依赖：
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.kafka</groupId>
<artifactId>spring-kafka</artifactId>
</dependency>
application.properties:
### kafka configure spring.kafka.bootstrap-servers=10.160.3.70:9092 spring.kafka.consumer.group-id=sea-test spring.kafka.consumer.enable-auto-commit=false spring.kafka.consumer.auto-offset-reset=earliest spring.kafka.consumer.max-poll-records=2000 #spring.kafka.consumer.key-deserializer=org.apache.kafka.common.serialization.StringDeserializer #spring.kafka.consumer.value-deserializer=org.apache.kafka.common.serialization.StringDeserializer spring.kafka.producer.retries=3 spring.kafka.producer.batch-size=16384 spring.kafka.producer.buffer-memory=33554432 spring.kafka.producer.linger=10 #spring.kafka.producer.key-serializer=org.apache.kafka.common.serialization.StringSerializer #spring.kafka.producer.value-serializer=org.apache.kafka.common.serialization.StringSerializer
KafkaConfig:
package com.icil.topic.config; import java.util.HashMap; import java.util.Map; import org.apache.kafka.clients.admin.AdminClient; import org.apache.kafka.clients.admin.AdminClientConfig; import org.apache.kafka.clients.consumer.ConsumerConfig; import org.apache.kafka.clients.producer.ProducerConfig; import org.apache.kafka.common.serialization.StringDeserializer; 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.config.ConcurrentKafkaListenerContainerFactory; import org.springframework.kafka.config.KafkaListenerContainerFactory; import org.springframework.kafka.core.DefaultKafkaConsumerFactory; import org.springframework.kafka.core.DefaultKafkaProducerFactory; import org.springframework.kafka.core.KafkaAdmin; import org.springframework.kafka.core.KafkaTemplate; import org.springframework.kafka.core.ProducerFactory; import org.springframework.kafka.listener.ContainerProperties; import com.google.common.collect.Maps; @Configuration @EnableKafka public class KafkaConfig { @Value("${spring.kafka.bootstrap-servers}") private String bootstrapServers; @Value("${spring.kafka.consumer.group-id}") private String groupId; @Value("${spring.kafka.consumer.enable-auto-commit}") private Boolean autoCommit; @Value("${spring.kafka.consumer.auto-offset-reset}") private String autoOffsetReset; @Value("${spring.kafka.consumer.max-poll-records}") private Integer maxPollRecords; @Value("${spring.kafka.producer.linger}") private int linger; @Value("${spring.kafka.producer.retries}") private Integer retries; @Value("${spring.kafka.producer.batch-size}") private Integer batchSize; @Value("${spring.kafka.producer.buffer-memory}") private Integer bufferMemory; //cankao :https://blog.csdn.net/tmeng521/article/details/90901925 public Map<String, Object> producerConfigs() { Map<String, Object> props = new HashMap<>(); props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers); //设置重试次数 props.put(ProducerConfig.RETRIES_CONFIG, retries); //达到batchSize大小的时候会发送消息 props.put(ProducerConfig.BATCH_SIZE_CONFIG, batchSize); //延时时间，延时时间到达之后计算批量发送的大小没达到也发送消息 props.put(ProducerConfig.LINGER_MS_CONFIG, linger); //缓冲区的值 props.put(ProducerConfig.BUFFER_MEMORY_CONFIG, bufferMemory); //序列化手段 props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class); props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class); //producer端的消息确认机制,-1和all都表示消息不仅要写入本地的leader中还要写入对应的副本中 props.put(ProducerConfig.ACKS_CONFIG, "-1");//单个brok 推荐使用'1' //单条消息的最大值以字节为单位,默认值为1048576 props.put(ProducerConfig.LINGER_MS_CONFIG, 10485760); //设置broker响应时间，如果broker在60秒之内还是没有返回给producer确认消息，则认为发送失败 props.put(ProducerConfig.REQUEST_TIMEOUT_MS_CONFIG, 60000); //指定拦截器(value为对应的class) //props.put(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG, "com.te.handler.KafkaProducerInterceptor"); //设置压缩算法(默认是木有压缩算法的) props.put(ProducerConfig.COMPRESSION_TYPE_CONFIG, "snappy");//snappy