一、 项目概述
随着智能手机的普及，在如今的电商网站中已经有越来越多的用户来自移动端， 相比起传统浏览器的登录方式 ，手机 APP 成为了更多用户访问电商网站的首选 。对 于电商企业来说 ，一般会通过各种不同的渠道对自己的 APP 进行市场推广，而这些 渠道的统计数据（比如，不同网站上广告链接的点击量、 APP 下载量）就成了市场 营销的重要商业指标。
二、代码
2.1 pom文件配置
pom文件如下:
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>1.10.1</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java_2.11</artifactId>
<version>1.10.1</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka_2.11</artifactId>
<version>1.10.1</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-core</artifactId>
<version>1.10.1</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients_2.11</artifactId>
<version>1.10.1</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-redis_2.11</artifactId>
<version>1.1.5</version>
</dependency>
<!-- https://mvnrepository.com/artifact/mysql/mysql-connector-java -->
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>8.0.19</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-statebackend-rocksdb_2.11</artifactId>
<version>1.10.1</version>
</dependency>
<!-- Table API 和 Flink SQL -->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-planner-blink_2.11</artifactId>
<version>1.10.1</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-planner_2.11</artifactId>
<version>1.10.1</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-api-java-bridge_2.11</artifactId>
<version>1.10.1</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-scala_2.11</artifactId>
<version>1.10.1</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-common</artifactId>
<version>1.10.1</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-csv</artifactId>
<version>1.10.1</version>
</dependency>
2.2 POJO类
AdClickEvent
private Long userId;
private Long adId;
private String province;
private String city;
private Long timestamp;
AdCountViewByProvince
private String province;
private String windowEnd;
private Long count;
BlackListUserWarning
private Long userId;
private Long adId;
private String warningMsg;
ChannelPromotionCount
private String channel;
private String behavior;
private String windowEnd;
private Long count;
MarketingUserBehavior
private Long userId;
private String behavior;
private String channel;
private Long timestamp;
2.3 自定义测试数据源
定义一个源数据的 POJO 类 MarketingUserBehavior，再 定义一个 SourceFunction， 用于产生用户行为源数据，命名为 SimulatedMarketingBehaviorSource：
// 实现自定义的模拟市场用户行为数据源
public static class SimulatedMarketingUserBehaviorSource implements SourceFunction<MarketingUserBehavior>{
// 控制是否正常运行的标示位
Boolean running = true;
// 定义用户行为和渠道的范围
List<String> behaviorList = Arrays.asList("CLICK", "DOWNLOAD", "INSTALL", "UNINSTALL");
List<String> channelList = Arrays.asList("app store", "wechat", "weibo");
Random random = new Random();
@Override
public void run(SourceContext<MarketingUserBehavior> ctx) throws Exception {
while(running){
// 随机生成所有字段
Long id = random.nextLong();
String behavior = behaviorList.get( random.nextInt(behaviorList.size()) );
String channel = channelList.get( random.nextInt(channelList.size()));
Long timestamp = System.currentTimeMillis();
// 发出数据
ctx.collect(new MarketingUserBehavior(id ,behavior, channel, timestamp));
Thread.sleep(100L);
}
}
@Override
public void cancel() {
running = false;
}
}
2.4 分渠道统计
另外定义一个窗口处理的输出结果 POJO 类 ChannelPromotionCount，并自定义 预聚合函数 AggregateFunction 和全窗口函数 ProcessWindowFunction 进行处理。
代码:
package com.zqs.flink.project.market_analysis;
/**
* @remark App Marketing By Channel
*/
import com.zqs.flink.project.market_analysis.beans.ChannelPromotionCount;
import com.zqs.flink.project.market_analysis.beans.MarketingUserBehavior;
import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.java.tuple.Tuple;
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.source.SourceFunction;
import org.apache.flink.streaming.api.functions.timestamps.AscendingTimestampExtractor;
import org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction;
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.sql.Timestamp;
import java.util.Arrays;
import java.util.List;
import java.util.Random;
public class AppMarketingByChannel {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
// 1. 从自定义数据源中读取数据
DataStream<MarketingUserBehavior> dataStream = env.addSource(new SimulatedMarketingUserBehaviorSource() )
.assignTimestampsAndWatermarks(new AscendingTimestampExtractor<MarketingUserBehavior>() {
@Override
public long extractAscendingTimestamp(MarketingUserBehavior element) {
