“ 使用Java编写Flink程序，统计从注册到下单过程中的不同uv数。”
通过本篇文章做到深入掌握如下知识点，欢迎探讨，共同学习和进步。
１、checkpoing应用
２、kafka数据源连接器构造，包含重要的水位线设置
３、windowAll窗口函数的应用
４、双流interval join应用
01
—
需求介绍
某一保险公司提供了一个h5注册页面供终端客户注册账号，用户注册后可选择通过h5页面下载APP，然后登录APP，最后在APP中购买保险产品。用户任何一步都有可能流失。在某一活动日，公司领导需实时观察一些用户行为指标，主要有注册用户数、app活跃用户数、注册且下载app用户数，注册且在app下单用户数。
注册用户数：实时１分钟统计当天的累计注册用户数
app活跃用户数：实时１分钟统计当天的app活跃用户数，和实时１分钟统计每小时的app活跃用户数
注册且下载app用户数：实时１分钟统计注册且下载app的用户数
注册且在app下单用户数：实时１分钟统计注册且在app下单的用户数
02
—
开发设计
１、用户注册数据接入kafka ods_user_rt topic中，涉及到的字段主要有user_id和register_time
２、用户行为数据接入kafka ods_traffic_rt topic中，涉及到的字段主要有user_id、event_type(行为事件类型，比如下载app和下单)、event_time(事件发生时间)
３、flink使用ods_user_rt和ods_traffic_rt两条流源进行实时计算，将计算结果保存到如下mysql结果表中，可直接使用提供的sinkapi Jdbc.sink()。
CREATE TABLE IF NOT EXISTS `ads_user_stats_rt`( `stats_time` VARCHAr(20) NOT NULL comment '统计时间', `register_cnt` INT comment '注册用户数', `register_download_cnt` INT comment '注册下载用户数', `register_download_order_cnt` INT comment '注册下单用户数', `active_user_uv_per_hour` INT comment 'app内小时累计活跃用户数', `active_user_uv_per_day` INT comment 'app内天累计活跃用户数', PRIMARY KEY ( `stats_time` ))ENGINE=InnoDB DEFAULT CHARSET=utf8;
４、注册用户数计算思路：单条流可完成计算，按天分窗，每个窗口从零点开始，比如2021-11-21 00:00:00，可每分钟提前触发结果，这里主要采用windowAll窗口函数。app内天累计活跃用户数，和app内小时累计活跃用户数计算方式类似。
５、注册下载数计算思路：需使用两条流计算，采用interval join算子，interval join仅支持inner join效果。interval join在这里应用的原理是行为流数据不晚于用户流８小时且不早于用户流１小时。相当于早到的用户流数据会进行状态缓冲，待限定时间范围内的相同用户行为流数据到来后完成join并输出。注册下单数计算思路同理。
6、因各个指标是１分钟触发一次，所以checkpoint执行间隙会大于１分钟，初步设计为５分钟执行一次checkpoint。
03
—
代码实现
// 实时程序代码
public class UserBehaviorStats {
private static SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
public static void main(String[] args) throws Exception {
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.enableCheckpointing(300000); // 设置检查点执行间隔为5分
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); // 设置时间类型
// kafka配置属性
Properties props = new Properties();
props.setProperty("bootstrap.servers", "localhost:9092");
props.setProperty("group.id", "demo");
// 构造用户数据源连接器
FlinkKafkaConsumer011 userRegisterConsumer =new FlinkKafkaConsumer011(
"ods_user_rt", //设置用户源数据主题
new JSonKeyValueDeserializationSchema(false),
props);
userRegisterConsumer.assignTimestampsAndWatermarks(new UserMessagePeriodicWatermarks());
userRegisterConsumer.setStartFromLatest();
// 构造流量数据源连接器
FlinkKafkaConsumer011 userBehaviorConsumer =new FlinkKafkaConsumer011(
"ods_traffic_rt", //设置流量源数据主题
new JSonKeyValueDeserializationSchema(false),
props);
userBehaviorConsumer.assignTimestampsAndWatermarks(new TrafficMessagePeriodicWatermarks());
userBehaviorConsumer.setStartFromLatest();
// 构造用户注册和行为流
DataStreamSource userRegisterDataStreamSource = env.addSource(userRegisterConsumer);
DataStreamSource userBehaviorDataStreamSource = env.addSource(userBehaviorConsumer);
// 天累计用户注册数
userRegisterDataStreamSource.map(x -> x.get("value").get("user_id").asText())
.windowAll(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8)))
.trigger(ContinuousEventTimeTrigger.of(Time.minutes(1)))
.process(getProcessAllWindowFunction())