return props; } @Bean //创建一个kafka管理类，相当于rabbitMQ的管理类rabbitAdmin,没有此bean无法自定义的使用adminClient创建topic public KafkaAdmin kafkaAdmin() { Map<String, Object> props = new HashMap<>(); //配置Kafka实例的连接地址 //kafka的地址，不是zookeeper props.put(AdminClientConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers); KafkaAdmin admin = new KafkaAdmin(props); return admin; } @Bean //kafka客户端，在spring中创建这个bean之后可以注入并且创建topic,用于集群环境，创建对个副本 public AdminClient adminClient() { return AdminClient.create(kafkaAdmin().getConfig()); } @Bean public ProducerFactory<String, String> producerFactory() { return new DefaultKafkaProducerFactory<>(producerConfigs()); } @Bean public KafkaTemplate<String, String> kafkaTemplate() { return new KafkaTemplate<>(producerFactory()); } @Bean public Map<String, Object> consumerConfigs() { Map<String, Object> props = Maps.newHashMap(); props.put(ConsumerConfig.GROUP_ID_CONFIG, groupId); props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, autoCommit); props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, autoOffsetReset); props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers); props.put(ConsumerConfig.MAX_POLL_RECORDS_CONFIG, maxPollRecords); // props.put(ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG, 180000); // props.put(ConsumerConfig.REQUEST_TIMEOUT_MS_CONFIG, 900000); // props.put(ConsumerConfig.MAX_POLL_INTERVAL_MS_CONFIG, 900000); props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class); props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class); return props; } @Bean public KafkaListenerContainerFactory<?> batchFactory() { ConcurrentKafkaListenerContainerFactory<Integer, String> factory = new ConcurrentKafkaListenerContainerFactory<>(); factory.setConsumerFactory(new DefaultKafkaConsumerFactory<>(consumerConfigs())); //设置为批量消费，每个批次数量在Kafka配置参数中设置ConsumerConfig.MAX_POLL_RECORDS_CONFIG factory.setBatchListener(true); // set the retry template // factory.setRetryTemplate(retryTemplate()); factory.getContainerProperties().setAckMode(ContainerProperties.AckMode.MANUAL); return factory; } }
如果topic需要初始化：可以配置//cankao :https://blog.csdn.net/tmeng521/article/details/90901925
@Configuration public class KafkaInitialConfiguration { //创建TopicName为topic.quick.initial的Topic并设置分区数为8以及副本数为1 @Bean//通过bean创建(bean的名字为initialTopic) public NewTopic initialTopic() { return new NewTopic("topic.quick.initial",8, (short) 1 ); } /** * 此种@Bean的方式，如果topic的名字相同，那么会覆盖以前的那个 * @return */ // //修改后|分区数量会变成11个 注意分区数量只能增加不能减少 @Bean public NewTopic initialTopic2() { return new NewTopic("topic.quick.initial",11, (short) 1 ); } @Bean //创建一个kafka管理类，相当于rabbitMQ的管理类rabbitAdmin,没有此bean无法自定义的使用adminClient创建topic public KafkaAdmin kafkaAdmin() { Map<String, Object> props = new HashMap<>(); //配置Kafka实例的连接地址 //kafka的地址，不是zookeeper props.put(AdminClientConfig.BOOTSTRAP_SERVERS_CONFIG, "127.0.0.1:9092"); KafkaAdmin admin = new KafkaAdmin(props); return admin; } @Bean //kafka客户端，在spring中创建这个bean之后可以注入并且创建topic public AdminClient adminClient() { return AdminClient.create(kafkaAdmin().getConfig()); } }
test 手动创建topic ,手动查看所有topic
@Autowired // adminClien需要自己生成配置bean private AdminClient adminClient; @Autowired private KafkaTemplate<String, String> kafkaTemplate; @Test//自定义手动创建topic和分区 public void testCreateTopic() throws InterruptedException { // 这种是手动创建 //10个分区，一个副本 // 分区多的好处是能快速的处理并发量，但是也要根据机器的配置 NewTopic topic = new NewTopic("topic.manual.create", 10, (short) 1); adminClient.createTopics(Arrays.asList(topic)); Thread.sleep(1000); } /** * 获取所有的topic * @throws Exception */ @Test public void getAllTopic() throws Exception { ListTopicsResult listTopics = adminClient.listTopics(); Set<String> topics = listTopics.names().get(); for (String topic : topics) { System.err.println(topic); } }
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