How do you balance software flexibility and performance?

Software should be "soft", as hardware is "hard". So, it must be easy to change. This requires a deep analysis and a good design before building a system. There a plenty of methodologies and techniques that can be used such as design patterns, domain driven design, test driven development, continuous integration and deployment, to name a few.

Software flexibility is most easily attained with strong interfaces and flexible implementations. In addition, software should be able to be refactored with minimal effort by ensuring proper automated tests.

The successful products I've worked on in the past had software flexibility built through fail-fast, iterative development, plug-and-play modules, and easier interfaces. Over the years, the only constant that has hit us hard more frequently is change in processes, tools, languages, or mindset. Being an adaptive, extensible architecture is not a good to have but a must-have, and if not embraced early on, products are neither innovative nor future-ready.

I always suggest a modular software architecture, allowing for agile development and quicker stakeholder decisioning. In addition, in the case of object oriented languages, inheritence does matter. As far as performance, in leveraging a modular software architecture, the runtime elements should also be separated from the queriable aspects. Thus, aspects executing at runtime will not be slowed by historical anaysis. The runtime system as a whole should also be instrumented by performance policy so that when thresholds are exceeded (memory, io, etc), operations teams will have situational awareness on the need for intervention. Another trade-off is the investment in recruiting a skilled architecture, development & QA team.

Balancing flexibility and performance is an ongoing process. It requires a combination of good software engineering practices, continuous monitoring, and a willingness to adapt as requirements change over time.

At a more detailed level performance can be viewed as a measure of efficiency, the efficiency with which the software is using the main resources it has available: compute/CPU, memory, storage, network. Software performance is measured through benchmarks. The results of benchmarks for one software package can be compared with those of other software packages or they can be compared with previous versions of the same package. When compared with previous versions, we can find performance boosts/improvements/enhancements or regressions. Of course we want to know exactly which parts are slow and Profilers are one class of tools we use to find that out.

The most important aspect of performance is related to user experience in comparison to system load. The 5,000th concurrent user should have the same experience as if only one user is on the system. Less visible software does not always need to be as performant but has to reliable.

Deliver as much as possible , using small as possible assets . this is the heartbeat , but it can not be strictly measured ,since there are a lot of constraints, regarding the product, you need to deal with. In our days in most of the not realtime systems you don't have to measure performance at a milisecond, also there is a lot of software assets that you can combine to serve a high scale system, such as queus , gateways, caching , scaling microservices, so the real challenge is to combine them all together letting each part do what it does the best.

Again, I agree with the definition of performance here. Each problem in each domain has some performance related requirements that are not usually well defined. Each solution to those problems will inevitably make a trade of performance, flexibility and complexity in order to resolve the problem. Some very high percentage of the time, engineers tend to first build to solve the problem, and then tune the solution for better performance. Often, that means that the performance you can obtain is limited to the possibilities in the solution space provided, rather than the highest performing solution that could be created.

As a DevOps engineer, my job involves ensuring that the software we deliver finds a harmonious balance between adaptability and performance. It's like conducting an orchestra of technology, this usually involves using automation for adaptability, continuous monitoring for real-time feedback, IaC for rapid deployment, and scaling gracefully. Security of course remains at the heart of performance, and effective collaboration among teams is essential to ensure that everyone's needs are met.

Often we get lost in a realm of chasing “one stop solutions” and “big bangs” before working on a deep understanding of the business so the technology can be aligned with the goals and objectives of the business. Once you understand the goals of the business you can then balance flexibility and performance through the architecture of the system. Building platforms that do not align with the business will limit both flexibility and performance. The technology architecture needs to align with the business architecture.

Everything has a cost. For example, strict service level agreements (SLAs) require investment in supporting software, and high availability comes with added expenses. We must find a balance based on customer needs and ROI. While microservices are popular, they aren't always necessary. Monolithic architectures often suffice. Don't adopt trends blindly; choose what suits your project's needs.

