Kafka: The Definitive Guide Neha Narkhede, Gwen Shapira, and Todd Palino Boston Kafka: The Definitive Guide by Neha Narkhede , Gwen Shapira , and Todd Palino Copyright © 2016 Neha Narkhede, Gwen Shapira, Todd Palino All rights reserved Printed in the United States of America Published by O’Reilly Media, Inc , 1005 Gravenstein Highway North, Sebastopol, CA 95472 O’Reilly books may be purchased for educational, business, or sales promotional use Online editions are also available for most titles ( http://safaribooksonline.com ) For more information, contact our corporate/ institutional sales department: 800-998-9938 or corporate@oreilly.com Editor: Shannon Cutt Production Editor: FILL IN PRODUCTION EDI‐ TOR Copyeditor: FILL IN COPYEDITOR July 2016: Proofreader: FILL IN PROOFREADER Indexer: FILL IN INDEXER Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Rebecca Demarest First Edition Revision History for the First Edition 2016-02-26: First Early Release See http://oreilly.com/catalog/errata.csp?isbn=9781491936160 for release details While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work Use of the information and instructions contained in this work is at your own risk If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights 978-1-491-93616-0 [LSI] Table of Contents Preface vii Meet Kafka 11 Publish / Subscribe Messaging How It Starts Individual Queue Systems Enter Kafka Messages and Batches Schemas Topics and Partitions Producers and Consumers Brokers and Clusters Multiple Clusters Why Kafka? Multiple Producers Multiple Consumers Disk-based Retention Scalable High Performance The Data Ecosystem Use Cases The Origin Story LinkedIn’s Problem The Birth of Kafka Open Source The Name Getting Started With Kafka 11 12 14 14 15 15 16 17 18 19 20 21 21 21 21 22 22 23 25 25 26 26 27 27 iii Installing Kafka 29 First Things First Choosing an Operating System Installing Java Installing Zookeeper Installing a Kafka Broker Broker Configuration General Broker Topic Defaults Hardware Selection Disk Throughput Disk Capacity Memory Networking CPU Kafka in the Cloud Kafka Clusters How Many Brokers Broker Configuration Operating System Tuning Production Concerns Garbage Collector Options Datacenter Layout Colocating Applications on Zookeeper Getting Started With Clients 29 29 29 30 32 33 34 36 39 40 40 40 41 41 41 42 43 44 44 47 47 48 49 50 Kafka Producers - Writing Messages to Kafka 51 Producer overview Constructing a Kafka Producer Sending a Message to Kafka Serializers Partitions Configuring Producers Old Producer APIs Kafka Consumers - Reading Data from Kafka 71 KafkaConsumer Concepts Consumers and Consumer Groups Consumer Groups - Partition Rebalance Creating a Kafka Consumer Subscribing to Topics The Poll Loop iv | Table of Contents 71 71 74 76 77 77 52 54 55 58 64 66 70 Commits and Offsets Automatic Commit Commit Current Offset Asynchronous Commit Combining Synchronous and Asynchronous commits Commit Specified Offset Rebalance Listeners Seek and Exactly Once Processing But How Do We Exit? Deserializers Configuring Consumers fetch.min.bytes fetch.max.wait.ms max.partition.fetch.bytes session.timeout.ms auto.offset.reset enable.auto.commit partition.assignment.strategy client.id Stand Alone Consumer - Why and How to Use a Consumer without a Group Older consumer APIs 79 80 81 82 84 85 86 88 90 91 95 95 96 96 96 97 97 97 98 98 99 Kafka Internals 101 Reliable Data Delivery 103 Building Data Pipelines 105 Cross-Cluster Data Mirroring 107 Administering Kafka 109 10 Stream Processing 111 11 Case Studies 113 A Installing Kafka on Other Operating Systems 115 Table of Contents | v Preface Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions Constant width Used for program listings, as well as within paragraphs to refer to program ele‐ ments such as variable or function names, databases, data types, environment variables, statements, and keywords Constant width bold Shows commands or other text that should be typed literally by the user Constant width italic Shows text that should be replaced with user-supplied values or by values deter‐ mined by context This element signifies a tip or suggestion This element signifies a general note vii This element indicates a warning or caution Using Code Examples Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/oreillymedia/title_title This book is here to help you get your job done In general, if example code is offered with this book, you may use it in your programs and documentation You not need to contact us for permission unless you’re reproducing a significant portion of the code For example, writing a program that uses several chunks of code from this book does not require permission Selling or distributing a CD-ROM of examples from O’Reilly books does require permission Answering a question by citing this book and quoting example code does not 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Safari Books Online offers a range of plans and pricing for enterprise, government, education, and individuals Members have access to thousands of books, training videos, and prepublication manuscripts in one fully searchable database from publishers like O’Reilly Media, Prentice Hall Professional, Addison-Wesley Professional, Microsoft Press, Sams, Que, viii | Preface CHAPTER Reliable Data Delivery Placeholder 103 CHAPTER Building Data Pipelines Placeholder 105 CHAPTER Cross-Cluster Data Mirroring Placeholder 107 CHAPTER Administering Kafka Placeholder 109 CHAPTER 10 Stream Processing Placeholder 111 CHAPTER 11 Case Studies Placeholder 113 APPENDIX A Installing Kafka on Other Operating Systems Installing on Windows Installing on OS X 115 About the Authors 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 Shapira is a Software Engineer at Cloudera, working on data ingest and focus‐ ing 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 and contributes to multiple industry blogs including O’Reilly Radar and Ingest.Tips Todd Palino 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 devel‐ oper 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 industry running infrastructure services, most recently as a Systems Engineer at Verisign, developing service manage‐ ment automation for DNS, networking, and hardware management, as well as man‐ aging hardware and software standards across the company