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。