一、前提
kafka的版本是 2.6.2
一般我们消费kafka的时候是指定消费组，是不会指定消费组内部消费kafka各个分区的分配策略，但是我们也可以指定消费策略，通过源码发现，我们可以有三种分区策略：
- RangeAssignor (默认）
- RoundRobinAssignor
- StickyAssignor
指定消费分区策略
props.put("partition.assignment.strategy", "org.apache.kafka.clients.consumer.RoundRobinAssignor");
kafka消费分区策略的分区入口类是：ConsumerCoordinator的performAssignment方法
@Override
protected Map<String, ByteBuffer> performAssignment(String leaderId,
String assignmentStrategy,
List<JoinGroupResponseData.JoinGroupResponseMember> allSubscriptions) {
//获取分区策略
ConsumerPartitionAssignor assignor = lookupAssignor(assignmentStrategy);
//存储消费组订阅的所有topic
Set<String> allSubscribedTopics = new HashSet<>();
//存储消费组内各个消费者对应的基本信息（比如元数据）
Map<String, Subscription> subscriptions = new HashMap<>();
Map<String, List<TopicPartition>> ownedPartitions = new HashMap<>();
for (JoinGroupResponseData.JoinGroupResponseMember memberSubscription : allSubscriptions) {
Subscription subscription = ConsumerProtocol.deserializeSubscription(ByteBuffer.wrap(memberSubscription.metadata()));
subscription.setGroupInstanceId(Optional.ofNullable(memberSubscription.groupInstanceId()));
subscriptions.put(memberSubscription.memberId(), subscription);
allSubscribedTopics.addAll(subscription.topics());
ownedPartitions.put(memberSubscription.memberId(), subscription.ownedPartitions());
}
//具体实现在类 AbstractPartitionAssignor （各个分区算法的抽象类）
Map<String, Assignment> assignments = assignor.assign(metadata.fetch(), new GroupSubscription(subscriptions)).groupAssignment();
...
log.info("Finished assignment for group at generation {}: {}", generation().generationId, assignments);
...
return groupAssignment;
}
AbstractPartitionAssignor 的 assign()
//各个分区策略具体的算法
public abstract Map<String, List<TopicPartition>> assign(Map<String, Integer> partitionsPerTopic,
Map<String, Subscription> subscriptions);
@Override
public GroupAssignment assign(Cluster metadata, GroupSubscription groupSubscription) {
Map<String, Subscription> subscriptions = groupSubscription.groupSubscription();
Set<String> allSubscribedTopics = new HashSet<>();
for (Map.Entry<String, Subscription> subscriptionEntry : subscriptions.entrySet())
allSubscribedTopics.addAll(subscriptionEntry.getValue().topics());
Map<String, Integer> partitionsPerTopic = new HashMap<>();
for (String topic : allSubscribedTopics) {
Integer numPartitions = metadata.partitionCountForTopic(topic);
if (numPartitions != null && numPartitions > 0)
partitionsPerTopic.put(topic, numPartitions);
else
log.debug("Skipping assignment for topic {} since no metadata is available", topic);
}
/构建参数 partitionsPerTopic：map，表示各个topic有多少个分区
//subscriptions ：map，表示消费者相关信息（消费者id，消费者对应的主题）
Map<String, List<TopicPartition>> rawAssignments = assign(partitionsPerTopic, subscriptions);
// this class maintains no user data, so just wrap the results
Map<String, Assignment> assignments = new HashMap<>();
for (Map.Entry<String, List<TopicPartition>> assignmentEntry : rawAssignments.entrySet())
assignments.put(assignmentEntry.getKey(), new Assignment(assignmentEntry.getValue()));
return new GroupAssignment(assignments);
}
下面说明下RangeAssignor与RoundRobinAssignor两种分区策略的区别
二、RangeAssignor 分区策略
RangeAssignor是默认分配的策略
public class RangeAssignor extends AbstractPartitionAssignor {
