Kafka 消費組消費者分配策略

時間 2021-01-01

標籤 javascript php css html java python nginx web apache django 欄目 Kafka 简体版

原文原文鏈接

微信公衆號：蘇言論
理論聯繫實際，暢言技術與生活。javascript

消費組和消費者是kafka中比較重要的概念，理解和掌握原理有利於優化kafka性能和處理消費積壓問題。Kafka topic 由多個分區組成，分區分佈在集羣節點上；php

Topic:topic01  PartitionCount:10       ReplicationFactor:2     Configs:
        Topic: topic01 Partition: 0    Leader: 1       Replicas: 1,4   Isr: 1,4
        Topic: topic01 Partition: 1    Leader: 2       Replicas: 2,5   Isr: 2,5
        Topic: topic01 Partition: 2    Leader: 3       Replicas: 3,1   Isr: 3,1
        Topic: topic01 Partition: 3    Leader: 4       Replicas: 4,2   Isr: 4,2
        Topic: topic01 Partition: 4    Leader: 5       Replicas: 5,3   Isr: 5,3
        Topic: topic01 Partition: 5    Leader: 1       Replicas: 1,3   Isr: 1,3
        Topic: topic01 Partition: 6    Leader: 2       Replicas: 2,4   Isr: 2,4
        Topic: topic01 Partition: 7    Leader: 3       Replicas: 3,5   Isr: 3,5
        Topic: topic01 Partition: 8    Leader: 4       Replicas: 4,1   Isr: 4,1
        Topic: topic01 Partition: 9    Leader: 5       Replicas: 5,2   Isr: 5,2

當外部程序消費topic數據時，kafka將其視爲消費組(ConsumerGroup)，每一個消費組包含1個或多個消費者(Consumer)，消費者數量最多能夠爲分區總數量，並非能夠無限量。當消費組中的任意一個消費者終止時，kafka會對消費組進行平衡(Rebalance)，再根據存活消費數和消費者分配策略從新分配消費者。在0.10.x版本中，kafka提供兩種分配策略(RangeAssignor、RoundRobinAssignor)，0.11.x 版本新增策略(StickyAssignor)，結構以下；css

1 RangeAssignor 策略

RangeAssignor 以主題爲單位，以數據順序排列可用分區，以字典順序排列消費者，將topic分區數除以消費者總數，以肯定分配給每一個消費者的分區數；若是沒有平均分配，那麼前幾個消費者將擁有一個額外的分區。實現代碼；html

for (String memberId : subscriptions.keySet())
            assignment.put(memberId, new ArrayList<TopicPartition>());

        for (Map.Entry<String, List<String>> topicEntry : consumersPerTopic.entrySet()) {
            String topic = topicEntry.getKey();
            List<String> consumersForTopic = topicEntry.getValue();

            Integer numPartitionsForTopic = partitionsPerTopic.get(topic);
            if (numPartitionsForTopic == null)
                continue;

            Collections.sort(consumersForTopic);

            int numPartitionsPerConsumer = numPartitionsForTopic / consumersForTopic.size();  //topic分區數除以消費者總數
            int consumersWithExtraPartition = numPartitionsForTopic % consumersForTopic.size();  //計算額外分區

            List<TopicPartition> partitions = AbstractPartitionAssignor.partitions(topic, numPartitionsForTopic);
            for (int i = 0, n = consumersForTopic.size(); i < n; i++) {
                int start = numPartitionsPerConsumer * i + Math.min(i, consumersWithExtraPartition);
                int length = numPartitionsPerConsumer + (i + 1 > consumersWithExtraPartition ? 0 : 1);
                assignment.get(consumersForTopic.get(i)).addAll(partitions.subList(start, start + length));
            }
        }

好比有兩個topic(topic1 ,topic2) ，每一個topic都有三個分區；java

topic1 ,分區：topic1p0,topic1p1,topic1p2
topic2 ,分區：topic2p0,topic2p1,topic2p2

和一個消費組(consumer_group1)，有(consumer1,consumer2)兩個消費者，使用RangeAssignor策略可能會獲得以下的分配：python

consumer1: [topic1p0,topic1p1,topic2p0,topic2p1]
consumer2: [topic1p2,topic2p2]

若是此時消費組(consumer_group1)有新的消費者consumer3加入，使用RangeAssignor策略可能會獲得以下的分配：nginx

consumer1: [topic1p0,topic2p0]
consumer2: [topic1p2,topic2p2]
consumer3: [topic1p1,topic2p1]

2 RoundRobinAssignor 策略

RoundRobinAssignor 是kafka默認策略，對全部分區和全部消費者循環分配，分區更均衡；實現代碼；web

Map<String, List<TopicPartition>> assignment = new HashMap<>();
        for (String memberId : subscriptions.keySet())
            assignment.put(memberId, new ArrayList<TopicPartition>());

        CircularIterator<String> assigner = new CircularIterator<>(Utils.sorted(subscriptions.keySet()));
        for (TopicPartition partition : allPartitionsSorted(partitionsPerTopic, subscriptions)) {
            final String topic = partition.topic();
            while (!subscriptions.get(assigner.peek()).topics().contains(topic))
                assigner.next();
            assignment.get(assigner.next()).add(partition);
        }

