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Holt - Scaling CouchDB

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Scaling CouchDB: summary, description and annotation

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This practical guide offers a short course on scaling CouchDB to meet the capacity needs of your distributed application. Through a series of scenario-based examples, this book lets you explore several methods for creating a system that can accommodate growth and meet expected demand. In the process, you learn about several tools that can help you with replication, load balancing, clustering, and load testing and monitoring. Apply performance tips for tuning your databaseReplicate data, using Futon and CouchDBs RESTful interfaceDistribute CouchDBs workload through load balancingLearn options.;Table of Contents; Preface; Conventions Used in This Book; Using Code Examples; Safari Books Online; How to Contact Us; Acknowledgments; Chapter 1. Defining Scaling Goals; What is Scalability?; Capacity Planning; The CAP Theorem; Consistency; Availability; Partition Tolerance; Chapter 2. Tuning and Designing for Scale; Performance Tips; Document Design; Chapter 3. Replication; Filters and Specifying Documents; Conflict Resolution; Picking the Same Revision as CouchDB; Picking a Conflicted Revision; Merging Revisions; Chapter 4. Load Balancing; CouchDB Nodes; Replication Setup.

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Scaling CouchDB
Replication, Clustering, and Administration
Bradley Holt
Editor
Mike Loukides

Copyright 2011 Bradley Holt

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Nutshell Handbook, the Nutshell Handbook logo, and the OReilly logo are registered trademarks of OReilly Media, Inc. Scaling CouchDB , the image of a chough, and related trade dress are trademarks of OReilly Media, Inc.

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While every precaution has been taken in the preparation of this book, the publisher and authors assume no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein.

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Preface
Conventions Used in This Book

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Using Code Examples

This book is here to help you get your job done. In general, you may use the code in this book in your programs and documentation. You do not need to contact us for permission unless youre 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 OReilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your products documentation does require permission.

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Acknowledgments

Id first like to thank Damien Katz, creator of CouchDB, and all of CouchDBs contributors. Id also like to thank all of the contributors to the other open source software tools referenced in this book. This includes contributors to the Apache HTTP Server, BigCouch, CouchDB Lounge, Pillow, and Tsung. Knut Ola Hellan (creator of Pillow) and Martin Brown (from Couchbase) both provided valuable feedback which helped to make this book better. Mike Loukides, this books editor, and the rest of the team at OReilly Media were very responsive and helpful.

Chapter 1. Defining Scaling Goals

Before you can scale CouchDB, you need to define your scaling goals. Once you have defined your goals then you can design your system. You should test the scalability of your system before it is deployed. See . You can monitor an individual node by issuing a GET HTTP request to /_stats which will return various statistics about the CouchDB node.

What is Scalability?

Its important to note the distinctions between performance, vertical scaling, and horizontal scaling. Performance typically refers to properties of a system such as response time or throughput. Vertical scaling (or scaling up) means adding computing capacity to a single node in a system. This could be through added memory, a faster CPU, or larger hard drives. While memory gets cheaper, CPUs get faster, and hard drives get larger every year, at any given moment theres an upward limit to vertical scaling. Also, the hardware for high capacity nodes is usually more expensive per unit of computing capacity (as defined by your application) than commodity hardware.

When someone uses the word scalability, they are often referring to horizontal scalability. Horizontal scaling (or scaling out) is when a systems computing capacity is scaled by adding new nodes. In theory, each new node adds the entire amount of that nodes computing capacity to the system. In practice, true horizontal scalability is rarely, if ever, achieved. The network overhead of nodes communicating with each other can detract from the overall computing capacity of the system. Also, few systems are configured such that there is no redundant computing work between nodes.

Capacity Planning

Capacity planning is often lumped together with scaling. With capacity planning, the focus is on creating a system that can meet an expected amount of demand. With scalability, the focus is on creating a system that is capable of accommodating growth. As mentioned before, both vertical and horizontal scaling each have their own limitations. Given these limitations, a combination of scalability and capacity planning is often warranted. Put another way, define a maximum capacity to which you need to scale and test so that your system can scale to meet that capacitythere is no such thing as infinite scalability, and attempting to create such a system would be prohibitively expensive. In , we will take a look at testing the capacity of your system through distributed load testing.

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