How a programming language shapes the future of your product technology? And why it's important to consider it now?

Over the past years of working with multiple companies in multiple markets (fintechs, banks, social apps, B2B), I came to this realisation that how much choosing the "right" programming language shapes the backbone of your software product — something that seems negligible at first but comes with a higher price that will affect you in the future, yes.

Something to keep in mind

So before I dive in, let me say this first; I've never been a too much strict advocate on being one-sided towards any particular programming language nor being biased based on my expertise or favouritism, whether it's Java or Golang, Kotlin or Scala, C# or Clojure, JavaScript or Ruby, every programming language is designed to solve and address variety of problems and has it's own pros and cons (or much better to say "trade-offs").

Now, point is you should never ever assume that the any language can solve and meet all your product problems doesn't matter whether you're bootstrapping something from scratch, or building something on top of an existing infrastructure, or scaling an existing product technology or planning to migrate. You should always consider these "trade-offs" in your mind - what are you willing to pay and what will you get in return.

Picking the "right" most programming language can be bit challenging as it heavily depends upon your circumstances, business philosophy and how you're going to scale it, definitely the answer to this is no-silver bullet as it varies from situation-to-situation and trade-offs you're going to make.

But, I've come up with a framework model that can help you analyse your situation, get a closer idea and help you pick the right most programming language that can closely fit all your software demands, read it below:

1. Back to Basics ??

• Paradigm

Programming paradigms shape how problems are approached in software development. For example, object-oriented programming (OOP), used in languages like Java, is ideal for building complex systems following Clean Architecture (SOLID) design principles, essential to build autonomous large-scale enterprise systems. In contrast, functional programming (FP), seen in languages like Scala, emphasizes immutability and is preferred for data-intensive tasks where predictable behavior is key.

For instance, Python supports both OOP and FP, making it versatile for tasks ranging from web development to data science. On the other hand, procedural programming with languages like C/C++ is suited for system-level programming where control over hardware is crucial.

• Readability

The syntax of a programming language plays a crucial role in code readability and maintainability. Languages like Python are known for their clean, readable syntax, which closely resembles natural language. This simplicity reduces the learning curve and makes it easier to onboard new developers, making Python a popular choice for rapid development and prototyping.

In contrast, languages like C++ offer powerful features but come with more complex syntax. While this can provide greater control and efficiency, it may also lead to steeper learning curves and more challenging maintenance, especially in large codebases.

JavaScript strikes a balance with its flexible syntax that supports various coding styles. But (hot take): However, this flexibility comes with a price to pay which can sometimes lead to inconsistent code if not managed with proper standards, clean practices and principles.

• Type Safety:

A programming language’s type system significantly impacts code reliability and development efficiency. Statically-typed languages like Java and C# enforce type checks at compile time, reducing runtime errors and enhancing code safety and preventing major crashes during runtime saving you the cost of suffering big incidents or downtimes if running on large-scale.

In contrast, dynamically-typed languages like JavaScript and Python perform type checks at runtime, offering greater flexibility and faster prototyping. But (hot take): This flexibility can lead to type-related errors that only surface during execution, which may require more rigorous testing to ensure code stability.

PS: TypeScript, a statically-typed superset of JavaScript, provides a middle ground by introducing static types to JavaScript. This blend of flexibility and safety has made TypeScript increasingly popular for building large-scale JavaScript applications.

• Concurrency and Parallelism:

Concurrency and parallelism are critical for modern applications that require high performance and responsiveness. Concurrency allows a program to manage multiple tasks at once, while parallelism enables tasks to run simultaneously on multiple processors.

Languages like Java provide robust concurrency support through threads and executors, making it a solid choice for building multi-threaded and real-time systems. Go is designed with concurrency in mind, using goroutines to efficiently manage thousands of concurrent tasks, making it ideal for scalable, cloud-native applications.

On the other hand, JavaScript handles concurrency using an event-driven model with its async/await syntax, which simplifies working with asynchronous code. This approach is well-suited for I/O-bound tasks like handling web requests in Node.js, though it doesn't support parallelism directly.

