CPIT.pdf

The Art of Capacity Planning Other resources from O’Reilly Related titles Apache Cookbook™ High Performance Web Apache 2 Pocket Reference Sites Building Scalable Web Sites Optimizing Oracle High Performance MySQL Performance Website Optimization oreilly.com oreilly.com is more than a complete catalog of O’Reilly books. You’ll also find links to news, events, articles, weblogs, sample chapters, and code examples. oreillynet.com is the essential portal for developers interested in open and emerging technologies, including new plat- forms, programming languages, and operating systems. Conferences O’Reilly brings diverse innovators together to nurture the ideas that spark revolutionary industries. We specialize in documenting the latest tools and systems, translating the innovator’s knowledge into useful skills for those in the trenches. Visit conferences.oreilly.com for our upcoming events. Safari Bookshelf (safari.oreilly.com) is the premier online reference library for programmers and IT professionals. Conduct searches across more than 1,000 books. Sub- scribers can zero in on answers to time-critical questions in a matter of seconds. Read the books on your Bookshelf from cover to cover or simply flip to the page you need. Try it today for free. The Art of Capacity Planning John Allspaw Beijing Cambridge Farnham Köln Sebastopol Taipei Tokyo The Art of Capacity Planning by John Allspaw Copyright © 2008 Yahoo! Inc. 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 (safari.oreilly.com). For more information, contact our corporate/institutional sales department: (800) 998-9938 or [email protected]. Editor: Andy Oram Cover Designer: Mark Paglietti Production Editor: Rachel Monaghan Interior Designer: Marcia Friedman Production Services: Octal Publishing, Inc. Illustrator: Robert Romano Indexer: Angela Howard Printing History: September 2008: First Edition. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. The Art of Capacity Planning and related trade dress are trademarks of O’Reilly Media, Inc. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and O’Reilly Media, Inc. was aware of a trademark claim, the designations have been printed in caps or initial caps. While every precaution has been taken in the preparation of this book, the publisher and author assume no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein. This book uses RepKover™, a durable and flexible lay-flat binding. ISBN: 978-0-596-51857-8 [M] To my father, James W. Allspaw, who taught me that engineering is about getting things done, not just thinking things up. CONTENTS PREFACE ix 1 GOALS, ISSUES, AND PROCESSES IN CAPACITY PLANNING 1 Quick and Dirty Math 3 Predicting When Your Systems Will Fail 3 Make Your System Stats Tell Stories 4 Buying Stuff: Procurement Is a Process 6 Performance and Capacity: Two Different Animals 6 The Effects of Social Websites and Open APIs 8 2 SETTING GOALS FOR CAPACITY 11 Different Kinds of Requirements and Measurements 12 Architecture Decisions 15 3 MEASUREMENT: UNITS OF CAPACITY 23 Aspects of Capacity Tracking Tools 24 Applications of Monitoring 31 API Usage and Its Effect on Capacity 59 Examples and Reality 60 Summary 61 4 PREDICTING TRENDS 63 Riding Your Waves 64 Procurement 80 The Effects of Increasing Capacity 83 Long-Term Trends 84 Iteration and Calibration 88 Summary 90 5 DEPLOYMENT 93 Automated Deployment Philosophies 93 Automated Installation Tools 96 Automated Configuration 98 Summary 103 vii A VIRTUALIZATION AND CLOUD COMPUTING 105 B DEALING WITH INSTANTANEOUS GROWTH 121 C CAPACITY TOOLS 127 INDEX 131 viii C O N T E N T S Chapter Preface S OMEWHERE AROUND 3 A.M. ON JULY 7TH, 2005, MY COWORKER, CAL HENDERSON, AND I WERE FINISHING up some final details before moving all of the traffic for our website, Flickr.com, to its new home: a Yahoo! data center in Texas. The original infrastructure in Vancouver was becom- ing more and more overloaded, and suffering from serious power and space constraints. Since Yahoo! had just acquired Flickr, it was time to bring new capacity online. It was about an hour after we changed DNS records to point to our shiny new servers that Cal happened to glance at the news. The London subway had just been bombed. Londoners responded with their camera phones, among other things. Over the next 24 hours, Flickr saw more traffic than ever before, as photos from the disaster were uploaded to the site. News outlets began linking to the photos, and traffic on our new servers went through the roof. It was not only a great example of citizen journalism, but also an object lesson—sadly, one born of tragedy—in capacity planning. Traffic can be sporadic and unpredictable at times. Had we not moved over to the new data center, Flickr.com wouldn’t have been available that day. ix Capacity planning has been around since ancient times, with roots in everything from economics to engineering. In a basic sense, capacity planning is resource management. When resources are finite, and come at a cost, you need to do some capacity planning. When a civil engineering firm designs a new highway system, it’s planning for capacity, as is a power company planning to deliver electricity to a metropolitan area. In some ways, their concerns have a lot in common with web operations; many of the basic concepts and concerns can be applied to all three disciplines. While systems administration has been around since the 1960s, the branch focused on serving websites is still emerging. A large part of web operations is capacity planning and management. Those are processes, not tasks, and they are composed of many different parts. Although every organization goes about it differently, the basic concepts are the same: Ensure proper resources (servers, storage, network, etc.) are available to handle expected and unexpected loads. Have a clearly defined procurement and approval system in place. Be prepared to justify capital expenditures in support of the business. Have a deployment and management system in place to manage the resources once they are deployed. Why I Wrote This Book One of my frustrations as an operations engineering manager was not having somewhere to turn to help me figure out how much equipment we’d need to keep running. Existing books on the topic of computer capacity planning were focused on the mathematical theory of resource planning, rather than the practical implementation of the whole process. A lot of literature addressed only rudimentary models of website use cases, and lacked specific information or advice. Instead, they tended to offer mathematical models designed to illustrate the principles of queuing theory, which is the foundation of traditional capac- ity planning. This approach might be mathematically interesting and elegant, but it doesn’t help the operations engineer when informed he has a week to prepare for some unknown amount of additional traffic—perhaps due to the launch of a super new fea- ture—or seeing his site dying under the weight of a link from the front page of Yahoo!, Digg, or CNN. I’ve found most books on web capacity planning were written with the implied assump- tion that concepts and processes found in non-web environments, such as manufacturing or industrial engineering, applied uniformly to website environments as well. While some of the theory surrounding such planning may indeed be similar, the practical application of those concepts doesn’t map very well to the short timelines of website development. In most web development settings, it’s been my observation that change happens too fast and too often to allow for the detailed and rigorous capacity investigations common to other fields. By the time the operations engineer comes up with the queuing model for his system, x PREFACE new code is deployed and the usage characteristics have likely already changed dramati- cally. Or some other technological, social, or real-world event occurs, making all of the modeling and simulations irrelevant. What I’ve found to be far more helpful, is talking to colleagues in the industry—people who come up against many of the same scaling and capacity issues. Over time, I’ve had contact with many different companies, each employing diverse architectures, and each experiencing different problems. But quite often they shared very similar approaches to solutions. My hope is that I can illustrate some of these approaches in this book. Focus and Topics This book is not about building complex models and simulations, nor is it about spending time running benchmarks over and over. It’s not about mathematical concepts such as Lit- tle’s Law, Markov chains, or Poisson arrival rates. What this book is about is practical capacity planning and management that can take place in the real world. It’s about using real tools, and being able to adapt to changing usage on a website that will (hopefully) grow over time. When you have a flat tire on the highway, you could spend a lot of time trying to figure out the cause, or you can get on with the obvious task of installing the spare and getting back on the road. This is the approach I’m presenting to capacity planning: adaptive, not theoretical. Keep in mind a good deal of the information in this book will seem a lot like common sense—this is a good thing. Quite often the simplest approaches to problem solving are the best ones, and capacity planning is no exception. This book will cover the process of capacity planning for growing websites, including mea- surement, procurement, and deployment. I’ll discuss some of the more popular and proven measurement tools and techniques. Most of these tools run in both LAMP and Windows-based environments. As such, I’ll try to keep the discussion as platform-agnostic as possible. Of course, it’s beyond the scope of this book to cover the details of every database, web server, caching server, and storage solution. Instead, I’ll use examples of each to illustrate the process and concepts, but this book is not meant to be an implementation guide. The intention is to be as generic as possible when it comes to explaining resource manage- ment—it’s the process itself we want to emphasize. For example, a database is used to store data and provide responses to queries. Most of the more popular databases allow for replicating data to other servers, which enhances redun- dancy, performance, and architectural decisions. It also assists the technical implementa- tion of replication with Postgres, Oracle, or MySQL (a topic for other books). This book covers what replication means in terms of planning capacity and deployment. Essentially, this book is about measuring, planning, and managing growth for a web appli- cation, regardless of the underlying technologies you choose. PREFACE xi Audience for This Book This book is for systems, storage, database, and network administrators, engineering man- agers, and of