本篇文章為大家展示了如何在spring boot中使用spring-kafka實現(xiàn)一個接收消息功能，內(nèi)容簡明扼要并且容易理解，絕對能使你眼前一亮，通過這篇文章的詳細(xì)介紹希望你能有所收獲。
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實現(xiàn)方法
pom.xml文件如下
<?xml version="1.0" encoding="UTF-8"?>4.0.0 org.linuxsogood.sync linuxsogood-sync 1.0.0-SNAPSHOT org.springframework.boot spring-boot-starter-parent 1.4.0.RELEASE 1.8 3.3.1 1.2.4 3.3.6 4.1.1 org.springframework.boot spring-boot-starter-web org.springframework.boot spring-boot-starter-jdbc org.springframework.boot spring-boot-starter-aop org.springframework.boot spring-boot-starter-freemarker org.springframework.kafka spring-kafka 1.1.0.RELEASE junit junit 4.12 test org.assertj assertj-core 3.5.2 org.hamcrest hamcrest-all 1.3 test org.mockito mockito-all 1.9.5 test org.springframework spring-test 4.2.3.RELEASE test org.springframework.boot spring-boot-starter-test test MySQL mysql-connector-java com.microsoft.sqlserver sqljdbc4 4.0.0 com.alibaba druid 1.0.11 org.mybatis mybatis ${mybatis.version} org.mybatis mybatis-spring ${mybatis.spring.version} org.mybatis.generator mybatis-generator-core 1.3.2 compile true com.github.pagehelper pagehelper ${pagehelper.version} tk.mybatis mapper ${mapper.version} com.alibaba fastjson 1.2.17 repo.spring.io.milestone Spring Framework Maven Milestone Repository https://repo.spring.io/libs-milestone mybatis_generator org.mybatis.generator mybatis-generator-maven-plugin 1.3.2 true true org.springframework.boot spring-boot-maven-plugin org.linuxsogood.sync.Starter
orm層使用了MyBatis,又使用了通用Mapper和分頁插件.
kafka消費(fèi)端配置
重慶分公司
重慶分公司
import org.linuxsogood.sync.listener.Listener;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.kafka.annotation.EnableKafka;
import org.springframework.kafka.config.ConcurrentKafkaListenerContainerFactory;
import org.springframework.kafka.config.KafkaListenerContainerFactory;
import org.springframework.kafka.core.ConsumerFactory;
import org.springframework.kafka.core.DefaultKafkaConsumerFactory;
import org.springframework.kafka.listener.ConcurrentMessageListenerContainer;
import java.util.HashMap;
import java.util.Map;
@Configuration
@EnableKafka
public class KafkaConsumerConfig {
@Value("${kafka.broker.address}")
private String brokerAddress;
@Bean
KafkaListenerContainerFactory> kafkaListenerContainerFactory() {
ConcurrentKafkaListenerContainerFactory factory = new ConcurrentKafkaListenerContainerFactory<>();
factory.setConsumerFactory(consumerFactory());
factory.setConcurrency(3);
factory.getContainerProperties().setPollTimeout(3000);
return factory;
}
@Bean
public ConsumerFactory consumerFactory() {
return new DefaultKafkaConsumerFactory<>(consumerConfigs());
}
@Bean
public Map consumerConfigs() {
Map propsMap = new HashMap<>();
propsMap.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, this.brokerAddress);
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, "firehome-group");
propsMap.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
return propsMap;
}
@Bean
public Listener listener() {
return new Listener();
}
} 生產(chǎn)者的配置.