@Override
public String name() {
return "range";
}
private Map<String, List<MemberInfo>> consumersPerTopic(Map<String, Subscription> consumerMetadata) {
Map<String, List<MemberInfo>> topicToConsumers = new HashMap<>();
for (Map.Entry<String, Subscription> subscriptionEntry : consumerMetadata.entrySet()) {
String consumerId = subscriptionEntry.getKey();
MemberInfo memberInfo = new MemberInfo(consumerId, subscriptionEntry.getValue().groupInstanceId());
for (String topic : subscriptionEntry.getValue().topics()) {
put(topicToConsumers, topic, memberInfo);
}
}
return topicToConsumers;
}
@Override
public Map<String, List<TopicPartition>> assign(Map<String, Integer> partitionsPerTopic,
Map<String, Subscription> subscriptions) {
//获取主题对应的消费者列表
//partitionsPerTopic 主题对应分区个数
//subscriptions 消费者的信息（消费者id，消费者对应的主题，消费者实例）
Map<String, List<MemberInfo>> consumersPerTopic = consumersPerTopic(subscriptions);
//打印输出，可以看到消费组group-one有两个消费者 consumer-group-one-1-504e90bc-c1cc-45d5-a687-5e4f98ee48c3 和 consumer-group-one-1-11580834-fc23-468e-ae11-edbc3c4a74bd
//其中： consumer-group-one-1-11580834-fc23-468e-ae11-edbc3c4a74bd 消费了 test_topic_partition_one 和 test_topic_partition_two
// consumer-group-one-1-504e90bc-c1cc-45d5-a687-5e4f98ee48c3 只消费了 test_topic_partition_one
// consumersPerTopic: {test_topic_partition_one=[MemberInfo [member.id: consumer-group-one-1-504e90bc-c1cc-45d5-a687-5e4f98ee48c3, group.instance.id: {}], MemberInfo [member.id: consumer-group-one-1-11580834-fc23-468e-ae11-edbc3c4a74bd, group.instance.id: {}]], test_topic_partition_two=[MemberInfo [member.id: consumer-group-one-1-11580834-fc23-468e-ae11-edbc3c4a74bd, group.instance.id: {}]]}
Map<String, List<TopicPartition>> assignment = new HashMap<>();
for (String memberId : subscriptions.keySet())
assignment.put(memberId, new ArrayList<>());
for (Map.Entry<String, List<MemberInfo>> topicEntry : consumersPerTopic.entrySet()) {
//获取topic
String topic = topicEntry.getKey();
//获取topic对应的消费者
List<MemberInfo> consumersForTopic = topicEntry.getValue();
//获取topic的分区数
Integer numPartitionsForTopic = partitionsPerTopic.get(topic);
if (numPartitionsForTopic == null)
continue;
Collections.sort(consumersForTopic);
//计算每个消费者至少消费几个分区
int numPartitionsPerConsumer = numPartitionsForTopic / consumersForTopic.size();
//计算剩余几个分区
int consumersWithExtraPartition = numPartitionsForTopic % consumersForTopic.size();
//获取主题分区列表
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);
//可以看到前面的消费者会多分配一个分区
int length = numPartitionsPerConsumer + (i + 1 > consumersWithExtraPartition ? 0 : 1);
//计算每个消费者对应的分区列表，可以看到前面的消费者会多分配一个分区
assignment.get(consumersForTopic.get(i).memberId).addAll(partitions.subList(start, start + length));
}
}
return assignment;
}
}
举例说明：构建消费组下两个消费者， test_topic_partition_one和test_topic_partition_two都是9个分区
进程一：
props.put("group.id", "group-one");
props.put("auto.offset.reset", "latest");
KafkaConsumer<String, byte[]> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Arrays.asList("test_topic_partition_one", "test_topic_partition_two"));
进程二：
props.put("group.id", "group-one");
props.put("auto.offset.reset", "latest");
KafkaConsumer<String, byte[]> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Arrays.asList("test_topic_partition_one"));
