What makes a robust system?

Millions of users interface with an application daily; when they open their apps, they expect to see the right information immediately. A robust system handles a large amount of data flow to the users fast, correct, and without crashing. Without a reliable system in place, the app becomes less user friendly. Fortunately, there are system principles that one could follow to create secure systems to manage data-intensive applications. Below are some principles to follow:

Principle 1: Readability

When a client requests data from the server, the server processes the request by retrieving data from the database, modifying that data, and returning it. Reading info from the database is expensive and time-consuming. The app can leverage existing tools like Redis Cache or ElasticSearch for faster reads and to reduce stress on the database. For example, the application can cache a response in a caching server and return it by reading from the cache instead of the database. Or, the app can index the keywords of the data in ElasticSearch for faster searches. Overall, the speed of data retrievals improves by integrating the application with caching solutions.

Principle 2: Reliability

A reliable system provides a correct response with low latency. It does not fail due to faults nor hang from system overload. If there are hardware or software failures, it still returns a response without crashing. One can achieve a reliable system by reducing mistakes, having a fault-tolerant methodology, and monitoring metrics. For example, to prevent bugs, one can monitor the error and warning logs and fix those errors in the code. Also, writing unit and integration tests or testing in the sandbox reduces business-logic flaws. Hardware failures can occur by turning off a system or when a disk crashes. Like the chaos monkey, fault-tolerant systems are put in place to allow the system to continue working during hardware failures. Usually, the cloud-based platform has a fault-tolerant system in place if you host your application on it. The fault-tolerant system prevents cascading crashes – meaning that, when one system fails, other reliant systems continue working. Overall, reliability allows the application to run regardless of any system errors.

Principle 3: Scalability

A scalable system allows the application to support the increased overload. Imagine an app deployed to one server; after a few days, there are many requests sent to that server. The server has trouble processing the heavy incoming traffic as it used up the CPU. It hangs, “the internet breaks,” and the server is down. A scalable server, in place, handles high traffic and massive calls without breaking. Measuring the request per second or the number of database/cache reads helps determine how to scale the server. The server can be scaled vertically or horizontally. Vertical scaling means adding more memory to one server and making it large. On the other hand, horizontal scaling has the application deployed across multiple servers. Having scalable servers reduces the system hanging.

Principle 4: Maintainability

Another critical part of a robust system is maintainability. In general, the applications should adopt a uniform programming language or tools. Imagine the applications across the architecture uses GoLang, Java, Python, JavaScript, Perl, and so on. It becomes challenging to hire an expert for all those languages or to bring him/her up to date with the language rules. Overall, keeping the language consistent allows for more natural adaption. Also, the code should be commented, documented, and written for the future to avoid refactoring. Writing unit and integration tests allow the code to be more manageable. To sum up, a maintainable system reduces chaos for the future!

Having a system follow these principles allows it to be robust. Have fun designing it!

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