Kafka源码系列之实现自己的kafka监控
Kafka源码系列之实现自己的kafka监控
一，基本思路介绍
Kafka作为一个好用的且应用很广泛的消息队列，在大数据处理系统中基本是必不可少的。当然，作为缓存消息的消息队列，我们对其进行流量监控及消费滞后告警就显得异常重要了。
读过前面的文章，<Kafka源码系列之源码解析SimpleConsumer的消费过程>和<Kafka源码系列之Consumer高级API性能分析>这两篇文章的兄弟姐妹应该看本篇文章会很简单。实际就是利用SimpleConsumer获取Partition最新的offset，用Zookeeper的工具获取消费者组的各个分区的消费偏移，两者做差就是lagSize。
但是实际kafka的消费者组的消费偏移存储，kafka支持两个版本的：
1，基于Zookeeper。OffsetFetchRequest.CurrentVersion为0。
2，基于kafka自身。OffsetFetchRequest.CurrentVersion为1(默认)。
那么要实现一个消费者消费滞后预警，就要兼容两种方式，那么我们就详细的来介绍这两种方式的实现。
二，重要工具类
1，ConsumerOffsetChecker
Kafka提供的检查消费者消费偏移，LogEndSize和lagsize的工具。我们实现自己的监控均可以模仿该类实现。本文也仅限于基于该类将实现过程。
2，ZkUtils
Kafka提供的操作Zookeeper的工具类。
3，SimpleConsumer
Kafka消费者实现类。Kafka的副本同步，低级消费者，高级消费者都是基于该类实现从kafka消费消息的。
4，OffsetRequest
消费者去获取分区数据偏移的请求类，对应的请求key是：RequestKeys.OffsetsKey。在kafka的服务端kafkaApis的处理函数是：handleOffsetRequest(request)
5，OffsetFetchRequest
这个是请求某个topic的某个消费组的消费偏移，对应的请求key:RequestKeys.OffsetFetchKey。在kafka的服务端kafkaApis的处理函数是：handleOffsetFetchRequest(request)
6，OffsetManager
偏移管理器。内部维护了一个Scheduler，会定时执行compact，进行偏移的合并。
三，源代码实现
1，首先是获得消费者的消费偏移
ConsumerOffsetChecker当main方法中首先是获得topic列表
val topicList = topics match {
case Some(x) => x.split(",").view.toList
case None => ZkUtils.getChildren(zkClient, groupDirs.consumerGroupDir + "/owners").toList
}接着是建立到Broker链接，然后从kafka获取消费者偏移
val topicPartitions = topicPidMap.flatMap { case(topic, partitionSeq) => partitionSeq.map(TopicAndPartition(topic, _)) }.toSeq
val channel = ClientUtils.channelToOffsetManager(group, zkClient, channelSocketTimeoutMs, channelRetryBackoffMs)
debug("Sending offset fetch request to coordinator %s:%d.".format(channel.host, channel.port))
channel.send(OffsetFetchRequest(group, topicPartitions))
val offsetFetchResponse = OffsetFetchResponse.readFrom(channel.receive().buffer)
debug("Received offset fetch response %s.".format(offsetFetchResponse))
offsetFetchResponse.requestInfo.foreach { case (topicAndPartition, offsetAndMetadata) =>
if (offsetAndMetadata == OffsetMetadataAndError.NoOffset) {
val topicDirs = new ZKGroupTopicDirs(group, topicAndPartition.topic)
// this group may not have migrated off zookeeper for offsets storage (we don't expose the dual-commit option in this tool
// (meaning the lag may be off until all the consumers in the group have the same setting for offsets storage)
try {
val offset = ZkUtils.readData(zkClient, topicDirs.consumerOffsetDir + "/%d".format(topicAndPartition.partition))._1.toLong
offsetMap.put(topicAndPartition, offset)
} catch {
case z: ZkNoNodeException =>
if(ZkUtils.pathExists(zkClient,topicDirs.consumerOffsetDir))
offsetMap.put(topicAndPartition,-1)
else
throw z
}
}
else if (offsetAndMetadata.error == ErrorMapping.NoError)
offsetMap.put(topicAndPartition, offsetAndMetadata.offset)
else {
println("Could not fetch offset for %s due to %s.".format(topicAndPartition, ErrorMapping.exceptionFor(offsetAndMetadata.error)))
}
}假如，获得的偏移信息为空，那么就从Zookeeper获取消费者偏移。
解决获取topic的分区的最大偏移，实际思路是构建simpleConsumer，然后由其 去请求偏移，再跟获取的消费者偏移做差就得到消费者最大偏移。
topicList.sorted.foreach {
topic => processTopic(zkClient, group, topic)
}topicPidMap.get(topic) match {
case Some(pids) =>
pids.sorted.foreach {
pid => processPartition(zkClient, group, topic, pid)
}
case None => // ignore
}在processPartition中
val offsetOpt = offsetMap.get(topicPartition)
val groupDirs = new ZKGroupTopicDirs(group, topic)
val owner = ZkUtils.readDataMaybeNull(zkClient, groupDirs.consumerOwnerDir + "/%s".format(pid))._1
ZkUtils.getLeaderForPartition(zkClient, topic, pid) match {
case Some(bid) =>
val consumerOpt = consumerMap.getOrElseUpdate(bid, getConsumer(zkClient, bid))
consumerOpt match {
case Some(consumer) =>
val topicAndPartition = TopicAndPartition(topic, pid)
val request =
OffsetRequest(immutable.Map(topicAndPartition -> PartitionOffsetRequestInfo(OffsetRequest.LatestTime, 1)))
val logSize = consumer.getOffsetsBefore(request).partitionErrorAndOffsets(topicAndPartition).offsets.head然后做差得到LagSize
val lagString = offsetOpt.map(o => if (o == -1) "unknown" else (logSize - o).toString)
println("%-15s %-30s %-3s %-15s %-15s %-15s %s".format(group, topic, pid, offsetOpt.getOrElse("unknown"), logSize, lagString.getOrElse("unknown"),
owner match {case Some(ownerStr) => ownerStr case None => "none"}))getConsumer方法中
private def getConsumer(zkClient: ZkClient, bid: Int): Option[SimpleConsumer] = {
try {
ZkUtils.readDataMaybeNull(zkClient, ZkUtils.BrokerIdsPath + "/" + bid)._1 match {
case Some(brokerInfoString) =>
Json.parseFull(brokerInfoString) match {
case Some(m) =>
val brokerInfo = m.asInstanceOf[Map[String, Any]]
val host = brokerInfo.get("host").get.asInstanceOf[String]
val port = brokerInfo.get("port").get.asInstanceOf[Int]
Some(new SimpleConsumer(host, port, 10000, 100000, "ConsumerOffsetChecker"))
case None =>
throw new BrokerNotAvailableException("Broker id %d does not exist".format(bid))
}
case None =>
throw new BrokerNotAvailableException("Broker id %d does not exist".format(bid))
}
} catch {
case t: Throwable =>
println("Could not parse broker info due to " + t.getCause)
None
}
}四，总结
该工具类的使用
bin/kafka-consumer-offset-checker.sh --group yourgroup -topic yourtopic --zookeeper localhost:2181
输出结果
Offset是消费者消费到的偏移，logsize是kafka数据的最大偏移，Lag是二者的差。也即
LagSize = LogSize - Offset
得到我们消费组的滞后情况后，我们就可以根据需求(比如，设定滞后多少消息后给出告警)，给出相应的告警。