问题导读：
1.kafka的消费者组的消费偏移存储，kafka支持两个版本?
2.ConsumerOffsetChecker类的作用是什么？
3.Kafka如何通过源码实现监控？
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
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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
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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)))
}
}
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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。
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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
}
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在processPartition中
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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
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然后做差得到LagSize
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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"
}))
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getConsumer方法中
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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
}
}
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四，总结
该工具类的使用
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1
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bin
/kafka-consumer-offset-checker
.sh --group yourgroup -topic yourtopic --zookeeper localhost:2181
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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。
转自：http://www.aboutyun.com/forum.php?mod=viewthread&tid=22215&extra=page%3D1&page=1&