course, capacity planners. It’s intended for anyone who hopes (or perhaps fears) their website will grow like those of Facebook, Flickr, MySpace, Twitter, and others—companies that underwent the trial-by- fire process of scaling up as their usage skyrocketed. The approaches in this text come from real experience with sites where traffic has grown both heavily and rapidly. If you expect the popularity of your site will dramatically increase the amount of traffic you experience, then please read this book. Organization of the Material Chapter 1, Goals, Issues, and Processes in Capacity Planning, presents the issues that arise over and over on heavily trafficked websites. Chapter 2, Setting Goals for Capacity, illustrates the various concerns involved with plan- ning for the growth of a web application, and how capacity fits into the overall picture of availability and performance. Chapter 3, Measurement: Units of Capacity, discusses capacity measurement and monitoring. Chapter 4, Predicting Trends, explains how to turn measurement data into forecasts, and how trending fits into the overall planning process. Chapter 5, Deployment, discusses concepts related to deployment; automation of installation, configuration, and management. Appendix A, Virtualization and Cloud Computing, discusses where virtualization and cloud services fit into a capacity plan. Appendix B, Dealing with Instantaneous Growth, offers insight into what can be done in capacity crisis situations, and some best practices for dealing with site outages. Appendix C, Capacity Tools, is an annotated list of measurement, installation, configuration, and management tools highlighted throughout the book. Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, filenames, Unix utilities, and command-line options. Constant width Indicates the contents of files, the output from commands, and generally anything found in programs. xii PREFACE Constant width bold Shows commands or other text that should be typed literally by the user, and parts of code or files highlighted to stand out for discussion. Constant width italic Shows text that should be replaced with user-supplied values. 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 you’re reproducing a significant portion of the code. For example, writing a pro- gram 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 require per- mission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission. We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “The Art of Capacity Planning by John Allspaw. Copyright 2008 Yahoo! Inc., 978-0-596-51857-8.” If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at [email protected]. We’d Like to Hear from You Please address comments and questions concerning this book to the publisher: O’Reilly Media, Inc. 1005 Gravenstein Highway North Sebastopol, CA 95472 800-998-9938 (in the United States or Canada) 707-829-0515 (international or local) 707-829-0104 (fax) We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at: http://www.oreilly.com/catalog/9780596518578 To comment or ask technical questions about this book, send email to: [email protected] For more information about our books, conferences, Resource Centers, and the O’Reilly Network, see our website at: http://www.oreilly.com PREFACE xiii Safari® Books Online When you see a Safari® Books Online icon on the cover of your favorite technology book, that means the book is available online through the O’Reilly Network Safari Bookshelf. Safari offers a solution that’s better than e-books. It’s a virtual library that lets you easily search thousands of top tech books, cut and paste code samples, download chapters, and find quick answers when you need the most accurate, current information. Try it for free at http://safari.oreilly.com. Acknowledgments It’s simply not possible to thank everyone enough in this single, small paragraph, but I will most certainly mention their names. Most of the material in this book was derived from experiences in the trenches, and there are many people who have toughed it out in those trenches alongside me. Peter Norby, Gil Raphaelli, Kevin Collins, Dathan Pattishall, Cal Henderson, Aaron Cope, Paul Hammond, Paul Lloyd, Serguei Mourachov and Chad Dick- erson need special thanks, as does Heather Champ and the entire Flickr customer care team. Thank you Flickr development engineering: you all think like operations engineers and for that I am grateful. Thanks to Stewart Butterfield and Caterina Fake for convincing me to join the Flickr team early on. Thanks to David Filo and Hugo Gunnarsen for forcing me to back up my hardware requests with real data. Major thanks go out to Kevin Murphy for providing so much material in the automated deployment chapter. Thanks to Andy Oram and Isabel Kunkle for editing, and special thanks to my good friend Chris Colin for excellent pre-pre-editing advice. Thanks to Adam Jacob, Matt St. Onge, Jeremy Zawodny, and Theo Schlossnagle for the super tech review. Much thanks to Matt Mullenweg and Don MacAskill for sharing their cloud infrastructure use cases. Most important, thanks to my wife, Elizabeth Kairys, for encouraging and supporting me in this insane endeavor. Accomplishing this without her would have been impossible. xiv PREFACE Chapter 1 CHAPTER ONE Goals, Issues, and Processes in Capacity Planning T HIS CHAPTER IS DESIGNED TO HELP YOU ASSEMBLE AND USE THE WEALTH OF TOOLS AND TECHNIQUES presented in the following chapters. If you do not grasp the concepts introduced in this chap- ter, reading the remainder of this book will be like setting out on the open ocean without knowing how to use a compass, sextant, or GPS device—you can go around in circles forever. When you break them down, capacity planning and management—the steps taken to organize the resources your site needs to run properly—are, in fact, simple processes. You begin by asking the question: what performance do you need from your website? First, define the application’s overall load and capacity requirements using specific metrics, such as response times, consumable capacity, and peak-driven processing. Peak-driven processing is the workload experienced by your application’s resources (web servers, data- bases, etc.) during peak usage. The process, illustrated in Figure 1-1, involves answering these questions: 1. How well is the current infrastructure working? Measure the characteristics of the workload for each piece of the architecture that comprises your applications—web server, database server, network, and so on—and compare them to what you came up with for your performance requirements mentioned above. 1 2. What do you need in the future to maintain acceptable performance? Predict the future based on what you know about past system performance then marry that prediction with what you can afford, and a realistic timeline. Determine what you’ll need and when you’ll need it. 3. How can you install and manage resources after you gather what you need? Deploy this new capacity with industry-proven tools and techniques. 4. Rinse, repeat. Iterate and calibrate your capacity plan over time. F I G U R E 1 - 1. The process for determining the capacity you need Your ultimate goal lies between not buying enough hardware and wasting your money on too much hardware. Let’s suppose you’re a supermarket manager. One of your tasks is to manage the schedule of cashiers. Your challenge is picking the right number of cashiers working at any moment. Assign too few, and the checkout lines will become long, and the customers irate. Schedule too many working at once, and you’re spending more money than neces- sary. The trick is finding the right balance. Now, think of the cashiers as servers, and the customers as client browsers. Be aware some cashiers might be better than others, and each day might bring a different amount of cus- tomers. Then you need to take into consideration your supermarket is getting more and more popular. A seasoned supermarket manager intuitively knows these variables exist, and attempts to strike a good balance between not frustrating the customers and not pay- ing too many cashiers. Welcome to the supermarket of web operations. 2 CHAPTER ONE Quick and Dirty Math The ideas I’ve just presented are hardly new, innovative, or complex. Engineering disci- plines have always employed back-of-the-envelope calculations; the field of web opera- tions is no different. Because we’re looking to make judgments and predictions on a quickly changing land- scape, approximations will be necessary, and it’s important to realize what that means in terms of limitations in the process. Being aware of when detail is needed and when it’s not is crucial to forecasting budgets and cost models. Unnecessary detail means wasted time. Lacking the proper detail can be fatal. Predicting When Your Systems Will Fail Knowing when each piece of your infrastructure will fail (gracefully or not) is crucial to capacity planning. Capacity planning for the web, more often than one would like to admit, looks like the approach shown in Figure 1-2. F I G U R E 1 - 2. Finding failure points Including this information as part of your calculations is mandatory, not optional. How- ever, determining the limits of each portion of your site’s backend can be tricky. An easily segmented architecture helps you find the limits of your current hardware configurations. You can then use those capacity ceilings as a basis for predicting future growth. For example, let’s assume you have a database server that responds to queries from your frontend web servers. Planning for capacity means knowing the answers to questions such as these: Taking into account the specific hardware configuration, how many queries per second (QPS) can the database server manage? How many QPS can it serve before performance degradation affects end user experience? Adjusting for periodic spikes and subtracting some comfortable percentage of headroom (or safety factor, which we’ll talk about later) will render a single number with which you can characterize that database configuration vis-à-vis the specific role. Once you find that “red line” metric, you’ll know: GOALS, ISSUES, AND PROCESSES IN CAPACITY PLANNING 3 The load that will cause the database to fail, which will allow you to set alert thresholds accordingly. What to expect from adding (or removing) similar database servers to the backend. When to start sizing another order of new database capacity. We’ll talk more about these last points in the coming chapters. One thing to note is the entire capacity planning process is going to be architecture-specific. This means the calcu- lations you make to predict increasing capacity may have other constraints specific to your particular application. For example, to spread out the load, a LAMP application might utilize a MySQL server as a master database in which all live