Microservices or Monolith? Why One Size Doesn’t Fit All in Software Development

Following the recommended methods and techniques may seem like common sense to many. It’s only natural to strive for excellence in software development. Adhering to accepted best practices is a reliable approach to ensuring high- quality software. In the early days of my career, and likely for most people, too, I was all in with this philosophy

Throughout my career journey, I’ve had the opportunity to work with various companies. Ranging from small startups to large corporations and from consumer goods to B2B products and software-based companies, as well as those dealing with physical goods too! I’ve been exposed to cutting-edge software solutions and standing established systems. Experiencing it all firsthand has been quite a learning curve for me! I’ve noticed that although certain software practices are usually effective, it’s essential to consider the situation you’re dealing with. Honestly, there is no one-size-fits-all solution.

Let’s delve into the realm of microservices for a moment now! Bashing systems are all the rage these days. Have you noticed that, too? I’ve found myself in discussions on this topic, and truth be told, I haven’t always been a staunch defender of monolith architectures, either. Who would be? Managing a codebase with 10 developers can quickly become chaotic—especially when making core code adjustments or updating libraries. Yeah! I totally understand why teams would opt to use microservices. It allows each team to manage their part of the system independently, and that can definitely help get rid of a lot of problems and challenges.

Before diving headfirst into microservices trends and technologies, let’s remember the challenges they bring along with them well. Each microservice having its own repository might seem advantageous, but things can get tricky when dealing with a feature that spans multiple services. Talk about a headache! Deployment procedures can be a whole other ball game altogether. If you ask me, ensuring that everything is rolled out in a precise sequence without breaking any existing compatibility can really increase stress levels. A monolith system setup like that doesn’t cause any concerns, as all the code is deployed collectively in one go. It’s a one-time deal!

It’s a real headache to keep track of all those monitoring tools and backups for each one individually… Let’s remember the importance of regularly testing those backups, too. Imagine finding out they’re no good when you’re in dire need of them!. Let me tell you from experience. What more of these microservices do you have under your belt? The more time you spend sorting through all these gritty details, the more It’s like being stuck in a loop of maintenance work!

And let’s consider this, too. For a company that’s expanding rapidly, working with microservices might complicate the process of bringing in developers quite a bit. Typically speaking, a single unified system is more straightforward for newcomers to get the hang of. Unless it’s all chaotic and disorganised. Admittedly, Docker does offer some simplification here; however, it does mean you have to update your Docker setup whenever there’s a new microservice or changes to an existing one occur. It’s like trying to hit a moving target!

If your team consists of a handful of developers, sticking with a monolithic architecture may be the most practical choice initially. As your team expands, the upkeep required for a monolith could eventually surpass the complexities of microservices, prompting you to think about transitioning. However, adopting microservices prematurely can lead to more complications than benefits—believe me.

Let’s explore the concept of connecting data for efficiency. That’s a clever idea! In SQL databases, you can achieve this through queries, and in NoSQL, by embedding additional data in a document. That’s a logical approach right there! Why go through the hassle of running multiple database queries when just one can get the job done efficiently? Optimising in this way can work wonders for scaling up and significantly improving performance in the short and medium run.

Here’s the catch; Over time, unexpected challenges may arise. Picture this scenario—you’ve decided to divide your system into microservices. However, if a large portion of your data is interdependent. It could significantly restrict your capability to proceed with the split. You could end up in a situation where you’re overwhelmed by data and struggling to determine how to divide it, only to discover that there’s no room for separation. Quite frustrating, isn’t it?. When transitioning between systems or platforms, you may have to modify a significant portion of the code to separate the data effectively.

Let’s discuss scalability in relation to SQL usage here. SQL servers are not notably recognised for their ability to scale horizontally, and a single server has its limits, too. Dealing with JOIN operations can pose a challenge when attempting to partition tables into different databases, as JOIN queries cannot be executed across multiple servers. Segmenting the database becomes a task as you must guarantee that all the data you intend to join resides within the same shard.

Ensuring that your data remains presents its own challenges, too. Numerous queries increase performance demands, and managing independent data sources adds complexity to the mix. So, how should this issue be tackled?

Honestly, there isn’t a solution to this question—the ideal approach varies depending on the type of software being developed and its target audience. Each option has its pros and cons that need to be weighed. Simply following a trend isn’t enough; it’s crucial to consider your specific circumstances. Every design choice should be backed by a rationale rather than just following the crowd. When the day wraps up, it ultimately comes down to what suits you best.

Article by Dave