首先，kafka的topic是由多个partitions物理分隔的。假设topic: testIn,有8个partitions
其次，我们编写的springcloud kafka stream程序，打成jar包后，可以部署多个不同的实例instances，假设部署了3个instance。
那么这3个instance是怎么分配这8个partitions的呢？
在spring.cloud.stream.kafka.bindings.input.consumer.autoRebalanceEnabled=true时(默认)，多个实例会自动均衡
的分配partitions,由ConsumerCoordinator来自动协调，不用您操心了（跟instanceCount 和instanceIndex 没关系了，但是concurrency还是同理的）， 当设置成false后：
主要是通过consumer的4个参数(org.springframework.cloud.stream.binder.ConsumerProperties)来决定的:
spring.cloud.stream.bindings.input.consumer.partitioned=true
准备启动多少个实例
spring.cloud.stream.bindings.input.consumer.instanceCount =3
该实例编号index,从0开始到instanceCount -1
spring.cloud.stream.bindings.input.consumer.instanceIndex =0
每个实例中启动多少个kafka consumer
spring.cloud.stream.bindings.input.consumer.concurrency =1
如果按照上面的配置的话(3个instance,每个instance的并行度1):
instance0将启动一个consumer0,消费p0,p3,p6;
instance1将启动一个consumer0,消费p1,p4,p7;
instance2将启动一个consumer0,消费p2,p5;
主要逻辑在org.springframework.cloud.stream.binder.kafka.KafkaMessageChannelBinder#createConsumerEndpoint这个方法中:
protected MessageProducer createConsumerEndpoint(final ConsumerDestination destination, final String group,
final ExtendedConsumerProperties<KafkaConsumerProperties> extendedConsumerProperties) {
boolean anonymous = !StringUtils.hasText(group);
Assert.isTrue(!anonymous || !extendedConsumerProperties.getExtension().isEnableDlq(),
"DLQ support is not available for anonymous subscriptions");
String consumerGroup = anonymous ? "anonymous." + UUID.randomUUID().toString() : group;
final ConsumerFactory<?, ?> consumerFactory = createKafkaConsumerFactory(anonymous, consumerGroup,
extendedConsumerProperties);
int partitionCount = extendedConsumerProperties.getInstanceCount()
* extendedConsumerProperties.getConcurrency();
//如果AutoRebalanceEnabled=false的话，当instanceCount*Concurrency比实际topic的partitions数量还要多的话，将报错，因为会启动空闲的consumer
Collection<PartitionInfo> allPartitions = provisioningProvider.getPartitionsForTopic(partitionCount,
extendedConsumerProperties.getExtension().isAutoRebalanceEnabled(),
new Callable<Collection<PartitionInfo>>() {
@Override
public Collection<PartitionInfo> call() throws Exception {
Consumer<?, ?> consumer = consumerFactory.createConsumer();
List<PartitionInfo> partitionsFor = consumer.partitionsFor(destination.getName());
consumer.close();
return partitionsFor;
}
});
Collection<PartitionInfo> listenedPartitions;
if (extendedConsumerProperties.getExtension().isAutoRebalanceEnabled() ||
extendedConsumerProperties.getInstanceCount() == 1) {
listenedPartitions = allPartitions;
}
else {
listenedPartitions = new ArrayList<>();
//为该Instance计算应该分派哪些partitions
for (PartitionInfo partition : allPartitions) {
// divide partitions across modules
if ((partition.partition()
% extendedConsumerProperties.getInstanceCount()) == extendedConsumerProperties
.getInstanceIndex()) {
listenedPartitions.add(partition);
}
}
}
this.topicsInUse.put(destination.getName(), new TopicInformation(group, listenedPartitions));
Assert.isTrue(!CollectionUtils.isEmpty(listenedPartitions), "A list of partitions must be provided");
final TopicPartitionInitialOffset[] topicPartitionInitialOffsets = getTopicPartitionInitialOffsets(
listenedPartitions);
final ContainerProperties containerProperties = anonymous
|| extendedConsumerProperties.getExtension().isAutoRebalanceEnabled()
? new ContainerProperties(destination.getName())
: new ContainerProperties(topicPartitionInitialOffsets);
//并行度取consumer参数中设置的Concurrency和实际处理的partitions子集中较小的一个。
int concurrency = Math.min(extendedConsumerProperties.getConcurrency(), listenedPartitions.size());
@SuppressWarnings("rawtypes")
//ConcurrentMessageListenerContainer的doStart方法将根据并行度创建consumer
final ConcurrentMessageListenerContainer<?, ?> messageListenerContainer =
new ConcurrentMessageListenerContainer(
consumerFactory, containerProperties) {
@Override
public void stop(Runnable callback) {
super.stop(callback);
}
};
messageListenerContainer.setConcurrency(concurrency);
