本篇是在《Kafka Stream简单示例（一）》和《Kafka Stream简单示例（二）---聚合 Aggregation--统计总和》以及《 Kafka Stream简单示例（三）---自定义Serde》基础上成文的，建议先阅读前三篇，以便清楚上下文关系需求背景。

第三篇《Kafka Stream简单示例（三）---自定义Serde》中，我们自定义了Statistic类的Serializer和Deserializer。现实中我们可能需要多个类都支持序列化和反序列，能否有泛型的Serializer和Deserializer，直接放入自己的类就可以完成工作？答案是，如果你的类，是POJO类型的，使用泛型JsonPOJOSerializer和JsonPOJODeserializer就可以。

注意：示例中的代码只是展示流程，非生产代码，仅供参考，由此导致的问题本人概不负责。

官方文档在这里，我用是kafka 1.0. 所以连接也是1.0版本的文档。 http://kafka.apache.org/10/documentation/streams/developer-guide/datatypes.html

项目需求

统计一分钟内（固定时间窗口Tumbling Window）内温度的总和与平均值。类似的还有，最大值，最小值。

主要流程和代码

完整的代码在这里，欢迎加星和fork。谢谢！

一个结果中必须同时含有总和与平均值，于是我们设计一个简单数据结构

@Data
@AllArgsConstructor
@NoArgsConstructor
public class Statistics {
    private Long avg;
    private Long sum;
    private Long count;
}

细心的人会发现本篇中的Statistic比《 Kafka Stream简单示例（三）---自定义Serde》中的Statistic多一个@NoArgsConstructor注解，这是因为我们后面使用反序列化是需要生成Statistics对象（使用默认的无参构造函数生成）。因此需要添加@NoArgsConstructor注解。

根据Serdes的要求，我们必须提供对应的Serializer和Deserializer。

SerdesClass.png

我们需要实现JsonPOJOSerializer和JsonPOJODeserializer。仍然才考LongSerializer和LongDeserializer的实现，我们实现了StatisticsSerializer和StatisticsDeserializer。
首先是序列化实现JsonPOJOSerializer

package com.yq.generic;

import com.fasterxml.jackson.databind.ObjectMapper;
import org.apache.kafka.common.serialization.Serializer;
import org.apache.kafka.common.errors.SerializationException;

import java.util.Map;

/**
 * 这个是官方例子的copy， 版权归官方。copy到本地是为了让我的例子也运行起来
 * https://github.com/apache/kafka/blob/1.0/streams/examples/src/main/java/org/apache/kafka/streams/examples/pageview/JsonPOJOSerializer.java
 * className: JsonPOJOSerializer
 *
 */


public class JsonPOJOSerializer implements Serializer {
    private final ObjectMapper objectMapper = new ObjectMapper();

    /**
     * Default constructor needed by Kafka
     */
    public JsonPOJOSerializer() {
    }

    @Override
    public void configure(Map props, boolean isKey) {
    }

    @Override
    public byte[] serialize(String topic, T data) {
        if (data == null)
            return null;

        try {
            return objectMapper.writeValueAsBytes(data);
        } catch (Exception e) {
            throw new SerializationException("Error serializing JSON message", e);
        }
    }

    @Override
    public void close() {
    }

}

其次是反序列化实现JsonPOJODeserializer

package com.yq.generic;

import com.fasterxml.jackson.databind.ObjectMapper;
import org.apache.kafka.common.serialization.Deserializer;
import org.apache.kafka.common.errors.SerializationException;

import java.util.Map;

/**
 * 这个是官方例子的copy， 版权归官方。copy到本地是为了让我的例子也运行起来
 * https://github.com/apache/kafka/blob/1.0/streams/examples/src/main/java/org/apache/kafka/streams/examples/pageview/JsonPOJODeserializer.java
 * className: JsonPOJOSerializer
 *
 */
public class JsonPOJODeserializer implements Deserializer {
    private ObjectMapper objectMapper = new ObjectMapper();

    private Class tClass;

    /**
     * Default constructor needed by Kafka
     */
    public JsonPOJODeserializer() {
    }

    @SuppressWarnings("unchecked")
    @Override
    public void configure(Map props, boolean isKey) {
        tClass = (Class) props.get("JsonPOJOClass");
    }

    @Override
    public T deserialize(String topic, byte[] bytes) {
        if (bytes == null)
            return null;

        T data;
        try {
            data = objectMapper.readValue(bytes, tClass);
        } catch (Exception e) {
            throw new SerializationException(e);
        }

        return data;
    }

    @Override
    public void close() {

    }
}

最后是我们的主流程。
第一步，我们需要先定义

final Serializer statisticsSerializer = new JsonPOJOSerializer<>();
        serdeProps.put("JsonPOJOClass", Statistics.class);
        statisticsSerializer.configure(serdeProps, false);

        final Deserializer statisticsDeserializer = new JsonPOJODeserializer<>();
        serdeProps.put("JsonPOJOClass", Statistics.class);
        statisticsDeserializer.configure(serdeProps, false);

        final Serde statisticsSerde = Serdes.serdeFrom(statisticsSerializer, statisticsDeserializer);

