High-Level Design(HLD) Interview Questions - System Design
Last Updated :
22 Sep, 2025
High-Level Design (HLD) gives a big-picture view. It shows the main parts of a system and how they fit together. HLD acts as a map, focusing on modularity, scalability, and smooth integration. The aim is clear development direction while meeting business goals and technical limits. Short sentences, longer explanations – HLD creates a balanced architectural blueprint.
1. What are the key components of a High-Level Design (HLD)?
HLD defines the overall system architecture and provides a bird’s-eye view of the solution. Key components:
- System Architecture -> Major layers (UI, API, DB, etc.).
- Components -> Describes major components (e.g., Authentication Service, Payment Service, Database Layer).
- Technology Stack -> Programming languages, frameworks, DB choice, infrastructure.
- Scalability Considerations -> Caching, partitioning, replication.
- Security Mechanisms -> Authentication, authorization, encryption, auditing.
- Integration Points -> External services, third-party APIs.
2. How do you decide between a Monolithic and Microservices Architecture in HLD?
The choice depends on system size, complexity, scalability needs, and team structure:
Monolithic Architecture
- Pros: Simple to develop, deploy, test, and monitor.
- Cons: Poor scalability, tightly coupled, difficult to maintain for large systems.
- Best when: Small applications, startups, MVPs, small team sizes.
Microservices Architecture
- Pros: Independent scaling, technology flexibility, fault isolation, faster iteration.
- Cons: Complex deployment, network overhead, service coordination.
- Best when: Large-scale systems, distributed teams, high scalability & agility needed.
Rule of Thumb: Start monolithic (if small) -> refactor to microservices as the system grows.
3. What are the trade-offs between a Relational and Non-Relational(NoSQL) database in an HLD?
Relational Databases (RDBMS)
- Structured schema, ACID transactions.
- Strong consistency, complex queries (SQL joins).
- Vertical scaling.
- Best for: Banking, ERP, inventory management.
NoSQL Databases (Key-Value, Document, Column, Graph)
- Flexible schema, horizontal scaling.
- High availability, fast read/write for large datasets.
- Eventual consistency (in many cases).
- Best for: Real-time apps, big data, social media, IoT.
Trade-off -> Choose RDBMS for consistency & structured data; choose NoSQL for scalability & unstructured data.
4. How do you ensure high availability in an HLD?
High availability (HA) means the system remains accessible with minimal downtime. Strategies:
- Redundancy -> Replicate critical components (servers, DB replicas).
- Load Balancing -> Distribute requests across multiple nodes.
- Failover Mechanisms -> Automatic switch to backup systems when one fails.
- Geo-distributed Deployment -> Deploy across multiple regions/zones.
- Disaster Recovery Plan -> Backup & restore strategies.
- Monitoring & Alerts -> Detect failures quickly.
5. Explain the concept of load balancing in the context of HLD.
Load balancing distributes incoming requests across multiple servers to ensure:
- Efficient Resource Utilization
- Improved Performance (lower response times)
- High Availability (no single point of failure)
Load Balancing Techniques:
- Round Robin -> Assign requests in sequence.
- Least Connections -> Route to server with fewest active connections.
- Weighted Distribution -> Assign based on server capacity.
- Geo Load Balancing -> Route based on user location.
Example: Nginx, HAProxy, AWS ELB, GCP Load Balancer.
6. What are the key considerations for designing a scalable system in HLD?
Scalability ensures the system can handle increased load without breaking. Considerations:
- Horizontal Scaling -> Add more servers (stateless services).
- Vertical Scaling -> Upgrade hardware (limited).
- Partitioning/Sharding -> Split large datasets across nodes.
- Caching -> Reduce DB load with in-memory stores (Redis, Memcached).
- Asynchronous Processing -> Use queues (Kafka, RabbitMQ) for background tasks.
- Database Replication -> Read replicas for heavy read loads.
- CDN -> Cache static content closer to users.
7. How do you handle security concerns in HLD?
Security must be built into the architecture, not added later. Key measures:
- Authentication & Authorization -> Implement RBAC/ABAC, OAuth2.0, JWT.
- Encryption -> Encrypt data in transit (TLS/HTTPS) and at rest (AES, KMS).
- Input Validation -> Prevent SQL injection, XSS.