data is written and maintained, and use a second, repli- cated slave database for read-only database operations. Adding more slave databases to scale the read-only traffic is generally an appropriate technique, but many large websites (including Flickr) have been forthright about their experiences with this approach, and the limits they’ve encountered. There is a limit to how many read-only slave databases you can add before you begin to see diminishing returns as the rate and volume of changes to data on the master database may be more than the replicated slaves can sus- tain, no matter how many you add. This is just one example where your architecture can have a large effect on your ability to add capacity. Expanding database-driven web applications might take different paths in their evolution toward scalable maturity. Some may choose to federate data across many master data- bases. They may split up the database into their own clusters, or choose to cache data in a variety of methods to reduce load on their database layer. Yet others may take a hybrid approach, using all of these methods of scaling. This book is not intended to be an advice column on database scaling, it’s meant to serve as a guide by which you can come up with your own planning and measurement process—one that is right for your environment. Make Your System Stats Tell Stories Server statistics paint only part of the picture of your system’s health. Unless they can be tied to actual site metrics, server statistics don’t mean very much in terms of characterizing your usage. And this is something you’ll need to know in order to track how capacity will change over time. For example, knowing your web servers are processing X requests per second is handy, but it’s also good to know what those X requests per second actually mean in terms of your users. Maybe X requests per second represents Y number of users employing the site simultaneously. It would be even better to know that of those Y simultaneous users, A percent are upload- ing photos, B percent are making comments on a heated forum topic, and C percent are poking randomly around the site while waiting for the pizza guy to arrive. Measuring those user metrics over time is a first step. Comparing and graphing the web server hits- per-second against those user interaction metrics will ultimately yield some of the cost of 4 CHAPTER ONE providing service to the users. In the examples above, the ability to generate a comment within the application might consume more resources than simply browsing the site, but it consumes less when compared to uploading a photo. Having some idea of which features tax your capacity more than others gives you context in which to decide where you’ll want to focus priority attention in your capacity planning process. These observations can also help drive any technology procurement justifications. Quite often, the person approving expensive hardware and software requests is not the same person making the requests. Finance and business leaders must sometimes trust implicitly that their engineers are providing accurate information when they request capi- tal for resources. Tying system statistics to business metrics helps bring the technology closer to the business units, and can help engineers understand what the growth means in terms of business success. Marrying these two metrics together can therefore help the awareness that technology costs shouldn’t automatically be considered a cost center, but rather a significant driver of revenue. It also means that future capital expenditure costs have some real context, so even those non-technical folks will understand the value tech- nology investment brings. For example, when presenting a proposal for an order of new database hardware, you should have the systems and application metrics on hand to justify the investment. But if you had the pertinent supporting data, you could say something along the lines of “…and if we get these new database servers, we’ll be able to serve our pages X percent faster, which means our pageviews—and corresponding ad revenues—have an opportunity to increase up to Y percent.” Backing up your justifications in this way can also help the busi- ness development people understand what success means in terms of capacity management. MEASURE, MEASURE, MEASURE Engineers like graphs for good reason: they tell a story better than numbers can by themselves, and let you know exactly how your system is performing. There are some industry-tested tools and tech- niques used in measuring system statistics, such as CPU, memory, and disk usage. A lot of them can be reused to measure anything you need, including application-level or business metrics. Another theme in this book is measurement, which should be considered a necessity, not an option. You have a fuel gauge on your car’s dashboard for a reason. Don’t make the mistake of not installing one on your systems. We’ll see more about this in Chapter 3. GOALS, ISSUES, AND PROCESSES IN CAPACITY PLANNING 5 Buying Stuff: Procurement Is a Process After you’ve completed all your measurements, made snap judgments about usage, and sketched out future predictions, you’ll need to actually buy things: bandwidth, storage appliances, servers, maybe even instances of virtual servers. In each case, you’ll need to explain to the people with the checkbooks why you need what you think you need, and why you need it when you think you need it. (We’ll talk more about predicting the future and presenting those findings in Chapter 4.) Procurement is a process, and should be treated as yet another part of capacity planning. Whether it’s a call to a hosting provider to bring new capacity online, a request for quotes from a vendor, or a trip to your local computer store, you need to take this important seg- ment of time into account. Smaller companies, while usually a lot less “liquid” than their larger bretheren, can really shine in this arena. Being small often goes hand-in-hand with being nimble. So while you might not be offered the best price on equipment as the big companies who buy in massive bulk, you’ll likely be able to get it faster, owing to a less cumbersome approval process. Quite often the person you might need to persuade is the CFO, who sits across the hall from you. In the early days of Flickr, we used to be able to get quotes from a vendor and simply walk over to the founder of the company (seated 20 feet away), who could cut and send a check. The servers would arrive in about a week, and we’d rack them in the data center the day they came out of the box. Easy! Yahoo! has a more involved cycle of vetting hardware requests that includes obtaining many levels of approval and coordinating delivery to various data centers around the world. Purchases having been made, the local site operation teams in each data center then must assemble, rack, cable, and install operating systems on each of the boxes. This all takes more time than when we were a startup. Of course, the flip side is, with such a large company we can leverage buying power. By buying in bulk, we can afford a larger amount of hardware for a better price. In either case, the concern is the same: the procurement process should be baked into your larger planning exercise. It takes time and effort, just like all the other steps. There is more about this in Chapter 4. Performance and Capacity: Two Different Animals The relationship between performance tuning and capacity planning is often misunder- stood. While they affect each other, they have different goals. Performance tuning opti- mizes your existing system for better performance. Capacity planning determines what your system needs and when it needs it, using your current performance as a baseline. 6 CHAPTER ONE COMMON SENSE STEPS AND METHODS Real-world observations are worth more than any theoretical measurement. Capacity planning— and the predictions that drive it—should come from the empirical observation of your site’s usage, not benchmarks made in artificial environments. Benchmarking and performance research have value, but shouldn’t be used as the sole indicators of capacity. Let’s face it: tuning is fun, and it’s addictive. But after you spend some time tweaking val- ues, testing, and tweaking some more, it can become a endless hole, sucking away time and energy for little or no gain. There are those rare and beautiful times when you stumble upon some obvious and simple parameter that can make everything faster—you find the one MySQL configuration parameter that doubles the cache size, or realize after some test- ing that those TCP window sizes set in the kernel can really make a difference. Great! But as illustrated in Figure 1-3, for each of those rare gems you discover, the amount of obvi- ous optimizations you find thereafter dwindles pretty rapidly. F I G U R E 1 - 3. Decreasing returns from performance tuning Capacity planning must happen without regard to what you might optimize. The first real step in the process is to accept the system’s current performance, in order to estimate what you’ll need in the future. If at some point down the road you discover some tweak that brings about more resources, that’s a bonus. GOALS, ISSUES, AND PROCESSES IN CAPACITY PLANNING 7 Here’s a quick example of the difference between performance and capacity. Suppose there is a butcher in San Francisco who prepares the most delectable bacon in the state of California. Let’s assume the butcher shop has an arrangement with a store in San Jose to sell their great bacon there. Every day, the butcher needs to transport the bacon from San Francisco to San Jose using some number of trucks—and the bacon has to get there within an hour. The butcher needs to determine what type of trucks, and how many of them he’ll need to get the bacon to San Jose. The demand for the bacon in San Jose is increasing with time. It’s hard having the best bacon in the state, but it’s a good problem to have. The butcher has three trucks that suffice for the moment. But he knows he might be dou- bling the amount of bacon he’ll need to transport over the next couple of months. At this point, he can either: Make the trucks go faster Get more trucks You’re probably seeing the point here. While the butcher might squeeze some extra horsepower out of the trucks by having them tuned up—or by convincing the drivers to break the speed limit—he’s not going to achieve the same efficiency gain that would come from simply purchasing more trucks. He has no choice but to accept the performance of each truck, and work from there. The moral of this little story? When faced with the question of capacity, try to ignore those urges to make existing gear faster, and focus instead on the topic at hand: finding out what you need, and when. One other note about performance tuning and capacity: there is no silver bullet formula to tell you when tuning is appropriate and when it’s not. It may be that simply buying more hardware is the correct thing to do, when weighed against engineering time spent on tun- ing the existing system. Striking this balance between optimization and capacity deploy- ment is a challenge and will differ from environment to environment. The Effects of Social Websites and Open APIs As more and more websites install Web 2.0 characteristics, web operations are becoming increasingly important, especially capacity management. If your site contains content gen- erated by your users, utilization and growth isn’t completely under the control of the site’s creators—a large portion of that control is in the hands of the user community, as shown by my example in the Preface concerning the London subway bombing. This can be scary for people accustomed to building sites with very predictable growth patterns, because it means capacity is hard to predict and needs to be on the radar of all those invested—both the business and the technology staff. The challenge for development and operations staff of a social website is to stay ahead of the growing usage by collecting enough data from that upward spiral to drive informed planning for the future. 