Distributed System Scalability Quiz

Horizontal scaling involves adding more servers to a system, distributing the load across multiple machines, which enhances redundancy and availability. In contrast, vertical scaling focuses on upgrading a single server's resources, such as CPU or RAM, to handle increased demand, which can lead to improved performance but may have limitations on capacity.

Round-robin is a load balancing algorithm that distributes incoming requests evenly across a group of servers in a circular manner. Each server receives a request in turn, ensuring that no single server is overwhelmed, which helps maintain balanced resource utilization and improves overall system performance.

Sharding a database enhances scalability by dividing data into smaller, manageable pieces called shards. Each shard can be stored on different servers, allowing for parallel processing and reducing the load on any single server. This distribution enables the system to handle more requests and increases overall performance as the database grows.

In the CAP theorem, a distributed system can only achieve two of the three guarantees: consistency, availability, and partition tolerance. When a network partition occurs, the system must choose between maintaining consistent data across nodes or ensuring that all requests receive a response, leading to the trade-off involving partition tolerance.

Caching in distributed systems stores frequently accessed data closer to the user, minimizing the time it takes to retrieve information. This reduces the load on databases by decreasing the number of requests they handle, resulting in faster response times and improved overall system performance.

Stateless services do not maintain any client-specific data between requests, allowing them to be easily replicated across multiple servers. This enables horizontal scaling, as new instances can be added without worrying about data consistency. In contrast, stateful services require managing session data, making scaling more complex and challenging.

Decoupling via message queues enables independent scaling of microservices by allowing them to communicate asynchronously. Each service can scale based on its specific load without affecting others, as message queues handle the data transfer. This flexibility enhances performance and resource utilization in a microservices architecture.

Database replication enhances scalability by allowing multiple replicas of the database to handle read requests. This distribution of reads across several servers reduces the load on the primary database, improves response times for users, and enables the system to handle a larger number of simultaneous queries efficiently.

Eventual consistency in distributed systems prioritizes availability and partition tolerance over immediate data correctness. This approach allows systems to remain operational even during network disruptions, accepting that data may be temporarily inconsistent. Eventually, the system reconciles these inconsistencies, ensuring that all nodes converge to the same state over time, thus enhancing overall system availability.

Read replicas help distribute read traffic, reducing load on the primary database. Connection pooling minimizes the overhead of establishing connections, optimizing resource use. Query caching stores the results of frequent queries, speeding up data retrieval and decreasing database load. Increasing server RAM alone may not address bottlenecks effectively without these techniques.

A reverse proxy enhances the scalability of web applications by distributing incoming traffic across multiple servers. This load balancing technique ensures that no single server is overwhelmed, improving performance, reliability, and response times. By efficiently managing requests, it helps maintain optimal resource utilization and enhances the overall user experience.

Asynchronous processing allows tasks to be executed independently, enabling the system to handle multiple requests simultaneously without waiting for each task to complete. This leads to better resource utilization and reduced bottlenecks, effectively distributing the system load compared to synchronous processing, where tasks are completed sequentially, potentially causing delays and inefficiencies.