一次性拉取多條數據,消費後再手動提交ACK,因爲要保存到數據庫去, 這過程如果失敗的話, 需要重新消費這些數據
所以 配置的時候,KAFKA不能自動提交 ,
批量消費數據
- 設置ENABLE_AUTO_COMMIT_CONFIG=false,禁止自動提交
- 設置AckMode=MANUAL_IMMEDIATE
- 監聽方法加入Acknowledgment ack 參數
package com.zenlayer.ad.kafuka;
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.KafkaTemplate;
import org.springframework.kafka.core.ProducerFactory;
import org.springframework.kafka.listener.AbstractMessageListenerContainer;
import java.util.HashMap;
import java.util.Map;
@Configuration
@EnableKafka
public class KafkaConfiguration {
/**
* @author zhff
* @version 2019/9/1 下午04:07
*/
@Value("${spring.kafka.bootstrap-servers}")
private String bootstrapServers;
@Value("${spring.kafka.consumer.enable-auto-commit}")
private Boolean autoCommit;
@Value("${spring.kafka.consumer.auto-commit-interval}")
private Integer autoCommitInterval;
@Value("${spring.kafka.consumer.group-id}")
private String groupId;
@Value("${spring.kafka.consumer.max-poll-records}")
private Integer maxPollRecords;
@Value("${spring.kafka.consumer.auto-offset-reset}")
private String autoOffsetReset;
@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;
/**
* 生產者配置信息
*/
@Bean
public Map<String, Object> producerConfigs() {
Map<String, Object> props = new HashMap<String, Object>();
props.put(ProducerConfig.ACKS_CONFIG, "0");//默認爲1,all和-1都是消費在服務副本里 也已經接收成功,防止數據丟失
props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
props.put(ProducerConfig.RETRIES_CONFIG, retries);
props.put(ProducerConfig.BATCH_SIZE_CONFIG, batchSize);
props.put(ProducerConfig.LINGER_MS_CONFIG, 1);
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;
}
/**
* 生產者工廠
*/
@Bean
public ProducerFactory<String, String> producerFactory() {
return new DefaultKafkaProducerFactory<>(producerConfigs());
}
/**
* 生產者模板
*/
@Bean
public KafkaTemplate<String, String> kafkaTemplate() {
return new KafkaTemplate<>(producerFactory());
}
/**
* 消費者配置信息
*/
@Bean
public Map<String, Object> consumerConfigs() {
Map<String, Object> props = new HashMap<String, Object>();
props.put(ConsumerConfig.GROUP_ID_CONFIG, groupId);
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.MAX_POLL_RECORDS_CONFIG, maxPollRecords);
props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, autoCommit);// 手動提交 配置 false
props.put(ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG, 120000);
props.put(ConsumerConfig.REQUEST_TIMEOUT_MS_CONFIG, 180000);
props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
return props;
}
/**
* 消費者批量工程
*/
@Bean
public KafkaListenerContainerFactory<?> batchFactory() {
ConcurrentKafkaListenerContainerFactory<Integer, String> factory = new ConcurrentKafkaListenerContainerFactory<>();
factory.setConsumerFactory(new DefaultKafkaConsumerFactory<>(consumerConfigs()));
// 設置爲批量消費,每個批次數量在Kafka配置參數中設置ConsumerConfig.MAX_POLL_RECORDS_CONFIG
factory.setBatchListener(true);
factory.setConcurrency(4);
factory.getContainerProperties().setAckMode(AbstractMessageListenerContainer.AckMode.MANUAL_IMMEDIATE);
factory.getContainerProperties().setPollTimeout(30000);
return factory;
}
}
配置文件 也可以把手動提交配置 寫成這樣
ack-mode: MANUAL_IMMEDIATE
spring:
kafka:
bootstrap-servers: 192.168.1.125:9092 192.168.1.126:9092 192.168.1.127:9092
producer:
# 重試次數
retries: 3
# 批量發送的消息數量
batch-size: 16384
# 32MB的批處理緩衝區
buffer-memory: 33554432
key-serializer: org.apache.kafka.common.serialization.StringSerializer
value-serializer: org.apache.kafka.common.serialization.StringSerializer
consumer:
# 默認消費者組
group-id: 0
# 最早未被消費的offset
auto-offset-reset: earliest
# 批量一次最大拉取數據量
max-poll-records: 3000
# 自動提交時間間隔, 這種直接拉到數據就提交 容易丟數據
auto-commit-interval: 2000
# 禁止自動提交
enable-auto-commit: false
# 批量拉取間隔,要大於批量拉取數據的處理時間,時間間隔太小會有重複消費
max.poll.interval.ms: 5000
topicName:
topic2: topic_collect1
topic5: topic_collect111
消費的方法如下, 方法比較簡單
@KafkaListener(id = "0", topics = "topic_collect", containerFactory = "batchFactory")
public void listen100(List<ConsumerRecord<String, String>> records, Acknowledgment ack) {
System.out.println(records.size() + "條數被消費");
try {
batchConsumer(records);
ack.acknowledge();
} catch (Exception ex) {
logger.error("消費數據出錯 ", ex.getStackTrace());
}
}