• Tooling, Productivity & Cross Platform:

The quality of a language's tooling can significantly impact development efficiency. Java boasts a mature ecosystem with powerful IDEs like IntelliJ IDEA and Eclipse, comprehensive build tools like Maven and Gradle, and extensive debugging and testing support. Python, with tools like PyCharm and integrated package managers such as pip, excels in simplicity and rapid development, especially in data science and automation. JavaScript’s tooling supports rapid front-end development and easy integration with backend services, especially in full-stack applications.

A programming language’s ability to operate across multiple platforms can be a decisive factor in product development. Java is renowned for its "write once, run anywhere" philosophy, thanks to the Java Virtual Machine (JVM). This cross-platform compatibility makes Java an excellent choice for enterprise applications that need to run consistently across different operating systems.

Python also offers strong cross-platform support, running seamlessly on Windows, macOS, and Linux. Its versatility is further enhanced by frameworks like Django and Flask for web development, and PyInstaller for packaging Python applications into standalone executables.

JavaScript is inherently cross-platform as it runs in web browsers, making it the backbone of web applications. Additionally (hot take), with Node.js you can easily write a web server in JS/TS while writing front-end code as well, giving you the flexibility to move in full-stack.

2. Running at Scale ??

I'll break scale into 3 parts:

1. Concurrency: Languages like Go are designed with built-in concurrency features, such as goroutines, which efficiently handle many simultaneous tasks. In contrast, Java uses threads and thread pools, offering robust but sometimes more resource-intensive concurrency support. (hot take) JavaScript handles concurrency through an event-driven model with asynchronous programming, ideal for I/O-bound tasks but requiring careful management of async operations to avoid performance bottlenecks.
2. Memory: Languages with automatic garbage collection, such as Java and Python, simplify memory management but can introduce latency due to garbage collection pauses. On the other hand, (hot take) languages like C++ give developers manual control over memory allocation, which can lead to better performance but requires meticulous management to avoid memory leaks and fragmentation.
3. Computation: C++ and Rust are known for their high execution speed due to close-to-metal operations and minimal runtime overhead. Java, with its JIT compilation, offers a balance between execution speed and portability. (hot take) Python, while highly productive and versatile, often lags in execution speed due to its interpreted nature.

3. Ecosystem ??

Picking a programming language that exactly fits the right ecosystem for the software product you're building is often most important thing. For example, if you've a web app then you can easily pick JavaScript (or TS) ecosystem for keeping it in full-stack cycle (NextJS or NodeJS backend, ReactJS or Angular frontend) essentially saving you the cost of running and maintaining a different backend stack, but me mindful of the trade-offs which JavaScript ecosystem has (loose testing, dynamically-typed, no type-safety and async I/O bounded tasks).

On the other hand, if you've right knowledge and resources than it's no-brainer not to pick more robust language or framework for building backends that supports wide variety of libraries and frameworks especially for large-scale distributed systems.

(hot take) Rust, Go, Java, Scala, etc. would be awesome choice for building core backbone of your tech product, I'd favour these languages more over dynamically-typed languages because they are more type-safe (compiled), more fine-grain memory control (or built-in memory management), can deal with high concurrency and high throughput (multi-threading and parallelism) and support wide libraries and frameworks to build distributed applications favouring developer productivity, testing and DevOps.

4. What business is asking for? ??

In the end, everything revolves around business and organisation philosophy which directly impacts how technology grows and scales, of-course scaling comes with alot of challenges but more important question is how well are you prepare to address these upcoming or growing concerns when your codebase grows, team expands, traffic increases and you start expanding geographically across multiple-regions and data centres.

How will you ensure strong testing coverage? What about high-availability of crucial systems processing huge chunks of data? How will you efficiently manage your compute costs on clouds striking balance between spending and generated revenue? How teams will write code, test and deploy it within couple of seconds without any external human dependency?

These are some of the highlighting questions which I believe any technical leader should consider before diving into just "building" part and building it the "right way".

So, which one are you picking?

Concluding it here, there's no silver-bullet to decide which one will suit you the best. All it depends upon your product, business and engineering philosophy, what I believe more often is understanding the correct trade-offs between each programming languages and answering some of the questions before you dive into it.