24 February, 2026 (Last Updated)

System Design Interview Questions and Answers

Are you preparing for system design interviews and wondering what kind of architectural questions you might face?

System design interviews test your ability to think at scale, make trade-offs, and design reliable, distributed systems that handle real-world traffic and failures.

This guide on system design interview questions and answers covers core concepts, intermediate-level component design, and advanced large-scale architecture discussions to help you build structured thinking and confidently approach system design rounds.

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System Design Interview Questions for Freshers

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1. What is system design, and why is it important in software engineering?

System design is the process of defining the architecture, components, data flow, and interactions required to build a scalable and reliable software system.

It involves decisions about:

• Backend services
• Databases
• APIs
• Scaling strategy
• Fault tolerance
• Data storage
• Communication between components

System design is important because:

• It ensures scalability as users grow.
• It prevents performance bottlenecks.
• It improves maintainability.
• It reduces system failure risks.
• It aligns technical architecture with business requirements.

2. What is scalability in system design?

Scalability refers to a system’s ability to handle increasing load without performance degradation.

A scalable system can:

• Handle more users
• Process more requests
• Store more data
• Maintain stable response time

3. What is the difference between vertical scaling and horizontal scaling?

Feature	Vertical Scaling	Horizontal Scaling
Definition	Increase power of a single machine	Add more machines
Example	Upgrade CPU/RAM	Add more servers
Complexity	Simple	More complex
Scalability Limit	Hardware limit	Practically unlimited
Fault Tolerance	Low	High

Vertical scaling means upgrading an existing server, while horizontal scaling means distributing load across multiple servers.

4. What is high availability?

High availability (HA) refers to designing systems to minimize downtime and ensure continuous operation.

It is achieved using:

• Redundant servers
• Load balancing
• Failover mechanisms
• Multi-region deployment
• Health checks

5. What is load balancing?

Load balancing distributes incoming traffic across multiple servers to ensure optimal resource utilization and prevent overload.

It improves:

• Performance
• Reliability
• Scalability

Common algorithms:

• Round Robin
• Least Connections
• IP Hash

Load balancing prevents a single server from becoming a bottleneck.

6. What is caching, and why is it used?

Caching stores frequently accessed data in fast storage to reduce response time and database load.

It is used to:

• Improve performance
• Reduce database queries
• Reduce latency
• Lower infrastructure cost

Caching is widely used in high-traffic systems like e-commerce and social media platforms.

7. What is database sharding?

Database sharding is the process of splitting a large database into smaller, independent pieces called shards.

Each shard contains a subset of data.

Benefits:

• Improves scalability
• Reduces query load
• Enables horizontal scaling

Sharding is useful when a single database cannot handle growing data volume.

8. What is replication in databases?

Replication is the process of copying data from one database server to another.

Types:

• Master-Slave replication
• Multi-Master replication

Benefits:

• High availability
• Fault tolerance
• Improved read performance

Replication ensures system reliability and load distribution.

9. What is CAP theorem?

CAP theorem states that a distributed system can only guarantee two of the following three properties:

• Consistency – All nodes see the same data at the same time.
• Availability – Every request receives a response.
• Partition Tolerance – System continues operating despite network failures.

You cannot guarantee all three simultaneously.

CAP theorem helps architects make trade-off decisions.

10. What is eventual consistency?

Eventual consistency is a consistency model where updates to data will propagate to all nodes eventually, but not immediately.

This means:

• Some nodes may temporarily have stale data.
• After some time, all nodes converge to the same state.

11. What is monolithic architecture?

Monolithic architecture is a software design approach where the entire application is built as a single unified codebase and deployed as one unit. All components, such asthe user interface, business logic, and database access are tightly integrated and run within the same process.

In a monolithic system, if a small feature needs modification, the entire application typically needs to be rebuilt and redeployed. This approach is simpler to develop initially and is suitable for small applications.

However, as the system grows, it becomes harder to scale, maintain, and deploy independently. For example, early-stage startups often begin with monolithic architecture due to its simplicity.

12. What is microservices architecture?

Microservices architecture is a design approach where an application is divided into small, independent services that communicate with each other over a network. Each service is responsible for a specific business capability and can be developed, deployed, and scaled independently.

