System Design Interview Questions and Answers (2025) | JaganInfo

System design is the process of defining the architecture, components, interfaces, and data of a system to satisfy specified requirements.

Key components include servers, databases, load balancers, caches, APIs, queues, and client applications.

Scalability is the ability of a system to handle increased load by adding resources either vertically or horizontally.

Load balancing distributes incoming network traffic across multiple servers to maximize throughput and minimize response time.

Caching stores frequently accessed data in faster storage to reduce latency and load on backend systems.

A database is a structured storage system that allows for efficient retrieval, insertion, and management of data.

SQL databases are relational and use structured schema; NoSQL databases are non-relational and support flexible schemas.

Vertical scaling adds resources to a single machine; horizontal scaling adds more machines to distribute load.

A Content Delivery Network caches content on servers located near users to reduce latency and improve performance.

APIs (Application Programming Interfaces) define methods for different components to communicate and exchange data.

Microservices architecture structures an application as a collection of loosely coupled services, enabling independent deployment and scalability.

Eventual consistency guarantees that, given enough time, all replicas of data will converge to the same value despite temporary inconsistencies.

Sharding partitions a database into smaller, faster, and more manageable pieces called shards, distributed across different servers.

Message queues enable asynchronous communication between services by storing messages until they are processed.

Use redundancy, automatic failover, health checks, graceful degradation, and monitoring to ensure reliability.

The CAP theorem states that in distributed systems, one can only simultaneously guarantee two of three: Consistency, Availability, and Partition tolerance.

Monolithic architectures are single unified applications, while microservices decompose applications into smaller, independent services.

Service discovery automatically detects and connects microservices dynamically as they scale up or down.

A circuit breaker prevents an application from repeatedly trying to execute an operation likely to fail, enabling faster recovery and resilience.

Queues are for point-to-point messaging with single consumer per message; topics support publish-subscribe with multiple subscribers receiving messages.

Partitioning divides a dataset into distinct chunks to improve query performance and scalability.

Rate limiting controls the number of requests a user or client can make in a time window to prevent abuse and ensure fair usage.

An approach where the system allows temporary inconsistencies but ensures data will synchronize over time.

Replication copies data across multiple machines for fault tolerance and improved read performance.

By employing techniques like distributed transactions, consensus algorithms, or using consistency models like strong or eventual consistency.

An API gateway acts as a single entry point, handling requests, routing, authentication, rate limiting, and caching in microservices architectures.

Synchronous requires sender to wait for receiver’s response; asynchronous allows sender to continue processing without immediate response.

Strong consistency ensures all clients see the same data simultaneously; eventual consistency allows temporary differences with eventual convergence.

CAP theorem states a distributed system can’t simultaneously guarantee Consistency, Availability, and Partition tolerance; it must choose two.

Indexing improves query performance by allowing faster data retrieval using data structures like B-trees or hash indexes.

A distributed system consists of multiple independent computers that appear to users as a single coherent system, coordinating to achieve a common goal.

Algorithms like Paxos and Raft help distributed systems agree on a single data value despite failures and message delays.

Eventual consistency allows temporary inconsistency; conflict resolution uses techniques like vector clocks or last-write-wins.

An operation is idempotent if performing it multiple times results in the same effect as performing it once, critical for safe retries.

Multi-tenancy allows multiple customers (tenants) to share the same application and infrastructure while keeping data isolated.

Backpressure controls data flow rate between components to prevent system overload during high traffic.

A pattern to detect failures and stop calls to failing services temporarily, helping maintain system stability.

Use redundancy, failover, data replication, monitoring, and disaster recovery strategies.

You must choose between consistency, availability, and partition tolerance when designing distributed systems based on application needs.

Quorum-based replication requires a majority of nodes to agree on reads and writes, balancing consistency and availability.

Sharding splits data across servers; challenges include hotspot management, cross-shard queries, and resharding complexity.

Caches shared across multiple nodes improving performance and scalability; e.g., Redis, Memcached clusters.

Use consistency models, cache invalidation strategies, and read-your-writes guarantees.

An architecture where events trigger services to communicate asynchronously, increasing scalability and decoupling.

Stream processing handles data in real-time as it flows; batch processing operates on large volumes of stored data at intervals.

Distributed tracing tracks requests as they propagate through distributed systems to diagnose latency and failures.

A dedicated infrastructure layer managing service-to-service communication, security, and observability in microservices.

Protocols that help distributed systems agree on a single value despite failures and asynchronous communication.

Design with redundancy, graceful degradation, retries, error handling, and automated failover mechanisms.

Estimate future load, monitor usage patterns, provision resources accordingly, and plan for scaling.