8 CHAPTER ONE ARCHITECTURE AND ITS EFFECT ON CAPACITY Your driving style affects your car’s mileage. A similar principle can be applied to web architectures. One of the recurring themes in this book will be how your website’s architecture can have a signifi- cant impact on how you use, consume, and manage capacity. Design has greater effect on the effective use of your capacity than any tuning and tweaking of your servers and network. Design also plays a large role in how easily and flexibly you can add or subtract capacity as the need arises. Although software and hardware tuning, optimization, and performance tweaking are related to capacity planning, they are not the same thing. This book focuses on tuning your architecture to allow for easier capacity management. Keeping the pieces of your architecture easily divisible and seg- mented can help you tackle a lot of load characterization problems—problems you’ll need to solve before you can create an accurate picture of what will be required to grow, and when. Providing web services via open APIs introduces a another ball of wax altogether, as your application’s data will be accessed by yet more applications, each with their own usage and growth patterns. It also means users have a convenient way to abuse the system, which puts more uncertainty into the capacity equation. API usage needs to be monitored to watch for emerging patterns, usage edge cases, and rogue application developers bent on crawling the entire database tree. Controls need to be in place to enforce the guidelines or Terms of Service (TOS), which should accompany any open API web service (more about that in Chapter 3). In my first year of working at Flickr, we grew from 60 photo uploads per minute to 660. We expanded from consuming 200 gigabytes of disk space per day to 880, and we bal- looned from serving 3,000 images a second to 8,000. And that was just in the first year. Capacity planning can become very important, very quickly. But it’s not all that hard; all you need to do is pay a little attention to the right factors. The rest of the chapters in this book will show you how to do this. I’ll split up this process into segments: 1. Determining your goals (Chapter 2) 2. Collecting metrics and finding your limits (Chapter 3) 3. Plotting out the trends and making forecasts based on those metrics and limits (Chapter 4) 4. Deploying and managing the capacity (Chapter 5) GOALS, ISSUES, AND PROCESSES IN CAPACITY PLANNING 9 Chapter 2 CHAPTER TWO Setting Goals for Capacity Y OU WOULDN ’ T BEGIN MIXING CONCRETE BEFORE YOU KNEW WHAT YOU WERE BUILDING. S IMILARLY , you shouldn’t begin planning for capacity before you determine your site’s requirements. Capacity planning involves a lot of assumptions related to why you need the capacity. Some of those assumptions are obvious, others are not. For example, if you don’t know that you should be serving your pages in less than three seconds, you’re going to have a tough time determining how many servers you’ll need to satisfy that requirement. More important, it will be even tougher to determine how many servers you’ll need to add as your traffic grows. Common sense, right? Yes, but it’s amazing how many organizations don’t take the time to assemble a rudimentary list of operational requirements. Waiting until users complain about slow responses or time-outs isn’t a good strategy. Establishing the acceptable speed or reliability of each part of your site can be a consider- able undertaking, but it will pay off when you’re planning for growth and need to know what standard you should maintain. This chapter shows you how to understand the dif- ferent types of requirements your management and customers will force you to deal with, and how architectural design helps you with this planning. 11 Different Kinds of Requirements and Measurements Now that we’re talking about requirements—which might be set by others, external to your group—we can look at the different types you’ll need to deal with. Your managers, your end-users, and your clients running websites with you, all have varying objectives and measure success in different ways. Ultimately, these requirements, or capacity goals, are interrelated and can be distilled into the following: Performance — External service monitoring — Business requirements — User expectations Capacity — System metrics — Resource ceilings Interpreting Formal Measurements Your site should be available not only to your colleagues performing tests on your website from a facility down the road, but also to real visitors who may be located on other conti- nents with slow connections. Some large companies choose to have site performance (and availability) constantly monitored by services such as Keynote (http://keynote.com) or Gomez (http://gomez.com). These commercial services deploy worldwide networks of machines that constantly ping your web pages to record the return time. Servers then keep track of all these metrics and build you a handy-dandy dashboard to evaluate how your site performance and uptime appears from many locations around the world. Because Keynote and Gomez are deemed “objective” third parties, those statistics can be used to enforce or guide Service Level Agreements (SLAs) arranged with partner compa- nies or sites (we’ll talk more about SLAs later). Keynote and Gomez can be considered enterprise-level services. There are also plenty of low-cost alternatives, including PingDom (http://pingdom.com), SiteUptime (http://siteuptime.com), and Alertra (http://alertra.com). It’s important to understand exactly what these services measure, and how to interpret the numbers they generate. Since most of them are networks of machines rather than peo- ple, it’s essential to be aware of how those web pages are being requested. Some things to consider when you’re looking at service monitoring systems include: Are they simulating human users? Are they caching objects like a normal web browser would? Why or why not? Can you determine how much time is spent due to network transfer versus server time, both in the aggregate, and for each object? Can you determine whether a failure or unexpected wait time is due to geographic net- work issues or measurement failures? 12 CHAPTER TWO If you believe your service monitoring systems are testing in a manner representative of your users when they visit your site, you have good reasons to trust the numbers. Also keep in mind, the metrics you use for capacity planning or site performance measurement might ultimately find their way onto an executive dashboard somewhere, viewed by a non-technical audience. CFOs, CTOs, business development folks, and even CEOs can become addicted to qualita- tive assessments of operations. This can be a double-edged sword. On the one hand, you’re being transparent about failures, which can help when you’re attempting to justify expenditures and organizational changes to support capacity. On the other hand, you’re also giving a frequently obsessive crowd more to obsess about, so when there are any anomalies in this data, you should be prepared to explain what they mean. Service Level Agreements So what exactly is an SLA? It’s an instrument that makes business people comfortable, much like insurance. But in broader, less anxious terms, an SLA is a metric that defines how a service should operate within agreed-upon boundaries. It puts some financial mus- cle into the metric by establishing a schedule of credits for meeting goals, or possibly pen- alties if the service does not achieve them. With websites, SLAs cover mostly availability and performance. Some SLAs guarantee a service will available for a pre-established percentage of time, such as 99.99%. What this means is that 0.01% of the time, the service can be unavail- able, and it will still be within the bounds of the SLA. Other SLAs require that demand for a service stay within reasonable limits; request rate limits or storage and upload limits are typical parameters. For example, you might find a web hosting company with something like verbiage below in its “Terms of Service” document: Acme Hosting, Inc. will use commercially reasonable efforts to make the SuperHostingPlan available with a Monthly uptime percentage (defined below) of at least 99.9% during any monthly billing cycle. In the event Acme Hosting, Inc. does not meet this commitment, you will be eligible to receive a service credit as described below. Monthly uptime percentage Credit percentage Between 99 and 99.9% 1 day credit Less than 99% 1 week credit Looks pretty reassuring, doesn’t it? The problem is, 99.9% uptime stretched over a month isn’t as great a number as one might think: 30 days = 720 hours = 43,200 minutes 99.9% of 43,200 minutes = 43,156.8 minutes 43,200 minutes – 43,156.8 minutes = 43.2 minutes SETTING GOALS FOR CAPACITY 13 This means for 43.2 minutes every month, this service can go down without penalty. If your site generates $3,000 worth of sales every minute, you could easily calculate how much money any amount of downtime will cost you (along with the less measurable con- sequence of disgruntled customers). Table 2-1 shows percentages of uptime on a yearly basis. T A B L E 2 - 1. SLA percentages and acceptable downtimes Uptime SLA Downtime per year 90.0% 36 days, 12 hours 95.0% 18 days, 6 hours 99.0% 87 hours, 36 minutes 99.50% 43 hours, 48 minutes 99.90% 8 hours, 45 minutes, 36 seconds 99.99% 52 minutes, 33 seconds 99.999% 5 minutes, 15 seconds 99.9999% 32 seconds The term five-nines is commonly heard in discussions about SLAs and availability. This refers to 99.999% availability, and it is used in marketing literature at least as much as it is in technical literature. Five-nines is usually used to indicate your site or system is deemed to be highly available. These SLA availability numbers aim to provide a level of confidence in a website’s service, but also imply you can equate downtime to lost revenue. I don’t believe this is actually accurate, as the straight math will bear out. If your service is unavailable for 10 minutes and it normally produces $3,000 of revenue every minute, then you might assume your business has lost $30,000. In reality, customers might just pick up where they left off and buy what they were in the process of buying when the outage occurred. Your business might be spending extra money on the customer service side to make up for an outage that has no impact on your earnings. The point is, while a true and accurate financial representation of an outage may be nei- ther true nor accurate, the importance of availability should be clear. Business Capacity Requirements The use of web services is becoming more and more prevalent in today’s Web 2.0 mashup-y world. While most web services offer open APIs for individual application developers to build upon, business-to-business relationships depend on them as well. Therefore, compa- nies usually tie revenue streams to having unfettered access to an API. This could mean a business relationship relies on a certain level of availability, or performance of your API, measured in a percentage uptime (such as 99.99%) or an agreed-upon rate of API requests. 