Scalable code is code that can handle increasing amounts of data, users, or complexity without compromising performance, reliability, or maintainability. It is a crucial skill for programmers who want to build applications that can grow and adapt to changing needs and demands. In this article, you will learn some tips and techniques to create scalable code in any programming language.
One of the most important factors that affect the scalability of your code is the choice of data structures and algorithms. Data structures are the ways you organize and store your data, such as arrays, lists, stacks, queues, trees, graphs, etc. Algorithms are the steps you follow to manipulate and process your data, such as sorting, searching, filtering, etc. Different data structures and algorithms have different trade-offs in terms of time and space complexity, which measure how fast and how much memory your code uses. You should choose the ones that best suit your problem domain and optimize your code for the most common or critical scenarios.
Overly complex code is a symptom of inappropriate data structures. Overly complex data structures are a symptom of inappropriate algorithms. Technical debt contributes to inefficient or complex data structures and algorithms. Anticipate likely future requirements before committing to a design. Design an overall framework that can withstand upgrades and enhancements. For scalability, insist on separation of unrelated data and functions. Carefully incorporate caching of heavily-referenced but infrequently-modified data. Avoid unnecessary generality. Minimize abstraction layers. Leverage frequent hardware upgrades to obtain higher transaction rates without software changes. Push validation into the UI. Design for speed. Automate stress tests.
Data structure plays an important role in the performance of any application. Choosing the right data structure resolves the many performance issues. For example:- You can use an array when you need fast random access and have a fixed or known size. Use a linked list when you have dynamic size requirements, and insertions or deletions in the middle are common.
To create scalable code, focus on modularity, allowing for easy updates and maintenance. Optimize for performance and resource efficiency to handle increased load. Implement strong, consistent coding standards for uniformity. Use version control systems for better collaboration and tracking changes. Regularly refactor and test your code to ensure quality. Finally, leverage cloud services and serverless architectures for flexibility in scaling.
DSA (Data Structures and Algorithms) is quite important for creating scalable code. It is very important to know DSA in depth and know when to apply each one of them. The correct use of Data Structures and Algorithms will make the code more scalable.
It looks trivial, but it can make or break a functionality. Having data that's tree-structured and not observing or knowing the operations of such a structure, can introduce a lot of complexity. How should you search for an element? BFS or DFS? Identifying them effectively and early will make the implementation trivial.
Another key factor that affects the scalability of your code is the design and architecture of your code. To ensure your code is well-designed and easy to maintain, following the SOLID principles can be beneficial. SOLID stands for Single responsibility, Open/closed, Liskov substitution, Interface segregation, and Dependency inversion. These guidelines for object-oriented programming emphasize creating code that is modular, reusable, testable, and adaptable. Single responsibility means each class or module should have one reason to change. Open/closed means each class or module should be open for extension but closed for modification. Liskov substitution requires each subclass or derived class to substitute its parent or base class without breaking the functionality. Interface segregation suggests each interface or abstraction should be small and specific and not force clients to depend on methods they do not use. Lastly, dependency inversion states each class or module should depend on abstractions and not on concrete implementations.
Using Open/Closed & Dependency Inversion for scalability I find highly effective. It's hard to build big projects when the dependency arrows point in all directions. Having models that depend on UI or API details will break the product along the way and will crash under its own weight. The bigger the scope of the product, the more important these principle become. Use the Single Responsibility principle, to constantly break classes up into smaller classes. This will enhance the clarity of the code. Open/Closed Principle is in the end the essential principle, allowing to defer hard and difficult implementation decisions for later. It not only saves on costs, in the end it will make the product more flexible, adapting to new requirements.
Software developers can use the SOLID principles to create more maintainable, flexible, and scalable software using five design principles. It was Robert C Martin who introduced these principles to object-oriented programming. For example: the first principle is Single repo which means that each class should have a single responsibility like a user class should handle user-related functionalities, and a logger class should handle logging, following the SRP.
Whatever the size of the project, * Unit tests, avoid them for the sake of coverage. The main intention is to test code with all possible test data set & verify the output is expected. * Sticking to above will stream line code in most possible ways, which will push us to explore different ways to make our Reliable and scalable. Few suggestions: * Responsibility oriented classes, this will avoid scattered commonly used code across projects. * Do use Interface instances rather concrete references. This reduces dependencies & tight coupling between the code/ features. * Keep an eye on growing features, they have all rights to mature as a framework/ module. * Embrace refactoring!
SOLID, like any other software engineering practice, is just a tool, not a dogma or rule. You can discard it if you need to. SOLID is a guidance to use OOP better, and it might impact performance because OOP can be slow. If you are scaling at the data level, you might need to optimize for crunching tons of data. In this scenario, DOD is better to maximize the hardware L1/L2 caches. And DOD don’t go well with OOP, and thus SOLID. So the inner parts of the code that are doing this heavy data crunching should not use SOLID. However, for the majority of the code, use SOLID since it will make development easier. It will usually be called only once per few millisecs compared to the inner part that will be called thousands of times in a millisec.
In addition to SOLID principles, applying Clean Architecture principles and modularity allows the modules to reusable and clean. Maintenance becomes of very low cost, code and feature extensions become easy, and can be tested with no effort required.
To improve the scalability of your code, you can use concurrency and parallelism, which enable you to execute multiple tasks simultaneously or in parallel. This technique can help you leverage the power of multi-core processors, distributed systems, or cloud computing to handle more workload and improve performance. However, it also comes with challenges such as synchronization, communication, coordination, and error handling. Therefore, it is important to use concurrency and parallelism wisely and carefully. Best practices include using appropriate tools and libraries such as threads, processes, coroutines, futures, promises; using appropriate data structures and algorithms such as queues, stacks, atomic variables; utilizing appropriate patterns and architectures such as producer-consumer, worker-pool, pipeline; and using techniques and strategies to avoid or resolve common problems like deadlock, livelock, race condition, starvation.
