依赖

<dependency>
    <groupId>org.springframework.kafka</groupId>
    <artifactId>spring-kafka</artifactId>
    <version>1.1.1.RELEASE</version>
</dependency>

配置

#============== kafka ===================
kafka.consumer.bootstrap-servers=10.93.21.21:9092
kafka.consumer.enable.auto.commit=true
kafka.consumer.session.timeout=6000
kafka.consumer.auto.commit.interval=100
kafka.consumer.auto.offset.reset=latest
kafka.consumer.topic=test
kafka.consumer.group.id=test
kafka.consumer.concurrency=10
kafka.producer.compression-type=lz4
kafka.producer.servers=10.93.21.21:9092
kafka.producer.retries=0
kafka.producer.batch.size=4096
kafka.producer.linger=1
kafka.producer.buffer.memory=40960

生产者

1)通过@Configuration、@EnableKafka,声明Config并且打开KafkaTemplate能力。

2)通过@Value注入application.properties配置文件中的kafka配置。

3)生成bean,@Bean

import java.util.HashMap;
import java.util.Map;

import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.common.serialization.StringSerializer;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.kafka.annotation.EnableKafka;
import org.springframework.kafka.core.DefaultKafkaProducerFactory;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.kafka.core.ProducerFactory;

@Configuration
@EnableKafka
public class KafkaProducerConfig {

    @Value("${kafka.producer.servers}")
    private String servers;
    @Value("${kafka.producer.retries}")
    private int retries;
    @Value("${kafka.producer.batch.size}")
    private int batchSize;
    @Value("${kafka.producer.linger}")
    private int linger;
    @Value("${kafka.producer.buffer.memory}")
    private int bufferMemory;


    public Map<String, Object> producerConfigs() {
        Map<String, Object> props = new HashMap<>();
        props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, servers);
        props.put(ProducerConfig.RETRIES_CONFIG, retries);
        props.put(ProducerConfig.BATCH_SIZE_CONFIG, batchSize);
        props.put(ProducerConfig.LINGER_MS_CONFIG, linger);
        props.put(ProducerConfig.BUFFER_MEMORY_CONFIG, bufferMemory);
        props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
        props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
        return props;
    }

    public ProducerFactory<String, String> producerFactory() {
        return new DefaultKafkaProducerFactory<>(producerConfigs());
    }

    @Bean
    public KafkaTemplate<String, String> kafkaTemplate() {
        return new KafkaTemplate<String, String>(producerFactory());
    }
}

写一个Controller。想topic=test,key=key,发送消息message

import com.kangaroo.sentinel.common.response.Response;
import com.kangaroo.sentinel.common.response.ResultCode;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.web.bind.annotation.*;
import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpServletResponse;


@RestController
@RequestMapping("/kafka")
public class CollectController {
    protected final Logger logger = LoggerFactory.getLogger(this.getClass());
    @Autowired
    private KafkaTemplate kafkaTemplate;

    @RequestMapping(value = "/send", method = RequestMethod.GET)
    public Response sendKafka(HttpServletRequest request, HttpServletResponse response) {
        try {
            String message = request.getParameter("message");
            logger.info("kafka的消息={}", message);
            kafkaTemplate.send("test", "key", message);
            logger.info("发送kafka成功.");
            return new Response(ResultCode.SUCCESS, "发送kafka成功", null);
        } catch (Exception e) {
            logger.error("发送kafka失败", e);
            return new Response(ResultCode.EXCEPTION, "发送kafka失败", null);
        }
    }

}

消费者

1)通过@Configuration、@EnableKafka,声明Config并且打开KafkaTemplate能力。

2)通过@Value注入application.properties配置文件中的kafka配置。

3)生成bean,@Bean

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.kafka.annotation.EnableKafka;
import org.springframework.kafka.config.ConcurrentKafkaListenerContainerFactory;
import org.springframework.kafka.config.KafkaListenerContainerFactory;
import org.springframework.kafka.core.ConsumerFactory;
import org.springframework.kafka.core.DefaultKafkaConsumerFactory;
import org.springframework.kafka.listener.ConcurrentMessageListenerContainer;

import java.util.HashMap;
import java.util.Map;

