Kafka Stream简单示例（二）---聚合 Aggregation--统计总和

淩亂°似流年 2022-03-10 01:40 412阅读 0赞

在[《Kafka Stream简单示例（一）》][Kafka Stream]基础上，我们稍作修改实现一个基于固定时间窗口统计总和的例子。

# 项目需求： #

统计每30秒内，按照key分组的总值。topic收到的消息格式：key:a, value:1， 例如如果kafka topic 30秒（Tumbling Window， 也就是固定窗口）， 收到消息key:a, value:1， key:b, value:5， key:a, value:3， 统计结果为key：a为 4（1+3）， key：b为5.

# 主要代码 #

备注：我的kafka版本是kafka\_2.12-1.0.0， 应用的kafka stream版本是1.0.2， 请大家注意版本差异。

<dependency>
                <groupId>org.apache.kafka</groupId>
                <artifactId>kafka-streams</artifactId>
                <version>1.0.2</version>
          </dependency>

项目依赖和第一篇相同。这里直接上代码，本示例代码还是在官方提供的代码基础上修改而来。  
核心在于提供以下3参数：  
**inal Initializer initializer**                          —提供初始化的值， 示例代码提供的初始值为0L  
**final Aggregator<? super K, ? super V, VR> aggregator**,           —怎么计算聚合， 我们key相同的值进行相加  
**final Materialized<K, VR, WindowStore<Bytes, byte\[\]>> materialized**  —进行状态标记

KStream<String, String> source = builder.stream(“iot-key”); —我们topic的内容为key:a, value:1这种格式  
.groupByKey()—按照key来统计， 也就是key为a的算一组，key为b的算一组  
.windowedBy(TimeWindows.of(TimeUnit.SECONDS.toMillis(TEMPERATURE\_WINDOW\_SIZE)))—时间窗口为30秒

KTable<Windowed, Long> 最终的结果为key为Windowed类型，value为Long类型

package com.yq;
    
    
    import org.apache.kafka.clients.consumer.ConsumerConfig;
    import org.apache.kafka.common.serialization.Serde;
    import org.apache.kafka.common.serialization.Serdes;
    import org.apache.kafka.common.utils.Bytes;
    import org.apache.kafka.streams.KafkaStreams;
    import org.apache.kafka.streams.StreamsBuilder;
    import org.apache.kafka.streams.StreamsConfig;
    import org.apache.kafka.streams.kstream.Aggregator;
    import org.apache.kafka.streams.kstream.Initializer;
    import org.apache.kafka.streams.kstream.KStream;
    import org.apache.kafka.streams.kstream.KTable;
    import org.apache.kafka.streams.kstream.Materialized;
    import org.apache.kafka.streams.kstream.Produced;
    import org.apache.kafka.streams.kstream.TimeWindows;
    import org.apache.kafka.streams.kstream.Windowed;
    import org.apache.kafka.streams.kstream.internals.WindowedDeserializer;
    import org.apache.kafka.streams.kstream.internals.WindowedSerializer;
    import org.apache.kafka.streams.state.WindowStore;
    
    import java.util.Properties;
    import java.util.concurrent.CountDownLatch;
    import java.util.concurrent.TimeUnit;
    /* * iot-key 输入的数据格式为，并且是在刚好在20秒的窗口被stream消费 * key:a, value:1, key:b, value:5, key:b. value:7, key:a. value:2, key:a, value3. key:b, value:3， * iot-key-sum结果为， * key:a, value1, key:b, value:5, key:b, value:12(5+7) key:a, value:3(1 + 2), key:a, value:(3+3) * , key:b, value:15 * * 本代码为演示使用没有异常处理，如果输入的value不是数字，会出现NumberFormatException异常 */
    public class TempAggregationSumDemo { 
    
        private static final int TEMPERATURE_WINDOW_SIZE = 30;
    
        public static void main(String[] args) throws Exception { 
    
            Properties props = new Properties();
            props.put(StreamsConfig.APPLICATION_ID_CONFIG, "streams-key-sum");
            props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
            props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
            props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
    
            props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "latest");
            props.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0);
    
            StreamsBuilder builder = new StreamsBuilder();
    
            KStream<String, String> source = builder.stream("iot-key");
            //KStream是一个由键值对构成的抽象记录流，每个键值对是一个独立的单元，即使相同的Key也不会覆盖，类似数据库的插入操作
            KTable<Windowed<String>, Long> sumWindowed = source
                    .groupByKey()
                    .windowedBy(TimeWindows.of(TimeUnit.SECONDS.toMillis(TEMPERATURE_WINDOW_SIZE)))
                    .aggregate(
                            new Initializer<Long>() { 
                                @Override
                                public Long apply() { 
                                    return 0L;
                                }
                            },
                            new Aggregator<String, String, Long>() { 
                                @Override
                                public Long apply(String aggKey, String newValue, Long aggValue) { 
                                    System.out.println("aggKey:" + aggKey+ ", newValue:"  +  newValue +", aggKey:" + aggValue );
                                    Long newValueLong = Long.valueOf(newValue);
                                    long newSum = aggValue.longValue() + newValueLong.longValue();
                                    return Long.valueOf(newSum);
                                }
                            },
                            Materialized.<String, Long, WindowStore<Bytes, byte[]>>as("time-windowed-aggregated-temp-stream-store")
                                    .withValueSerde(Serdes.Long())
                    );
    
            WindowedSerializer<String> windowedSerializer = new WindowedSerializer<>(Serdes.String().serializer());
            WindowedDeserializer<String> windowedDeserializer = new WindowedDeserializer<>(Serdes.String().deserializer(), TEMPERATURE_WINDOW_SIZE);
            Serde<Windowed<String>> windowedSerde = Serdes.serdeFrom(windowedSerializer, windowedDeserializer);;
    
            sumWindowed.toStream().to("iot-key-sum", Produced.with(windowedSerde, Serdes.Long()));
            final KafkaStreams streams = new KafkaStreams(builder.build(), props);
            final CountDownLatch latch = new CountDownLatch(1);
    
            // attach shutdown handler to catch control-c
            Runtime.getRuntime().addShutdownHook(new Thread("streams-key-shutdown-hook") { 
                @Override
                public void run() { 
                    streams.close();
                    latch.countDown();
                }
            });
    
            try { 
                streams.start();
                latch.await();
            } catch (Throwable e) { 
                System.exit(1);
            }
            System.exit(0);
        }
    }

# 运行效果 #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3J1c3NsZQ_size_16_color_FFFFFF_t_70]

[Kafka Stream]: https://blog.csdn.net/russle/article/details/88091448
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3J1c3NsZQ_size_16_color_FFFFFF_t_70]: /images/20220310/44f237288eed4e159f988763fb7cbd59.png