Sharpen your system design instincts with these critical questions on real-time communication, heavy load handling, database choices, caching, and synchronization patterns at the system level.
Which approach best supports real-time communication between a client and server to minimize latency in message delivery?
Explanation: Web-socket enables persistent, bidirectional communication between client and server for real-time updates, ensuring minimal delay in message delivery. Batch processing and scheduled jobs are used for periodic data handling, not real-time needs. FTP transfer is designed for file transfers and is not suitable for interactive or instant communication scenarios.
In a large-scale system dealing with heavy user traffic, which component is most effective for distributing requests evenly across servers?
Explanation: A load balancer efficiently distributes incoming network or application traffic across multiple servers, improving reliability and scalability. A single server risks overload and downtime. Peer-to-peer mesh is not standard for centralized web services. Static HTML files alone do not handle server-side traffic management.
When a relational database grows too large to fit on one server, what strategy allows for horizontal scaling and increased capacity?
Explanation: Sharding splits a large database into smaller, manageable pieces across multiple servers, allowing for horizontal scale. Replication creates copies but does not distribute write load. A single monolithic database becomes a bottleneck. RAM upgrades help temporarily but don't address structural scaling needs.
What system-level design is typically used to reduce database reads and serve frequently accessed data quickly?
Explanation: Caching solutions like in-memory data stores reduce latency by storing frequently accessed data near the application, decreasing load on the primary database. Writing directly to disk is slow. SSH tunnels are for secure communication, not data access speed. Multiple login servers address authentication, not data retrieval.
In a system where users can modify the same file across multiple devices, which design pattern is crucial to prevent conflicting edits?
Explanation: Synchronization with conflict resolution ensures consistent data by detecting and resolving concurrent modifications, preventing data loss or corruption. Eventual consistency may permit conflicting changes temporarily. Round-robin scheduling is for task distribution, not conflict handling. Token bucket rate limiting manages request rates, not data consistency.