Systems should perform according to how they are designed. Flexibility is competence in the face of changes in directionality. Being highly flexible is wonderful, but pivoting in response to changing requirements boils down to a matter of speed, skill and communication. Product Owners/Managers must be deeply in tune with their engineers, and their engineers must be deeply competent with their technologies to design, re-design, and iterate enhancements with speed.

Flexibility and performance are orthogonal requirements only if we are building a generic application. Application built for a very specific purpose will have flexibility and performance working together.

In most cases you actually don't. The most flexible things are very narrow in what they do. The fastest things that can scale the most are likewise very narrow in what they do. Basically, if you want flexible and performant - you simply do less. The problem becomes when we start stringing together all of these simple components into larger and more complex systems. So I say this with an emphasis on not trying to reduce the problem to absurdity... but we should attempt to avoid doing that. Good systems are as simple as possible. When we stray from simplicity, we run into problems with our solutions being flexible and scalable.

As for me, it's always better to stick to flexibility first rather than performance as currently computation resources costs less than employee time spending on development/support. If this piece of functionality is getting to bottleneck, only than it makes sense to spend some effort to performance.

Donald E. Knut: "... Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97 % of the time: pre mature optimization is the root of all evil." First of all create the software. If, and only if, it needs, then attack real bottlenecks. Use Pareto, if you solve 20% of bottlenecks, your software may be 80% faster.

DevOps can be a key to balancing software flexibility and performance with strategic decisions. Identify the trade-offs by considering factors like time-to-market, user experience, and scalability. Designing software with modularity and application into loosely coupled components with microservices architecture can be beneficial in achieving flexibility. Automated testing to ensure that changes do not introduce performance regressions. Implement CI/CD pipelines to streamline the release process and catch issues early. IaC principles allow flexibility in scaling resources based on demand and it is consistently reproducible. Review and adjust capacity planning with future usage patterns so that infrastructure can handle increased load.

The simple answer is solid architecture and processes that define the software. Too often, we don't consider the big picture, especially within new applications. Leveraging proven patterns is also commonly a great architectural principle. For example, semiconductor manufacturing is one of the more complex systems environments on the planet. Sure the are others, but from the amount of data and control, it's very high on the list. One pattern that has greatly enabled semi are logical microservices. The logical part comes from clear architecture as to the design and what really should be coupled and decoupled. Another is the use of a memory resident AI/ML based system that allows the centralization of the business logic.

They are mutually exclusive. it should be balanced at times in anticipation of future requirements. In my experience we need to trade off loosely coupled architecture, configuration model, unnecessary use of cutting edge tech to reduce OPEX, maintenance cost, operations hence the better performance. At the end focus should be on solving the problem software is intended to with best user experience in market.

In a prior job, a coworker introduced me to a useful mnemonic: YAGNI (You Ain't Gonna Need It). Put simply, it's a reminder that most often, you should only code for the use cases that exist NOW, not also for future requests that haven't been made yet. It is of course always good to write code that can be reused and extended, but don't try to be too flexible and anticipate what a future integrator might conceivably want - doing this usually leads to unused code that naturally can't be optimized for the cases it's actually being used for, and you can make many mistakes along the way on how to address future actual use cases.

In my experience Having enough discussions to fully understand the requirements allows for creating code that is necessary and flexible to meet future needs Not everything needs to be built out initially, so long as the mvp points are there This facilitates testing that can be targeted to address potential performance issues and have those identified early in the life cycle, thus permitting time to address before go live

Follow the SOLID principles as much as possible to the spirit. They are really solid in making your application flexible. But remember and be cautious not to over do. Now comes performance. Here as well, follow resource model. Know your resources and select fitting framework or language. If working on existing project, see for options to divide and conquer.

If you don't make it flexible, you make it dead. Concept on early stage is almost never what it is when matured... That said it may be not bad choice. Make it cheap and fast, let it die when new requirements arrive and build new one. Depends on what you are building and how easy it is to replace it.