Achieving Data consistency is a common challenge when scaling stateful applications, as it involves ensuring that data is synchronized across multiple instances of the application.

A distributed cache reduces the load on the database by storing frequently accessed data, reducing the need to query the database, and thereby enhancing the scalability of a web application.

A potential downside of round-robin load balancing in a heterogeneous server environment is the uneven distribution of load, as servers may have different capacities or capabilities, leading to suboptimal performance.

Containerization aids in scaling web applications by providing enhanced portability, allowing applications to run consistently across different environments, facilitating easier scaling and deployment.

Load balancing is critical in a monolithic architecture to distribute traffic evenly across servers.

Horizontal scaling involves adding more resources to the existing servers.

A Load balancer distributes network or application traffic across a number of servers.

In cloud environments, Horizontal scaling refers to the automated process of adding or removing resources as needed, also known as scaling out or in.

Downtime during traffic spikes suggests a possible lack of horizontal scaling, where the application fails to dynamically add more instances to handle increased load. Monitoring and alerting can help detect such issues early.

The doPut() method is used by an HTTP servlet to handle PUT requests, allowing the servlet to process and respond to PUT requests.