第二步。就像SerDe内置的Serdes.Long()或者 Serdes.String()，可以直接使用statisticsSerde。

KTable的格式是 KTable, Statistics>。 aggregate函数的初始值和返回都是Statistics类型，结果存储的格式Materialized.>as("time-windowed-aggregated-temp-stream-store")
.withValueSerde(statisticsSerde) ，也是Statistics类型。

package com.yq.generic;


import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import com.yq.customized.Statistics;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.common.serialization.Deserializer;
import org.apache.kafka.common.serialization.Serde;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.common.serialization.Serializer;
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.KeyValueMapper;
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.HashMap;
import java.util.Map;
import java.util.Properties;
import java.util.concurrent.CountDownLatch;
import java.util.concurrent.TimeUnit;

/**
 *  http://kafka.apache.org/10/documentation/streams/developer-guide/datatypes.html
 * 统计60秒内，温度值的最大值  topic中的消息格式为数字，30， 21或者{"temp":19, "humidity": 25}
 */
public class TemperatureAvgGenericSerDeDemo {
    private static final int TEMPERATURE_WINDOW_SIZE = 60;

    public static void main(String[] args) throws Exception {

        Properties props = new Properties();
        props.put(StreamsConfig.APPLICATION_ID_CONFIG, "streams-temp-avg");
        props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "127.0.0.1:9092");
        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, "earliest");
        props.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0);

        StreamsBuilder builder = new StreamsBuilder();

        KStream source = builder.stream("iot-temp");

        Map serdeProps = new HashMap<>();

        final Serializer statisticsSerializer = new JsonPOJOSerializer<>();
        serdeProps.put("JsonPOJOClass", Statistics.class);
        statisticsSerializer.configure(serdeProps, false);

        final Deserializer statisticsDeserializer = new JsonPOJODeserializer<>();
        serdeProps.put("JsonPOJOClass", Statistics.class);
        statisticsDeserializer.configure(serdeProps, false);

        final Serde statisticsSerde = Serdes.serdeFrom(statisticsSerializer, statisticsDeserializer);

        KTable, Statistics> max = source
                .selectKey(new KeyValueMapper() {
                    @Override
                    public String apply(String key, String value) {
                        return "stat";
                    }
                })
                .groupByKey()
                .windowedBy(TimeWindows.of(TimeUnit.SECONDS.toMillis(TEMPERATURE_WINDOW_SIZE)))
                .aggregate(
                        new Initializer() {
                            @Override
                            public Statistics apply() {
                                Statistics avgAndSum = new Statistics(0L,0L,0L);
                                return avgAndSum;
                            }
                        },
                        new Aggregator() {
                            @Override
                            public Statistics apply(String aggKey, String newValue, Statistics aggValue) {
                                //topic中的消息格式为{"temp":19, "humidity": 25}
                                System.out.println("aggKey:" + aggKey + ",  newValue:" + newValue + ", aggKey:" + aggValue);
                                Long newValueLong = null;
                                try {
                                    JSONObject json = JSON.parseObject(newValue);
                                    newValueLong = json.getLong("temp");
                                }
                                catch (ClassCastException ex) {
                                    try {
                                        newValueLong = Long.valueOf(newValue);
                                    }
                                     catch (NumberFormatException e) {
                                         System.out.println("Exception:" + e.getMessage());
                                         //异常返回原值
                                         return aggValue;
                                    }
                                }
                                catch (Exception e) {
                                    System.out.println("Exception:" + e.getMessage());
                                    //异常返回原值
                                    return aggValue;
                                }


                                aggValue.setCount(aggValue.getCount() + 1);
                                aggValue.setSum(aggValue.getSum() + newValueLong);
                                aggValue.setAvg(aggValue.getSum() / aggValue.getCount());

                                return aggValue;
                            }
                        },
                        Materialized.>as("time-windowed-aggregated-temp-stream-store")
                                .withValueSerde(statisticsSerde)
                );

        WindowedSerializer windowedSerializer = new WindowedSerializer<>(Serdes.String().serializer());
        WindowedDeserializer windowedDeserializer = new WindowedDeserializer<>(Serdes.String().deserializer(), TEMPERATURE_WINDOW_SIZE);
        Serde> windowedSerde = Serdes.serdeFrom(windowedSerializer, windowedDeserializer);

        max.toStream().to("iot-temp-stat", Produced.with(windowedSerde, statisticsSerde));

        final KafkaStreams streams = new KafkaStreams(builder.build(), props);
        final CountDownLatch latch = new CountDownLatch(1);


        Runtime.getRuntime().addShutdownHook(new Thread("streams-temperature-shutdown-hook") {
            @Override
            public void run() {
                streams.close();
                latch.countDown();
            }
        });

        try {
            streams.start();
            latch.await();
        } catch (Throwable e) {
            System.exit(1);
        }
        System.exit(0);
    }
}

效果截图

图中已经有文字说明，结合代码能更清楚了解Kafka Stream。

Kafka Stream简单示例（四）---定义更通用的Serde

项目需求

主要流程和代码

效果截图

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