return element.getTimestamp();
}
});
// 2. 分渠道开窗统计
SingleOutputStreamOperator<ChannelPromotionCount> resultStream = dataStream
.filter(data -> !"UNINSTALL".equals(data.getBehavior()))
.keyBy("channel", "behavior")
.timeWindow(Time.hours(1), Time.seconds(5)) // 定义滑窗
.aggregate(new MarketingCountAgg(), new MarketingCountResult());
resultStream.print();
env.execute("app marketing by channel job");
}
// 实现自定义的模拟市场用户行为数据源
public static class SimulatedMarketingUserBehaviorSource implements SourceFunction<MarketingUserBehavior>{
// 控制是否正常运行的标示位
Boolean running = true;
// 定义用户行为和渠道的范围
List<String> behaviorList = Arrays.asList("CLICK", "DOWNLOAD", "INSTALL", "UNINSTALL");
List<String> channelList = Arrays.asList("app store", "wechat", "weibo");
Random random = new Random();
@Override
public void run(SourceContext<MarketingUserBehavior> ctx) throws Exception {
while(running){
// 随机生成所有字段
Long id = random.nextLong();
String behavior = behaviorList.get( random.nextInt(behaviorList.size()) );
String channel = channelList.get( random.nextInt(channelList.size()));
Long timestamp = System.currentTimeMillis();
// 发出数据
ctx.collect(new MarketingUserBehavior(id ,behavior, channel, timestamp));
Thread.sleep(100L);
}
}
@Override
public void cancel() {
running = false;
}
}
// 实现自定义的增量聚合函数
public static class MarketingCountAgg implements AggregateFunction<MarketingUserBehavior, Long, Long>{
@Override
public Long createAccumulator() {
return 0L;
}
@Override
public Long add(MarketingUserBehavior 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;
}
}
// 实现自定义全窗口函数
public static class MarketingCountResult extends ProcessWindowFunction<Long, ChannelPromotionCount, Tuple, TimeWindow>{
@Override
public void process(Tuple tuple, Context context, Iterable<Long> elements, Collector<ChannelPromotionCount> out) throws Exception {
String channel = tuple.getField(0);
String behavior = tuple.getField(1);
String windowEnd = new Timestamp(context.window().getEnd()).toString();
Long count = elements.iterator().next();
out.collect(new ChannelPromotionCount(channel, behavior, windowEnd, count));
}
}
}
测试记录:
2.5 不分渠道（总量）统计
同样我们还可以考察不分渠道的市场推广统计，这样得到的就是所有渠道推广 的总量，创建 AppMarketingStatistics 类。
代码如下:
package com.zqs.flink.project.market_analysis;
import com.zqs.flink.project.market_analysis.beans.ChannelPromotionCount;
import com.zqs.flink.project.market_analysis.beans.MarketingUserBehavior;
import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.common.functions.MapFunction;
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.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.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.sql.Timestamp;
/**
* @remark App Marketing Statistics
*/
public class AppMarketingStatistics {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
// 1. 从自定义数据源中读取数据
DataStream<MarketingUserBehavior> dataStream = env.addSource(new AppMarketingByChannel.SimulatedMarketingUserBehaviorSource())
.assignTimestampsAndWatermarks(new AscendingTimestampExtractor<MarketingUserBehavior>() {
@Override
public long extractAscendingTimestamp(MarketingUserBehavior element) {
return element.getTimestamp();
}
});
// 2. 开窗统计总量
SingleOutputStreamOperator<ChannelPromotionCount> resultStream = dataStream
.filter(data -> !"UNINSTALL".equals(data.getBehavior()))
.map(new MapFunction<MarketingUserBehavior, Tuple2<String, Long>>() {
@Override
public Tuple2<String, Long> map(MarketingUserBehavior value) throws Exception {
return new Tuple2<>("total", 1L);
}
})
.keyBy(0)
.timeWindow(Time.hours(1), Time.seconds(5))
.aggregate( new MarketingStatisticsAgg(), new MarketingStatisticsResult() );
resultStream.print();
env.execute("app marketing by channel job");
}
public static class MarketingStatisticsAgg implements AggregateFunction<Tuple2<String, Long>, Long, Long>{
@Override
public Long createAccumulator() {
return 0L;
}
@Override
public Long add(Tuple2<String, 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;
}
}
public static class MarketingStatisticsResult implements WindowFunction<Long, ChannelPromotionCount, Tuple, TimeWindow>{
@Override
public void apply(Tuple tuple, TimeWindow window, Iterable<Long> input, Collector<ChannelPromotionCount> out) throws Exception {
String windowEnd = new Timestamp( window.getEnd() ).toString();
Long count = input.iterator().next();
out.collect(new ChannelPromotionCount("total", "total", windowEnd, count));
}
}
}
测试记录:
