What Makes an Application Scalable?

What Does a Scalable Application Mean?

Applications that are depended on by thousands of users may see peaks or dips in demand during the day and managing the cost of running the infrastructure can be challenging. Scalable applications are applications that are able to increase their resources to serve requests as they come.

What types of Scaling are there?

There are two basic examples of scaling for applications,

  • Vertical Scaling, where an application’s resources are increased due to demand, such as increasing the RAM available to an application host. Vertical Scaling is also sometimes referred to as scale-up.
  • Horizontal Scaling, where an application is spread across different nodes in a cluster. This is most appropriate for applications that require a lot of resources. Horizontal Scaling is also referred to as scale-out.

Scalable applications have many uses, including;

  • Allowing cost reduction during low utilisation by scaling down clusters or pods serving the application.
  • Improving quality of service during peak load times by scaling up horizontally or vertically by utilising resources made available to the application autoscaler.This will ensure that your application always has the right amount of capacity to handle the current traffic demand.
  • Faster processing as features can use the optimal storage technology for data and information.

Best Practice for Scalable Applications

Applications usually are best compartmentalised into components for both design and maintainability, monolithic architectures for code bases and applications have caused application elements to become obscure and entrenched, using a more distributed approach from the start can reduce cost and technical debt as components can be re-written or swapped out.

  • Splitting transient application functions in to their own libraries or APIs can allow the front end and back end to operate independently, this can allow processes that take time to be acted on based on events rather than cause waiting or processing as a batch.
  • Data storage should be independent to the service that owns the data and therefore should use the best storage type and mechanisms available such as caching or streams of data. Slow returns should be accounted for and independent from the user interface.
  • Applications should not be strained by resources, whenever possible applications should perform the same for every user or function regardless of workload or work-type. Rather than wait for functions to complete, have a common goal of eventual data integrity.
  • When implementing concepts like micro-services you should endeavour to ensure standard practices like a common set of languages or behaviours to improve maintainability.
  • Complexity can sometimes be harder to manage than an equivalent monolithic application, even though each function should be simpler.
  • Isolate failure to individual services.

By Aidan

I am a software engineer with a passion for business and information systems, FMCG, management and electronics.

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