13. Compare monolithic and microservices architectures.

Feature	Monolithic Architecture	Microservices Architecture
Structure	Single unified codebase	Multiple independent services
Deployment	Entire app deployed together	Services deployed independently
Scalability	Hard to scale specific components	Individual services can scale
Fault Isolation	Failure affects whole system	Failure isolated to one service
Complexity	Simpler initially	More complex infrastructure
Maintenance	Harder as system grows	Easier modular maintenance

Monolithic architecture is easier to start with, while microservices are better suited for large-scale distributed systems requiring independent scaling and flexibility.

14. What is a reverse proxy?

A reverse proxy is a server that sits between clients and backend servers, forwarding client requests to appropriate internal servers and returning responses back to clients.

Unlike a forward proxy (which represents clients), a reverse proxy represents backend servers. It provides several benefits:

• Load balancing
• SSL termination
• Security filtering
• Caching
• Hiding internal server details

15. What is an API gateway?

An API gateway is a centralized entry point for managing and routing requests to multiple backend services in a distributed system. It is commonly used in microservices architecture.

The API gateway handles:

• Authentication and authorization
• Request routing
• Rate limiting
• Logging and monitoring
• Request aggregation

16. What is the difference between SQL and NoSQL databases?

Feature	SQL Databases	NoSQL Databases
Structure	Structured tables	Flexible schemas
Schema	Fixed schema	Dynamic schema
Scalability	Vertical scaling	Horizontal scaling
Data Model	Relational	Document, key-value, graph, column
Example	MySQL, PostgreSQL	MongoDB, Cassandra

SQL databases are suitable for structured data and strong consistency requirements, such as banking systems. NoSQL databases are ideal for large-scale distributed systems with flexible data models and high scalability needs.

17. What is the difference between synchronous and asynchronous communication?

Feature	Synchronous Communication	Asynchronous Communication
Execution	Waits for response	Does not wait for response
Dependency	Blocking	Non-blocking
Performance	Slower under load	More scalable
Example	REST API call	Message queue event

In synchronous communication, a service waits for a response before proceeding. In asynchronous communication, a service sends a request and continues execution without waiting, often using message queues like Kafka or RabbitMQ. Asynchronous systems improve scalability and resilience.

18. What is latency vs throughput?

Latency is the time taken for a request to travel from client to server and receive a response. It is typically measured in milliseconds.

Throughput refers to the number of requests or transactions a system can process per unit time, usually measured in requests per second.

19. What is fault tolerance?

Fault tolerance is the ability of a system to continue operating correctly even when one or more components fail.

It is achieved through:

• Redundant servers
• Data replication
• Failover mechanisms
• Health checks
• Auto-recovery systems

20. What is a CDN and why is it used?

A CDN (Content Delivery Network) is a geographically distributed network of servers that cache and deliver content closer to users.

When a user requests content:

• The request is routed to the nearest CDN edge server.
• Cached content is served quickly.
• If not cached, it fetches from origin server.

CDNs reduce latency, improve performance, reduce server load, and enhance availability. They are widely used for delivering images, videos, scripts, and static website assets globally.

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System Design Interview Questions for Intermediate

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1. How would you design a URL shortening service?

A URL shortening service converts long URLs into shorter, unique links and redirects users to the original URL when accessed.

Core components:

• API layer to accept long URLs
• Unique ID generator
• Database to store mapping (short → long URL)
• Redirection service
• Caching layer for frequently accessed links

Design steps:

• Generate a unique ID using Base62 encoding.
• Store mapping in database.
• Return shortened URL.
• On access, look up ID and redirect (HTTP 302).

To scale:

• Use database sharding.
• Cache popular URLs.
• Add rate limiting.
• Use CDN for faster redirection.

2. How do you design a scalable web application?

Designing a scalable web application requires separating components and enabling horizontal scaling.

Architecture:

• Client→ Load Balancer → Application Servers
• Application Servers → Database
• Caching layer (Redis)
• CDN for static assets

Key strategies:

• Stateless application servers.
• Horizontal scaling using auto-scaling groups.
• Database replication for read scalability.
• Caching to reduce database load.
• Asynchronous processing using message queues.

This architecture ensures the system handles increasing traffic without degradation.

3. How does database indexing improve performance?

Database indexing improves performance by creating a data structure that allows faster lookups.

Without index:

• Database scans entire table (O(n)).
• With index:
• Uses B-Tree or Hash index (O(log n)).

