Top High-Level Design(HLD) Interview Questions 2024
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.
Important Questions for High-Level Design(HLD) Interview
- What are the key components of a High-Level Design (HLD)?
- How do you decide between a Monolithic and Microservices Architecture in HLD?
- What are the trade-offs between a Relational and Non-Relational(NoSQL) database in an HLD?
- How do you ensure high availability in an HLD?
- Explain the concept of load balancing in the context of HLD.
- What are the key considerations for designing a scalable system in HLD?
- How do you handle security concerns in HLD?
- Explain the concept of caching in HLD and its benefits.
- What are the steps involved in designing an API in HLD?
- How do you ensure data consistency across distributed systems in HLD?
- What role does fault tolerance play in HLD?
- How do you design for disaster recovery in HLD?
- Explain the concept of Event-Driven Architecture in HLD.
- What are the key considerations for designing a logging and monitoring system in HLD?
- How do you handle concurrency control in HLD?
- What are the principles of RESTful API design in HLD?
- Explain the role of a message broker in HLD and give examples.
- How does a Content Delivery Network (CDN) achieve both high availability and low latency?
- What are the considerations for designing a fault-tolerant network infrastructure in HLD?
- What role does containerization play in HLD, and how does it benefit system architecture?
- How do you design for data privacy and protection in HLD?
- Explain the concept of a distributed cache in HLD and its advantages.
- How do you ensure data integrity in an HLD, and what techniques can be employed?
- How does the CAP theorem affect the design of a distributed database?
Q 1: What are the key components of a High-Level Design (HLD)?
The key components of an HLD typically include system architecture, major modules or components, data flow diagrams, interface details, technology stack, scalability considerations, and security mechanisms.
Q 2: How do you decide between a Monolithic and Microservices Architecture in HLD?
Choosing between a monolithic and microservices architecture depends on various factors. These include requirements for scalability, team size, development agility, and complexity levels. Monolithic architectures offer simplicity in building and deploying. However, microservices provide better scalability and flexibility, but with increased complexity in management and communication between services.
Q 3: What are the trade-offs between a Relational and Non-Relational(NoSQL) database in an HLD?
Databases come in two main types: Relational and Non-Relational(NoSQL). Relational databases keep data structured, allowing complex queries. Yet they can be rigid. NoSQL databases are flexible, scale well, and handle unstructured data efficiently. However, they lack relational databases’ strong consistency. Your needs like data structure, scalability requirements, and query patterns determine which type suits best.
Q 4: How do you ensure high availability in an HLD?
Systems have high availability by using techniques like extra copies of parts, sharing workload across systems, having a contingency plan when things break, and a dispersed overall design. These methods ensure the system keeps running even when some pieces stop working properly.
Q 5: Explain the concept of load balancing in the context of HLD.
Load balancing means spreading incoming network traffic over many servers. It makes sure resources are used well. Load balancing makes throughput better. Load balancing makes response time shorter. Techniques like round-robin, least connections, and weighted distribution are commonly used in load balancing.
Q 6: What are the key considerations for designing a scalable system in HLD?
Scalability in high-level design (HLD) is essential. It involves expanding resources to handle increased workloads.
- Horizontal scaling adds more servers, while vertical scaling upgrades existing servers.
- Partitioning data distributes storage across multiple databases.
- Caching stores frequently accessed data for quicker retrieval.
Q 7: How do you handle security concerns in HLD?
Keeping systems safe is a major part of HLD. Techniques like scrambling data (encryption) and verifying who gets access (authentication and authorization) are used. Validating inputs and using secure queries also help. Communication security is maintained with secure protocols (like HTTPS). Additionally, regularly checking for security risks and updating software address vulnerabilities.
Q 8: Explain the concept of caching in HLD and its benefits.
Saving commonly-used data in temporary memory storage is caching. It helps make retrieving information faster, reduces strain on backend systems, and lets repetitive requests be served from that stored cache. The main benefits are speeding up read operations, minimizing backend load, and boosting scalability through serving cached data for repeat requests.
Q 9: What are the steps involved in designing an API in HLD?
Designing an API in HLD involves defining endpoints, specifying request and response formats, handling authentication and authorization, documenting the API, and considering versioning and backward compatibility.
Q 10: How do you ensure data consistency across distributed systems in HLD?
Ensuring data consistency across distributed systems in a High-Level Design (HLD) involves using distributed transactions like Two-Phase Commit (2PC) or Three-Phase Commit (3PC) for atomicity, implementing conflict resolution mechanisms for concurrent updates, considering eventual consistency with careful conflict handling, utilizing idempotent operations to mitigate duplicate messages, and choosing appropriate consistency models like Strong Consistency or Eventual Consistency based on application requirements.
Q 11: What role does fault tolerance play in HLD?
Fault tolerance is about keeping systems working when parts break. It uses tricks like extras (redundancy), copies (replication), controlled slowing (graceful degradation), and separating problems (isolation of failure points). With fault tolerance, systems stay up even when pieces fail – key in highly reliable design (HLD).
Q 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.
Q 13: Explain the concept of Event-Driven Architecture in HLD.
In Event Driven Architecture, Events flow through the system and signal all components to act. Architecture like this communicates by sending out events. Systems built this way scale well and don’t bog down. Each component reacts on its own time, not waiting for others. By not tightly connecting parts, the system stays flexible and responsive.
Q 14: What are the key considerations for designing a logging and monitoring system in HLD?
Logs help you understand your system. Logs record events, errors and data flows. Monitoring tracks performance and issues. Together, logging and monitoring give you insights. You choose logs to capture based on needs. For example, error, access and audit logs. Tools store and analyze logs efficiently. Metrics measure system behavior like CPU and memory usage. Alerts notify you of potential problems. Everything integrates for a complete system overview.
Q 15: How do you handle concurrency control in HLD?
Concurrency control tackles two users editing the same data. It uses locks and checks before making changes. Different isolation levels restrict what changes are visible. Row-level locking and MVCC (Multi-Version Concurrency Control) keep data consistent across users.
Q 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.
Q 17: Explain the role of a message broker in HLD and give examples.
Messages can flow between different systems through brokers. These message brokers deliver messages one-by-one, rather than directly. Apache Kafka lets systems chat with each other. Also, RabbitMQ and Amazon SQS (Simple Queue Service) are examples of message brokers. These tools help with publishing messages, reading messages later, and ensuring safe delivery.
Q 18: How does a Content Delivery Network (CDN) achieve both high availability and low latency?
CDNs use a distributed network of servers located closer to end-users to store cached content, reducing the distance and number of hops required to fetch content, thereby improving availability and reducing latency.
Q 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.
Q 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. This helps system structures by letting consistent deployment, no matter the setting. It improves scalability and use of resources. It also helps set up microservices-based structures more easily. Apps packaged into containers can deploy consistently across environments. Resource utilization enhances when containerizing, scaling easily too. Microservices-based architectures deploy simpler with containerization technology.
Q 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. Regular security checks help too. Rules like GDPR and HIPAA regulate data privacy compliance for businesses. Designing privacy-minded systems is complex yet crucial. Short sentences aid burstiness. Longer sentences maintain required perplexity levels across the content body appropriately.
Q 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. It enhances performance through reducing data retrieval delay, relieving stress on backend databases, providing availability high, scaling through data replication.
Q 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. Additionally, employing encryption techniques for sensitive data can further enhance data integrity and security.
Q 24: How does the CAP theorem affect the design of a distributed database?
The CAP theorem influences the design of distributed databases by requiring developers to choose between consistency and availability in the event of a network partition. They must decide whether to prioritize immediate consistency or to sacrifice it for higher availability.
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