Scale Processes

Scale Processes


Performance Scaling & Performance Tuning

Pain Points

  • Is there a performance scaling or performance tuning problem you have because of your data sizes?
  • Is this a case where you have an application designed for a much smaller input dataset size than initially anticipated, and therefore, the application cannot handle and is overwhelmed with the bigger data sizes?
  • Are your service level agreements at risk due to a sudden production crisis?

Our Solution For You

Putting together an application quickly to handle a “hello world” scenario is simple enough to do. However, this only serves to fulfil an initial dog and pony show.

At Menerva, we address this early on as we realize that any successful data related implementation is one that scales well and does not collapse when volumes start increasing. It can only happen if the non-functional requirements for a data processing system such as high availability, scalability and reliability are factored into the design from the start.

Starting with the first sprint, we address this by doing the following from the onset:

  • Developing the application with the necessary scalability constructs “coded in” so it will continue to perform when volumes increase.
  • Ensuring that the necessary controls are in place to scale the implementation if the actual volumes are way higher than even the anticipated volumes.

In other words, when data volumes change, our focus is on scaling the infrastructure, not making changes within the application codebase. That is, the same application deployed on a more massive cluster with higher memory, CPU support and other resources should perform better. It is only possible if there are no performance bottlenecks in the application code.

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