4. How do you design a rate limiter?

A rate limiter controls how many requests a user can make within a defined time period.

Common algorithms:

• Fixed Window Counter
• Sliding Window
• Token Bucket
• Leaky Bucket

Implementation considerations:

• Store counters in Redis for distributed systems.
• Use IP or user ID for tracking.
• Return HTTP 429 when limit exceeded.

Rate limiting prevents abuse and protects system resources.

5. What is the difference between strong consistency and eventual consistency?

Feature	Strong Consistency	Eventual Consistency
Data Accuracy	Immediate consistency	Eventually consistent
Read Behavior	Always latest data	May return stale data
Availability	Lower during partition	Higher availability
Use Case	Banking systems	Social media feeds

Strong consistency ensures all nodes reflect the same data immediately. Eventual consistency allows temporary inconsistencies but improves scalability and availability.

6. How would you design a distributed cache?

A distributed cache stores frequently accessed data across multiple nodes.

Design components:

• Cache nodes (Redis cluster)
• Consistent hashing for data distribution
• Replication for fault tolerance
• Eviction policies (LRU, LFU)

Flow:

• Check cache.
• If miss, fetch from database.
• Store result in cache.
• Return data.

Challenges include cache invalidation and maintaining consistency between cache and database.

7. What are different database partitioning strategies?

Database partitioning splits data into smaller segments.

Types:

• Horizontal Partitioning (Sharding) – Rows split across servers.
• Vertical Partitioning – Columns split across tables.
• Range Partitioning – Based on value ranges.
• Hash Partitioning – Based on hash function.
• List Partitioning – Based on specific values.

8. How do you design a notification system?

A notification system delivers messages such as emails, SMS, or push notifications.

Architecture:

• User triggers event.
• Event stored in message queue.
• Worker processes queue.
• Notification service sends message.

Components:

• API layer
• Message queue (Kafka, RabbitMQ)
• Notification workers
• Retry mechanism
• Logging and monitoring

Asynchronous processing ensures system scalability and reliability.

9. How would you design a logging system?

A logging system collects, stores, and analyzes application logs.

Architecture:

• Application servers generate logs.
• Logs sent to log collector.
• Centralized storage (Elasticsearch).
• Visualization tool (Kibana).
• Alerting system.

Key considerations:

• Structured logging (JSON).
• Log rotation.
• Log retention policy.
• High availability storage.

Centralized logging improves observability and debugging.

10. How do you design a file storage system?

A file storage system must support upload, storage, and retrieval of files.

Architecture:

• Upload API
• Storage layer (Object storage like S3)
• Metadata database
• CDN for file delivery

Design considerations:

• Use object storage for scalability.
• Store metadata separately.
• Enable file chunking for large uploads.
• Use replication for durability.
• Implement access control.

This ensures scalability, durability, and performance.

11. What is message queue, and when would you use it?

A message queue is a communication mechanism that allows services to exchange data asynchronously. Instead of directly calling another service and waiting for a response, a service publishes a message to a queue, and another service processes it independently.

You would use a message queue when:

• You want asynchronous processing.
• You need to decouple services.
• You want to handle traffic spikes smoothly.
• You need reliable event-driven architecture.

12. Compare REST and gRPC in distributed systems.

Feature	REST	gRPC
Protocol	HTTP/1.1	HTTP/2
Data Format	JSON	Protocol Buffers (binary)
Performance	Moderate	High performance
Streaming	Limited	Supports streaming
Use Case	Public APIs	Internal microservices

REST is widely used for public APIs due to simplicity and readability. gRPC is more efficient and suitable for internal service-to-service communication because it uses binary serialization and supports bidirectional streaming.

13. What is service discovery?

Service discovery is a mechanism that enables services in a distributed system to dynamically find and communicate with each other.

Instead of hardcoding IP addresses, services register themselves with a registry (e.g., Consul, Eureka, etcd). When a service needs to call another service, it queries the registry to get the current instance location.

Service discovery is essential in microservices environments where instances scale up or down dynamically.

14. What is circuit breaker pattern?

The circuit breaker pattern prevents a system from repeatedly trying to execute a failing operation, which could cause cascading failures.

It operates in three states:

• Closed (normal operation)
• Open (fail fast, do not call downstream service)
• Half-open (test if service has recovered)

This improves system resilience and stability.