@Configuration
@EnableKafka
public class KafkaConsumerConfig {

    @Value("${kafka.consumer.servers}")
    private String servers;
    @Value("${kafka.consumer.enable.auto.commit}")
    private boolean enableAutoCommit;
    @Value("${kafka.consumer.session.timeout}")
    private String sessionTimeout;
    @Value("${kafka.consumer.auto.commit.interval}")
    private String autoCommitInterval;
    @Value("${kafka.consumer.group.id}")
    private String groupId;
    @Value("${kafka.consumer.auto.offset.reset}")
    private String autoOffsetReset;
    @Value("${kafka.consumer.concurrency}")
    private int concurrency;
    @Bean
    public KafkaListenerContainerFactory<ConcurrentMessageListenerContainer<String, String>> kafkaListenerContainerFactory() {
        ConcurrentKafkaListenerContainerFactory<String, String> factory = new ConcurrentKafkaListenerContainerFactory<>();
        factory.setConsumerFactory(consumerFactory());
        factory.setConcurrency(concurrency);
        factory.setBatchListener(true);
        factory.getContainerProperties().setPollTimeout(1500);
        return factory;
    }

    public ConsumerFactory<String, String> consumerFactory() {
        return new DefaultKafkaConsumerFactory<>(consumerConfigs());
    }


    public Map<String, Object> consumerConfigs() {
        Map<String, Object> propsMap = new HashMap<>();
        propsMap.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, servers);
        propsMap.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, enableAutoCommit);
        propsMap.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, autoCommitInterval);
        propsMap.put(ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG, sessionTimeout);
        propsMap.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        propsMap.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        propsMap.put(ConsumerConfig.GROUP_ID_CONFIG, groupId);
        propsMap.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, autoOffsetReset);
        propsMap.put(ConsumerConfig.MAX_POLL_RECORDS_CONFIG, 50);
        return propsMap;
    }

}

Listener简单的实现demo如下:只是简单的读取并打印key和message值

@KafkaListener中topics属性用于指定kafka topic名称,topic名称由消息生产者指定,也就是由kafkaTemplate在发送消息时指定。

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.kafka.annotation.KafkaListener;

public class Listener {
    protected final Logger logger = LoggerFactory.getLogger(this.getClass());


    @KafkaListener(topics = {"test"})
    public void listen(ConsumerRecord<?, ?> record) {
        logger.info("kafka的key: " + record.key());
        logger.info("kafka的value: " + record.value().toString());
    }
}

springboot 消费kafka

并发消费。我们使用的是ConcurrentKafkaListenerContainerFactory并且设置了factory.setConcurrency(4); (topic有4个分区,为了加快消费将并发设置为4,也就是有4个KafkaMessageListenerContainer)

批量消费。factory.setBatchListener(true); 以及 propsMap.put(ConsumerConfig.MAX_POLL_RECORDS_CONFIG, 50); 一个设启用批量消费,一个设置批量消费每次最多消费多少条消息记录。重点说明一下,我们设置的ConsumerConfig.MAX_POLL_RECORDS_CONFIG是50,并不是说如果没有达到50条消息,我们就一直等待。官方的解释是”The maximum number of records returned in a single call to poll().”, 也就是50表示的是一次poll最多返回的记录数。 每间隔max.poll.interval.ms我们就调用一次poll。每次poll最多返回50条记录。

分区消费。对于只有一个分区的topic,不需要分区消费,因为没有意义。下面的例子是针对有2个分区的情况(我的完整代码中有4个listenPartitionX方法,我的topic设置了4个分区),读者可以根据自己的情况进行调整。

public class MyListener {
    private static final String TPOIC = "topic02";

    @KafkaListener(id = "id0", topicPartitions = { @TopicPartition(topic = TPOIC, partitions = { "0" }) })
    public void listenPartition0(List<ConsumerRecord<?, ?>> records) {
        log.info("Id0 Listener, Thread ID: " + Thread.currentThread().getId());
        log.info("Id0 records size " +  records.size());

        for (ConsumerRecord<?, ?> record : records) {
            Optional<?> kafkaMessage = Optional.ofNullable(record.value());
            log.info("Received: " + record);
            if (kafkaMessage.isPresent()) {
                Object message = record.value();
                String topic = record.topic();
                log.info("p0 Received message={}",  message);
            }
        }
    }

    @KafkaListener(id = "id1", topicPartitions = { @TopicPartition(topic = TPOIC, partitions = { "1" }) })
    public void listenPartition1(List<ConsumerRecord<?, ?>> records) {
        log.info("Id1 Listener, Thread ID: " + Thread.currentThread().getId());
        log.info("Id1 records size " +  records.size());

        for (ConsumerRecord<?, ?> record : records) {
            Optional<?> kafkaMessage = Optional.ofNullable(record.value());
            log.info("Received: " + record);
            if (kafkaMessage.isPresent()) {
                Object message = record.value();
                String topic = record.topic();
                log.info("p1 Received message={}",  message);
            }
        }
}

如果我们的topic有多个分区,经过以上步骤可以很好的加快消息消费。如果只有一个分区,因为已经有一个同名group id在消费了,所以只会有一个在消费数据,另一个不消费数据,但是可以作为从节点,一旦主节点挂了,从节点就可以开始消费数据。

01-16 20:30