14 CHAPTER TWO Let’s assume your website provides postal codes, given various inputs to the API you’ve built. You might allow only one API call per minute to a regular or non-commercial user, but a shipping company might enter into a contract permitting it to call your API up to 10 times per second. Website capacity planning is as much about justifying capital expendi- tures as it is about technical issues, such as scaling, architectures, software, and hardware. Because capacity concerns can have such a large impact on business operations, they should be considered early in the process of development. User Expectations Obviously, the end goal of capacity planning is a smooth and speedy experience for your users. Several factors can affect the user’s experience beside capacity. It’s possible to have plenty of capacity but a slow website nonetheless. Designing fast web pages is beyond the scope of this book, but you can find a lot of great information in Steve Souders’ excellent book, High Performance Web Sites (O’Reilly). Even though capacity is only one part of making the end-user experience fast, that experi- ence is still one of the real-world metrics that we’ll want to measure and track in order to make forecasts. For example, when serving static web content, you may reach an intolerable amount of latency at high volumes before any system-level metrics (CPU, disk, memory) raise a red flag. Again, this can have more to do with the construction of the web page than the capacity of the servers sending the content. But as capacity is one of the more expensive pieces to change, it warrants investigation. Perceived slowness of a web page could be the result of a page that is simply too heavy, and not from a lack of capacity. (This is one of the fundamentals of Souders’ book.) It’s a good idea to determine whether this is the case when any user-perceived slowness is analyzed. The problem can be solved by either 1) adding capacity, or, 2) changing the page weight. The first solution can sometimes involve more cost than solution two. At Flickr, we serve tens of thousands of photos per second. Each photo server can serve a known and specific rate of images before reaching its maximum. We don’t define this maximum in terms of disk I/O, or CPU, or memory, but in terms of how many images we can serve without the “time to serve” for each image exceeding the specified amount of time. Architecture Decisions Your architecture is the basic layout of how all of the backend pieces—both hardware and software—are joined. Its design plays a crucial role in your ability to plan and manage capacity. Designing the architecture can be a complex undertaking, but there are a couple of great books available to help you: Cal Henderson’s Building Scalable Web Sites (O’Reilly) and Theo Schlossnagle’s Scalable Internet Architectures (Pearson). SETTING GOALS FOR CAPACITY 15 Your architecture affects nearly every part of performance, reliability, and management. Establishing good architecture almost always translates to easier effort when planning for capacity. Providing Measurement Points Both for measurements purposes as well as for rapid response to changing conditions, you want your architecture to be designed so you can easily split it into parts that perform dis- crete tasks. In an ideal world, each component of the backend should have a single job to do, but it could still do multiple jobs well, if needed. At the same time, its effectiveness on each job should be easy to measure. For instance, let’s look at a simple, database-driven web application just starting on its path toward world domination. To get the most bang for our buck, we have our web server and our database residing on the same hardware server. This means all the moving parts share the same hardware resources, as shown in Figure 2-1. F I G U R E 2 - 1. A simple, single-server web application architecture Let’s suppose you’ve already read Chapter 3 (cheating, are we?) and you have configured measurements for both system and application-level statistics for your server. You can measure the system statistics of this server via sar or rrdtool, and maybe even application- level measurements such as web resource requests or database queries-per-second. 16 CHAPTER TWO The difficulty with the setup in Figure 2-1 is you can’t easily distinguish which system sta- tistics correspond with the different pieces of the architecture. Therefore, you can’t answer basic questions that are likely to arise, such as: Is the disk utilization the result of the web server sending out a lot of static content from the disk, or rather, the database’s queries being disk-bound? How much of the filesystem cache, CPU, memory, and disk utilization is being con- sumed by the web server, and how much is being used for the database? With careful research, you can make some estimates about which daemon is using which resource. In the best case, the resource demands of the different daemons don’t contend with one another. For example, the web server might be bound mostly by CPU and not need much memory, whereas the database might be memory-bound without using much CPU. But even in this ideal scenario, if usage continues to grow, the resource contention will grow to warrant splitting the architecture into different hardware components (Figure 2-2). At that point, you’d really like to know how much CPU, cache, disk space, bus bandwidth, and so on, each daemon actually needs. F I G U R E 2 - 2. Separation of web server and database SETTING GOALS FOR CAPACITY 17 Splitting the nodes in this fashion makes it easier to understand the capacity demands, as the resources on each server are now dedicated to each piece of the architecture. It also means you can measure each server and its resource demands more distinctly. You could come to conclusions with the single-component configuration, but with less ease and accuracy. Of course, this division of labor also produces performance gains, such as pre- venting frontend client-side traffic from interfering with database traffic, but let’s forget about performance for the moment. If we’re recording system and application-level statistics, you can quantify what each unit of capacity means in terms of usage. With this new architecture, you can answer a few questions that you couldn’t before, such as: Database server How do increases in database queries-per-second affect the following? Disk utilization I/O Wait (percent of time the database waits due to network or disk operations) RAM usage CPU usage Web server How do increases in web server requests-per-second affect the following? Disk utilization I/O Wait RAM usage CPU usage Being able to answer these questions is key to establishing how (and when) you’ll want to add more capacity to each piece. Providing Scaling Points Now that you have a good idea of what’s required for each piece of this simple architec- ture, you can get a sense for whether you’ll want different hardware configurations. At Flickr, for the most part, our MySQL database installations happen to be disk-bound, so there’s no compelling reason to buy two quad-core CPUs for each database box. Instead, we spend money on more disk spindles and memory to help with filesystem performance and caching. We know this to be our ideal database hardware configuration—for our data- base. We have different configurations for our image serving machines, our web servers, and our image processing machines; all according to what in-box resources they rely on most. The last piece we’re missing in this discussion on architecture is what drives capacity fore- casting: resource ceilings. The questions posed earlier regarding the effects of usage on resources, point to an obvious culmination: when will the database or web server die? 18 CHAPTER TWO Each server in our example possesses a finite amount of the following hardware resources: Disk throughput Disk storage CPU RAM Network High loads will bump against the limits of one or more of those resources. Somewhere just below that critical level is where you’ll want to determine your ceiling for each piece of your architecture. Your ceiling is the critical level of a particular resource (or resources) that cannot be crossed without failure. Armed with your current ceilings, you can start to assemble your capacity plan. We’ll talk more about examples of ceilings in Chapter 3. As you can see, changing architecture in simple ways can help you understand for what purposes your capacity is being used. When thinking about architecture design, keep in mind the division of labor and the “small pieces, loosely joined” theory can go a long way toward giving you clues as to how your site is being used. We’ll touch more on architec- ture decisions throughout the book, and particularly in Chapter 3. Hardware Decisions (Vertical, Horizontal, and Diagonal Scaling) Choosing the right hardware for each component of your architecture can greatly affect costs. At the very least, when it comes to servers, you should have a basic idea (gleaned from measurement and usage patterns) of where you want to invest your money. Before perusing your vendor’s current pricing, be aware of what you’re trying to achieve. Will this server be required to do a lot of CPU work? Will it need to perform a lot of mem- ory work? Is it a