2.6 黑名单过滤
代码:
package com.zqs.flink.project.market_analysis;
/**
* @remark Ad Statistics By Province
*/
import com.zqs.flink.project.market_analysis.beans.AdClickEvent;
import com.zqs.flink.project.market_analysis.beans.AdCountViewByProvince;
import com.zqs.flink.project.market_analysis.beans.BlackListUserWarning;
import org.apache.flink.api.common.functions.AggregateFunction;
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.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 org.apache.flink.util.OutputTag;
import sun.awt.SunHints;
import java.net.URL;
import java.sql.Timestamp;
public class AdStatisticsByProvince {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
env.setParallelism(1);
// 1. 从文件中读取数据
URL resource = AdStatisticsByProvince.class.getResource("/AdClickLog.csv");
DataStream<AdClickEvent> adClickEventStream = env.readTextFile(resource.getPath())
.map( line -> {
String[] fields = line.split(",");
return new AdClickEvent(new Long(fields[0]), new Long(fields[1]), fields[2], fields[3], new Long(fields[4]));
})
.assignTimestampsAndWatermarks(new AscendingTimestampExtractor<AdClickEvent>() {
@Override
public long extractAscendingTimestamp(AdClickEvent element) {
return element.getTimestamp() * 1000L;
}
});
// 2. 对同一个用户点击同一个广告的行为进行检测预警
SingleOutputStreamOperator<AdClickEvent> filterAdClickStream = adClickEventStream
.keyBy("userId", "adId") // 基于用户id和广告id做分组
.process(new FilterBlackListUser(100));
// 3. 基于省份分组，开窗聚合
SingleOutputStreamOperator<AdCountViewByProvince> adCountResultStream = filterAdClickStream
.keyBy(AdClickEvent::getProvince)
.timeWindow(Time.hours(1), Time.minutes(5))
.aggregate(new AdCountAgg(), new AdCountResult());
adCountResultStream.print();
filterAdClickStream.getSideOutput(new OutputTag<BlackListUserWarning>("blacklist"){
}).print("blacklist-user");
env.execute("ad count by province job");
}
public static class AdCountAgg implements AggregateFunction<AdClickEvent, Long, Long>{
@Override
public Long createAccumulator() {
return 0L;
}
@Override
public Long add(AdClickEvent value, Long accumulator) {
return accumulator + 1;
}
@Override
public Long getResult(Long accumulator) {
return accumulator + 1;
}
@Override
public Long merge(Long a, Long b) {
return a + b;
}
}
public static class AdCountResult implements WindowFunction<Long, AdCountViewByProvince, String, TimeWindow>{
@Override
public void apply(String province, TimeWindow window, Iterable<Long> input, Collector<AdCountViewByProvince> out) throws Exception {
String windowEnd = new Timestamp( window.getEnd()).toString();
Long count = input.iterator().next();
out.collect( new AdCountViewByProvince(province, windowEnd, count));
}
}
// 实现自定义处理函数
public static class FilterBlackListUser extends KeyedProcessFunction<Tuple, AdClickEvent, AdClickEvent>{
// 定义属性值: 点击次数上限
private Integer countUpperBound;
public FilterBlackListUser(Integer countUpperBound){
this.countUpperBound = countUpperBound;
}
// 定义状态, 保存当前用户对某一广告的点击次数
ValueState<Long> countState;
// 定义一个标志状态，保存当前用户是否已经被发送到黑名单
ValueState<Boolean> isSentState;
@Override
public void open(Configuration parameters) throws Exception {
countState = getRuntimeContext().getState(new ValueStateDescriptor<Long>("ad-count", Long.class, 0L));
isSentState = getRuntimeContext().getState(new ValueStateDescriptor<Boolean>("is-sent", Boolean.class, false));
}
@Override
public void processElement(AdClickEvent value, Context ctx, Collector<AdClickEvent> out) throws Exception {
// 判断当前用户对同一广告的点击次数，如果不够上限，就count加1正常输出；如果达到上限，直接过滤掉，并侧输出流输出黑名单报警
// 首先获取当前的count值
Long curCount = countState.value();
// 1. 判断是否是第一个数据，如果是的话，注册一个第二天0点的定时器
if (curCount == 0){
Long ts = (ctx.timerService().currentProcessingTime() / (24*60*60*1000) + 1) * (24*60*60*1000) - 8*60*60*1000;
ctx.timerService().registerProcessingTimeTimer(ts);
}
// 2. 判断是否报警
if (curCount >= countUpperBound){
// 判断是否输出到黑名单过，如果没有的话就输出到侧输出流
if (!isSentState.value()){
isSentState.update(true); // 更新状态
ctx.output( new OutputTag<BlackListUserWarning>("blacklist"){
},
new BlackListUserWarning(value.getUserId(), value.getAdId(), "click over " + countUpperBound + "times." ));
}
return; // 不再执行下面操作
}
// 如果没有返回，点击次数加1，更新状态，正常输出当前数据到主流
countState.update(curCount + 1);
out.collect(value);
}
@Override
public void onTimer(long timestamp, OnTimerContext ctx, Collector<AdClickEvent> out) throws Exception {
// 清空所有状态
countState.clear();
isSentState.clear();
}
}
}
测试记录:
