Flink DataStream 按分钟或日期统计数据量

一、环境版本

环境	版本
Flink	1.17.0
Kafka	2.12
MySQL	5.7.33

二、MySQL建表脚本

create table user_log
(
    id      int auto_increment comment '主键'
        primary key,
    uid     int    not null comment '用户id',
    event   int    not null comment '用户行为',
    logtime bigint null comment '日志时间'
)
    comment '用户日志表，作为验证数据源';

三、用户日志类

新建maven项目

用以定义Kafka和MySQL中Schema

/**
 * 用户日志类
 */
@Data
public class UserLog {
    //用户uid
    private int uid;
    //用户行为
    private int event;
    //日志时间
    private Date logtime;
    
    //获取日期，用于按日期统计数据
    public String getFormatDate() {
        return DateUtil.format(logtime, "yyyyMMdd");
    }
    
    //获取时间，精确到分钟
    public String getFormatTime() {
        return DateUtil.format(logtime, "yyyy-MM-dd HH:mm") + ":00";
    }
}
}

四、用户数据生成器

/**
 * 用户数据生成器
 */
public class UserLogGenerator {
    public static void main(String[] args) throws Exception {
        // 1.获取执行环境
        StreamExecutionEnvironment env =
                StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        // 2.自定义数据生成器Source
        DataGeneratorSource<UserLog> dataGeneratorSource = new DataGeneratorSource<>(
                // 指定GeneratorFunction 实现类
                new GeneratorFunction<Long, UserLog>(){

                    // 定义随机数数据生成器
                    public RandomDataGenerator generator;

                    @Override
                    public void open(SourceReaderContext readerContext) throws Exception {
                        generator = new RandomDataGenerator();
                    }

                    @Override
                    public UserLog map(Long aLong) throws Exception {
                        UserLog userLog = new UserLog();
                        //随机生成用户uid
                        userLog.setUid(generator.nextInt(1, 50));
                        //随机生成用户行为
                        userLog.setEvent(generator.nextInt(1, 2));
                        //随机生成用户数据时间
                        userLog.setLogtime(DateUtil.offset(new DateTime(), DateField.MILLISECOND, generator.nextInt(-2000, 2000)));
                        return userLog;
                    }
                },
                // 指定输出数据的总行数
//                60 * 60 * 10,
                120,
                // 指定每秒发射的记录数
                RateLimiterStrategy.perSecond(10),
                // 指定返回值类型， 将Java的StockPrice封装成到TypeInformation
                TypeInformation.of(UserLog.class)
        );

        DataStreamSource<UserLog> dataGeneratorSourceStream = env.fromSource(dataGeneratorSource, WatermarkStrategy.noWatermarks(), "dataGeneratorSource");
        //输出生成数据
//        dataGeneratorSourceStream.print();

        //kafka数据写入
        KafkaSink<UserLog> kafkaSink = KafkaSink.<UserLog>builder()
                .setBootstrapServers("hadoop01:9092")
                .setRecordSerializer(
                        KafkaRecordSerializationSchema.<UserLog>builder()
                                .setTopic("userLog")
                                .setValueSerializationSchema((SerializationSchema<UserLog>) userLog -> JSONUtil.toJsonStr(userLog).getBytes())
                                .build()
                ).build();
        dataGeneratorSourceStream.sinkTo(kafkaSink);

        //MySQL数据写入，用以数据验证
        SinkFunction<UserLog> jdbcSink = JdbcSink.sink(
                "insert into user_log (uid, event, logtime) values (?, ?, ?)",
                new JdbcStatementBuilder<UserLog>() {
                    @Override
                    public void accept(PreparedStatement preparedStatement, UserLog userLog) throws SQLException {
                        preparedStatement.setInt(1, userLog.getUid());
                        preparedStatement.setInt(2, userLog.getEvent());
                        preparedStatement.setLong(3, userLog.getLogtime().getTime());
                    }
                }
                ,
                JdbcExecutionOptions.builder()
                        .withBatchSize(1000)
                        .withBatchIntervalMs(200)
                        .withMaxRetries(5)
                        .build(),
                new JdbcConnectionOptions.JdbcConnectionOptionsBuilder()
                        .withUrl("jdbc:mysql://192.168.31.116:3306/demo")
                        .withDriverName("com.mysql.cj.jdbc.Driver")
                        .withUsername("root")
                        .withPassword("root")
                        .build()
        );
        dataGeneratorSourceStream.addSink(jdbcSink);

        env.execute();
    }
}

五、DataStream按分钟或日期统计PV和UV

/**
 * 计算PV和UV
 */
public class UserLogPVUVCount {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);


        KafkaSource<String> kafkaSource = KafkaSource.<String>builder()
                .setBootstrapServers("hadoop01:9092")
                .setTopics("userLog")
                .setValueOnlyDeserializer(new SimpleStringSchema())
                .setStartingOffsets(OffsetsInitializer.earliest())
                .build();

        DataStreamSource<String> kafkaSourceStream = env.fromSource(kafkaSource, WatermarkStrategy.noWatermarks(), "kafkasource");
//        kafkaSourceStream.print();


        //kafka数据反序列化
        SingleOutputStreamOperator<UserLog> userLogStream = kafkaSourceStream.map(s -> JSONUtil.toBean(s, UserLog.class));

        //计算pv和uv，按日期统计时需将getFormatTime改为getFormatDate，并且注释'一分钟为窗口'代码和反注释'一天为窗口'代码
        SingleOutputStreamOperator<Tuple3<String, String, Integer>> userPVUVStream =
        userLogStream.keyBy((KeySelector<UserLog, String>) UserLog::getFormatTime)
                // 一天为窗口，指定时间起点比时间戳时间早8个小时
//                .window(TumblingProcessingTimeWindows.of(Time.days(1), Time.hours(-8)))
                // 一分钟为窗口
                .window(TumblingProcessingTimeWindows.of(Time.minutes(1)))
                // 10s触发一次计算，更新统计结果
                .trigger(ContinuousProcessingTimeTrigger.of(Time.seconds(10)))
                // 剔除超过时间范围的数据
                .evictor(TimeEvictor.of(Time.seconds(0),true))
                // 计算pv uv
                .process(new MyProcessWindowFunction());

        userPVUVStream.print();
        env.execute();
    }
}

/**
 * 自定义窗口处理函数，计算PV和UV
 */
public class MyProcessWindowFunction extends ProcessWindowFunction<UserLog, Tuple3<String, String, Integer>, String, TimeWindow> {
    // UV
    private transient MapState<Integer, String> uvState;
    // PV
    private transient ValueState<Integer> pvState;
    @Override
    public void open(Configuration parameters) throws Exception {
        super.open(parameters);
        uvState = this.getRuntimeContext().getMapState(new MapStateDescriptor<>("uv", Integer.class, String.class));
        pvState = this.getRuntimeContext().getState(new ValueStateDescriptor<>("pv", Integer.class));

        //ttl过期机制
        StateTtlConfig ttlConfig = StateTtlConfig
                //1分钟过期
                .newBuilder(Time.minutes(1))
                .setUpdateType(StateTtlConfig.UpdateType.OnCreateAndWrite)
                .setStateVisibility(StateTtlConfig.StateVisibility.NeverReturnExpired)
                .build();

        // 开启ttl
        ValueStateDescriptor<Integer> pvStateDescriptor = new ValueStateDescriptor<>("pv", Integer.class);
        MapStateDescriptor<Integer, String> uvStateDescriptor = new MapStateDescriptor<>("uv", Integer.class, String.class);
        pvStateDescriptor.enableTimeToLive(ttlConfig);
        uvStateDescriptor.enableTimeToLive(ttlConfig);
        pvState = this.getRuntimeContext().getState(pvStateDescriptor);
        uvState = this.getRuntimeContext().getMapState(uvStateDescriptor);
    }


    @Override
    public void process(String s, ProcessWindowFunction<UserLog, Tuple3<String, String, Integer>, String, TimeWindow>.Context context, Iterable<UserLog> iterable, Collector<Tuple3<String, String, Integer>> collector) throws Exception {
        Integer pv = 0;
        Iterator<UserLog> iterator = iterable.iterator();
        while (iterator.hasNext()){
            pv = pv + 1;
            Integer userId = iterator.next().getUid();
            uvState.put(userId,null);
        }
        pvState.update((pvState.value() == null ? 0 : pvState.value()) + pv);
        int uv = 0;
        Iterator<Integer> uvIterator = uvState.keys().iterator();
        while (uvIterator.hasNext()){
            uvIterator.next();
            uv = uv + 1;
        }
        collector.collect(Tuple3.of(s, "uv", uv));
        collector.collect(Tuple3.of(s, "pv", pvState.value()));
    }
}

