# Get a list of unique commands run on this machine $ cat /var/log/auth.log | cut -d':' -f11 | sort | uniq
Architecting a software system for malleability
The past few years of software development has given me this one beautiful insight:
I can’t predict the future
To illustrate this point on a personal level, it wasn’t even my plan to be a software developer. My undergraduate studies were in sports physiology, and the intention was to follow that up with sports medicine. However, through the various twists of fate inherent in life that was not to be, and I wound up helping building and shipping eCommerce stores.
The vagaries of life do not extend only to me, however. They’re an inherent part of life. The psychologist Dan Gilbert says in his talk “The psychology of your future self”:
We asked people how much they expected to change over the next 10 years, and also how much they had changed over the last 10 years, and what we found, well, … people underestimate how much their personalities will change in the next decade.
So, I didn’t know I would be here 6 years ago, and based on Gilberts assessment we don’t know who we’ll be in another 10 years. It follows that its extremely difficult to see where our technology should develop over the next 10 years. We can assess that by reviewing the last 10 years;
The Apple App Store, Chrome, Android and Bitcoin were released in 2008
Maps with GPS reached Android, in 2009
Both the iPad and car2go (short term, instant car rentals) were released in 2010
Google+ was launched and Adobe begun to sunset Flash in 2011
Windows 8, 4k TV, Windows phone, Curiosity on Mars, Google Glass, 802.11ac and Space X flying to the ISS were all in 2012
Oculus Rift, the Smart Watch and Touch ID landed in 2013
Self driving cars began to emerge in 2014
Apple Pay, Project Loon and was released in 2015
IOT began to appear in earnest in 2016
Self driving trucks, reinforcement learning (AlphaGo) and the smart speaker made great strides in 2017
Cheap neural networks (Tensor Flow) executing on phones, bluetooth headphones that automatically translate and the GDPR were part of 2018
Which brings us to our current year of 2019. Each of those technologies had an impact on the market, shifting the balance of power in various industries dramatically and providing new opportunities for those who are lucky enough to find the talent, capital and drive to take advantage of them.
The lesson to draw from these changes is that the world changes at a far higher rate than one would naively imagine and when designing our software systems we should factor in this high rate of change so that we do not drive ourselves or our project partners into financial ruin attempting to innovate around their next business offering
Platforms that have successfully adapted
It stands to reason that if we were to assess how to design our own software to be maximally adaptable we should look at what others have done in the past that have successfully adapted to industry changes.
Apple, Cisco and Intel are all hardware (in addition to software) companies, so for our purpose we’ll dismiss them as a targets. Google, Microsoft, Facebook and Adobe are all primarily software companies however, so can serve as good lessons how how to build systems that are well structured over time. Google and Facebook are famously “internet” heavy companies, but both Adobe and Microsoft have pivoted in recent years to be much more internet driven. Microsoft have famously stated Windows 10 will be their last version of Windows and Adobe is making significant moves into internet driven business with “experience cloud”.
So, these companies are moving towards software that is:
Delivered primarily via the internet
Developed and delivered to users in increments, and adapted based on user feedback
Sold via an “ongoing revenue” model, be that subscriptions or advertising
The thing that these companies have in common is that their products are all designed around software that embraces continual change, in any arbitrary direction.
Software design requirements
To understand how to design software it’s first worth unpacking why we’re building software in the first place. Generally speaking I build software to make a computer solve a problem in a reliable way to derive some sort of useful work out of it. Programs can be as simple as:
To Magento 1’s behemoth 1.7 million lines of code:
$ sloccount clean-magento-ee Total Physical Source Lines of Code (SLOC) = 1,730,997 Development Effort Estimate 502.62
Regardless, software programs exist for some human purpose; to take some human input and return some human output (at some point or other).
Designing for reasonability
Softwares utility is a function of its predictability; of our understanding of how we can use it to accomplish work. Perhaps the best example of this is the Unix utility called cat:
$ cat foo bar
This program takes the contents of the file “foo” and prints them to screen, showing “bar”. The particularly remarkable part about cat is not this behaviour, but rather:
That it was initially designed in 1971
That it hasn’t changed
It is the very essence of a predictable program. There is a whole swathe of unix programs that follow this trend; enunciated by Peter H. Salus with:
Write programs that do one thing and do it well.
