A critical part of any application development process is capacity planning. The goal is to build a system that meets your business requirements, both now and in the future. But, for the last 25 years, most of the application development and capacity planning techniques we've developed make the implicit assumption that the size of the application is relatively stable. Though some amount of growth of an application is nothing new (and, therefore, neither is growth of the underlying hardware platform), never before has it been reasonable to design a system that can grow in scope beyond its initial implementation by a factor of ten or more. I don't know about you, but for me, an application that quickly grows by an order of magnitude no longer fits into the "relatively stable size" category. Building a scalable warehouse means creating an environment where this type of rapid growth and rapid change are the norm. Since many of the original underlying premises about slow, stable application growth no longer hold true, we have to modify our approach to capacity planning. There are two main areas of difference. First, traditional non-scalable platforms had several components whose capacities were fixed. (For example, uniprocessors had a fixed CPU capacity.) However, many modern scalable platforms can scale nearly all of their subcomponents (CPU, memory, I/O bandwidth) by an order of magnitude. This adds more degrees of freedom to the capacity planning problem, which means that we have to specify more things than we used to. Also, the ability to change the capacity of nearly all components requires a more in-depth understanding of the complex interactions between all of the system components.

Second, since the needs of your enterprise change so quickly, the system capacity requirements will also change quickly. This requires you to think of capacity planning not as a single event in the application development cycle, but rather as a continuous process that runs throughout the entire life of the warehouse. In other words, capacity planning becomes highly iterative in the scalable world, and each iteration has multiple steps.

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