 热门页面
*/
public class HotPages {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
env.setParallelism(1);
//读取文件
URL resource = HotPages.class.getResource("/apache.log");
DataStream<String> inputStream = env.readTextFile(resource.getPath());
DataStream<ApacheLogEvent> dataStream = inputStream
.map(line -> {
String[] fields = line.split(" ");
SimpleDateFormat simpleDateFormat = new SimpleDateFormat("dd/MM/yyyy:HH:mm:ss");
Long timestamp = simpleDateFormat.parse(fields[3]).getTime();
return new ApacheLogEvent(fields[0], fields[1], timestamp, fields[5], fields[6]);
})
.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<ApacheLogEvent>(Time.seconds(1)) {
@Override
public long extractTimestamp(ApacheLogEvent element) {
return element.getTimestamp();
}
});
dataStream.print("data");
// 分组开窗聚合
// 定义一个侧输出流标签
OutputTag<ApacheLogEvent> lateTag = new OutputTag<ApacheLogEvent>("late"){};
SingleOutputStreamOperator<PageViewCount> windowAggStream = dataStream
.filter(data -> "GET".equals(data.getMethod())) // 过滤get请求
.filter(data -> {
String regex = "^((?!\\.(css|js|png|ico)$).)*$";
return Pattern.matches(regex, data.getUrl());
})
.keyBy(ApacheLogEvent:: getUrl) // 按照url分组
.timeWindow(Time.minutes(10), Time.seconds(5))
.allowedLateness(Time.minutes(1))
.sideOutputLateData(lateTag)
.aggregate(new PageCountAgg(), new PageCountResult());
windowAggStream.print("agg");
windowAggStream.getSideOutput(lateTag).print("late");
// 收集同一窗口count数据，排序输出
DataStream<String> resultStream = windowAggStream
.keyBy(PageViewCount::getWindowEnd)
.process(new TopNHotPages(3));
resultStream.print();
env.execute("hot pages job");
}
// 自定义聚合函数
public static class PageCountAgg implements AggregateFunction<ApacheLogEvent, Long, Long> {
@Override
public Long createAccumulator() {
return 0L;
}
@Override
public Long add(ApacheLogEvent value, Long accumulator) {
return accumulator + 1;
}
@Override
public Long getResult(Long accumulator) {
return accumulator;
}
@Override
public Long merge(Long a, Long b) {
return a + b;
}
}
// 实现自定义的窗口函数
public static class PageCountResult implements WindowFunction<Long, PageViewCount, String, TimeWindow>{
@Override
public void apply(String url, TimeWindow window, Iterable<Long> input, Collector<PageViewCount> out) throws Exception {
out.collect(new PageViewCount(url, window.getEnd(), input.iterator().next() ));
}
}
// 实现自定义的处理函数
public static class TopNHotPages extends KeyedProcessFunction<Long, PageViewCount, String>{
private Integer topSize;
public TopNHotPages(Integer topSize){
this.topSize = topSize;
}
// 定义状态，保存当前所有pageViewCount到Map中
MapState<String, Long> pageViewCountMapState;
@Override
public void open(Configuration parameters) throws Exception {
pageViewCountMapState = getRuntimeContext().getMapState(new MapStateDescriptor<String, Long>("page-count-map", String.class, Long.class));
}
@Override
public void processElement(PageViewCount value, Context ctx, Collector<String> out) throws Exception {
pageViewCountMapState.put(value.getUrl(), value.getCount());
ctx.timerService().registerEventTimeTimer(value.getWindowEnd() + 1);
// 注册一个1分钟之后的定时器,用来清空状态
ctx.timerService().registerEventTimeTimer(value.getWindowEnd() + 60 + 1000L);
}
@Override
public void onTimer(long timestamp, OnTimerContext ctx, Collector<String> out) throws Exception {
// 先判断是否到了窗口关闭清理时间,如果是,直接清空状态返回
if ( timestamp == ctx.getCurrentKey() + 60 * 1000L ){
pageViewCountMapState.clear();
return;
}
ArrayList<Map.Entry<String, Long>> pageViewCounts = Lists.newArrayList(pageViewCountMapState.entries());
pageViewCounts.sort(new Comparator<Map.Entry<String, Long>>() {
@Override
public int compare(Map.Entry<String, Long> o1, Map.Entry<String, Long> o2) {
if(o1.getValue() > o2.getValue())
return -1;
else if(o1.getValue() < o2.getValue())
return 1;
else
return 0;
}
});
// 格式化成String输出
StringBuilder resultBuilder = new StringBuilder();
