- 出版社: O'Reilly Media, Inc, USA (2017年10月10日)
- 平装: 322页
- 语种： 英语
- ISBN: 9781491936160
- 条形码: 9781491936160
- 商品尺寸: 17.8 x 1.7 x 23.3 cm
- ASIN: 1491936169
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Kafka - The Definitive Guide (英语) 平装 – 2017年10月10日
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Neha Narkhede is Cofounder and Head of Engineering at Confluent, a company backing the popular Apache Kafka messaging system. Prior to founding Confluent, Neha led streams infrastructure at LinkedIn where she was responsible for LinkedIn s petabyte scale streaming infrastructure built on top of Apache Kafka and Apache Samza. Neha specializes in building and scaling large distributed systems and is one of the initial authors of Apache Kafka. In the past she has worked on search within the database at Oracle and holds a Masters in Computer Science from Georgia Tech.Gwen is a Software Engineer at Cloudera, working on data ingest and focusing on Apache Kafka. She is a frequent contributor to the Apache Kafka project, she has contributed Kafka integration to Apache Flume, and is a committer on Apache Sqoop.Gwen has 15 years of experience working with customers to design scalable data architectures.Formerly a solution architect at Cloudera, senior consultant at Pythian, Oracle ACE Director, and board member at NoCOUG. Gwen is a frequent speaker at industry conferences andcontributes to multiple industry blogs including O Reilly Radar and Ingest.Tips.Todd is a Staff Site Reliability Engineer at LinkedIn, tasked with keeping the largest deployment of Apache Kafka, Zookeeper, and Samza fed and watered. He is responsible for architecture, day-to-day operations, and tools development, including the creation of an advanced monitoring and notification system. Todd is the developer of the open source project Burrow, a Kafka consumer monitoring tool, and can be found sharing his experience on Apache Kafka at industry conferences and tech talks. Todd has spent over 20 years in the technology industryrunning infrastructure services, most recently as a Systems Engineer at Verisign, developing service management automation for DNS, networking, and hardware management, as well as managing hardware and software standards across the company."
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It details many configuration parameters that affect clustering, replication, message delivery.
It also offers valuable material for system administrators who need to manage and monitor a running cluster.
It's not very good for programmers: Java API coverage is partial and inadequate.
The chapters are uncoordinated and poorly integrated with some repeated material.
Many errors in the code samples and the text.
Chapters 3, 4, and 11 discuss the programming API.
The first two deal with message producers and consumers, present the Java API for publishing and consuming messages and discuss delivery semantics. They also detail configuration options that can be used to customize message producers and consumers. Security and access control is mentioned but never really discussed. There is not a single full program that can be run but several snippets full of errors.
Chapter 11 offers a tutorial introduction to stream processing: what it is and what problems it solves. Three code examples illustrate Kafka Streams, the Stream framework that comes with Kafka and provides a high level abstraction for manipulating data streams. The chapter gives you a taste of what you can do with Kafka Streams but doesn't do much to teach how to use it.
Chapter 2 gives a tutorial on Kafka installation and discusses several configuration options that may help in tuning a Kafka cluster. Basic ZooKeeper knowledge can help understand.
Chapter 5 delves into the internals of replication, partitions, request processing, and message storage on physical files.
Chapter 6 discusses data delivery guarantees. It revisits producer and consumer issues related to message delivery, and how to configure brokers and topics. It also explains how "at least once" delivery is easily achievable while "exactly once" delivery is not.
Chapter 7 briefly explores the Kafka Connect architecture: a producer/consumer alternative to exchange data between Kafka and another data storage system.
Chapters 8-10 have a more sysadmin-oriented content.
Chapter 8 explores cross cluster data mirroring, why you may need it, available alternative architectures/models and issues of lost or duplicated data you may come across. It also introduces Kafka's own cluster mirroring tool MirrorMaker, its configuration and tuning.
Chapter 9 covers command line tools to create and manage topics and partitions.
Chapter 10 is on monitoring a Kafka cluster and explores JMX metrics exposed by brokers, producers and consumers that can help in monitoring and detecting problems. Basic JMX knowledge is required to follow along.