network-bound gateway? Today, the difference between horizontal and vertical scaling architectures are quite well known in the industry, but it bears reviewing in order to put capacity planning into context. Being able to scale horizontally means having an architecture that allows for adding capac- ity by simply adding similarly functioning nodes to the existing infrastructure. For instance, a second web server to share the burden of website visits. Being able to scale vertically is the capability of adding capacity by increasing the resources internal to a server, such as CPU, memory, disk, and network. Since the emergence of tiered and shared-nothing architectures, horizontal scaling has been widely recognized for its advantages over vertical scaling as it pertains to web applications. Being able to scale horizontally means designing your application to handle various levels of database abstraction and distribution. You can find great approaches to horizontal application development techniques in the aforementioned books by Henderson and Schlossnagle. SETTING GOALS FOR CAPACITY 19 The danger of relying solely on vertical scaling is, as you continue to upgrade components of a single computer, the cost rises dramatically. You also introduce the risk of a single point of failure (SPOF). Horizontal scaling involves the more complex issue of increasing the potential failure points as you expand the size of the server farm. In addition, you inherently introduce some challenges surrounding any synchronization you’ll need between the nodes. Diagonal scaling (a term coined by myself) is the process of vertically scaling the horizon- tally scaled nodes you already have in your infrastructure. Over time, CPU power and RAM become faster, cheaper, and cooler, and disk storage becomes larger and less expen- sive, so it can be cost effective to keep some vertical scaling as part of your plan, but applied to horizontal nodes. What this all boils down to is, for all of your nodes bound on CPU or RAM, you can “upgrade” to fewer servers with more CPU and RAM. For disk-bound boxes, it can also mean you may be able to replace them with fewer machines that have more disk spindles. As an example, I’ll take a recent Flickr upgrade. Initially, we had 67 dual-CPU, 4 GB RAM, single SATA drive web servers. For the most part, our frontend layer is CPU-bound, handling requests from client browsers, making backend database calls, and taking photo uploads. These 67 machines were equipped with Intel Xeon 2.80 GHz CPUs running Apache and PHP. When it was time to add capacity, we decided to try the new Quad Core CPU boxes. We found the dual quad core machines had roughly three times the processing power of the existing dual CPU boxes. With 8 CPU cores of Intel Xeon L5320 1.86 GHz CPUs, we were able to replace 67 existing boxes with only 18 new boxes. Figure 2-3 illustrates how much the server load average (across the entire cluster) dropped as a result. Figure 2-3 shows the reduction in load average when the 67 machines were removed from the production pool and the 18 new boxes were allowed to take over for the same production load. This certainly makes for a very dramatic-looking graph, but load average might not be the best metric to illustrate this diagonal scaling exercise. Figure 2-4 represents the same time period as Figure 2-3, except it details the number of apache requests-per-second when the older servers were replaced. The shades of lines on the graph represent a single server, allowing you to clearly see when the newer servers took over. Note the amount of apache requests-per-second actually went up by as much as 400 after the replacement, implying the older machines were very close to their own bottlenecks. Let’s take a look at Table 2-2 to learn what this meant in terms of resources. 20 CHAPTER TWO F I G U R E 2 - 3. Load average drop by replacing 67 boxes with 18 higher capacity boxes F I G U R E 2 - 4. Serving more traffic with fewer servers T A B L E 2 - 2. Comparing server architectures Power (kW) at 60% Servers CPU RAM Disk of peak usage 67 2 (2 cores) 4 GB 1 x 80 GB SATA 8.763 18 2 (8 cores) 4 GB 1 x 146 GB SATA 2.332 SETTING GOALS FOR CAPACITY 21 Based on traffic patterns, if we assume the servers are working at an average of about 60 percent of their peak, this means we’re using roughly 30 percent of the electrical power we were using previously. We’ve also saved 49U of rack space because each server needs only 1U of space. That’s more than one full, standard 42U rack emptied as a result of diag- onal scaling. Not bad. Disaster Recovery Disaster recovery is saving business operations (along with other resources, such as data, which we won’t consider in this book) after a natural or human-induced catastrophe. By catastrophe, I’m not implying the failure of a single server, but a complete outage that’s usually external to the operation of the website infrastructure. Examples of such disasters include data center power or cooling outages, as well as physi- cal disasters, such as earthquakes. It can also include incidents, such as construction acci- dents or explosions that affect the power, cooling, or network connectivity relied upon by your site. Regardless of the cause, the effect is the same: you can’t serve your website. Continuing to serve traffic under failure conditions is obviously an important part of web operations and architecture design. Contingency planning clearly involves capacity man- agement. Disaster recovery (DR) is only one part of what is termed Business Continuity Planning (BCP), which is the larger logistical plan to ensure continuity of business in the face of different failure event scenarios. In most cases, the solution is to deploy complete architectures in two (or more) separate physical locations, which means multiplying your infrastructure costs. It also means multi- plying the nodes you’ll need to manage, doubling all of the data replication, code, and configuration deployment, and multiplying all of your monitoring and measurement applications by the number of data centers you deploy. Clearly, DR plans raise both economic and technical concerns. DR and BCP are large topics in and of themselves, and are beyond the scope of this book. If this topic is of particular interest to you, there are many books available dedicated specifically to this subject. 22 CHAPTER TWO Chapter 3 CHAPTER THREE Measurement: Units of Capacity The only man who behaves sensibly is my tailor; he takes my measurements anew every time he sees me, while all the rest go on with their old measurements and expect me to fit them. —George Bernard Shaw I F YOU DON ’ T HAVE A WAY TO MEASURE YOUR CURRENT CAPACITY , YOU CAN ’ T CONDUCT CAPACITY planning—you’ll only be guessing. Fortunately, a seemingly endless range of tools is avail- able for measuring computer performance and usage. I’m willing to bet that moments after the first computer program was written, another one was written to measure and record how fast the first one performed. Most operating systems come with some basic built-in utilities that can measure various performance and consumption metrics. Most of these utilities usually provide a way to record results as well. Additional popular open source tools are easy to download and run on virtually any modern system. For capacity planning, your measurement tools should provide, at minimum, an easy way to: Record and store data over time Build custom metrics Compare metrics from various sources Import and export metrics 23 As long as you choose tools that can in some way satisfy this criteria, you don’t need to spend much time pondering which to use. What is more important is what metrics you choose to measure, and what metrics to which you pay particular attention. In this chapter, I’ll discuss the specific statistics you’ll want to measure for different pur- poses, and show the results in graphs to help you better interpret them. There are plenty of other sources of information on how to set up particular tools to generate the measure- ments; most professional system administrators already have such tools installed. ACCEPTING THE OBSERVER EFFECT Measuring your systems introduces yet another task your server will be asked to perform in order to function properly. Some system resources are going to be consumed for the purposes of collection and transfer of metrics. Good monitoring tools make an effort to be lightweight and not get in the way of a server’s primary work, but there will always be some amount of overhead. This means simply measuring your system’s resources will in some small way (hopefully, very small) affect the system’s behavior, and by extension, the very measurements you end up recording. This is commonly known as the “observer effect.” My philosophy is to accept the burden on the server and the slight distortion in the data collected as a cost of doing business. Giving up some percentage of CPU, disk, memory, and network resources to provide clear and useful measurement data is a small price to pay for monitoring your system’s over- all health and capacity. Aspects of Capacity Tracking Tools This chapter is about automatically and routinely measuring server behavior over a pre- defined amount of time. By monitoring normal behavior over days, weeks, and months, you’ll be able to see both patterns that recur regularly, and trends over time that help you predict when you need to increase capacity. We’ll also discuss deliberately increasing the load through artificial scaling using methods that closely simulate what will happen to your site in the future. This will also help you predict the need to increase capacity. For the tasks in this chapter, you need tools that collect, store, and display (usually on a graph) metrics over time. They can be used to drive capacity predictions as well as prob- lem resolution. 