.addSink(getMysqlSink("INSERT INTO ads_user_stats_rt(stats_time,register_cnt) VALUES(?,?) ON DUPLICATE KEY UPDATE register_cnt = ?"));
// 天累计活跃用户数
userBehaviorDataStreamSource.map(x -> x.get("value").get("user_id").asText())
.windowAll(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8)))
.trigger(ContinuousEventTimeTrigger.of(Time.minutes(1)))
.process(getProcessAllWindowFunction())
.addSink(getMysqlSink("INSERT INTO ads_user_stats_rt(stats_time,active_user_uv_per_day) VALUES(?,?) ON DUPLICATE KEY UPDATE active_user_uv_per_day = ?"));
// 按小时累计活跃注册数
userBehaviorDataStreamSource.map(x -> x.get("value").get("user_id").asText())
.windowAll(TumblingEventTimeWindows.of(Time.hours(1)))
.trigger(ContinuousEventTimeTrigger.of(Time.minutes(1)))
.process(getProcessAllWindowFunction())
.addSink(getMysqlSink("INSERT INTO ads_user_stats_rt(stats_time,active_user_uv_per_hour) VALUES(?,?) ON DUPLICATE KEY UPDATE active_user_uv_per_hour = ?"));
// 天累计用户注册且下载用户数
userRegisterDataStreamSource.map(x -> x.get("value").get("user_id").asText())
.keyBy(x -> x)
.intervalJoin(userBehaviorDataStreamSource.keyBy(x -> x.get("value").get("user_id").asText()))
.between(Time.hours(-1), Time.hours(8))
.process(new ProcessJoinFunction() {
@Override
public void processElement(String s, ObjectNode s2, Context context, Collector collector) throws Exception
{
if ("download".equals(s2.get("value").get("event_type").asText())) {
collector.collect(s2.get("value").get("user_id").asText());
}
}
}).windowAll(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8)))
.trigger(ContinuousEventTimeTrigger.of(Time.minutes(1)))
.process(getProcessAllWindowFunction())
.addSink(getMysqlSink("INSERT INTO ads_user_stats_rt(stats_time,register_download_cnt) VALUES(?,?) ON DUPLICATE KEY UPDATE register_download_cnt = ?"));
// 天累计用户注册且下单用户数
userRegisterDataStreamSource.map(x -> x.get("value").get("user_id").asText())
.keyBy(x -> x)
.intervalJoin(userBehaviorDataStreamSource.keyBy(x -> x.get("value").get("user_id").asText()))
.between(Time.hours(-1), Time.hours(8))
.process(new ProcessJoinFunction() {
@Override
public void processElement(String s, ObjectNode s2, Context context, Collector collector) throws Exception
{
if ("clickOrder".equals(s2.get("value").get("event_type").asText())) {
collector.collect(s2.get("value").get("user_id").asText());
}
}
}).windowAll(TumblingEventTimeWindows.of(Time.days(1), Time.hours(-8)))
.trigger(ContinuousEventTimeTrigger.of(Time.minutes(1)))
.process(getProcessAllWindowFunction())
.addSink(getMysqlSink("INSERT INTO ads_user_stats_rt(stats_time,register_download_order_cnt) VALUES(?,?) ON DUPLICATE KEY UPDATE register_download_order_cnt = ?"));
env.execute("flink kafka user behavior sample");
}
private static ProcessAllWindowFunction getProcessAllWindowFunction() {
return new ProcessAllWindowFunction() {
@Override
public void process(Context context, Iterable iterable, Collector collector) throws Exception
{
HashSet hashSet = new HashSet<>();
for(String str : iterable) {
hashSet.add(str);
}
AdsUserStatsRt adsUserStatsRt = new AdsUserStatsRt();