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;
import java.util.HashMap;
import java.util.Map;
@Configuration
@EnableKafka
public class KafkaProducerConfig {
@Value("${kafka.broker.address}")
private String brokerAddress;
@Bean
public ProducerFactory producerFactory() {
return new DefaultKafkaProducerFactory<>(producerConfigs());
}
@Bean
public Map producerConfigs() {
Map props = new HashMap<>();
props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, this.brokerAddress);
props.put(ProducerConfig.RETRIES_CONFIG, 0);
props.put(ProducerConfig.BATCH_SIZE_CONFIG, 16384);
props.put(ProducerConfig.LINGER_MS_CONFIG, 1);
props.put(ProducerConfig.BUFFER_MEMORY_CONFIG, 33554432);
props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
return props;
}
@Bean
public KafkaTemplate kafkaTemplate() {
return new KafkaTemplate(producerFactory());
}
} 監(jiān)聽,監(jiān)聽里面,寫的就是業(yè)務(wù)邏輯了,從kafka里面得到數(shù)據(jù)后,具體怎么去處理. 如果需要開啟kafka處理消息的廣播模式,多個監(jiān)聽要監(jiān)聽不同的group,即方法上的注解@KafkaListener里的group一定要不一樣.如果多個監(jiān)聽里的group寫的一樣,就會造成只有一個監(jiān)聽能處理其中的消息,另外監(jiān)聽就不能處理消息了.也即是kafka的分布式消息處理方式.
在同一個group里的監(jiān)聽,共同處理接收到的消息,會根據(jù)一定的算法來處理.如果不在一個組,但是監(jiān)聽的是同一個topic的話,就會形成廣播模式
import com.alibaba.fastjson.JSON;
import org.linuxsogood.qilian.enums.CupMessageType;
import org.linuxsogood.qilian.kafka.MessageWrapper;
import org.linuxsogood.qilian.model.store.Store;
import org.linuxsogood.sync.mapper.StoreMapper;
import org.linuxsogood.sync.model.StoreExample;
import org.apache.commons.lang3.StringUtils;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.kafka.annotation.KafkaListener;
import java.util.List;
import java.util.Optional;
public class Listener {
private static final Logger LOGGER = LoggerFactory.getLogger(Listener.class);
@Autowired
private StoreMapper storeMapper;
/**
* 監(jiān)聽kafka消息,如果有消息則消費(fèi),同步數(shù)據(jù)到新烽火的庫
* @param record 消息實體bean
*/
@KafkaListener(topics = "linuxsogood-topic", group = "sync-group")
public void listen(ConsumerRecord<?, ?> record) {
Optional<?> kafkaMessage = Optional.ofNullable(record.value());
if (kafkaMessage.isPresent()) {
Object message = kafkaMessage.get();
try {
MessageWrapper messageWrapper = JSON.parseObject(message.toString(), MessageWrapper.class);
CupMessageType type = messageWrapper.getType();
//判斷消息的數(shù)據(jù)類型,不同的數(shù)據(jù)入不同的表
if (CupMessageType.STORE == type) {
proceedStore(messageWrapper);
}
} catch (Exception e) {
LOGGER.error("將接收到的消息保存到數(shù)據(jù)庫時異常, 消息:{}, 異常:{}",message.toString(),e);
}
}
}
/**
* 消息是店鋪類型,店鋪消息處理入庫
* @param messageWrapper 從kafka中得到的消息
*/
private void proceedStore(MessageWrapper messageWrapper) {
Object data = messageWrapper.getData();
Store cupStore = JSON.parseObject(data.toString(), Store.class);
StoreExample storeExample = new StoreExample();
String storeName = StringUtils.isBlank(cupStore.getStoreOldName()) ? cupStore.getStoreName() : cupStore.getStoreOldName();
storeExample.createCriteria().andStoreNameEqualTo(storeName);
List stores = storeMapper.selectByExample(storeExample);
org.linuxsogood.sync.model.Store convertStore = new org.linuxsogood.sync.model.Store();
org.linuxsogood.sync.model.Store store = convertStore.convert(cupStore);
//如果查詢不到記錄則新增
if (stores.size() == 0) {
storeMapper.insert(store);
} else {
store.setStoreId(stores.get(0).getStoreId());
storeMapper.updateByPrimaryKey(store);
}
}
} 上述內(nèi)容就是如何在spring boot中使用spring-kafka實現(xiàn)一個接收消息功能，你們學(xué)到知識或技能了嗎？如果還想學(xué)到更多技能或者豐富自己的知識儲備，歡迎關(guān)注創(chuàng)新互聯(lián)行業(yè)資訊頻道。