resultBuilder.append("=================================================\n");
resultBuilder.append("窗口结束时间:").append(new Timestamp(timestamp -1)).append("\n");
// 遍历列表，取top n输出
for (int i = 0; i < Math.min(topSize, pageViewCounts.size()); i++){
Map.Entry<String, Long> currentItemViewCount = pageViewCounts.get(i);
resultBuilder.append("NO ").append(i + 1).append(":")
.append(" 页面URL = ").append(currentItemViewCount.getKey())
.append(" 浏览量 = ").append(currentItemViewCount.getValue())
.append("\n");
}
resultBuilder.append("======================================\n\n");
// 控制输出频率
Thread.sleep(1000L);
out.collect(resultBuilder.toString());
}
}
}
测试记录:
3.3 页面访问量
代码:
PageView
package com.zqs.flink.project.networkflowanalysis;
import com.zqs.flink.project.networkflowanalysis.beans.UserBehavior;
import com.zqs.flink.project.networkflowanalysis.beans.PageViewCount;
import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.streaming.api.functions.timestamps.AscendingTimestampExtractor;
import org.apache.flink.streaming.api.functions.windowing.WindowFunction;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;
import java.net.URL;
import java.util.Random;
/**
* @author 只是甲
* @date 2021-10-18
* @remark page view 统计
*/
public class PageView {
public static void main(String[] args) throws Exception{
// 1.创建执行环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(4);
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
// 2. 读取数据, 创建DataStream
URL resource = PageView.class.getResource("/UserBehavior.csv");
DataStream<String> inputStream = env.readTextFile(resource.getPath());
// 3. 转换为POJO， 分配时间戳和watermark
DataStream<UserBehavior> dataStream = inputStream
.map(line -> {
String[] fields = line.split(",");
return new UserBehavior(new Long(fields[0]), new Long(fields[1]), new Integer(fields[2]), fields[3], new Long(fields[4]));
})
.assignTimestampsAndWatermarks(new AscendingTimestampExtractor<UserBehavior>() {
@Override
public long extractAscendingTimestamp(UserBehavior element) {
return element.getTimestamp() * 1000L;
}
});
// 4. 分组开窗聚合，得到每个窗口内各个商品的count值
SingleOutputStreamOperator<Tuple2<String, Long>> pvResultStream0 =
dataStream
.filter(data -> "pv".equals(data.getBehavior())) // 过滤pv行为
.map(new MapFunction<UserBehavior, Tuple2<String, Long>>() {
@Override
public Tuple2<String, Long> map(UserBehavior value) throws Exception {
return new Tuple2<>("pv", 1L);
}
})
.keyBy(0) // 按商品分组
.timeWindow(Time.hours(1)) // 开1小时滚动窗口
.sum(1);
// 并行任务改进， 设计随机key，解决数据倾斜问题
SingleOutputStreamOperator<PageViewCount> pvStream = dataStream.filter(data -> "pv".equals(data.getBehavior()))
.map(new MapFunction<UserBehavior, Tuple2<Integer, Long>>() {
@Override
public Tuple2<Integer, Long> map(UserBehavior value) throws Exception {
Random random = new Random();
return new Tuple2<>(random.nextInt(10), 1L);
}
})
.keyBy(data -> data.f0)
.timeWindow(Time.hours(1))
.aggregate(new PvCountAgg(), new PvCountResult());
// 将各分区数据汇总起来
DataStream<PageViewCount> pvResultStream = pvStream
.keyBy(PageViewCount::getWindowEnd)
.process(new TotalPvCount());
pvResultStream.print();
env.execute("pv count job");
}
// 实现自定义预聚合函数
public static class PvCountAgg implements AggregateFunction<Tuple2<Integer, Long>, Long, Long>{
@Override
public Long createAccumulator() {
return 0L;
}
@Override
public Long add(Tuple2<Integer, Long> value, Long accumulator) {
return accumulator + 1;
}
@Override
public Long getResult(Long accumulator) {
return accumulator;
}
@Override
public Long merge(Long a, Long b) {
return a + b;
}
}
// 实现自定义窗口
public static class PvCountResult implements WindowFunction<Long, PageViewCount, Integer, TimeWindow>{
@Override
public void apply(Integer integer, TimeWindow window, Iterable<Long> input, Collector<PageViewCount> out) throws Exception {
out.collect( new PageViewCount(integer.toString(), window.getEnd(), input.iterator().next()));