通过上面的分配算法可以得到：
消费者：consumer-group-one-1-11580834-fc23-468e-ae11-edbc3c4a74bd消费的分区为：
test_topic_partition_one-0,
test_topic_partition_one-1,
test_topic_partition_one-2,
test_topic_partition_one-3,
test_topic_partition_one-4,
test_topic_partition_two-0,
test_topic_partition_two-1,
test_topic_partition_two-2,
test_topic_partition_two-3,
test_topic_partition_two-4,
test_topic_partition_two-5,
test_topic_partition_two-6,
test_topic_partition_two-7,
test_topic_partition_two-8
消费者：consumer-group-one-1-504e90bc-c1cc-45d5-a687-5e4f98ee48c3消费的分区为：
test_topic_partition_one-5,
test_topic_partition_one-6,
test_topic_partition_one-7,
test_topic_partition_one-8
如果进程二也消费两个主题，则对应的关系变成
通过上面的分配算法可以得到：
消费者：consumer-group-one-1-11580834-fc23-468e-ae11-edbc3c4a74bd消费的分区为：
test_topic_partition_one-0,
test_topic_partition_one-1,
test_topic_partition_one-2,
test_topic_partition_one-3,
test_topic_partition_one-4,
test_topic_partition_two-0,
test_topic_partition_two-1,
test_topic_partition_two-2,
test_topic_partition_two-3,
test_topic_partition_two-4,
消费者：consumer-group-one-1-504e90bc-c1cc-45d5-a687-5e4f98ee48c3消费的分区为：
test_topic_partition_one-5,
test_topic_partition_one-6,
test_topic_partition_one-7,
test_topic_partition_one-8,
test_topic_partition_two-5,
test_topic_partition_two-6,
test_topic_partition_two-7,
test_topic_partition_two-8
可以看到第一个消费者比第二个消费者多消费一个test_topic_partition_one的分区，而且是连续的。同时可以看到分类是按照topic粒度区分的，也就是每个消费者消费一个topic的分区与其他topic是无关的。可以会导致第一个实例运行压力较大的问题。
三、RoundRobinAssignor 分区策略
public class RoundRobinAssignor extends AbstractPartitionAssignor {
@Override
public Map<String, List<TopicPartition>> assign(Map<String, Integer> partitionsPerTopic,
Map<String, Subscription> subscriptions) {
Map<String, List<TopicPartition>> assignment = new HashMap<>();
//存储消费组下所有的消费者，构建两个消费者
// 其中一个：consumer-group-one-1-6c946240-3ffc-4bba-806d-7d7a0ccc1ad5
// 另一个：consumer-group-one-1-d227d230-8adc-4d4e-a092-77b63c07855a
List<MemberInfo> memberInfoList = new ArrayList<>();
for (Map.Entry<String, Subscription> memberSubscription : subscriptions.entrySet()) {
assignment.put(memberSubscription.getKey(), new ArrayList<>());
memberInfoList.add(new MemberInfo(memberSubscription.getKey(),
memberSubscription.getValue().groupInstanceId()));
}
//排序后的消费者
CircularIterator<MemberInfo> assigner = new CircularIterator<>(Utils.sorted(memberInfoList));
for (TopicPartition partition : allPartitionsSorted(partitionsPerTopic, subscriptions)) {
final String topic = partition.topic();
//轮询指定消费者的分区
while (!subscriptions.get(assigner.peek().memberId).topics().contains(topic)) {
assigner.next();
}
assignment.get(assigner.next().memberId).add(partition);
}
return assignment;
}
//获取排序后的所有主题分区
private List<TopicPartition> allPartitionsSorted(Map<String, Integer> partitionsPerTopic,
Map<String, Subscription> subscriptions) {
SortedSet<String> topics = new TreeSet<>();
for (Subscription subscription : subscriptions.values())
topics.addAll(subscription.topics());
List<TopicPartition> allPartitions = new ArrayList<>();
for (String topic : topics) {
Integer numPartitionsForTopic = partitionsPerTopic.get(topic);
if (numPartitionsForTopic != null)