24 CHAPTER THREE Examples of these tools include: Cacti (http://cacti.net) Munin (http://munin.projects.linpro.no/) Ganglia (http://ganglia.info) Hyperic HQ (http://hyperic.com) The tools don’t need to be fancy. In fact, for some metrics, I still simply load them into Excel and plot them there. Appendix C contains a more comprehensive list of capacity planning tools. It’s important to start out by understanding the types of monitoring to which this chapter refers. Companies in the web operations field use the term monitoring to describe all sorts of operations—generating alerts concerning system availability, data collection and its analysis, real-world and artificial end user interaction measurement—the list goes on and on. Quite often this causes confusion. I suspect many commercial vendors who align on any one of those areas exploit this confusion to further their own goals, much to our det- riment as end users. This chapter is not concerned with system availability, the health of your servers, or notifi- cation management—the sorts of activities offered by Nagios, Zenoss, OpenNMS, and other popular network monitoring systems. Some of these tools do offer some of the fea- tures we need for our monitoring purposes, such as the ability to display and store metrics. But they exist mostly to help you recognize urgent problems and avoid imminent disas- ters. For the most part, they function a lot like extremely complex alarm clocks and smoke detectors. Metric collection systems, on the other hand, act more like court reporters, who observe and record what’s going on without taking any action whatsoever. As it pertains to our goals, the term monitoring refers to metric collection systems used to collect, store, and display system and application-level metrics of your infrastructure. Fundamentals and Elements of Metric Collection Systems Nearly every major commercial and open source metric collection system employs the same architecture. As depicted in Figure 3-1, this architecture usually consists of an agent that runs on each of the physical machines being monitored, and a single server that aggre- gates and displays the metrics. As the number of nodes in your infrastructure grows, you will probably have more than a single server performing aggregation, especially in the case of multiple data center operations. The agent’s job is to periodically collect data from the machine on which it’s running and send a summary to the metric aggregation server. The metric aggregation server stores the metrics for each of the machines it’s monitoring, which can then be displayed by various methods. Most aggregation servers use some sort of database; one specialized format known as Round-Robin Database (RRD) is particularly popular. MEASUREMENT: UNITS OF CAPACITY 25 F I G U R E 3 - 1. The fundamental pieces of most metric collection systems Round-Robin Database and RRDTool RRDTool is probably the most commonly used utility for storing system and network data—at least for those using the LAMP stack. I’m only going to offer you an overview here, but you can find a full description of it on the “about” page and in the tutorials of its RRDTool website at http://rrdtool.org. The key characteristics of system monitoring data concern its size: there’s a lot of it, and it’s constantly increasing. Thus, ironically, you need to do capacity planning just for the data you’re collecting for capacity planning! The Round-Robin Database (RRDTool) utility solves that by making an assumption that you’re interested in fine details only for the recent past. As you move backward in the stored data, it’s acceptable to lose some of the details. After some maximum time defined by the user (say, a year), you let data disappear completely. This approach sets a finite limit on how much data you’re storing, with the tradeoff being the degree of detail as time moves on. RRDTool can also be used to generate graphs from this data and show views on the vari- ous time slices for which you’ve recorded data. It also contains utilities to dump, restore, and manipulate RRD data, which come in handy when you drill down into some of the nitty-gritty details of capacity measurement. The metric collection tools mentioned earlier in “Aspects of Capacity Tracking Tools” are frontends to RRDTool. Ganglia The charts in this chapter were generated by Ganglia (http://ganglia.info). I had several rea- sons for choosing this frontend to present examples and illustrate useful monitoring practices. First, Ganglia is the tool we currently use for this type of monitoring at Flickr. 26 CHAPTER THREE We chose it based partly on some general reasons that might make it a good choice for you as well: it’s powerful (offering good support for the criteria I listed at the beginning of the chapter) and popular. But in addition, Ganglia was developed originally as a grid manage- ment and measurement mechanism aimed at high performance computing (HPC) clus- ters. Ganglia works well for Flickr’s infrastructure because our architecture is similar to HPC environments, in that our backend is segmented into different clusters of machines that each play a different role. The principles in this chapter, however, are valuable regardless of which monitoring tool you use. Fundamentally, Ganglia works similarly to most metric collection and storage tools. Its metric collection agent is called gmond and the aggregation server piece is called gmetad. The metrics are displayed using a PHP-based web interface. SNMP The Simple Network Management Protocol (SNMP) is a common mechanism for gather- ing metrics for most networking and server equipment. Think of SNMP as a standardized monitoring and metric collection protocol. Most routers, switches, and servers support it. SNMP collects and sends more types of metrics than most administrators choose to mea- sure. Because most networking equipment and embedded devices are closed systems, you can’t run user-installed applications, such as a metric collection agent like gmond. How- ever, as SNMP has long been a standard for networking devices, it provides an easy way to extract metrics from those devices without depending on an agent. Treating Logs As Past Metrics Logs are a great way to inject metrics into your measurement systems, and it underscores one of our criteria for being able to create custom metrics within your monitoring system. Web servers can log a wealth of information. When you see a spike in resources on a graph, you can often drill down to the access and error logs to find the exact moment those resources jumped. Thus, logs make problem identification easier. Most databases have options to log queries that exceed a certain amount of time, allowing you to identify and fix those slow-running queries. Almost everything you use—mail servers, load bal- ancers, firewalls–has the ability to create logs, either directly or via a Unix-style syslog facility. As an example, at Flickr we count the number of web server error and access log lines per minute and include those metrics into Ganglia’s graphs. Monitoring As a Tool for Urgent Problem Identification As will be mentioned in the upcoming section, “Applications of Monitoring,” problem notification is a separate area of expertise from capacity planning, and generally uses dif- ferent tools. But some emerging problems are too subtle to trigger health checks from tools such as Nagios. However, the tools we cover in this chapter can be pressed into ser- vice to warn you of impending trouble. The techniques in this section can also quickly show you the effects of an optimization. MEASUREMENT: UNITS OF CAPACITY 27 Figure 3-2 shows some anomalous behavior we once discovered on Flickr through Gan- glia. It represents several high-level views of some of Flickr’s clusters. F I G U R E 3 - 2. Using metric collection to identify problems Without even looking into the details, you can see from the graphs on the left that some- thing unusual has just happened. These graphs cover the load and running processes on the cluster, whereas the groups on the right display combined reports on the memory usage for those clusters. The X axes for all of the graphs correspond to the same time period, so it’s quite easy to see the number of running processes (notably in the WWW cluster) dip in conjunction with the spike in the GEO cluster. The WWW cluster obviously contains Apache frontend machines serving flickr.com, and our GEO cluster is a collection of servers that perform geographic lookups for features such as photo geotagging. By looking at this one web page, I can ascertain where the prob- lem originated (GEO) and where its effects were felt (all other clusters). As it turns out, 28 CHAPTER THREE this particular event occurred when one of our GEO servers stalled on some of its requests. The connections from our web servers accumulated as a result. When we restarted the GEO server, the web servers gradually recovered. When faults occur with your website, there is tremendous value in being able to quickly gather status information. You want to be able to get fast answers to the following questions: What was the fault? When did the fault occur? What caused the fault? In this example, Figure 3-2 helped us pinpoint the source of the trouble because we could correlate the event’s effects (via the timeline) on each of our clusters. Network Measurement and Planning Capacity planning goes beyond servers and storage to include the network to which they’re all connected. The implementation details of routing protocols and switching architectures are not within the scope of this book, but your network is just like any of your other resource: finite in capacity, and well worth measuring. Networks are commonly viewed as plumbing for servers, and the analogy is apt. When your network is operating well, data simply flows. When it doesn’t, everything comes to a grinding halt. This isn’t to say that subtle and challenging problems don’t crop up with networking: far from it. But for the most part, network devices are designed to do one task well, and their limits should be clear. Network capacity in hosted environments is often a metered and strictly controlled resource; getting data about your usage can be difficult, depending on the contract you have with your network provider. As a sanity check on your inbound and outbound net- work usage, aggregate your outward-facing server network metrics and compare them with the bill you receive from your hosting provider. When you own your own racks and switches, you can make educated decisions about how to divide the hosts across them according to the network capacity they’ll need. For example, at Flickr, our photo cache servers demand quite a bit from their switches, because all they do is handle requests for downloads of photos. We’re careful not to put too many of them on one switch so the servers have enough bandwidth. Routers and switches are like servers in that they have various metrics that can be extracted (usually with the SNMP protocol) and recorded. While their main metrics are the bytes in and out per second (or packets in and out if the payloads are small), they often expose other metrics as well, such as CPU usage and current network sessions. All of these metrics should be measured on a periodic basis with a network graphing tool, such as MRTG, or some other utility that can store the history for each metric. Unlike Ganglia and other metric collection tools, MRTG is built with SNMP in mind. Simply because your switch and router are well below your limits of network capacity doesn’t MEASUREMENT: UNITS OF CAPACITY 29 mean you’re not nearing CPU usage ceilings on those devices—all of those metrics should be monitored with alerting thresholds as well. Load Balancing Load balancers have been a source of much joy and pain in the field of web operations. Their main purpose is to