You're building a web application. How can you ensure it scales to meet demand?

For achieving a more scalable architecture, consider using horizontal scaling. In most applications, two types of tasks exist: critical tasks, which must be executed immediately, and non-critical tasks, which can be queued for later processing. Keeping this distinction in mind during architecture design ensures that critical tasks, like database updates during checkout, are performed concurrently with the API call. Non-critical tasks, like sending an email notification, can be placed in a queue and processed later on separate servers. This approach creates a highly scalable system. Therefore, before implementing horizontal scaling, prioritize identifying and separating critical and non-critical tasks to avoid unnecessary delays.

Arquiteturas são elaboradas para que um produto em formato de software tenha capacidade de adaptar-se a novos contextos ou momentos de mercado, para que o produto resista caso haja um novo direcionamento não esperado, pois feliz ou infelizmente estas situações acontecem na vida real com mais frequência do que gostaríamos. Acredito que uma boa estratégia seja divisão por responsabilidades do software, por exemplo, a interface do usuário (ui), o acesso a dados (data access), as funcionalidades (feature), os utilitários (util) e os domínios do negócio (domain) estejam em lugares separados. Desta forma temos capacidade de realizar substituições tecnológicas com pouco trabalho e complexidade conforme as mudanças são executadas no projeto.

Completely agree on architecture being pivotal for scalability, modularity, and reliability. I’ll give an e-commerce web application as an example to show how a microservices architecture can enhance these aspects: In a traditional monolithic setup, the entire application is a single, interlinked unit handling the user interface, business logic, data storage, and communication internally. This can lead to challenges in scaling and maintenance as the application grows. On the other hand, a microservices architecture breaks down the application into smaller, independent services, each handling a specific function and communicating through APIs.

To optimize code, adopt a component-based approach, as exemplified in React JS, to promote code reuse. Create functions for frequently repeated tasks, and leverage techniques like dynamic programming to store previous results for future calculations, effectively utilizing CPU and reducing bandwidth by minimizing API calls. Additionally, consider server-side rendering, employed in Next.js, to further optimize CPU and bandwidth usage while enhancing application speed through the generation of static sites on the server, thus lessening the browser's workload.

Load balancers offer two main ways to optimize your system: horizontal scaling and task specialization. For horizontal scaling, you set up multiple servers performing the same task, efficiently distributing the workload. This approach is ideal for high-traffic scenarios where raw processing power needs a boost. Alternatively, you can utilize task specialization, assigning specific tasks to different servers based on their capabilities. This optimizes resource utilization by leveraging each server's strengths, preventing bottlenecks and reducing redundant processing. Both approaches improve performance and scalability, allowing you to tailor your architecture to your specific needs.

It distributes incoming traffic across multiple servers, preventing any single server from becoming a bottleneck. This ensures efficient resource utilization, optimal performance, and enhanced reliability. Load balancing enables horizontal scaling by adding or removing servers dynamically, adapting to varying loads. It enhances fault tolerance, minimizes downtime, and provides a seamless user experience, making it indispensable for handling increased traffic and achieving high availability in web applications.

While autoscaling is a fantastic feature for hosting, it presents two potential challenges. Firstly, scaling servers without addressing the database can quickly create a bottleneck. Increasing API calls with more servers puts immense pressure on a single database, potentially affecting performance. Secondly, relying on third-party dependencies with rate limits can backfire in a scaled environment. As API calls climb with additional servers, hitting those limits can cripple functionality. Therefore, effectively utilizing autoscaling requires careful consideration of database scalability and managing third-party dependencies to avoid hitting their rate limits.

Datadog can be very helpful here, or for a self hosted solution, ELK (Elasticsearch + Kibana + Logstash) can also work really well

Before building scalable architecture the one needs to answer: - what would be considered as scalable solution? - what type of SLA should architecture support? - what type of traffic application should support? - where are.the users coming from? - what will be primary devices used to access the application? - etc... Based, one responses the will need to build an architecture which will be expanded in the future.

Making sure data doesn't live in the process hosts (memory or disk) but instead lives in a shared resource like Redis and/or Postgres is paramount for any horizontally scalable architecture to function