The wisdom of this minimalistic approach is difficult to overstate. Programs that are easily predictable and follow a “standard” approach have some distinct advantages:
The time to understand and fit them in to our architecture is minimal
Their potential use cases are large
Their interoperability with other systems is large
Additionally, keeping the feature set limited makes it much simpler to maintain this software, especially while retaining knowledge of the use cases it is being used for — both those initially designed and those accrued over time.
This dramatically reduces both the cost of maintaining one piece of software, and the likelihood that this particular piece of software will change over time.
Designing for interrogability
Generally speaking we do not design software just for ourselves, but additionally to solve problems on behalf of others (usually for some monetary compensation). This creates a disconnect between:
How we understand the problem, and design the software to be used
How the software is actually used
John Allspaw refers to this as “above/below the line”, in which each user, developer and other stakeholder has a different conceptual model for how the software “works”. That model is only grounded in “reality” by interrogating the software to ensure that it’s actually functioning as initially designed. To make design decisions as to how the software should be further reduced, restructured or replaced we need to know how the software is being use.
We can start this process by interrogating cat. cat is written in c and runs on unix . Unix (particularly Linux in this example) exposes a whole set of tools to allow inspecting both cat and other applications, such as strace , ltrace, perf with additional tools like sysdig . However, while these tools give us an extremely good idea what the application is doing in specific invocations they are cost prohibitive to run the entire time. Instead, we need to move to less granular tools. Unfortunately, this comes with a tradeoff — we need to guess ahead of time what we need to instrument.
There are a three broad way of doing so:
Without going too far into the detail, an application should be designed such that it exposes the detail required to understand how its working. This is useful both for understanding when the application is not working correctly as well as understanding how its used under normal conditions.
When choosing how to instrument an application the property that is perhaps the most useful is being able to ask questions of the software — to interrogate it. Logs are perhaps the simplest way to do this, allowing us to check internal program state at a later point when an issue is reported. But time series data is a very close second, and allows querying for application behaviour over time. This allows making judgements about how people are using the app, rather than just snap-shotting application internal state over time. The Prometheus documentation explains how to instrument an application to maximise its interrogability.
By understanding how its used we can modify our program to make those use cases easier or more efficient. We can additionally drop some of the functionality that is not being used over time to maintain program simplicity and reduce the cost of maintenance and risk.
As software is used more frequently it will be better understood by its users. That is also where software engineers should invest the most time ensuring the software is designed in such a way it is easy for users to understand and reason about as designing for simplicity will further increase uptake, forming a virtuous cycle until an “optimal simplicity” level is reached.
Design with a focus on solving the users problem
The process of shipping software is a complex one, involving:
Business process modelling
General architecture design
Software component design
Each of these disciplines is a complex one that involves a staggering amount of research, discipline, and effort over time. Accordingly it’s more likely than not that each component will have specialists, each of whom seek to do the best job they possibly can.
It’s important while designing and implementing this system that the goal is to solve a users problem. One can get lost in the minutiae of one’s own discipline, creating a relative work of art — at the expense of the system as a whole, and the user with their problem.
To solve the users problem each stakeholder needs to subjugate their own ideal solution in favour of a solution that favours the customers happiness. To retain this focus while developing the design needs to put the customer at the forefront of all decisions; each decision justified in relation to how that decision helps the customer solves their problem.
By doing so, while each component of the system may be even more complex or less elegant for those who have built it, the vast majority of users will experience a simpler, easier to understand system.
Designing unsurprising software
Software that is “surprising” is software that is unpredictable. Unpredictable software is harder for users to make use of, in turn driving usage of the application in unpredictable ways. This unpredictable usage means either either:
A high amount of refactoring to make the unusual mechanism the standard use case
A high amount of refactoring to shift users to the standard model
Regardless, quite a bit needs to be changed. Accordingly the goal while developing software should be to be the “least surprising” or “least astonishing”. This principle is captured as the “principle of least astonishment”:
“People are part of the system. The design should match the user’s experience, expectations, and mental models.”
