springboot中实现kafka指定offset消费
kafka消费过程难免会遇到需要重新消费的场景,例如我们消费到kafka数据之后需要进行存库操作,若某一时刻数据库down了,导致kafka消费的数据无法入库,为了弥补数据库down期间的数据损失,有一种做法我们可以指定kafka消费者的offset到之前某一时间的数值,然后重新进行消费。
首先创建kafka消费服务
@Service
@Slf4j
//实现CommandLineRunner接口,在springboot启动时自动运行其run方法。
public class TspLogbookAnalysisService implements CommandLineRunner {
@Override
public void run(String... args) {
//do something
}
}
kafka消费模型建立
kafka server中每个主题存在多个分区(partition),每个分区自己维护一个偏移量(offset),我们的目标是实现kafka consumer指定offset消费。
在这里使用consumer–>partition一对一的消费模型,每个consumer各自管理自己的partition。
@Service
@Slf4j
public class TspLogbookAnalysisService implements CommandLineRunner {
//声明kafka分区数相等的消费线程数,一个分区对应一个消费线程
private static final int consumeThreadNum = 9;
//特殊指定每个分区开始消费的offset
private List<Long> partitionOffsets = Lists.newArrayList(1111,1112,1113,1114,1115,1116,1117,1118,1119);
private ExecutorService executorService = Executors.newFixedThreadPool(consumeThreadNum);
@Override
public void run(String... args) {
//循环遍历创建消费线程
IntStream.range(0, consumeThreadNum)
.forEach(partitionIndex -> executorService.submit(() -> startConsume(partitionIndex)));
}
}
kafka consumer对offset的处理
声明kafka consumer的配置类
private Properties buildKafkaConfig() {
Properties kafkaConfiguration = new Properties();
kafkaConfiguration.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "");
kafkaConfiguration.put(ConsumerConfig.GROUP_ID_CONFIG, "");
kafkaConfiguration.put(ConsumerConfig.MAX_POLL_RECORDS_CONFIG, "");
kafkaConfiguration.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, "");
kafkaConfiguration.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, "");
kafkaConfiguration.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, "");
kafkaConfiguration.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG,"");
kafkaConfiguration.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "");
...更多配置项
return kafkaConfiguration;
}
创建kafka consumer,处理offset,开始消费数据任务
private void startConsume(int partitionIndex) {
//创建kafka consumer
KafkaConsumer<String, byte[]> consumer = new KafkaConsumer<>(buildKafkaConfig());
try {
//指定该consumer对应的消费分区
TopicPartition partition = new TopicPartition(kafkaProperties.getKafkaTopic(), partitionIndex);
consumer.assign(Lists.newArrayList(partition));
//consumer的offset处理
if (collectionUtils.isNotEmpty(partitionOffsets) && partitionOffsets.size() == consumeThreadNum) {
Long seekOffset = partitionOffsets.get(partitionIndex);
log.info("partition:{} , offset seek from {}", partition, seekOffset);
consumer.seek(partition, seekOffset);
}
//开始消费数据任务
kafkaRecordConsume(consumer, partition);
} catch (Exception e) {
log.error("kafka consume error:{}", ExceptionUtils.getFullStackTrace(e));
} finally {
try {
consumer.commitSync();
} finally {
consumer.close();
}
}
}
消费数据逻辑,offset操作
private void kafkaRecordConsume(KafkaConsumer<String, byte[]> consumer, TopicPartition partition) {
while (true) {
try {
ConsumerRecords<String, byte[]> records = consumer.poll(TspLogbookConstants.POLL_TIMEOUT);
//具体的处理流程
records.forEach((k) -> handleKafkaInput(k.key(), k.value()));
//🌿很重要:日志记录当前consumer的offset,partition相关信息(之后如需重新指定offset消费就从这里的日志中获取offset,partition信息)
if (records.count() > 0) {
String currentOffset = String.valueOf(consumer.position(partition));
log.info("current records size is:{}, partition is: {}, offset is:{}", records.count(), consumer.assignment(), currentOffset);
}
//offset提交
consumer.commitAsync();
} catch (Exception e) {
log.error("handlerKafkaInput error{}", ExceptionUtils.getFullStackTrace(e));
}
}
}
原文:AppMaster - The no-code platform for building web & mobile apps