六、数据验证

1. 启动 UserLogGenerator
2. 启动 UserLogCount

(2025-08-13 10:37:00,uv,45)
(2025-08-13 10:37:00,pv,118)
(2025-08-13 10:36:00,uv,2)
(2025-08-13 10:36:00,pv,2)

3. 在MySQL中验证查询

转换时间戳

时间戳	转换前	转换后
w_start	2025-08-13 10:36:00	1755052560000
w_end	2025-08-13 10:37:00	1755052620000

# 与Flink输出一致
select count(distinct uid) from user_log where logtime< 1755052620000 and logtime>=1755052560000;

select count(distinct uid) from user_log where logtime>= 1755052620000 ;

七、POM文件

<project>
  <groupId>dblab</groupId>
  <artifactId>demo</artifactId>
  <modelVersion>4.0.0</modelVersion>
  <name> </name>
  <packaging>jar</packaging>
  <version>1.0</version>
  <repositories>
    <repository>
      <id>central-repos</id>
      <name>Central Repository</name>
      <url>http://repo.maven.apache.org/maven2</url>
    </repository>
    <repository>
      <id>alimaven</id>
      <name>aliyun maven</name>
      <url>https://maven.aliyun.com/nexus/content/groups/public/</url>
    </repository>
  </repositories>
  <properties>
    <flink.version>1.17.0</flink.version>
  </properties>
  <dependencies>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-streaming-java</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-clients</artifactId>
      <version>${flink.version}</version>
    </dependency>
<!--    <dependency>-->
<!--      <groupId>org.apache.flink</groupId>-->
<!--      <artifactId>flink-connector-files</artifactId>-->
<!--      <version>${flink.version}</version>-->
<!--    </dependency>-->
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-connector-kafka</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-connector-datagen</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-table-api-java-bridge</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-table-planner-loader</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-table-runtime</artifactId>
      <version>${flink.version}</version>
    </dependency>
<!--    <dependency>-->
<!--      <groupId>org.apache.flink</groupId>-->
<!--      <artifactId>flink-connector-files</artifactId>-->
<!--      <version>${flink.version}</version>-->
<!--    </dependency>-->
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-table-common</artifactId>
      <version>${flink.version}</version>
    </dependency>
<!--    <dependency>-->
<!--      <groupId>org.apache.flink</groupId>-->
<!--      <artifactId>flink-csv</artifactId>-->
<!--      <version>${flink.version}</version>-->
<!--    </dependency>-->
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-connector-jdbc</artifactId>
      <version>3.1.1-1.17</version>
    </dependency>
    <dependency>
      <groupId>mysql</groupId>
      <artifactId>mysql-connector-java</artifactId>
      <version>8.0.33</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-json</artifactId>
      <version>${flink.version}</version>
    </dependency>


    <dependency>
      <groupId>org.projectlombok</groupId>
      <artifactId>lombok</artifactId>
      <version>1.18.26</version>
      <scope>provided</scope>
    </dependency>

    <dependency>
      <groupId>cn.hutool</groupId>
      <artifactId>hutool-all</artifactId>
      <version>5.8.39</version>
    </dependency>

  </dependencies>
  <build>
    <plugins>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-assembly-plugin</artifactId>
        <version>3.0.0</version>
        <configuration>
          <descriptorRefs>
            <descriptorRef>jar-with-dependencies</descriptorRef>
          </descriptorRefs>
        </configuration>
        <executions>
          <execution>
            <id>make-assembly</id>
            <phase>package</phase>
            <goals>
              <goal>single</goal>
            </goals>
          </execution>
        </executions>
      </plugin>
    </plugins>
  </build>
</project>

八、参考鸣谢

轻松通关Flink第33讲：Flink 计算 PV、UV 代码实现

Flink计算pv和uv的通用方法