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首先，我们来看看消费。Kafka提供了非常简单的消费API，使用者只需初始化Kafka的Broker Server地址，然后实例化KafkaConsumer类即可拿到Topic中的数据。一个简单的Kafka消费实例代码如下所示：
public class JConsumerSubscribe extends Thread {
public static void main(String[] args) { JConsumerSubscribe jconsumer = new JConsumerSubscribe(); jconsumer.start(); } /** 初始化Kafka集群信息. */ private Properties configure() { Properties props = new Properties(); props.put("bootstrap.servers", "dn1:9092,dn2:9092,dn3:9092");// 指定Kafka集群地址
props.put("group.id", "ke");// 指定消费者组
props.put("enable.auto.commit", "true");// 开启自动提交
props.put("auto.commit.interval.ms", "1000");// 自动提交的时间间隔
// 反序列化消息主键 props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
// 反序列化消费记录 props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
return props;
} /** 实现一个单线程消费者. */ @Override public void run() { // 创建一个消费者实例对象 KafkaConsumer<String, String> consumer = new KafkaConsumer<>(configure()); // 订阅消费主题集合 consumer.subscribe(Arrays.asList("test_kafka_topic"));
// 实时消费标识 boolean flag = true;
while (flag) {
// 获取主题消息数据 ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
for (ConsumerRecord<String, String> record : records)
// 循环打印消息记录 System.out.printf("offset = %d, key = %s, value = %s%n", record.offset(), record.key(), record.value());
} // 出现异常关闭消费者对象 consumer.close();
}}
上述代码我们就可以非常便捷地拿到Topic中的数据。但是，当我们调用poll方法拉取数据的时候，Kafka Broker Server做了那些事情。接下来，我们可以去看看源代码的实现细节。核心代码如下：
org.apache.kafka.clients.consumer.KafkaConsumer
private ConsumerRecords<K, V> poll(final long timeoutMs, final boolean includeMetadataInTimeout) {
acquireAndEnsureOpen(); try {
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if (timeoutMs < 0) throw new IllegalArgumentException("Timeout must not be negative");
if (this.subscriptions.hasNoSubscriptionOrUserAssignment()) {
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throw new IllegalStateException("Consumer is not subscribed to any topics or assigned any partitions");
} // poll for new data until the timeout expires
long elapsedTime = 0L;
do {
client.maybeTriggerWakeup(); final long metadataEnd; if (includeMetadataInTimeout) {
final long metadataStart = time.milliseconds(); if (!updateAssignmentMetadataIfNeeded(remainingTimeAtLeastZero(timeoutMs, elapsedTime))) {
return ConsumerRecords.empty();
} metadataEnd = time.milliseconds(); elapsedTime += metadataEnd – metadataStart; } else {
while (!updateAssignmentMetadataIfNeeded(Long.MAX_VALUE)) {
log.warn("Still waiting for metadata");
} metadataEnd = time.milliseconds(); } final Map<TopicPartition, List<ConsumerRecord<K, V>>> records = pollForFetches(remainingTimeAtLeastZero(timeoutMs, elapsedTime)); if (!records.isEmpty()) {
// before returning the fetched records, we can send off the next round of fetches
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// and avoid block waiting for their responses to enable pipelining while the user
// is handling the fetched records.
//
// NOTE: since the consumed position has already been updated, we must not allow
// wakeups or any other errors to be triggered prior to returning the fetched records.
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if (fetcher.sendFetches() > 0 || client.hasPendingRequests()) {
client.pollNoWakeup(); } return this.interceptors.onConsume(new ConsumerRecords<>(records));
} final long fetchEnd = time.milliseconds(); elapsedTime += fetchEnd – metadataEnd; } while (elapsedTime < timeoutMs);
return ConsumerRecords.empty();
} finally {
release(); } }
上述代码中有个方法pollForFetches，它的实现逻辑如下：
private Map<TopicPartition, List<ConsumerRecord<K, V>>> pollForFetches(final long timeoutMs) {
final long startMs = time.milliseconds();
long pollTimeout = Math.min(coordinator.timeToNextPoll(startMs), timeoutMs);
// if data is available already, return it immediately
final Map<TopicPartition, List<ConsumerRecord<K, V>>> records = fetcher.fetchedRecords();
if (!records.isEmpty()) {
return records;
}
// send any new fetches (won't resend pending fetches)
fetcher.sendFetches();
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// We do not want to be stuck blocking in poll if we are missing some positions
// since the offset lookup may be backing off after a failure
// NOTE: the use of cachedSubscriptionHashAllFetchPositions means we MUST call
// updateAssignmentMetadataIfNeeded before this method.
if (!cachedSubscriptionHashAllFetchPositions && pollTimeout > retryBackoffMs) {
pollTimeout = retryBackoffMs;
}
client.poll(pollTimeout, startMs, () -> {
// since a fetch might be completed by the background thread, we need this poll condition
// to ensure that we do not block unnecessarily in poll()
return !fetcher.hasCompletedFetches();
});
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// after the long poll, we should check whether the group needs to rebalance
// prior to returning data so that the group can stabilize faster
if (coordinator.rejoinNeededOrPending()) {
return Collections.emptyMap();
}
return fetcher.fetchedRecords();
}
上述代码中加粗的位置，我们可以看出每次消费者客户端拉取数据时，通过poll方法，先调用fetcher中的fetchedRecords函数，如果获取不到数据，就会发起一个新的sendFetches请求。而在消费数据的时候，每个批次从Kafka Broker Server中拉取数据是有最大数据量限制，默认是500条，由属性(max.poll.records)控制，可以在客户端中设置该属性值来调整我们消费时每次拉取数据的量。
提示：这里需要注意的是，max.poll.records返回的是一个poll请求的数据总和，与多少个分区无关。因此，每次消费从所有分区中拉取Topic的数据的总条数不会超过max.poll.records所设置的值。
而在Fetcher的类中，在sendFetches方法中有限制拉取数据容量的限制，由属性(max.partition.fetch.bytes)，默认1MB。可能会有这样一个场景，当满足max.partition.fetch.bytes限制条件，如果需要Fetch出10000条记录，每次默认500条，那么我们需要执行20次才能将这一次通过网络发起的请求全部Fetch完毕。
这里，可能有同学有疑问，我们不能将默认的max.poll.records属性值调到10000吗?可以调，但是还有个属性需要一起配合才可以，这个就是每次poll的超时时间(Duration.ofMillis(100))，这里需要根据你的实际每条数据的容量大小来确定设置超时时间，如果你将最大值调到10000，当你每条记录的容量很大时，超时时间还是100ms，那么可能拉取的数据少于10000条。