Unfortunately what users find surprising is context specific. While designing an alarm clock users might expect that once they turn off an alarm the alarm goes away until the next occurrence, they might expect that hospital monitors switch alarms back on themselves after a period of time. Accordingly, designing software that does “what the user expects” requires an in depth understanding of that user, and the context in which they’re using the software.
That is surprisingly hard to come by; the study of software development is such a complex one it precludes a depth first knowledge of other fields. However, one can take two strategies to help design software in an unsurprising way:
Design software after an already established pattern. Design hospital software like other hospital software, and alarm clocks like other alarm clocks.
Work closely with users, soliciting and integrating their feedback
Even the most intractable problems can be made simpler and easier for users to understand with a deliberate design of software to match their conceptual models.
Designing software on balance
Given the above requirements perhaps the hardest thing to do is to strike a balance across them, and design the software for simplicity relative to each designer or consumer of that project.
cat, for example, may be simple to me as a developer but it is likely not simple for my grandmother.
Each stakeholder has a different model of the software:
Users model it in terms of the problems they’re trying to solve
The UX team model and optimize for users usage of the application
The business logic team attempt to model the user in the software
The business owners model it in terms of a return on investment
This makes it hard for the software architect to be able to make the software simple relative to all users. However, there are ways in which it’s possible to determine how to evolve the software to suit the stakeholders over time.
As the software evolves and the stakeholders learn more about each other it will become clear that there are commonalities in how those users see the software. For example, in the case of an eCommerce store the user, UX, business logic and business owners all have approximately the same notion of what an “order” or “shipment” needs, though with varying degrees of detail.
By writing the software to deliberately communicate its own nature with all stakeholders, writing supporting documentation to clearly explain that software where the software is incapable of explaining itself and minimising the amount of “views” that the software has the software itself can remain simple, and all stakeholders have a similar mental model of the software.
Once these patterns are established continue reusing them, reinforcing a consistent way of reasoning about that software.
Understanding what we’re designing
To understand what we’re designing, we first need to think in terms of the problem we’re solving.
In a past life I spent considerable time training to be a boxer (more specifically, a Thai boxer). Though it was only a habit, it was an activity that I fundamentally enjoyed. It additionally necessitated the purchase of some equipment. To participate, I would need.
1x. 16 ounce Boxing gloves
2x. Mouth guards
4x Singlets, Shorts & Wraps
1x. Groin guard
1x. Shin Guards (Heavy)
1x. Shin guards (Light)
The software journey we’ll consider then is the one that hopes to connect me with the equipment I need to continue my boxing profession.
Modelling the buying and usage journey
In the above equipment there is little value for it to be particularly well styled, emphatic or otherwise different — there is little fashion in the world of “boxing equipment”; they’re essentially commodity goods. Above all I would prize:
As a buyer of this equipment, I’m likely to undertake the following steps:
Discover the need for this equipment as I join (or rejoin) a boxing gymnasium
Discuss with my peers what a set of reliable equipment would be. If it’s available on site, I would likely simply purchase it there.
Further research what equipment might be available, and look for reviews that help me determine what brand of equipment I would like
Make the purchase of this equipment, and use it for a period while training
Purchase either the same or new equipment once that had been worn beyond its utility.
Each of those components have some reflection in software; from joining the boxing club to evaluating the equipment after a period of use for reuse.
Designing the software itself
Given our understanding of the principles required to design resilient software, let’s try and help our boxer find the equipment they need.
Launch and Iterate
As we’ve established, we’re poor predictors of the future. So to understand our problem we need to start solving it.
The simplest way the users buying journey can be modelled is simply a cash transaction for equipment at the boxing gymnasium. This is a solution completely without software, but as a process is a reasonably elegant solution:
It’s simple, and reuses existing primitives (cash, equipment)
It’s extremely low cost and easy to implement
This allows us to start filling out our business process. Things like “where do we purchase our goods from” or “where do we store our goods” or “what do users want to know about our goods” all start to come up and need solving.
