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Vertical is often thought of as the “easier” of the two methods. When scaling a system vertically, you add more power to an existing instance. This can mean more memory (RAM), faster storage such as Solid State Drives (SSDs), or more powerful processors (CPUs).
The reason this is thought to be the easier option is that hardware is often trivial to upgrade on cloud platforms like AWS, where servers are already virtualized. There is also very little (if any) additional configuration you are required to do at the software level.
Performance of a system is measured by many different metrics – one of the main ones is response time. Interestingly, scaling your system may increase response times. If you move away from the type of system architecture that has all of the components (database, application code, caching) on one server to a type of system architecture that separates these components onto their own servers then the response time will naturally increase as you now have network latency and other considerations. Let’s look at two popular system architecture types below.
Each application is different but the key is to identify key services that may be a bottleneck and the first ones to cripple under increased load pressure. One of the most common bottlenecks can be the database.
A microservices system architecture is the process of splitting up core services into their own ecosystems. A key part of your application may be an image processing service that can save, delete, cache and manipulate images. This service could be set up as its own infrastructure which means that it would be separated from the other application services. You’ll often hear the term separation of concerns when referring to microservices. Although each core service having its own infrastructure can make scalability easier, it can still add a lot of complexity to your application.
The database is used to store data in an application. You may use a traditional relational database such as MySQL or a NoSQL database such as MongoDB. In simple terms, the database is used to write data (save it) and read it (view it). The database can often be one of the first components to fall down under high load pressure in an application environment.
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