November 2022
Beginner to intermediate
346 pages
11h
Chinese
Content preview from Kafka权威指南（第2版）
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134
｜
第
7
章
消费者在读取下一批数据之前需要花一些时间来处理它们，所以这个指标会有些波动。关
键在于要确保消费者最终会赶上去，而不是越落越远。因为会出现正常波动，所以为这个
指标配置告警有一定难度。
Burrow
是
LinkedIn
公司开发的一款消费者滞后检测工具
，其
可以让这个指标的配置变得容易一些。
监控数据流是为了确保所有生成的数据会被及时地读取（这里的“及时”视具体的业务需
求而定）。为了确保数据能够被及时读取，需要知道数据是什么时候生成的。从
0.10.0
版本
开始，
Kafka
在所有消息里加入了生成消息的时间戳
。（但需要注意的是，发送消息的应用
程序或配置了相应参数的
broker
都可以覆盖这个时间戳。
）
为了确保所有消息在合理的时间内被读取，应用程序需要记录生成消息的数量（一般用每
秒多少条消息来表示）。消费者需要记录单位时间内已读取消息的数量以及消息生成时间
与读取时间之间的时间差（根据消息的时间戳来判断）。然后，我们需要一个系统将生产
者和消费者记录的消息数量收集起来（确保没有丢失消息），确保二者之间的时间差在合
理的范围内。要实现这种端到端的监控系统，不仅耗时，还有一定难度，到目前为止还没
有开源的实现。不过，作为
Confluent
Control Center
的一部分，
Confluent
提供了一个商业
版实现。
除了监控客户端和端到端数据流，
broker
还
提供了一些指标，用于说明
broker
发送给客户
端的错误响应率。建议收集
kafka.server:type=BrokerTopicMetrics,name=FailedProduce- ...
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