15. What is idempotency in distributed systems?

Idempotency means that performing the same operation multiple times produces the same result as performing it once.

Implementation strategy:

• Use unique request identifiers.
• Store processed request IDs.
• Return the same response for duplicate requests.

Idempotency is critical in distributed systems where network failures can cause retries.

16. How do you choose between SQL and NoSQL for a new system?

Choosing between SQL and NoSQL depends on system requirements.

Use SQL when:

• Strong consistency is required.
• Data relationships are complex.
• Transactions are critical.

Use NoSQL when:

• Schema flexibility is required.
• High scalability is needed.
• Large volumes of unstructured data exist.

17. How do you handle database migration in production?

Database migration in production must be handled carefully to avoid downtime.

Best practices:

• Use backward-compatible schema changes.
• Apply migrations in phases.
• Avoid dropping columns immediately.
• Use feature flags.
• Test migrations in staging.
• Use blue-green deployment strategy.

18. What is data replication lag?

Data replication lag occurs when replicas do not immediately reflect updates made to the primary database.

In master-replica systems:

• Writes occur on primary.
• Reads may occur on replicas.
• Replicas may temporarily return stale data.

Replication lag is measured in milliseconds or seconds and can impact systems requiring strong consistency.

Monitoring replication delay is critical in distributed databases.

19. What is backpressure in distributed systems?

Backpressure is a mechanism to prevent a fast producer from overwhelming a slower consumer in a distributed system.

If producers generate data faster than consumers can process it:

• Queues may overflow.
• System performance degrades.
• Failures may occur.

Solutions:

• Apply rate limiting.
• Use bounded queues.
• Implement flow control mechanisms.
• Scale consumers horizontally.

Backpressure ensures system stability under heavy load.

20. How would you design a simple chat application?

Design components:

• Client applications (web/mobile).
• API servers.
• WebSocket connections for real-time communication.
• Message queue for processing.
• Database for message storage.
• Presence service for online status.

Architecture flow:

• User sends message via WebSocket.
• Message stored in database.
• Message pushed to recipient.
• Offline messages retrieved later.

Scaling strategies:

• Use load balancer for API servers.
• Use distributed database.
• Partition users by region.
• Use CDN for media content.

A chat application requires low latency, real-time communication, and scalable storage.

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System Design Interview Questions for Experienced

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1. How would you design a scalable social media platform?

Designing a scalable social media platform requires handling massive user traffic, high write volume, and real-time feed updates.

High-level architecture includes:

• API layer behind load balancers
• Microservices (User service, Post service, Feed service)
• NoSQL database for posts
• SQL database for user data
• Distributed cache (Redis)
• CDN for images and media
• Message queue for asynchronous processing

Feed generation can be implemented using either:

• Fan-out on write (push model) for active users
• Fan-out on read (pull model) for large follower counts

Sharding by user ID ensures horizontal scalability. Replication and multi-region deployment ensure high availability.

2. How do you handle distributed transactions?

Distributed transactions involve multiple services or databases that must either all succeed or all fail. Traditional two-phase commit (2PC) can ensure atomicity but introduces latency and availability issues in distributed systems.

In modern architectures, distributed transactions are commonly handled using the Saga pattern. A saga breaks a large transaction into smaller local transactions, each with a compensating action in case of failure. Sagas can be orchestrated centrally or choreographed using events.

3. How would you design YouTube or a video streaming service?

A video streaming system requires storage, encoding, distribution, and delivery optimization.

Core components:

• Video upload service
• Distributed object storage
• Encoding pipeline (transcoding to multiple resolutions)
• Metadata database
• CDN for global distribution
• Recommendation service

Flow:

• User uploads video.
• Video stored in distributed storage.
• Background workers transcode video.
• CDN caches content near users.

To scale:

• Use chunked uploads.
• Store metadata separately.
• Use consistent hashing for storage.
• Use CDN to reduce origin server load.

4. What is a consensus algorithm? Explain Raft or Paxos.

A consensus algorithm ensures that distributed nodes agree on a single value or system state despite failures.

Raft is a widely used consensus algorithm designed for understandability and reliability. It works through:

• Leader election
• Log replication
• Safety guarantees

In Raft:

1. A leader node is elected.
2. All write requests go through the leader.
3. The leader replicates logs to followers.
4. Once a majority acknowledges, the entry is committed.