- Secure APIs -> Use API gateways, rate limiting, and request validation.
- Regular Patching -> Keep software and dependencies updated.
- Monitoring & Logging -> Track suspicious activity.
- Zero-Trust Principles -> Never trust, always verify; least privilege access.
8. Explain the concept of caching in HLD and its benefits.
Caching stores frequently accessed data in a fast-access layer (like in-memory) to reduce repeated backend/database calls.
Types: Client-side, CDN, server-side (Redis, Memcached), and application-level caching.
Benefits:
- Reduces response time (faster reads).
- Lowers load on backend databases.
- Improves scalability by serving repeated requests from cache.
- Enhances user experience with low-latency responses.
9. What are the steps involved in designing an API in HLD?
Designing an API involves:
- Identify resources & use cases (what needs to be exposed).
- Define endpoints (REST: /users/{id}, /orders; GraphQL schema if applicable).
- Specify request/response formats (JSON/XML).
- Authentication & Authorization (OAuth 2.0, JWT, API keys).
- Error handling & status codes (e.g., 404 Not Found, 500 Server Error).
- Versioning strategy (/v1, /v2 APIs).
- Rate limiting & throttling for fairness and security.
- Documentation (Swagger/OpenAPI).
10. How do you ensure data consistency across distributed systems in HLD?
Consistency Models: Strong, Eventual, or Causal (choose based on needs).
Techniques:
- Distributed transactions (2PC/3PC) for atomic operations.
- Idempotent operations to handle retries safely.
- Conflict resolution policies (last write wins, vector clocks, CRDTs).
- Eventual consistency for high availability, strong consistency for critical paths.
Trade-off: Availability vs Consistency (as per CAP theorem).
11. What role does fault tolerance play in HLD?
Fault tolerance ensures the system continues operating even when components fail.
Techniques:
- Redundancy (replicating servers, databases).
- Replication (data copies in multiple nodes/regions).
- Graceful degradation (non-critical features disabled during failures).
- Failure isolation (microservices, circuit breakers).
Importance: Improves reliability, minimizes downtime, and ensures user trust.
12. How do you design for disaster recovery in HLD?
Disaster recovery in HLD involves creating backup systems, implementing data replication across geographically distributed locations, establishing failover mechanisms, and regularly testing the recovery process to ensure its effectiveness.
- Backups: Regular, automated, and tested restores.
- Geo-replication: Data mirrored across multiple regions/data centers.
- Failover mechanisms: Automatic switch to standby servers/clusters.
- Recovery Point Objective (RPO) & Recovery Time Objective (RTO) defined in design.
- DR Drills: Regularly test recovery plans for effectiveness.
13. Explain the concept of Event-Driven Architecture in HLD.
EDA is a loosely coupled system where components communicate via events instead of direct calls. Flow: Producer -> Event Bus (e.g., Kafka, RabbitMQ) -> Consumers.
Benefits:
- Asynchronous communication.
- High scalability and resilience.
- Decoupled components (independent evolution).
Use cases: Real-time analytics, IoT systems, order processing pipelines.
14. What are the key considerations for designing a logging and monitoring system in HLD?
Logging:
- Structured logs (JSON format).
- Levels: INFO, WARN, ERROR, DEBUG.
- Contextual info (trace IDs, request IDs).
- Centralized log aggregation (ELK stack, Splunk).
Monitoring:
- Metrics (CPU, memory, request latency, error rates).
- Dashboards (Grafana, Prometheus).
- Alerts (threshold-based, anomaly detection).
Considerations: Scalability of log storage, correlation between logs/metrics, real-time alerting, and compliance (audit logs).
15. How do you handle concurrency control in HLD?
Concurrency control ensures data consistency when multiple users/processes access the same resource simultaneously.
- Techniques: Locks (row/record-level, optimistic/pessimistic), Multi-Version Concurrency Control (MVCC), and isolation levels (Read Committed, Repeatable Read, Serializable).
- Design Choice: For read-heavy systems -> MVCC; for strict financial transactions -> locking with higher isolation.
- Ensures no lost updates, dirty reads, or inconsistent states.
16. What are the principles of RESTful API design in HLD?
RESTful API design principles include using HTTP methods for CRUD operations, representing resources with URIs, stateless communication, employing standard status codes, and supporting content negotiation through request headers.
