- Rule 1Don't Overengineer the Solution
- Rule 2Design Scale into the Solution (D-I-D Process)
- Rule 3Simplify the Solution 3 Times Over
- Rule 4Reduce DNS Lookups
- Rule 5Reduce Objects Where Possible
- Rule 6Use Homogenous Networks
- Summary

## Rule 3—Simplify the Solution 3 Times Over

Whereas Rule 1 dealt with avoiding surpassing the "usable" requirements and eliminating complexity, this rule discusses taking another pass at simplifying everything from your perception of your needs through your actual design and implementation. Rule 1 is about fighting against the urge to make something overly complex, and Rule 3 is about attempting to further simplify the solution by the methods described herein. Sometimes we tell our clients to think of this rule as "asking the 3 how's." How do I simplify my scope, my design, and my implementation?

### How Do I Simplify the Scope?

The answer to this question of simplification is to apply the Pareto Principle (also known as the 80-20 rule) frequently. What 80% of your benefit is achieved from 20% of the work? In our case, a direct application is to ask "what 80% of your revenue will be achieved by 20% of your features." Doing significantly less (20% of the work) and achieving significant benefits (80% of the value) frees up your team to perform other tasks. If you cut unnecessary features from your product, you can do 5x as much work, and your product would be significantly less complex! With 4/5ths fewer features, your system will no doubt have fewer dependencies between functions and as a result will be able to scale both more effectively and cost effectively. Moreover, the 80% of the time that is freed up can be used to both launch new product offerings as well as invest in thinking ahead to the future scalability needs of your product.

We're not alone in our thinking on how to reduce unnecessary features while keeping a majority of the benefit. The folks at 37signals are huge proponents of this approach, discussing the need and opportunity to prune work in both their book *Rework*
^{2} and in their blog post titled "You Can Always Do Less."^{3} Indeed, the concept of the "minimum viable product" popularized by Eric Reis and evangelized by Marty Cagan is predicated on the notion of maximizing the "amount of validated learning about customers with the least effort."^{4} This "agile" focused approach allows us to release simple, easily scalable products quickly. In so doing we get greater product throughput in our organizations (organizational scalability) and can spend additional time focusing on building the minimal product in a more scalable fashion. By simplifying our scope we have more computational power as we are doing less.

### How Do I Simplify My Design?

With this new smaller scope, the job of simplifying our implementation just became easier. Simplifying design is closely related to the complexity aspect of overengineering. Complexity elimination is about cutting off unnecessary trips in a job, and simplification is about finding a shorter path. In Rule 1, we gave the example of only asking a database for that which you need; `select(*) from schema_name.table_name` became `select (column) from schema_name.table_name`. The approach of design simplification suggests that we first look to see if we already have the information being requested within a local shared resource like local memory. Complexity elimination is about doing less work, and design simplification is about doing that work faster and easier.

Imagine a case where we are looking to read some source data, perform a computation on intermediate tokens from this source data, and then bundle up these tokens. In many cases, each of these verbs might be broken into a series of services. In fact, this approach looks similar to that employed by the popular "map-reduce" algorithm. This approach isn't overly complex, so it doesn't violate Rule 1. But if we know that files to be read are small and we don't need to combine tokens across files, it might make sense to take the simple path of making this a simple monolithic application rather than decomposing it into services. Going back to our timecard example, if the goal is simply to compute hours for a single individual it makes sense to have multiple cloned monolithic applications reading a queue of timecards and performing the computations. Put simply, the step of design simplification asks us how to get the job done in an easy to understand, cost-effective, and scalable way.

### How Do I Simplify My Implementation?

Finally, we get to the question of implementation. Consistent with Rule 2—the D-I-D Process for Scale, we define an implementation as the actual coding of a solution. This is where we get into questions such as whether it makes more sense to solve a problem with recursion or iteration. Should we define an array of a certain size, or be prepared to allocate memory dynamically as we need it? Do I make the solution, open-source the solution, or buy it? The answers to all these solutions have a consistent theme: "How can we leverage the experiences of others and existing solutions to simplify our implementation?"

Given that we can't be the best at building everything, we should first look to find widely adopted open source or third-party solutions to meet our needs. If those don't exist, we should look to see if someone within our own organization has developed a scalable solution to solve the problem. In the absence of a proprietary solution, we should again look externally to see if someone has described a scalable approach to solve the problem that we can legally copy or mimic. Only in the absence of finding one of these three things should we embark on attempting to solve the solution ourselves. The simplest implementation is almost always one that has already been implemented and proven scalable.