繼續以上例topic和消費組爲例，RoundRobinAssignor 策略可能會獲得以下的分配；apache

consumer1: [topic1p0,topic1p1,topic2p2,]
consumer2: [topic2p0,topic2p1,topic1p2]

3 StickyAssignor 策略

StickyAssignor 策略是最複雜且是0.11.x 版本出現的新策略，該策略主要做用：django

使topic分區分配儘量均勻的分配給消費者
當某個消費者終止觸發從新分配時，儘量保留現有分配，將已經終止的消費者所分配的分區移動到另外一個消費者，避免所有分區從新平衡，節省開銷。

這個策略自0.11.x 版本出現後，一直到新版本有不一樣bug被發現，低版本慎用。

4 java多線程消費實例

public class KafkaTopicConsumer {   
    private KafkaConsumer<String, String> consumer;
    private int consumerId=0; //消費實例id
    private final long timeOut=10000;
    public KafkaTopicConsumer(int consumerId){
        this.consumerId=consumerId;
        Properties props = new Properties();
        props.put("client.id", "client-" + consumerId);
        props.put("bootstrap.servers","192.168.1.10:9092,192.168.1.11:9092");
        props.put("group.id", "test-group03");
        props.put("zookeeper.session.timeout.ms", "4000");
        props.put("zookeeper.sync.time.ms", "200");
        props.put("enable.auto.commit", "false");
        props.put("auto.offset.reset", "earliest");
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        //設置分區策略
        props.put("partition.assignment.strategy", "org.apache.kafka.clients.consumer.RangeAssignor");
        consumer = new KafkaConsumer<String, String>(props);
        consumer.subscribe(Arrays.asList("topic1","topic2"));
    }

    public void consume() {
        while (true){
            ConsumerRecords<String, String> records=consumer.poll(timeOut);
            System.out.println("records count:"+records.count());
            for (ConsumerRecord<String, String> record : records) {
              System.out.println(String.format("client-id = %d , topic = %s, partition = %d , offset = %d, key = %s, value = %s", this.consumerId,record.topic(), record.partition(), record.offset(), record.key(), record.value()));
            }
            consumer.commitSync();
        }
    }

    public static void main(String[] args) {
        int threadSize=Integer.parseInt(args[0]);
        for (int i = 0; i < threadSize; i++) {
            int id = i;
            new Thread() {
                @Override
                public void run() {
                    new KafkaTopicConsumer(id).consume();
                }
            }.start();
        }
    }//
}

啓動三個多線程實例消費，分區分配到每一個消費者的狀況；

GROUP                          TOPIC                          PARTITION  CURRENT-OFFSET  LOG-END-OFFSET  LAG             OWNER
test-group03                   topic2                0          0               3333            3333            client-0_/192.168.1.13
test-group03                   topic1                0          500             3333            2833            client-0_/192.168.1.13
test-group03                   topic2                2          0               3333            3333            client-2_/192.168.1.13
test-group03                   topic1                2          500             3333            2833            client-2_/192.168.1.13
test-group03                   topic2                1          500             3334            2834            client-1_/192.168.1.13
test-group03                   topic1                1          0               3334            3334            client-1_/192.168.1.13

對於大的topic，將topic單獨消費以免數據積壓和topic各自影響數據處理速度，好比文章開始時提到的10分區的topic(topic01)，根據硬件資源和分區策略設置合理的消費者，數據量大時最優的消費者數量爲分區總數。

GROUP                          TOPIC                          PARTITION  CURRENT-OFFSET  LOG-END-OFFSET  LAG             OWNER
test-group02                   topic01                       6          373460          1026328         652868          client-6_/192.168.1.13
test-group02                   topic01                       2          375660          1048756         673096          client-2_/192.168.1.13
test-group02                   topic01                       5          374625          1013157         638532          client-5_/192.168.1.13
test-group02                   topic01                       3          347001          1066967         719966          client-3_/192.168.1.13
test-group02                   topic01                       0          375570          1013261         637691          client-0_/192.168.1.13
test-group02                   topic01                       9          376545          1094088         717543          client-9_/192.168.1.13
test-group02                   topic01                       8          347082          1066948         719866          client-8_/192.168.1.13
test-group02                   topic01                       7          375100          1048827         673727          client-7_/192.168.1.13
test-group02                   topic01                       1          372447          1026467         654020          client-1_/192.168.1.13
test-group02                   topic01                       4          377052          1093926         716874          client-4_/192.168.1.13

5 總結

Kafka提供三種分配策略(RangeAssignor、RoundRobinAssignor、StickyAssignor)，其中StickyAssignor策略是0.11.x 版本新增的，每種策略不盡相同，RangeAssignor策略以主題爲單位，以數據順序排列可用分區，以字典順序排列消費者計算分配；RoundRobinAssignor 對全部分區和全部消費者循環均勻分配；但這兩種分配策略當有消費者終止或加入時均會觸發消費組平衡；StickyAssignor 策略當某個消費者終止時，儘量保留現有分配，將已經終止的消費者所分配的分區移動到另外一個消費者，避免所有分區從新平衡，節省開銷；對於topic分區數較多、數量較大使用StickyAssignor策略有較大優點。