具体描述
Why a book about logs? That’s easy: the humble log is an abstraction that lies at the heart of many systems, from NoSQL databases to cryptocurrencies. Even though most engineers don’t think much about them, this short book shows you why logs are worthy of your attention.
Based on his popular blog posts, LinkedIn principal engineer Jay Kreps shows you how logs work in distributed systems, and then delivers practical applications of these concepts in a variety of common uses—data integration, enterprise architecture, real-time stream processing, data system design, and abstract computing models.
作者简介
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. Twitter: @jaykreps
目录信息
读后感
Why a book about logs? That’s easy: the humble log is an abstraction that lies at the heart of many systems, from NoSQL databases to cryptocurrencies. Even though most engineers don’t think much about them, this short book shows you why logs are worthy of...
评分i heart logs。。。。实话实说，在项目开发过程中，是否很多开发人员并不关心日志呢？这一点恰恰发生在刚接触这行没多久的菜鸟程序员，当然或多或少也包括一些老油条。 在分布式系统中，各个节点又是集群的，当我们的日志不健全是，一旦系统在某一个模块出现瓶颈，没有办法将...
评分Why a book about logs? That’s easy: the humble log is an abstraction that lies at the heart of many systems, from NoSQL databases to cryptocurrencies. Even though most engineers don’t think much about them, this short book shows you why logs are worthy of...
评分i heart logs。。。。实话实说，在项目开发过程中，是否很多开发人员并不关心日志呢？这一点恰恰发生在刚接触这行没多久的菜鸟程序员，当然或多或少也包括一些老油条。 在分布式系统中，各个节点又是集群的，当我们的日志不健全是，一旦系统在某一个模块出现瓶颈，没有办法将...
评分i heart logs。。。。实话实说，在项目开发过程中，是否很多开发人员并不关心日志呢？这一点恰恰发生在刚接触这行没多久的菜鸟程序员，当然或多或少也包括一些老油条。 在分布式系统中，各个节点又是集群的，当我们的日志不健全是，一旦系统在某一个模块出现瓶颈，没有办法将...
用户评价
worst OReilly book ever
评分如何把LinkedIn遇到的distsys的painpoint拆解成log+serving layer 并独立成一个component 以及各种思路（比较了lambda arch)
评分内容很赞，放到四年前的确是奇书，不过今天来看，很多内容早已普及。
评分收获很少很少
评分收获很少很少
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