Kafka： ------ 事务控制、消费者隔离级别read_committed与read_uncommitted区别

导入依赖

  <dependency>
      <groupId>org.apache.kafka</groupId>
      <artifactId>kafka-clients</artifactId>
      <version>2.2.0</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/log4j/log4j -->
    <dependency>
      <groupId>log4j</groupId>
      <artifactId>log4j</artifactId>
      <version>1.2.17</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/org.slf4j/slf4j-api -->
    <dependency>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-api</artifactId>
      <version>1.7.25</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/org.slf4j/slf4j-log4j12 -->
    <dependency>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-log4j12</artifactId>
      <version>1.7.25</version>
    </dependency>
    <dependency>
      <groupId>org.apache.commons</groupId>
      <artifactId>commons-lang3</artifactId>
      <version>3.8.1</version>
    </dependency>

事务控制

首先应该进行事务提交级别设置

生产者应该开启事务

//开启事务
properties.put(ProducerConfig.TRANSACTIONAL_ID_CONFIG,"transaction-id"+ UUID.randomUUID());

消费者应该设置提交事务的隔离级别

//设置事务的隔离级别 为已提交
properties.put(ConsumerConfig.ISOLATION_LEVEL_CONFIG,"read_committed");

读取结果

• 生产者成功提交数据 控制台没错 消费者隔离级别是read_committed 读取到五条数据。
  
  生产者逻辑
  消费者控制台五条全部读取
• 生产者提交数据失败 控制台报错 消费者隔离级别是read_committed 只能读取成功提交的数据 所以一条数据也没有读取到。
  

生产者逻辑

生产者控制台报错

消费者没有得到数据

• 生产者提交数据失败 控制台报错 消费者隔离级别是read_uncommitted 可以读取未提交的数据 i是3的时候报错 所以可以读取四个 i是0 1 2 3时候send的数据。
  
  生产者代码逻辑
  
  生产者控制台报错
  
  消费者控制台读取四条数据 到 i=3 截止
  

设置为read_committed时候是生产者已提交的数据才能读取到
设置为read_uncommitted时候可以读取到未提交的数据(报错终止前的数据)

生产者消费者具体代码实现

生产者Only

package com.baizhi.jsy.transaction;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.errors.ProducerFencedException;
import org.apache.kafka.common.serialization.StringSerializer;
import java.util.Properties;
import java.util.UUID;
public class ProductKafkaTransactionnOnly {
    public static void main(String[] args) {
        //创建生产者
        Properties properties = new Properties();
        properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "Centos:9092");
        properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());
        properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());
        //优化参数
        properties.put(ProducerConfig.BATCH_SIZE_CONFIG, 1024 * 1024);//生产者尝试缓存记录，为每一个分区缓存一个mb的数据
        properties.put(ProducerConfig.LINGER_MS_CONFIG, 500);//最多等待0.5秒.
        //开启幂等性 acks必须是-1
        properties.put(ProducerConfig.ACKS_CONFIG,"-1");
        //允许超时最大时间
        properties.put(ProducerConfig.REQUEST_TIMEOUT_MS_CONFIG,5000);
        //失败尝试次数
        properties.put(ProducerConfig.RETRIES_CONFIG,3);
        //开幂等性  精准一次写入
        properties.put(ProducerConfig.ENABLE_IDEMPOTENCE_CONFIG,true);
        //开启事务
        properties.put(ProducerConfig.TRANSACTIONAL_ID_CONFIG,"transaction-id"+ UUID.randomUUID());
        KafkaProducer<String, String> kafkaProducer = new KafkaProducer<String, String>(properties);
        //初始化事务
        kafkaProducer.initTransactions();
        try {
            //开启事务
            kafkaProducer.beginTransaction();
            for (int i=0;i<5;i++){
                ProducerRecord<String, String> record = new ProducerRecord<>(
                        "topic01",
                        "Transaction",
                        "Test committed  Transaction1");
                kafkaProducer.send(record);
                kafkaProducer.flush();
              	if (i==3){            
                      Integer b=i/0;
                }
            }
            //事务提交
            kafkaProducer.commitTransaction();
        } catch (ProducerFencedException e) {
            //终止事务
            kafkaProducer.abortTransaction();
            e.printStackTrace();
        }
        kafkaProducer.close();
    }
}

消费者

package com.baizhi.jsy.transaction;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.serialization.StringDeserializer;
import java.time.Duration;
import java.util.Arrays;
import java.util.Iterator;
import java.util.Properties;
public class ConsumerKafkaReadCommitted {
    public static void main(String[] args) {
        //创建消费者
        Properties properties = new Properties();
        properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,"Centos:9092");
        properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
        properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class.getName());
        properties.put(ConsumerConfig.GROUP_ID_CONFIG,"group01");

        //设置事务的隔离级别   如果事务没有提交  读取不到
        properties.put(ConsumerConfig.ISOLATION_LEVEL_CONFIG,"read_committed");
        KafkaConsumer<String, String> kafkaConsumer = new KafkaConsumer<String, String>(properties);
        kafkaConsumer.subscribe(Arrays.asList("topic01"));
        try {
            while (true){
                //设置间隔多长时间取一次数据
                ConsumerRecords<String, String> consumerRecords = kafkaConsumer.poll(Duration.ofSeconds(1));
                //判断数据是否是空的
                if(!consumerRecords.isEmpty()){
                    Iterator<ConsumerRecord<String, String>> iterator = consumerRecords.iterator();
                    while (iterator.hasNext()){
                        ConsumerRecord<String, String> next = iterator.next();
                        String topic = next.topic();
                        String key = next.key();
                        String value = next.value();
                        long offset = next.offset();
                        int partition = next.partition();
                        long timestamp = next.timestamp();
                        System.out.println("key = " + key+"\t"+"offset = " + offset+"\t"+"value = " + value+"\t"+"partition = " + partition+"\t"+"timestamp = " + timestamp+"\t"+"topic = " + topic);
                    }
                }
            }
        } catch (Exception e) {
            e.printStackTrace();
        }finally {
            kafkaConsumer.close();
        }

    }
}