Implement functional checks in an application that external tools can access through exposed endpoints at regular intervals. This can help to verify that applications and services are performing correctly. Context and problem It’s a good practice, and often a business requirement, to monitor web applications and back-end services, to ensure they’re available and performing correctly.
The deployment stamp pattern involves deploying multiple independent copies of application components, including data stores. Each individual copy is called a stamp, or sometimes a service unit or scale unit. This approach can improve the scalability of your solution, allow you to deploy instances across multiple regions, and separate your customer data. Context and problem When hosting an application
Building distributed systems can get complicated. So can building a monolithic one, to be fair. But the difference is most of us choose more complexity than we need by going distributed. Any experienced developer or architect will tell you that most people actually don’t need to embrace microservices entirely. All the ones I’ve spoken to
We live in an age where massive scale, Internet-facing systems like Google, Amazon, Facebook and the like are engineering icons. They handle vast volumes of requests every second and manage data repositories of unprecedented size. Getting precise traffic numbers on these systems is not easy for commercial-in-confidence reasons. Still, the ability of these sites to
Data consistency is hardest part of the microservices architecture. Because in a traditional monolith application, a shared relational database handles data consistency. In a microservices architecture, each microservice has its own data store if you are using database per service pattern. So databases are distributed among the applications. Each application may use different technologies to manage their data like