Wait!! ask yourself: Do this code actually need to have concurrency or parallelism? Understand the business (this needs to take you first to a design, and later to the code). Use it if you actually need it, but remember the KISS (Keep it Simple, Stupid) and YAGNI (You aren't gonna need it) principles. If you need it, add it, if you don't need it, just don't!!! You don't need another piece of complex code for no reason.
Before jumping into concurrency you need to be 100% clear about what you are optimizing for. It's highly important not to make premature optimizations, for some future needs that might not happen at all. Concurrency increases complexity a lot, so make sure to bump up the complexity slowly. Exhaust any other possibility before jumping to a parallelism. Here are 5 steps to implement concurrency effectively: 1. measure performance of an existing implementation 2. identify the bottlenecks 3. make a spike to demonstrate an optimized version 4. encapsulate the multithreading logic
In my second internship, I learned the importance of concurrency and parallelism for scaling code effectively. To enhance this, I created a Parallel Executor using GPars and Agent in Groovy, designed to manage the sub-processes spawned by the system. This development led to a notable 25% improvement in performance. Additionally, I ensured that these processes were executed in a thread-safe manner, prioritizing the stability and reliability of the system.
concurrency and parallelism should also take in to account how it's deployed. if the code is written as a 12 factor app, it will be relatively easy to scale by simply adding more instances of the code on more machines (eg in kubernetes). On the other hand, if the code does things like maintain complex state internally, then parallelism is much harder to achieve safely.
Finally, another way to create scalable code is to refactor and optimize your code regularly and continuously. Refactoring is the process of improving the quality and readability of your code without changing its functionality, while optimization is the process of improving the performance and efficiency of your code by reducing its time and space complexity. Refactoring and optimizing your code should involve eliminating code smells, such as duplication or long methods, applying design patterns, using code analysis tools like linters or profilers, and using testing tools like unit tests or load tests. By taking these steps, you can make your code more scalable, reliable, and maintainable.
Refactoring and optimizing your code is much easier if you're familiar with the tools you're using to diagnose problems in your code. If areas of your code is slow, it can help to know your CPU profiling tool to get a break down of where the work is going. Another tool is the inspect tab to see what network calls are being made and why they're necessary. Knowing how to use your tools is a step towards refactoring and optimizing your code better. It'll explain where the bottlenecks are and you'll be more informed on how to address them
The funny thing about refactoring is that every developer understands the importance of refactoring yet somehow only a few of them make it a practice. You can NEVER write a good quality code on the first try, you always write a code that works in the first iteration and the second iteration should be making that code better, and scalable. Writing good-quality code is not optional, it serves as a foundation for an error-prone application. Pair programming and PR reviews are a few techniques you can review your code and read other people's code to learn more. For project managers, I recommend allocating a dedicated portion, around 10% of the project budget and deadline, for code refactoring.
I always live by Kent Beck’s quote: “first you make it work, then you make it good, then you make it (runs) fast.” This means you need to refactor your code until it is good, clean and fast. You shouldn’t settle only with “it works”. Some pointers: - Unit tests are your friend. A good unit test will help guard your code from unintended behaviour caused by your refactoring. - Try to get a compromise from both good and fast code. From my experience, a fast code tends to be hard to read, and vice versa. Eg. DOD is very fast but it is hard to extend. So try to make it good and fast enough. - The exception is the inner loop. You need to make it very fast so just use anything that runs fast. Like DOD. Anything else is not as important.
I've never written "the most" efficient snippet of code on the first try. There's always ways to improve your code. The ability to refactor, break up, and ultimately simplify your code is incredibly important. Refactoring should be a final step done once your code is in working condition, and as your codebase grows it should be periodically under review to ensure that the quality of the codebase doesn't deteriorate.
Writing code is not a one-time Job. It involves making edits and understanding to get better every time. The more we put effort into making the code simpler the better our chances of making the code scalable.
It's hard to overstate how important it is to KEEP IT SIMPLE. Simple lowers the learning curve to the next developer. Simple makes for finding problems faster and cheaper. Simple means that you can refactor without it feeling like a chore. Simple makes room for robust and efficient. Take the inspiration from the UNIX philosophy. Each tool doing one, simple (but not easy) thing, that you can chain together to achieve complex. But start with simple.
Architect for scale, design for performance Design for testability, and run fast tests on every commit. * What works to scale to 100x wont work for 10000x, so your code will have to change in ways you can’t predict. Use the simplest, fastest data structures that support the problem you are trying to solve * In the inner loop, you better hope that’s an array
True, but many confuse least work with simplicity. Separating concerns seems complex, but it's vital. Copying a giant function may seem 'simpler,' but refactoring pays off. There's a middle ground before over-engineering. Legos are simpler than spaghetti. More upfront work saves trouble in endless bug-chasing sprints, making the initial effort worthwhile.
Write it like a stranger has to debug it at 3am. Any scalable code will require 24/7 support. Often those taking care of such systems did not create them. So make it easy to understand. After all the other side of writing these systems is caring for them and you might just be on the other end of that pager.
Imagine coding is like building with building blocks. Learning the basic rules of how to stack and arrange the blocks is super important. It's like making sure the tower you build won't fall easily. Now, when we use special tools (frameworks) to build bigger and fancier towers, we shouldn't forget those basic rules, so our towers can keep getting taller without collapsing. Understanding the simple rules helps us make really big and cool things with our building blocks!