allPartitions.addAll(AbstractPartitionAssignor.partitions(topic, numPartitionsForTopic));
}
return allPartitions;
}
@Override
public String name() {
return "roundrobin";
}
}
举例说明：构建消费组下两个消费者， test_topic_partition_one和test_topic_partition_two都是9个分区
进程一：
props.put("group.id", "group-one");
props.put("auto.offset.reset", "latest");
//指定轮询策略
props.put("partition.assignment.strategy", "org.apache.kafka.clients.consumer.RoundRobinAssignor");
KafkaConsumer<String, byte[]> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Arrays.asList("test_topic_partition_one", "test_topic_partition_two"));
props.put("group.id", "group-one");
props.put("auto.offset.reset", "latest");
//指定轮询策略
props.put("partition.assignment.strategy", "org.apache.kafka.clients.consumer.RoundRobinAssignor");
KafkaConsumer<String, byte[]> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Arrays.asList("test_topic_partition_one"));
通过上面的分配算法可以得到：
消费者：consumer-group-one-1-6c946240-3ffc-4bba-806d-7d7a0ccc1ad5消费的分区为：
test_topic_partition_one-0,
test_topic_partition_one-2,
test_topic_partition_one-4,
test_topic_partition_one-6,
test_topic_partition_one-8,
test_topic_partition_two-0,
test_topic_partition_two-1,
test_topic_partition_two-2,
test_topic_partition_two-3,
test_topic_partition_two-4,
test_topic_partition_two-5,
test_topic_partition_two-6,
test_topic_partition_two-7,
test_topic_partition_two-8
消费者：consumer-group-one-1-504e90bc-c1cc-45d5-a687-5e4f98ee48c3消费的分区为：
test_topic_partition_one-1,
test_topic_partition_one-3,
test_topic_partition_one-5,
test_topic_partition_one-7
可以看到test_topic_partition_one分区是轮流的分配给两个消费者的
对应的日志
2024-08-19 14:28:34 INFO [org.apache.kafka.clients.consumer.internals.ConsumerCoordinator Line:626] [Consumer clientId=consumer-group-one-1, groupId=group-one] Finished assignment for group at generation 44: {consumer-group-one-1-6c946240-3ffc-4bba-806d-7d7a0ccc1ad5=Assignment(partitions=[test_topic_partition_one-0, test_topic_partition_one-2, test_topic_partition_one-4, test_topic_partition_one-6, test_topic_partition_one-8, test_topic_partition_two-0, test_topic_partition_two-1, test_topic_partition_two-2, test_topic_partition_two-3, test_topic_partition_two-4, test_topic_partition_two-5, test_topic_partition_two-6, test_topic_partition_two-7, test_topic_partition_two-8]), consumer-group-one-1-d227d230-8adc-4d4e-a092-77b63c07855a=Assignment(partitions=[test_topic_partition_one-1, test_topic_partition_one-3, test_topic_partition_one-5, test_topic_partition_one-7])}
如果进程二也消费两个主题，则对应的关系变成
消费者：consumer-group-one-1-6c946240-3ffc-4bba-806d-7d7a0ccc1ad5消费的分区为：
test_topic_partition_one-0,
test_topic_partition_one-2,
test_topic_partition_one-4,
test_topic_partition_one-6,
test_topic_partition_one-8,
test_topic_partition_two-1,
test_topic_partition_two-3,
test_topic_partition_two-5,
test_topic_partition_two-7
消费者：consumer-group-one-1-504e90bc-c1cc-45d5-a687-5e4f98ee48c3消费的分区为：
test_topic_partition_one-1,
test_topic_partition_one-3,
test_topic_partition_one-5,
test_topic_partition_one-7
test_topic_partition_two-0,
test_topic_partition_two-2,
test_topic_partition_two-4,
test_topic_partition_two-6,
test_topic_partition_two-8
也就是会把所有的分区轮流分给两个消费者，所以这种模式就和主题个数与主题分区有关了。
最后
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