adsUserStatsRt.setStatsTime(simpleDateFormat.format(new Date(context.window().getStart())));
adsUserStatsRt.setFieldValue(hashSet.size());
collector.collect(adsUserStatsRt);
}
};
}
private static SinkFunction getMysqlSink(String sql) {
return JdbcSink.sink(sql,
new JdbcStatementBuilder() {
@Override
public void accept(PreparedStatement preparedStatement, AdsUserStatsRt adsUserStatsRt) throws SQLException
{
preparedStatement.setString(1, adsUserStatsRt.getStatsTime());
preparedStatement.setInt(2, adsUserStatsRt.getFieldValue());
preparedStatement.setInt(3, adsUserStatsRt.getFieldValue());
}
},
JdbcExecutionOptions.builder()
.withBatchSize(1000)
.withBatchIntervalMs(200)
.withMaxRetries(5)
.build(),
new JdbcConnectionOptions.JdbcConnectionOptionsBuilder()
.withUrl("jdbc:mysql://localhost:3306/traffic?useSSL=false")
.withDriverName("com.mysql.jdbc.Driver")
.withUsername("root")
.withPassword("123456")
.build());
}
// 用户数据源时间戳分配器
public class UserMessagePeriodicWatermarks implements AssignerWithPeriodicWatermarks {
private long lastTs = Long.MIN_VALUE;
@Nullable
@Override
public Watermark getCurrentWatermark()
{
return new Watermark(lastTs);
}
@Override
public long extractTimestamp(ObjectNode jsonNodes, long l)
{
lastTs = jsonNodes.get("value").get("register_time").asLong();
return lastTs;
}
}
// 用户行为数据源时间戳分配器
public class TrafficMessagePeriodicWatermarks implements AssignerWithPeriodicWatermarks {
private long lastTs = Long.MIN_VALUE;
@Nullable
@Override
public Watermark getCurrentWatermark()
{
return new Watermark(lastTs);
}
@Override
public long extractTimestamp(ObjectNode jsonNodes, long l)
{
lastTs = jsonNodes.get("value").get("event_time").asLong();
return lastTs;
}
}
// 用于mysql sink的结果pojo
public class AdsUserStatsRt {
private String statsTime;
private String fieldName;
private Integer fieldValue;
public String getStatsTime() {
return statsTime;
}
public void setStatsTime(String stats_time) {
this.statsTime = stats_time;
}
public String getFieldName() {
return fieldName;
}
public void setFieldName(String fieldName) {
this.fieldName = fieldName;
}
public Integer getFieldValue() {
return fieldValue;
}
public void setFieldValue(Integer fieldValue) {
this.fieldValue = fieldValue;
}
}
public class UserBehaviorProducerTest {
private static volatile int flag1 = 0, flag2 = 0;
private static volatile List users = new ArrayList();
public static void main(String[] args) throws Exception {
// 构造kafka producer
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("acks", "all");
props.put("retries", 0);
props.put("batch.size", 16384);
props.put("linger.ms", 5000);
props.put("buffer.memory", 33554432);
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
Producer producer = new KafkaProducer(props);
// 用户注册消息线程
Thread userRegisterThread = new Thread(new Runnable() {
@Override
public void run()
{
int totalUserCount = 5000; // 设置注册用户数阀值,
String user_id = "";
for (int i = 0; i < totalUserCount; i++) {
JSonObject jsonObject = new JSonObject();
if (i < 1000) {
user_id = UUID.randomUUID().toString();
} else {
user_id = "fake_id"; // 满1000后不再生成新的用户,用固定用户消息来保持flink里用户流的水位线在不断推进
}