This ensures consistency across distributed systems such as etcd or Kubernetes control planes. Consensus algorithms are essential in distributed databases and coordination systems.

5. How would you design a distributed search engine?

A distributed search engine must support indexing, querying, and ranking.

Architecture includes:

• Web crawler
• Indexer service
• Distributed inverted index
• Query processing layer
• Ranking algorithm
• Cache layer

Data is partitioned across shards. Each shard maintains part of the index. Queries are broadcast to shards, results aggregated, and ranked.

Systems like Elasticsearch use this model. Replication ensures availability and fast query processing.

6. What is the eventual consistency trade-off in global systems?

Eventual consistency allows data updates to propagate asynchronously across distributed nodes. While this improves availability and performance, it introduces temporary inconsistencies.

In global systems, enforcing strong consistency across regions increases latency due to cross-region communication. Eventual consistency reduces latency but may allow stale reads.

Architects must evaluate whether the application requires immediate consistency or can tolerate temporary inconsistencies.

7. How would you design a ride-sharing system?

A ride-sharing system requires real-time location tracking and matching.

Components:

• User service
• Driver service
• Location tracking (GPS updates)
• Matching engine
• Pricing service
• Payment service

Flow:

• User requests ride.
• Matching service finds nearby drivers using geospatial indexing.
• Real-time updates via WebSockets.
• Payment processed after ride completion.

To scale:

• Partition drivers by geographic region.
• Use in-memory cache for location data.
• Use message queues for event handling.
• Deploy regionally to reduce latency.

8. How do you handle data partitioning at massive scale?

At massive scale, data must be partitioned across multiple machines to distribute load and storage.

Common strategies include:

• Hash-based partitioning
• Range-based partitioning
• Geographic partitioning

Consistent hashing is often used to evenly distribute keys across nodes while minimizing data movement when nodes are added or removed.

Proper partitioning avoids hotspots and improves system scalability.

9. How would you design a payment processing system?

A payment system requires reliability, security, and strong consistency.

Core components:

• API gateway
• Authentication service
• Transaction service
• Ledger database (strong consistency)
• Fraud detection service
• Message queue for asynchronous events

Design principles:

• Use idempotency keys for retries.
• Ensure ACID compliance.
• Encrypt sensitive data.
• Implement audit logging.

High availability and multi-region failover are critical to prevent downtime.

10. How do you design for disaster recovery?

Disaster recovery ensures system continuity during catastrophic failures.

Key strategies include:

• Multi-region deployment
• Automated backups
• Data replication
• Failover systems
• Regular disaster recovery testing

Recovery objectives include:

• RTO (Recovery Time Objective)
• RPO (Recovery Point Objective)

11. How would you design a real-time messaging system like WhatsApp?

A messaging system requires low latency and high throughput.

Components:

• WebSocket servers
• Message queue
• Distributed database
• Presence service
• Media storage

Flow:

• User sends message via persistent connection.
• Message stored in database.
• If recipient online, push instantly.
• If offline, store for later delivery.

Scaling strategies:

• Partition users across servers.
• Use sharding for messages.
• Use consistent hashing.
• Multi-region deployment.

The system must support eventual consistency and handle millions of concurrent connections.

12. How do you ensure observability in large systems?

Observability enables teams to understand system behavior and diagnose issues effectively.

It consists of three pillars:

• Logs
• Metrics
• Traces

Centralized logging systems aggregate logs from all services. Metrics track performance indicators such as latency and error rates. Distributed tracing tracks request flow across services.

Tools like Prometheus, Grafana, and OpenTelemetry are commonly used. Observability is essential for maintaining reliability in complex distributed architectures.

13. How would you design an e-commerce platform at scale?

An e-commerce platform includes multiple independent services:

• Product catalog
• Inventory management
• Order processing
• Payment service
• Search service

Design decisions:

• Use caching for product pages.
• Use message queues for order processing.
• Use database replication.
• Use CDN for images.
• Implement inventory locking to avoid overselling.

Sharding by product ID or user ID ensures scalability.

14. How would you refactor a monolithic system into microservices?

Refactoring a monolith into microservices requires gradual decomposition.

Steps include:

1. Identify bounded contexts and business domains.
2. Extract independent modules as services.
3. Introduce APIs for communication.
4. Gradually migrate traffic.
5. Implement service discovery and monitoring.