- Use HTTP methods properly (GET, POST, PUT, DELETE).
- Resource-based URIs (e.g.,
/users/{id}
). - Statelessness: No session state on server, each request must contain context.
- Standard status codes: 200 OK, 404 Not Found, 500 Internal Error.
- Content negotiation: Support JSON/XML via headers.
- Versioning:
/api/v1/
to ensure backward compatibility.
17. Explain the role of a message broker in HLD and give examples.
A message broker decouples services and enables asynchronous communication.
- Roles: Message routing, buffering, delivery guarantees, and scaling event-driven systems.
- Examples: Kafka (stream processing, high throughput), RabbitMQ (task queues, reliability), Amazon SQS (cloud-native queue).
- Benefits: Loose coupling, fault isolation, better scalability, and resilience.
18. How does a Content Delivery Network (CDN) achieve both high availability and low latency?
- Low latency: CDN caches content at edge servers near users, reducing round-trip time.
- High availability: Distributed servers, automatic failover, and load balancing ensure uptime even if some nodes fail.
- Extra: CDNs use replication, Anycast routing, and intelligent caching strategies.
19. What are the considerations for designing a fault-tolerant network infrastructure in HLD?
Designing a fault-tolerant network infrastructure needs backup parts. It should be divided into sections with protocols adapting to traffic changes. Install devices that balance loads and block threats and also establishing disaster recovery plans for network outages.
- Redundancy: Multiple network paths, backup servers.
- Isolation: Segment critical services to limit blast radius.
- Protocols: Dynamic routing (BGP/OSPF) for rerouting traffic.
- Load balancing: Evenly distribute requests.
- Security: Firewalls, intrusion detection.
- Disaster Recovery: Hot/warm DR sites for continuity.
20. What role does containerization play in HLD, and how does it benefit system architecture?
Containerization allows putting apps and dependencies into isolated containers, like with Docker.
- Consistency: Works the same across environments.
- Scalability: Containers scale horizontally with orchestration (K8s).
- Resource efficiency: Lighter than VMs.
- Supports Microservices: Each service isolated but easy to deploy.
- Resilience: Fault isolation - failure in one container doesn’t crash others.
21. How do you design for data privacy and protection in HLD?
Protecting data privacy involves encryption, limiting access, anonymizing identities, and secure protocols like TLS/SSL.
- Encryption: Data at rest (AES), data in transit (TLS/SSL).
- Access control: RBAC/ABAC, least privilege.
- Data anonymization: Masking PII.
- Compliance: GDPR, HIPAA.
- Auditing & monitoring: Track data access.
- Regular security reviews: Pen testing, vulnerability scans.
22. Explain the concept of a distributed cache in HLD and its advantages.
A distributed cache stores data accessed often across nodes in an environment distributed.
Advantages:
- Faster response (reduces DB calls).
- Scalability (horizontal distribution).
- Fault tolerance (replicated cache).
- Handles high read loads efficiently.
23. How do you ensure data integrity in an HLD, and what techniques can be employed?
Data integrity in an HLD can be ensured through techniques such as data validation, constraints at the database level, implementing referential integrity, using transactions for atomicity, consistency, isolation, and durability (ACID properties), checksums or hashing for data verification, and employing error handling and logging mechanisms to track and rectify inconsistencies.
- Database constraints: Primary key, foreign key, unique, not null.
- Validation: Input validation at API and DB layers.
- Transactions: Enforce ACID properties.
- Checksums/Hashing: Verify data during transmission.
- Error handling: Retry, logging inconsistencies.
- Encryption: Protect sensitive data integrity + confidentiality.
24. How does the CAP theorem affect the design of a distributed database?
CAP theorem states a distributed system can only guarantee 2 out of 3:
- Consistency: All nodes see the same data.
- Availability: System responds even during failures.
- Partition tolerance: System works despite network splits.
Impact on design:
- CP systems (e.g., MongoDB, HBase) -> prioritize consistency over availability.
- AP systems (e.g., Cassandra, DynamoDB) -> prioritize availability over consistency.
- Choice depends on application: banking (CP) vs. social media feeds (AP).
Explore
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High Level Design(HLD)
Low Level Design(LLD)
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Interview Guide for System Design
System Design Interview Questions & Answers