kafka的消费者分区分配策略

这篇具有很好参考价值的文章主要介绍了kafka的消费者分区分配策略。希望对大家有所帮助。如果存在错误或未考虑完全的地方，请大家不吝赐教，您也可以点击"举报违法"按钮提交疑问。

kafka有三种分区分配策略

1.RoundRobin

2.Range

3.Sticky

一、RoundRobin

RoundRobin策略很简单~假设我们有三个Topic10个Partition，上图！

假设顺序为A-0,A-1,A-2...C-2

kafka的消费者分区分配策略

不难看出轮询策略是将partition当做最小分配单位，将所有topic的partition都看作一个整体。然后为消费者轮询分配partition。当然得到此结果的前提是Consumer Group种的消费者订阅信息是一致的，如果订阅信息不一致，得到的结果也不均匀，下面举个例子：

kafka的消费者分区分配策略

如图，Consumer0订阅Topic-A、B，Consumer1订阅Topic-B、C

顺序注意图中的Seq，先分配TopicA

第一轮 : Consumer-0: Topic-A-Partition0

由于Consumer-1没有订阅Topic-A，所以只能找到Topic-B给Consumer-1分配

于是 Consumer-1: Topic-B-Partition0

------------------------------------------------------------------------------------------------------

第二轮: Consumer-0: Topic-A-Partition0,Topic-A-Partition1

Consumer-1: Topic-B-Partition0,Topic-B-Partition1

------------------------------------------------------------------------------------------------------

第三轮: Consumer-0: Topic-A-Partition0,Topic-A-Partition1，Topic-A-Partition2

Consumer-1: Topic-B-Partition0,Topic-B-Partition1，Topic-B-Partition2

------------------------------------------------------------------------------------------------------

第四、五、六轮：

Consumer-0: Topic-A-Partition0,Topic-A-Partition1，Topic-A-Partition2

Consumer-1: Topic-B-Partition0,Topic-B-Partition1，Topic-B-Partition2,Topic-C-Partition-0,Topic-C-Partition-1,Topic-C-Partition-2

------------------------------------------------------------------------------------------------------

可以看到Consumer-1多消费了3个分区。所以在Consumer Group有订阅消息不一致的情况下，我们最好不要选用RoundRobin。

二、Range(默认的分配策略)

Range策略不同于RoundRobin之处在于Range策略是面向Topic分配的(RoundRobin面向Partition)

，假设顺序为Topic-A,Topic-B,Topic-C，那么分配过程为：

第一轮: Consumer-0: Topic-A-Partition0

Consumer-1:Topic-A-Partition1

Consumer-2:Topic-A-Partition2

------------------------------------------------------------------------------------------------------------------------------

第二轮: Consumer-0: Topic-A-Partition0,Topic-B-Partition0,Topic-B-Partition1

Consumer-1:Topic-A-Partition1,Topic-B-Partition2

Consumer-2:Topic-A-Partition2,Topic-B-Partition3

-------------------------------------------------------------------------------------------------------------------------------

第三轮: Consumer-0: Topic-A-Partition0,Topic-B-Partition0,Topic-B-Partition1,Topic-C-Partition-0

Consumer-1:Topic-A-Partition1,Topic-B-Partition2,Topic-C-Partition-1

Consumer-2:Topic-A-Partition2,Topic-B-Partition3,Topic-C-Partition-2

最终结果如图，很明显我们可以看到它的特点是以topic为主进行划分的，假设消费者数量为N，主题分区数量为M，则有当前主题分配数量 = M%N==0? M/N +1 : M/N ;

kafka的消费者分区分配策略

Range策略的缺点在于如果Topic足够多、且分区数量不能被平均分配时，会出现消费过载的情景，举一个例子

kafka的消费者分区分配策略

可以看到此种情况已经相差3个分区，如果主题进一步扩大差距会愈发明显。

--------------------------------------------------------------------------------------------------------------------

三、Sticky

kafka在0.11版本引入了Sticky分区分配策略，它的两个主要目的是:

1.分区的分配要尽可能的均匀，分配给消费者者的主题分区数最多相差一个；

2.分区的分配尽可能的与上次分配的保持相同。

当两者发生冲突时，第一个目标优先于第二个目标。以RoundRobin的不均衡为例， kafka的消费者分区分配策略

此时的结果明显非常不均衡，如果使用Sticky策略的话结果应该是如此：

kafka的消费者分区分配策略

在这里我给出实际测试结果参考，稍后会将代码贴出供读者自行测试。

Sticky：

consumer-0:

topic :Topic-A;;;partition:2
topic :Topic-A;;;partition:1
topic :Topic-B;;;partition:2
topic :Topic-A;;;partition:0
topic :Topic-B;;;partition:0

consumer-1:

topic :Topic-B;;;partition:1
topic :Topic-C;;;partition:1
topic :Topic-C;;;partition:0
topic :Topic-C;;;partition:2

