Fortunately, Excel is one of many programs that feature curve fitting. Finding a good equation to fit the data can be just as much art as science. In this case, since our data is on a time line, the missing values in which we’re interested are in the future. You can then use that equation to make educated guesses about missing values within the data. This is the process by which you attempt to find a mathematical equation that mimics the data you’re looking at. But is our growth actually linear? It may not be.Įxcel calls this next step “adding a trend line,” but some readers might know this process as curve fitting. As I just suggested, we could simply draw a straight line that extends from our measured data to the point at which it intersects our current limit line. Some examples of how metrics measurement can be useful include:įigure 4-2. Cumulative disk consumption and available spaceĭetermining when we’re going to reach our space limitation is our next step. Trends can also inform community management, customer care and support, product management, and finance. Being aware of any recurring patterns can be invaluable when making decisions later on. When we looked at disk space consumption in Chapter 3, we stumbled upon some weekly upload patterns. Recognizing trends is valuable for many reasons, not just for capacity planning. It may also lead us to other questions: has that Sunday peak changed over time, and if so, how has it changed with respect to the other days of the week? Has the highest upload day always been Sunday? Does that change as we add new members residing on the other side of the International Date Line? Is Sunday still the highest upload day on holiday weekends? These questions can all be answered once you have the data, and the answers in turn could provide a wealth of insight with respect to planning new feature launches, operational outages, or maintenance windows. Once you have gathered historical data on capacity, you can begin analyzing it with an eye toward recognizing any trends and recurring patterns.įor example, in the last chapter I recounted how at Flickr, we discovered Sunday has been historically the highest photo upload day of the week. Conversely, the company financial officers will not hold you in high regard either when you’ve purchased a lot of equipment that lay idle, only to see its price drop a few months later.Ī good capacity plan depends on knowing your needs for your most important resources, and how those needs change over time. If you can wait six months before buying a piece of equipment, you will likely end up with faster and less expensive equipment at that time.Ĭertainly, you don’t want to be caught unprepared when growth takes place-this book is all about saving you from that career-threatening situation. Moore’s now-famous axiom in 1965 that postulates the number of transistors on an integrated circuit approximately doubles every eighteen months) holds true forever, we can predict that manufacturers will continue to lower costs over time. This rule is derived directly from the obvious trend in computing costs: all forms of hardware are becoming cheaper, even as they become faster and more reliable. As we’ll see later, the key to making accurate predictions is having an adjustable forecasting process.īefore you get too excited about charting massive growth and putting new servers in place to handle the deluge, let me remind you of one of the key economic factors you need to deal with: buying equipment too early is wasteful. By putting all of this historical data into perspective, you can generate estimates for what you’ll need to sustain the growth of your website. Outside of those rare bursts and spikes of load on your system, the long-term view is hopefully one of steadily increasing usage. It’s also the art of slicing and dicing up your historical data, and making educated guesses about the future. But let’s stop here for a moment to remember an irritating little detail: it’s impossible to accurately predict the future.įorecasting capacity needs is part intuition, and part math. Now you’ll be able to use this data (excluding the barking statistics) like a crystal ball, and predict the future like Nostradamus. You probably didn’t gain anything from graphing the peak barking periods of your neighbor’s dog-but hey, you did it, and I’m proud of you. You’re graphing everything you can get your hands on, as often as you can. I’M ASSUMING YOU’VE MADE A FEW PASSES THROUGH Chapter 3 AND HAVE JUST DEPLOYED A SUPER-AWESOME, totally amazing, monitoring, trending, graphing, and measurement system.
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