users.add(user_id);
jsonObject.put("user_id", user_id);
jsonObject.put("register_time", System.currentTimeMillis());
producer.send(new ProducerRecord("ods_user_rt", jsonObject.toJSonString()), new Callback() {
@Override
public void onCompletion(Recordmetadata metadata, Exception exception)
{
if (exception != null) {
System.out.println("Failed to send message with exception " + exception);
}
}
});
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
flag1 = 1;
}
});
userRegisterThread.start();
// 用户行为消息线程
Thread userBehaviorThread = new Thread(new Runnable() {
@Override
public void run()
{
int totalMessageCount = 500;
Random random = new Random();
int operateIndex;
for (int i = 0; i < totalMessageCount; i++) {
operateIndex = random.nextInt(2);
if (operateIndex == 0) {
JSonObject jsonObject = new JSonObject();
if (users.size() == 0) {
jsonObject.put("user_id", "fake_id");
} else {
jsonObject.put("user_id", users.get(random.nextInt(users.size())));
}
jsonObject.put("event_type", "download");
jsonObject.put("event_time", System.currentTimeMillis());
jsonObject.put("plantform_type", "app");
producer.send(new ProducerRecord("ods_traffic_rt", jsonObject.toJSonString()), new Callback() {
@Override
public void onCompletion(Recordmetadata metadata, Exception exception)
{
if (exception != null) {
System.out.println("Failed to send message with exception " + exception);
}
}
});
} else if (operateIndex == 1) {
JSonObject jsonObject = new JSonObject();
if (users.size() == 0) {
jsonObject.put("user_id", "fake_id");
} else {
jsonObject.put("user_id", users.get(random.nextInt(users.size())));
}
jsonObject.put("event_type", "clickOrder");
jsonObject.put("event_time", System.currentTimeMillis());
jsonObject.put("plantform_type", "app");
producer.send(new ProducerRecord("ods_traffic_rt", jsonObject.toJSonString()), new Callback() {
@Override
public void onCompletion(Recordmetadata metadata, Exception exception)
{
if (exception != null) {
System.out.println("Failed to send message with exception " + exception);
}
}
});
}
try {
Thread.sleep(5000);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
flag2 = 1;
}
});
userBehaviorThread.start();
// 等待用户注册和行为消息线程结束后关闭producer
Thread waitThread = new Thread(new Runnable() {
@Override
public void run()
{
while(true) {
if (flag1 == 1 && flag2 == 1) {
producer.close();
}
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}
});
waitThread.start();
}
}
04
—
遇到的坑
1、因数据源连接器的水位线直接取于消息中的时间字段值，当用户注册消息发送到1000个用户后，不再发送消息，而行为消息还在不断发送，那么实时统计的与用户流相关的指标都会计算错误，不会再输出一个更新值。如用户注册数、用户注册下载数、用户注册下单数。主要原因在于用户注册流水位线处于停止状态。为了解决这个问题，当发送1000个用户注册消息后，需要继续发送一个假的用户注册消息数据来不断更新水位值。
// 用户注册消息线程
Thread userRegisterThread = new Thread(new Runnable() {
@Override
public void run()
{
int totalUserCount = 5000; // 设置注册用户数阀值,
String user_id = "";
for (int i = 0; i < totalUserCount; i++) {
JSonObject jsonObject = new JSonObject();
if (i < 1000) {
user_id = UUID.randomUUID().toString();
} else {
user_id = "fake_id"; // 满1000后不再生成新的用户,用固定用户消息来保持flink里用户流的水位线在不断推进
}
users.add(user_id);
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