spring boot与kafka集成的简单实例
本文介绍了spring boot与kafka集成的简单实例,分享给大家,具体如下: 引入相关依赖 <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter</artifactId> </dependency> <dependency> <groupId>org.springframework.kafka</groupId> <artifactId>spring-kafka</artifactId> <version>1.1.1.RELEASE</version> </dependency> 从依赖项的引入即可看出,当前spring boot(1.4.2)还不支持完全以配置项的配置来实现与kafka的无缝集成。也就意味着必须通过java config的方式进行手工配置。 定义kafka基础配置 与redisTemplate及jdbcTemplate等类似。spring同样提供了org.springframework.kafka.core.KafkaTemplate作为kafka相关api操作的入口。 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.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 { public Map<String,Object> producerConfigs() { Map<String,Object> props = new HashMap<>(); props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"192.168.179.200:9092"); props.put(ProducerConfig.RETRIES_CONFIG,0); props.put(ProducerConfig.BATCH_SIZE_CONFIG,4096); props.put(ProducerConfig.LINGER_MS_CONFIG,1); props.put(ProducerConfig.BUFFER_MEMORY_CONFIG,40960); 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()); } } KafkaTemplate依赖于ProducerFactory,而创建ProducerFactory时则通过一个Map指定kafka相关配置参数。通过KafkaTemplate对象即可实现消息发送。 kafkaTemplate.send("test-topic","hello"); or kafkaTemplate.send("test-topic","key-1","hello"); 监听消息配置 import org.apache.kafka.clients.consumer.ConsumerConfig; import org.apache.kafka.common.serialization.StringDeserializer; 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 { @Bean public KafkaListenerContainerFactory<ConcurrentMessageListenerContainer<String,String>> kafkaListenerContainerFactory() { ConcurrentKafkaListenerContainerFactory<String,String> factory = new ConcurrentKafkaListenerContainerFactory<>(); factory.setConsumerFactory(consumerFactory()); factory.setConcurrency(3); factory.getContainerProperties().setPollTimeout(3000); 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,"192.168.179.200:9092"); propsMap.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG,false); propsMap.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG,"100"); propsMap.put(ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG,"15000"); propsMap.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class); propsMap.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class); propsMap.put(ConsumerConfig.GROUP_ID_CONFIG,"test-group"); propsMap.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG,"latest"); return propsMap; } @Bean public Listener listener() { return new Listener(); } } 实现消息监听的最终目标是得到监听器对象。该监听器对象自行实现。 import org.apache.kafka.clients.consumer.ConsumerRecord; import org.springframework.kafka.annotation.KafkaListener; import java.util.Optional; public class Listener { @KafkaListener(topics = {"test-topic"}) public void listen(ConsumerRecord<?,?> record) { Optional<?> kafkaMessage = Optional.ofNullable(record.value()); if (kafkaMessage.isPresent()) { Object message = kafkaMessage.get(); System.out.println("listen1 " + message); } } } 只需用@KafkaListener指定哪个方法处理消息即可。同时指定该方法用于监听kafka中哪些topic。 注意事项 定义监听消息配置时,GROUP_ID_CONFIG配置项的值用于指定消费者组的名称,如果同组中存在多个监听器对象则只有一个监听器对象能收到消息。 @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。 KEY_DESERIALIZER_CLASS_CONFIG与VALUE_DESERIALIZER_CLASS_CONFIG指定key和value的编码、解码策略。kafka用key值确定value存放在哪个分区中。 后记 时间是解决问题的有效手段之一。 在spring boot 1.5版本中即可实现spring boot与kafka Auto-configuration 以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持编程小技巧。 (编辑:李大同) 【声明】本站内容均来自网络,其相关言论仅代表作者个人观点,不代表本站立场。若无意侵犯到您的权利,请及时与联系站长删除相关内容! |