Resolving solved problems
Given our scenario our boxing gym has been holding equipment but is struggling to understand what equipment sells well, what sells badly and how much stock is remaining. In terms of our previously defined principles the process is not interrogable.
In this the use cases are fairly common, and there are already solutions that have largely solved these problems.
Dropping in a solution that solves “enough” of the problem is usually a good next step. Things like VendHQ, Square, Xero can solve the vast majority of these needs, and where they’re not yet solved a human process can make up the difference.
These solutions are perhaps not the most technically elegant. However, they’re already shaped by user demand and are thus the most conceptually simple to our user — they solve the users problem better than we’d be able to ourselves.
Be careful about solutions that solve more than the problems that need to be solved now. It is harder to remove process than it is to add it, and unless there is a demand for a feature it is likely redundant. That increases complexity for no discernable gain.
Building additional services
Our boxing gymnasium is now successfully selling equipment to its members, however the gym has only limited staff and does not have the time to explain the tradeoff between the various pieces of equipment prior to the start of the class.
To address this, they need software that will allow them to list their services on some sort of consumable format — the defacto implementation being on the internet.
Depending on the software chosen previously it’s possible that our boxing gym can simply “switch on” an integration with Shopify or Magento that allows them to reuse their existing data. If so, this is the best solution in this case. The gymnasium can continue to use their existing services with limited additional learning required to list their services online.
However, if such an integration is not available it is worth beginning to reevaluate the entire business stack such that a single solution can solve all problems. While this means a higher initial invest, it will be a significantly lower invest in terms of learning, diagnostics and any further development over essentially any timescale.
Designing a unique service
Our boxing gym has now grown and sells equipment both in its gymnasium and online. However it would like to develop a new feature that doesn’t exist on the market — the ability to sell equipment directly from other gymnasiums.
This requirement is so unique that no existing software can be used to model this particular requirement. Either existing software will have to be repurposed, or new software designed.
Whether to repurpose existing software or redesign new software essentially depends on the total feature set required for the new software. If the business is well understood and the requirements limited designing new software offers some compelling benefits:
The software can be designed to take advantage of business efficiencies
The software is well known by the implementing team
The software in absolute terms is not as complex
However, comes at the significant risk of losing track of the implementing team. If that team disappears, a new team will need to relearn the entirety of the business. Accordingly, if the software is being contracted out using a “standard” solution with minimal customisation buys insurance against relations with that contractor going sideways.
For the purpose of this we’ll assume that the development team is in house and has a vested interested in the success of the project.
Perhaps the best thing to do is to rebuild the business logic entirely. This means losing many features that are inherent in commercial or open source software, but it also dramatically reduces the absolute complexity of the system. This allows much faster development targeted directly for the needs of the business.
The result is software that is simpler, more targeted and in better control of the business — presuming the development team is capable of such software design.
Downsides of malleable software
Malleable software is exceedingly hard to design. There are some significant downsides to it:
As described in the example of the boxing gym owner, it was not economical to design software from scratch until the business requirement was such that no software existed that could be easily ported to the businesses need.
Designing software from scratch is an extremely expensive exercise. Developers are a scarce resource and developers that are driven by the results of the business even rarer.
It’s often a better balance to reuse existing primitives for services rather than take the leap for fully customized, malleable software. The more customized software is, the more expensive it is to maintain.
The process of understanding, designing and implementing software is an exceedingly difficult task. It requires an in depth knowledge of the problem, patience to put forward designs and rework them and the ability to implement the designs in software.
Software that is malleable does pay off, but only over a long period of time. The upfront investment is significant, and is better offset by incrementalism and the shifting to a self hosted solution only as there are no other options available.
However, once the initial design of the solution has been completed and presuming upkeep is not cost prohibitive, a solution that is more malleable will open more business opportunity.
Designing software is a complex process, needing to balance the needs of all stakeholders while keeping true to the vision that it intends to solve over a long period of time and with many different hands.
However, hopefully this article has provided some general background as to how software can be designed in such a way that it is more malleable, reducing the costs over the long term.