}
}
// 实现自定义处理函数,把相同窗口分组统计的count值叠加
public static class TotalPvCount extends KeyedProcessFunction<Long, PageViewCount, PageViewCount>{
// 定义状态， 保存当前的总Count值
ValueState<Long> totalCountState;
@Override
public void open(Configuration parameters) throws Exception {
totalCountState = getRuntimeContext().getState(new ValueStateDescriptor<Long>("total-count", Long.class, 0L));
}
@Override
public void processElement(PageViewCount value, Context ctx, Collector<PageViewCount> out) throws Exception {
totalCountState.update( totalCountState.value() + value.getCount() );
ctx.timerService().registerEventTimeTimer(value.getWindowEnd() + 1);
}
@Override
public void onTimer(long timestamp, OnTimerContext ctx, Collector<PageViewCount> out) throws Exception {
// 定时器出发, 所有分组count值都到齐, 直接输出当前的总count值
Long totalCount = totalCountState.value();
out.collect(new PageViewCount("pv", ctx.getCurrentKey(), totalCount));
// 清空状态
totalCountState.clear();
}
}
}
测试记录:
3.4 页面独立访问量
代码:
UniqueVisitor
package com.zqs.flink.project.networkflowanalysis;
/**
* @author 只是甲
* @date 2021-10-18
* @remark unique page view 统计
*/
import com.zqs.flink.project.networkflowanalysis.beans.UserBehavior;
import com.zqs.flink.project.networkflowanalysis.beans.PageViewCount;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.timestamps.AscendingTimestampExtractor;
import org.apache.flink.streaming.api.functions.windowing.AllWindowFunction;
import org.apache.flink.streaming.api.functions.windowing.WindowFunction;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;
import java.net.URL;
import java.util.HashSet;
public class UniqueVisitor {
public static void main(String[] args) throws Exception {
// 1. 创建执行环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
// 2. 读取数据， 创建DataStream
URL resource = UniqueVisitor.class.getResource("/UserBehavior.csv");
DataStream<String> inputStream = env.readTextFile(resource.getPath());
// 3. 转换为POJO, 分配时间戳和watermark
DataStream<UserBehavior> dataStream = inputStream
.map(line -> {
String[] fields = line.split(",");
return new UserBehavior(new Long(fields[0]), new Long(fields[1]), new Integer(fields[2]), fields[3], new Long(fields[4]));
})
.assignTimestampsAndWatermarks(new AscendingTimestampExtractor<UserBehavior>() {
@Override
public long extractAscendingTimestamp(UserBehavior element) {
return element.getTimestamp() * 1000L;
}
});
// 开窗统计uv值
SingleOutputStreamOperator<PageViewCount> uvStream = dataStream.filter(data -> "pv".equals(data.getBehavior()))
.timeWindowAll(Time.hours(1))
.apply(new UvCountResult());
uvStream.print();
env.execute("uv count job");
}
// 实现自定义全窗口函数
public static class UvCountResult implements AllWindowFunction<UserBehavior, PageViewCount, TimeWindow>{
@Override
public void apply(TimeWindow window, Iterable<UserBehavior> values, Collector<PageViewCount> out) throws Exception {
// 定义一个Set结构，保存窗口中所有的userId,自动去重
HashSet<Long> uidSet = new HashSet<>();
for (UserBehavior ub: values)
uidSet.add(ub.getUserId());
out.collect( new PageViewCount("uv", window.getEnd(), (long)uidSet.size()));
}
}
}
测试记录:
3.5 布隆过滤器实现独立访问量
代码:
UvWithBloomFilter
package com.zqs.flink.project.networkflowanalysis;
/**
* @author 只是甲
* @date 2021-10-18
* @remark unique page view 布隆过滤器
*/
import com.zqs.flink.project.networkflowanalysis.beans.UserBehavior;
import com.zqs.flink.project.networkflowanalysis.beans.PageViewCount;
// import kafka.server.DynamicConfig;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.timestamps.AscendingTimestampExtractor;
import org.apache.flink.streaming.api.functions.windowing.ProcessAllWindowFunction;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.triggers.Trigger;
import org.apache.flink.streaming.api.windowing.triggers.TriggerResult;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;
import redis.clients.jedis.Jedis;
import java.net.URL;
public class UvWithBloomFilter {
