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This chapter is from the book

Rule 2—Design Scale into the Solution (D-I-D Process)

Our firm is focused on helping clients through their scalability needs, and as you might imagine customers often ask us "When should we invest in scalability?" The somewhat flippant answer is that you should invest (and deploy) the day before the solution is needed. If you could deploy scale improvements the day before you needed them, you would delay investments to be "just in time" and gain the benefits that Dell brought to the world with configure-to-order systems married with just in time manufacturing. In so doing you would maximize firm profits and shareholder wealth.

But let's face it—timing such an investment and deployment "just in time" is simply impossible, and even if possible it would incur a great deal of risk if you did not nail the date exactly. The next best thing to investing and deploying "the day before" is AKF Partners' Design-Implement-Deploy or D-I-D approach to thinking about scalability. These phases match the cognitive phases with which we are all familiar: starting to think about and designing a solution to a problem, building or coding a solution to that problem, and actually installing or deploying the solution to the problem. This approach does not argue for nor does it need a waterfall model. We argue that agile methodologies abide by such a process by the very definition of the need for human involvement. One cannot develop a solution to a problem of which they are not aware, and a solution cannot be manufactured or released if it is not developed. Regardless of the development methodology (agile, waterfall, hybrid, or whatever), everything we develop should be based on a set of architectural principles and standards that define and guide what we do.

Design

We start with the notion that discussing and designing something is significantly less expensive than actually implementing that design in code. Given this relatively low cost we can discuss and sketch out a design for how to scale our platform well in advance of our need. Whereas we clearly would not want to put 10x, 20x, or 100x more capacity than we would need in our production environment, the cost of discussing how to scale something to those dimensions is comparatively small. The focus then in the (D)esign phase of the D-I-D scale model is on scaling to between 20x and infinity. Our intellectual costs are high as we employ our "big thinkers" to think through the "big problems." Engineering and asset costs, however, are low as we aren't writing code or deploying costly systems. Scalability summits, a process in which groups of leaders and engineers gather to discuss scale limiting aspects of the product, are a good way to identify the areas necessary to scale within the design phase of the D-I-D process. Table 1.1 lists the parts of the D-I-D process.

Table 1.1. D-I-D Process for Scale

Design

Implement

Deploy

Scale Objective

20x to Infinite

3x to 20x

1.5x to 3x

Intellectual Cost

High

Medium

Low to Medium

Engineering Cost

Low

High

Medium

Asset Cost

Low

Low to Medium

High to Very High

Total Cost

Low/Medium

Medium

Medium

Implement

As time moves on, and as our perceived need for future scale draws near, we move to (I)mplementing our designs within our software. We reduce our scope in terms of scale needs to something that's more realistic, such as 3x to 20x our current size. We use "size" here to identify that element of the system that is perceived to be the greatest bottleneck of scale and therefore in the greatest need of modification for scalability. There may be cases where the cost of scaling 100x (or greater) our current size is not different than the cost of scaling 20x, and if this is the case we might as well make those changes once rather than going in and making those changes multiple times. This might be the case if we are going to perform a modulus of our user base to distribute (or share) them across multiple (N) systems and databases. We might code a variable Cust_MOD that we can configure over time between 1 (today) and 1,000 (5 years from now). The engineering (or implementation) cost of such a change really doesn't vary with the size of N so we might as well make it. The cost of these types of changes are high in terms of engineering time, medium in terms of intellectual time (we already discussed the designs earlier in our lifecycle), and low in terms of assets as we don't need to deploy 100x our systems today if we intend to deploy a modulus of 1 or 2 in our first phase.

Deployment

The final phase of the D-I-D process is (D)eployment. Using our modulus example above, we want to deploy our systems in a just in time fashion; there's no reason to have idle assets sitting around diluting shareholder value. Maybe we put 1.5x of our peak capacity in production if we are a moderately high growth company and 5x our peak capacity in production if we are a hyper growth company. We often guide our clients to leverage the "cloud" for burst capacity so that we don't have 33% of our assets waiting around for a sudden increase in user activity. Asset costs are high in the deployment phase, and other costs range from low to medium. Total costs tend to be highest for this category as to deploy 100x of your necessary capacity relative to demand would kill many companies. Remember that scale is an elastic concept; it can both expand and contract, and our solutions should recognize both aspects of scale. Flexibility is therefore key as you may need to move capacity around as different systems within your solution expand and contract to customer demand.

Referring to Table 1.1, we can see that while each phase of the D-I-D process has varying intellectual, engineering, and asset costs, there is a clear progression of overall cost to the company. Designing and thinking about scale comes relatively cheaply and thus should happen frequently. Ideally these activities result in some sort of written documentation so that others can build upon it quickly should the need arise. Engineering (or developing) the architected or designed solutions can happen later and cost a bit more overall, but there is no need to actually implement them in production. We can roll the code and make small modifications as in our modulus example above without needing to purchase 100x the number of systems we have today. Finally the process lends itself nicely to purchasing equipment just ahead of our need, which might be a six-week lead time from a major equipment provider or having one of our systems administrators run down to the local server store in extreme emergencies.

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