if (!extendedConsumerProperties.getExtension().isAutoCommitOffset()) {
messageListenerContainer.getContainerProperties()
.setAckMode(AbstractMessageListenerContainer.AckMode.MANUAL);
messageListenerContainer.getContainerProperties().setAckOnError(false);
}
else {
messageListenerContainer.getContainerProperties()
.setAckOnError(isAutoCommitOnError(extendedConsumerProperties));
}
if (this.logger.isDebugEnabled()) {
this.logger.debug(
"Listened partitions: " + StringUtils.collectionToCommaDelimitedString(listenedPartitions));
}
final KafkaMessageDrivenChannelAdapter<?, ?> kafkaMessageDrivenChannelAdapter = new KafkaMessageDrivenChannelAdapter<>(
messageListenerContainer);
kafkaMessageDrivenChannelAdapter.setBeanFactory(this.getBeanFactory());
ErrorInfrastructure errorInfrastructure = registerErrorInfrastructure(destination, consumerGroup,
extendedConsumerProperties);
if (extendedConsumerProperties.getMaxAttempts() > 1) {
kafkaMessageDrivenChannelAdapter.setRetryTemplate(buildRetryTemplate(extendedConsumerProperties));
kafkaMessageDrivenChannelAdapter.setRecoveryCallback(errorInfrastructure.getRecoverer());
}
else {
kafkaMessageDrivenChannelAdapter.setErrorChannel(errorInfrastructure.getErrorChannel());
}
return kafkaMessageDrivenChannelAdapter;
}org.springframework.kafka.listener.ConcurrentMessageListenerContainer#doStart方法：
@Override
protected void doStart() {
if (!isRunning()) {
ContainerProperties containerProperties = getContainerProperties();
TopicPartitionInitialOffset[] topicPartitions = containerProperties.getTopicPartitions();
if (topicPartitions != null
&& this.concurrency > topicPartitions.length) {
this.logger.warn("When specific partitions are provided, the concurrency must be less than or "
+ "equal to the number of partitions; reduced from " + this.concurrency + " to "
+ topicPartitions.length);
this.concurrency = topicPartitions.length;
}
setRunning(true);
//根据并行度生成N个KafkaMessageListenerContainer，
//每个KafkaMessageListenerContainer的构造函数中会初始化一个KafkaConsumer
for (int i = 0; i < this.concurrency; i++) {
KafkaMessageListenerContainer<K, V> container;
if (topicPartitions == null) {
container = new KafkaMessageListenerContainer<>(this.consumerFactory, containerProperties);
}
else {
//从前面分给Instance的partitions子集中，再指派一些partitions给具体的并行consumer
container = new KafkaMessageListenerContainer<>(this.consumerFactory, containerProperties,
partitionSubset(containerProperties, i));
}
if (getBeanName() != null) {
container.setBeanName(getBeanName() + "-" + i);
}
if (getApplicationEventPublisher() != null) {
container.setApplicationEventPublisher(getApplicationEventPublisher());
}
container.setClientIdSuffix("-" + i);
container.start();
this.containers.add(container);
}
}
}
//具体的二级子集生成策略
private TopicPartitionInitialOffset[] partitionSubset(ContainerProperties containerProperties, int i) {
TopicPartitionInitialOffset[] topicPartitions = containerProperties.getTopicPartitions();
if (this.concurrency == 1) {
return topicPartitions;
}
else {
int numPartitions = topicPartitions.length;
if (numPartitions == this.concurrency) {
return new TopicPartitionInitialOffset[] { topicPartitions[i] };
}
else {
int perContainer = numPartitions / this.concurrency;
TopicPartitionInitialOffset[] subset;
if (i == this.concurrency - 1) {
subset = Arrays.copyOfRange(topicPartitions, i * perContainer, topicPartitions.length);
}
else {
subset = Arrays.copyOfRange(topicPartitions, i * perContainer, (i + 1) * perContainer);
}
return subset;
}
}
}看完以上核心代码，应该就很清楚partitions的分配策略了，下面我们改一下场景，启动2个instance,每个instance的concurrency=3，那么：
instance0将分到[p0,p2,p4,p6]这4个分区，然后会启动3个consumer,consumer0将消费[p0],consumer1消费[p2],consumer2消费[p4,p6]。
instance0将分到[p1,p3,p5,p7]这4个分区，然后会启动3个consumer,consumer0将消费[p1],consumer1消费[p3],consumer2消费[p5,p7]。
可以看到每个instance的consumer2其实会多消费一个分区，不是很均匀，所以应该尽可能能整除，比如启动2个instance，concurrency=4，或者concurrency=2。当concurrency=2时，每个实例的每个consumer将消费两个partition。
看到这里，你应该对这几个参数有了一个比较深刻的理解了，这样在stream程序扩容或者缩容的时候应该能正确的配置参数.