public static void main(String[] args) throws Exception {
// 1. 创建执行环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
// 2. 读取数据，创建DataStream
URL resource = UniqueVisitor.class.getResource("/UserBehavior.csv");
DataStream<String> inputStream = env.readTextFile(resource.getPath());
// 3. 转换为POJO，分配时间戳和watermark
DataStream<UserBehavior> dataStream = inputStream
.map(line -> {
String[] fields = line.split(",");
return new UserBehavior(new Long(fields[0]), new Long(fields[1]), new Integer(fields[2]), fields[3], new Long(fields[4]));
})
.assignTimestampsAndWatermarks(new AscendingTimestampExtractor<UserBehavior>() {
@Override
public long extractAscendingTimestamp(UserBehavior element) {
return element.getTimestamp() * 1000L;
}
});
// 开窗统计uv值
SingleOutputStreamOperator<PageViewCount> uvStream = dataStream
.filter(data -> "pv".equals(data.getBehavior()))
.timeWindowAll(Time.hours(1))
.trigger( new MyTrigger() )
.process( new UvCountResultWithBloomFliter() );
uvStream.print();
env.execute("uv count with bloom filter job");
}
// 自定义触发器
public static class MyTrigger extends Trigger<UserBehavior, TimeWindow>{
@Override
public TriggerResult onElement(UserBehavior element, long timestamp, TimeWindow window, TriggerContext ctx) throws Exception {
// 每一条数据来到, 直接触发窗口计算,并且直接清空窗口
return TriggerResult.FIRE_AND_PURGE;
}
@Override
public TriggerResult onProcessingTime(long time, TimeWindow window, TriggerContext ctx) throws Exception {
return TriggerResult.CONTINUE;
}
@Override
public TriggerResult onEventTime(long time, TimeWindow window, TriggerContext ctx) throws Exception {
return TriggerResult.CONTINUE;
}
@Override
public void clear(TimeWindow window, TriggerContext ctx) throws Exception {
}
}
// 自定义一个布隆过滤器
public static class MyBloomFilter {
// 定义位图的大小,一般需要定义为2的整次幂
private Integer cap;
public MyBloomFilter(Integer cap){
this.cap = cap;
}
// 实现一个hash函数
public Long hashCode(String value, Integer seed){
Long result = 0l;
for (int i = 0; i < value.length(); i++){
result = result * seed + value.charAt(i);
}
return result & (cap - 1);
}
}
// 实现自定义的处理函数
public static class UvCountResultWithBloomFliter extends ProcessAllWindowFunction<UserBehavior, PageViewCount, TimeWindow>{
// 定义jedis连接和布隆过滤器
Jedis jedis;
MyBloomFilter myBloomFilter;
@Override
public void open(Configuration parameters) throws Exception {
jedis = new Jedis("10.31.1.122", 6379);
myBloomFilter = new MyBloomFilter(1 << 29); // 要处理1亿个数据，用64MB大小的位图
}
@Override
public void process(Context context, Iterable<UserBehavior> elements, Collector<PageViewCount> out) throws Exception {
// 将位图和窗口count值全部存入redis，用windowEnd作为key
Long windowEnd = context.window().getEnd();
String bitmapKey = windowEnd.toString();
// 把count值存成一张hash表
String countHashName = "uv_count";
String countKey = windowEnd.toString();
// 1. 取当前的userId
Long userId = elements.iterator().next().getUserId();
// 2. 计算位图中的offset
Long offset = myBloomFilter.hashCode(userId.toString(), 61);
// 3. 用redis的getbit命令，判断对应位置的值
Boolean isExist = jedis.getbit(bitmapKey, offset);
if ( !isExist ){
// 如果不存在，对应位图的位置置1
jedis.setbit(bitmapKey, offset, true);
// 更新redis中保存的count值
Long uvCount = 0L; // 初始count值
String uvCountString = jedis.hget(countHashName, countKey);
if ( uvCountString != null && !"".equals(uvCountString) )
uvCount = Long.valueOf(uvCountString);
jedis.hset(countHashName, countKey, String.valueOf(uvCount + 1));
out.collect(new PageViewCount("uv", windowEnd, uvCount + 1));
}
}
@Override
public void close() throws Exception {
super.close();
}
}
}
测试记录:
参考:
- https://www.bilibili.com/video/BV1qy4y1q728
- https://ashiamd.github.io/docsify-notes/#/study/BigData/Flink/%E5%B0%9A%E7%A1%85%E8%B0%B7Flink%E5%85%A5%E9%97%A8%E5%88%B0%E5%AE%9E%E6%88%98-%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0?id=_1432-%e5%ae%9e%e6%97%b6%e6%b5%81%e9%87%8f%e7%bb%9f%e8%ae%a1%e7%83%ad%e9%97%a8%e9%a1%b5%e9%9d%a2