Last year as Amazon kick-started the Prime Day- its annual sale festival, users from across the US started complaining about site crashes. And while such events are common across internet companies, the fact that even Amazon, which carries the highest expertise in the domain and runs the largest cloud computing platform isn’t immune to such problems really puts forward the complexities involved in scaling any real-time application.

The reason the problem of scaling in real-time is so tough because it is extremely complex- consisting of an array of dynamic components, the failure in any of which can bring the entire system crashing down. Be it a UI component that’s consuming too many resources, a database component that’s overwhelmed by requests or simply a server component that fails to secure additional infrastructure to serve skewed traffic. Now as you might guess the solution to a multi-faced problem has to be multi-pronged. And while there can never be an air-tight solution, having a clear understanding of nuances would certainly help you make informed choices:

Beyond hardware

One of the common misconceptions is that scaling is all about additional infrastructure and while that’s correct to some degree (mostly for self-hosted apps), it is actually software that causes a majority of the issues. For instance, if you have auto-scaling enabled on your app, the system is supposed to scale back and forth depending automatically on real-time traffic. And because AWS, Azure or any other cloud service provider you may use have no dearth of physical infrastructure, apps should never crash due to high traffic but they do.

Tech bottlenecks

While every app development tool has some degree of scalability-related features built into it, some are obviously better at scaling than others. The differences essentially boil down to their individual approach to how they process requests. For instance, you can choose your application to process requests synchronously or asynchronously- a decision that doesn’t make much of a difference in low or moderate traffic. But when the traffic peaks, asynchronous applications are known to fare much better. The same goes for each of the components- framework, database, payment gateway, etc. involved in your app. Each of those components has a threshold- something that must be taken into the equation while designing your app.

Database vs. caching

This perhaps the biggest conundrum for app developers take eventually plays a vital role in determining the performance and scalability of any application. In general, the database has low memory cost but a high computational cost. Inversely, caches have high memory cost but lower computational cost. And because caches are clearly faster than database queries, it poses a very critical challenge- what components to cache and what components to store in the database? Though there is no Goldilocks principle to help you in the process, you must strike some kind of balance to not just optimize performance but also manage your application development cost.

Domino’s effect

To quote a computer science professor referring to Amazon’s case we discussed earlier, “These problems tend to feed on themselves — people retry loading, making the problem worse, or services complete partially. So shutting off services is often the better, but obviously bad, option.” The point is, once a problem creeps in, it generally gets worse and thus to avoid a full-blown crash, you must have some kind of backup plan ready to implement.

Overall, there are countless ways for any application to crash and it for that reason that even the most technologically advanced companies aren’t immune to this problem. That said, there are always pre-emptive steps you can take to minimize the risks involved. For that, however, you’d need expert mobile app development firms on your side to not just build a robust and resilient application but also troubleshoot when and if soothing goes wrong.