笔者描述的逻辑仅供参考，是在仅笔者描述的情况下才出现的结果，实际结果以读者自行测试为准。

1.导入kafka依赖

 <dependency>
       <groupId>org.apache.kafka</groupId>
       <artifactId>kafka_2.12</artifactId>
       <version>1.1.1</version>
 </dependency>

2.启动zookeeper，启动kafka；(windows版本)

3.命令行切换到kafka目录下；执行以下命令创建Topic & partition

kafka-topics.bat --create --zookeeper 127.0.0.1:2181 --replication-factor 2 --partitions 3 --topic Topic-A

kafka-topics.bat --create --zookeeper 127.0.0.1:2181 --replication-factor 2 --partitions 3 --topic Topic-B

kafka-topics.bat --create --zookeeper 127.0.0.1:2181 --replication-factor 2 --partitions 3 --topic Topic-C

测试代码：

consumer-0:

  public static void main(String[] args) {
        Properties properties = new Properties();
        properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "127.0.0.1:9092,127.0.0.1:9092,127.0.0.1:9092");
        properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        properties.put(ConsumerConfig.GROUP_ID_CONFIG, "group-0");
        //设置分配策略
        properties.put(ConsumerConfig.PARTITION_ASSIGNMENT_STRATEGY_CONFIG,RangeAssignor.class.getName());

        KafkaConsumer<String, String> consumer = new KafkaConsumer<>(properties);

        consumer.subscribe(Arrays.asList("Topic-A","Topic-B"), new ConsumerRebalanceListener() {

            @Override
            public void onPartitionsRevoked(Collection<TopicPartition> collection) {
                System.err.println("onPartitionsRevoked=========================================== ");
                for (TopicPartition pt: collection
                     ) {
                    System.err.println("topic :"+pt.topic() + ";;;partition:"+pt.partition());
                }
            }

            @Override
            public void onPartitionsAssigned(Collection<TopicPartition> collection) {
                System.err.println("onPartitionsAssigned=========================================== ");
                for (TopicPartition pt: collection
                ) {
                    System.err.println("topic :"+pt.topic() + ";;;partition:"+pt.partition());
                }
            }

            @Override
            public void onPartitionsLost(Collection<TopicPartition> partitions) {
                ConsumerRebalanceListener.super.onPartitionsLost(partitions);
            }
        });

        while (true) {
            ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
            for (ConsumerRecord<String, String> record : records) {

                System.out.printf("offset = %d, key = %s, value = %s%n", record.offset(), record.key(), record.value());
                TopicPartition topicPartition = new TopicPartition(record.topic(), record.partition());
            }
        }
    }

consumer-1

  public static void main(String[] args) {
        Properties properties = new Properties();
        properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "127.0.0.1:9092,127.0.0.1:9092,127.0.0.1:9092");
        properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        properties.put(ConsumerConfig.GROUP_ID_CONFIG, "group-0");
        //设置分配策略
        properties.put(ConsumerConfig.PARTITION_ASSIGNMENT_STRATEGY_CONFIG,RangeAssignor.class.getName());

        KafkaConsumer<String, String> consumer = new KafkaConsumer<>(properties);

        consumer.subscribe(Arrays.asList("Topic-B","Topic-C"), new ConsumerRebalanceListener() {

            @Override
            public void onPartitionsRevoked(Collection<TopicPartition> collection) {
                System.err.println("onPartitionsRevoked=========================================== ");
                for (TopicPartition pt: collection
                     ) {
                    System.err.println("topic :"+pt.topic() + ";;;partition:"+pt.partition());
                }
            }

            @Override
            public void onPartitionsAssigned(Collection<TopicPartition> collection) {
                System.err.println("onPartitionsAssigned=========================================== ");
                for (TopicPartition pt: collection
                ) {
                    System.err.println("topic :"+pt.topic() + ";;;partition:"+pt.partition());
                }
            }

            @Override
            public void onPartitionsLost(Collection<TopicPartition> partitions) {
                ConsumerRebalanceListener.super.onPartitionsLost(partitions);
            }
        });

        while (true) {
            ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
            for (ConsumerRecord<String, String> record : records) {

                System.out.printf("offset = %d, key = %s, value = %s%n", record.offset(), record.key(), record.value());
                TopicPartition topicPartition = new TopicPartition(record.topic(), record.partition());
            }
        }
    }

关于kafka的消费者分区分配策略就聊到这里，感谢大家，如果错误描述请指正，下章我们讨论kafka消费者重复消以及漏消费的问题

Kafka消费者重复消费数据以及漏消费问题_一念花开_的博客-CSDN博客文章来源地址https://www.toymoban.com/news/detail-456928.html

到了这里，关于kafka的消费者分区分配策略的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！