distribute load among pools, or clusters of machines, and they can range from the simplest to the most complex beasts in your data center. Load balanc- ing is usually implemented on the frontend of the architecture, playing traffic cop to web servers that respond to data requests from user’s browsers. But load balancers have also been used to spread load across databases, middle-layer application servers, geographically dispersed data centers, and mail servers; the list continues on. Load balancers establish load distribution based on a relatively short list of algorithms, and enable you specify the protocols to balance across the available servers serving the traffic. Scalable Internet Architectures by Theo Schlossnagle (Pearson) contains some excellent insights into load balancers and their role in web architectures. For our purposes, load balancers provide a great framework for capacity management, because they allow the easy expansion and removal of capacity in a production environ- ment. They also offer us a place to experiment safely with various amounts of live web traffic so we can track the real effect it has on our server’s resources. You’ll see later why this is useful in helping to find your server’s ceilings. This can be the joy found with load balancing: convenience in deploying and researching capacity. But there is also pain. Because load balancers are such an integral part of the architecture, failures can be spectacular and dramatic. Not all situations call for load balancing. Even when load balancing is needed, not all balancing algorithms are appropriate. Jeremy Zawodny recounted a story in the first edition of High Performance MySQL (O’Reilly) in which databases at Yahoo! were being load balanced with a “least connec- tions” scheme. This scheme works quite well when balancing web servers: it ensures the server with the smallest number of requests has more traffic directed to it. The reason it works with web servers is web requests are almost always short-lived and on average don’t vary to a great extent in size or latency. The paradigm falls apart, however, with databases because not all queries are the same in terms of size and time to process, and the results of those queries can be quite large. The lesson Zawodny leaves us is just because a database has relatively few current connections does not mean it can tolerate more load. A second concern with load balancing databases is how to check the health of specific servers within the pool to determine if they all remain capable of receiving traffic. As men- tioned earlier, databases are application-specific beasts, so what will work for my applica- tion might not work for yours. For me, replication slave lag may be the determining factor for health, whereas for you, it could be the current rate of SELECT statements. 30 CHAPTER THREE Further complications in load balancing include uncommon protocols, complicated bal- ancing algorithms, and the tuning needed to ensure load balancing is working optimally for your application. Applications of Monitoring The remainder of this chapter uses examples to demonstrate some of the important moni- toring techniques you need to know and perform. Application-Level Measurement As mentioned earlier, server statistics paint only a part of the capacity picture. You should also measure and record higher-level metrics specific to your application—not specific to one server, but to the whole system. CPU and server disk usage on a web server doesn’t tell the whole tale of what’s happening to each web request, and a stream of web requests can involve multiple pieces of hardware. At Flickr, we have a dashboard that collects these application-level metrics. They are col- lected on both a daily and cumulative basis. Some of the metrics can be drawn from a database, such as the number of photos uploaded. Others can come from aggregating some of the server statistics, such as total disk space consumed across disparate machines. Data collection techniques can be as simple as running a script from a cron job and putting results into its own database for future mining. Some of the metrics currently tracked at Flickr are: Photos uploaded (daily, cumulative) Photos uploaded per hour Average photo size (daily, cumulative) Processing time to segregate photos based on their different sizes (hourly) User registrations (daily, cumulative) Pro account signups (daily, cumulative) Number of photos tagged (daily, cumulative) API traffic (API keys in use, requests made per second, per key) Number of unique tags (daily, cumulative) Number of geotagged photos (daily, cumulative) We also track certain financial metrics, such as payments received (which lie outside the scope of this book). For your particular application, a good exercise would be to spend some time correlating business and financial data to the system and application metrics you’re tracking. For example, a Total Cost of Ownership (TCO) calculation would be incomplete without some indication of how much these system and application metrics cost the business. MEASUREMENT: UNITS OF CAPACITY 31 Imagine being able to correlate the real costs to serve a single web page with your applica- tion. Having these calculations would not only put the architecture into a different context from web operations (business metrics instead of availability, or performance metrics), but they can also provide context for the more finance-obsessed, non-technical upper man- agement who might have access to these tools. I can’t overemphasize the value inherent to identifying and tracking application metrics. Your efforts will be rewarded by imbuing your system statistics with context beyond server health, and will help guide your forecasts. During the procurement process, TCO calculations will prove to be invaluable, as we’ll see later. Now that we’ve covered the basics of capacity measurement, let’s take a look at which measurements you—the manager of a potentially fast-growing website—will likely want to pay special attention. I’ll discuss the common elements of web infrastructure and list considerations for measuring their capacity and establishing their upper limits. I’ll also provide some examples taken from Flickr’s own capacity planning to add greater rele- vance. The examples are designed to illustrate useful metrics you may wish to track as well. They are not intended to suggest Flickr’s architecture or implementation will fit every application’s environment. Storage Capacity The topic of data storage is vast. For our purposes, I’m going to focus only on the segments of storage that directly influence capacity planning for a high data volume website. One of the most effective storage analogies is that of a glass of water. The analogy com- bines a finite limit (the size of the glass) with a variable (the amount of water that can be put into and taken out of the glass at any given time). This helps you to visualize the two major factors to consider when choosing where and how to store your data: The maximum capacity of the storage media The rate at which the data can be accessed Traditionally, most web operations have been concerned with the first consideration—the size of the glass. However, most commercial storage vendors have aligned their product lines with both considerations in mind. In most cases, there are two options: large, slow, inexpensive disks (usually using ATA/SATA), and smaller, fast, expensive disks (SCSI and SAS technologies). Even though the field of data storage has matured, there are still many emerging—and possibly disruptive—technologies of which you should be aware. The popularity of solid- state drives and the hierarchical storage schemes that incorporate them may soon become the norm, as the costs of storage continue to drop and the raw I/O speed of storage has remained flat in recent years. 32 CHAPTER THREE Consumption rates When planning the storage needs for your application, the first and foremost consider- ation should be the consumption rate. This is the growth in your data volume measured against a specific length of time. For sites that consume, process, and store rich media files, such as images, video, and audio, keeping an eye on storage consumption rates can be critical to the business. But consumption is important to watch even if your storage doesn’t grow much at all. Disk space is about the easiest capacity metric to understand. Even the least technically inclined computer user understands what it means to run out of disk space. For storage consumption, the central question is: When will I run out of disk space? A real-world example: Tracking storage consumption At Flickr, we consume a lot of disk space as photos are uploaded and stored. I’ll use this simple case as an example of planning for storage consumption. When photos are uploaded, they are divided into different groups based on size, and sent to a storage appliance. We collect a wide range of metrics related to this process, including: How much time it takes to process each image into its various sizes How many photos were uploaded The average size of the photos How much disk space is consumed by those photos Later, we’ll see why we chose to measure these, but for the moment our focus is on the last item: the total disk space consumption over time. We collect and store this number on a daily basis. The daily time slice has enough detail to show weekly, monthly, seasonal, and holiday trends. Thus, it can be used to predict when we’ll need to order more storage hardware. Table 3-1 presents disk space consumption (for photos only) for a two-week period in 2005. T A B L E 3 - 1. Sample statistics on daily disk space consumption Date Total usage (GB) Daily usage (GB) 07/26/05 14321.83 138.00 07/27/05 14452.60 130.77 07/28/05 14586.54 133.93 07/29/05 14700.89 114.35 07/30/05 14845.72 144.82 07/31/05 15063.99 218.27 08/01/05 15250.21 186.21 MEASUREMENT: UNITS OF CAPACITY 33 T A B L E 3 - 1. Sample statistics on daily disk space consumption (continued) Date Total usage (GB) Daily usage (GB) 08/02/05 15403.82 153.61 08/03/05 15558.81 154.99 08/04/05 15702.35 143.53 08/05/05 15835.76 133.41 08/06/05 15986.55 150.79 08/07/05 16189.27 202.72 08/08/05 16367.88 178.60 The data in Table 3-1 is derived from a cron job that runs a script to record the output from the standard Unix df command on our storage appliances. The data is then aggre- gated and included on a metrics dashboard. (We also collect data in much smaller incre- ments [minutes] using Ganglia, but this is not relevant to the current example.) When we plot the data from Table 3-1, two observations become clear, as shown in Figure 3-3. F I G U R E 3 - 3. Table of daily disk consumption We can quickly see that the dates 7/31 and 8/07 were high upload periods. In fact, the 31st of July and the 7t

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