Strangler pattern is often used, where new services replace parts of the monolith incrementally.

This approach reduces risk while improving scalability and maintainability.

15. How would you design a distributed key-value store?

A distributed key-value store must provide scalability and availability.

Core concepts:

• Partitioning using consistent hashing
• Replication for fault tolerance
• Gossip protocol for cluster communication
• Eventual consistency model

Data is distributed across nodes using hash-based partitioning. Replication ensures durability. Systems like DynamoDB and Cassandra use this architecture.

Trade-offs must be made between consistency and availability.

16. How do you secure APIs in distributed architecture?

Securing APIs involves multiple layers of protection.

Key measures include:

• HTTPS encryption
• OAuth 2.0 or JWT authentication
• Role-based access control
• API gateways for centralized enforcement
• Rate limiting
• Input validation
• Logging and monitoring

Zero Trust principles ensure that no service is implicitly trusted. Security must be integrated into design, not added later.

17. How would you design a global load-balancing system?

A global load balancer distributes traffic across multiple geographic regions.

Architecture:

• DNS-based routing
• Health checks
• Geo-based routing
• Failover mechanisms

If one region fails, traffic automatically routes to another region. Latency-based routing improves user experience.

Global load balancing ensures availability and performance at worldwide scale.

18. What are common bottlenecks in distributed systems?

Common bottlenecks include:

• Database write contention
• Network latency
• Synchronous service dependencies
• Poor indexing
• Single points of failure
• Inefficient caching strategy

19. How would you design a multi-region high-availability system?

A multi-region system ensures service continuity across geographic locations.

Design elements:

• Active-active deployment across regions
• Data replication
• Global load balancer
• Automated failover
• Consistency management

Challenges include:

• Replication lag
• Data conflicts
• Increased latency

Trade-offs must balance consistency, availability, and performance.

20. What trade-offs do you consider between consistency, availability, and performance?

Distributed systems must balance trade-offs defined by the CAP theorem.

• Strong consistency may increase latency.
• High availability may allow stale reads.
• Performance optimizations may reduce strict guarantees.

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Scenario-Based Questions for System Design Interviews

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1. A news website experiences heavy traffic during breaking news events and crashes frequently. How would you redesign the system to handle sudden traffic spikes?

To handle sudden traffic spikes, the system must support horizontal scalability and traffic distribution. The first step is to place a load balancer in front of multiple stateless application servers, so incoming requests are distributed evenly. Static assets such as images and scripts should be served via a CDN to reduce load on origin servers.

Caching frequently accessed pages using Redis or an in-memory cache reduces repeated database queries. The database can be replicated for read scalability. Auto-scaling groups can automatically add more servers during peak traffic. This design ensures availability and stability during traffic surges.

2. A startup stores all application data in a single database, and performance is degrading as user growth increases. What architectural changes would you suggest?

As data volume grows, a single database becomes a bottleneck. The system can be improved by introducing read replicas to handle read-heavy traffic. Frequently accessed data should be cached using a distributed cache like Redis.

If the write volume grows significantly, database sharding can distribute data across multiple nodes. Separating services (e.g., user service and order service) into independent databases can also reduce contention. These changes improve scalability and reduce load on the primary database.

3. You are designing an online ticket booking system. How would you prevent overselling of tickets during high-demand events?

Overselling occurs when multiple users attempt to book the same seat simultaneously. To prevent this, the system must enforce strong consistency on seat allocation.

Techniques include database transactions with row-level locking or optimistic locking using version numbers. A distributed locking mechanism (e.g., Redis-based lock) can ensure only one transaction updates a seat at a time.

Additionally, a short reservation timeout system can temporarily block seats while users complete payment. This approach balances consistency and user experience while maintaining scalability.

4. A notification system sometimes delays message delivery during peak traffic. How would you redesign it to ensure reliable and scalable delivery?

Delays often indicate synchronous processing or resource saturation. The system should be redesigned to use asynchronous messaging through a message queue such as Kafka or RabbitMQ.

Incoming notification requests should be written to the queue, and worker services should process messages independently. Horizontal scaling of workers ensures high throughput. Implementing retry mechanisms and dead-letter queues ensures reliability.

This event-driven architecture improves performance and prevents delays under heavy load.

5. You are tasked with designing a globally distributed financial transaction system that must maintain consistency while supporting millions of users. How would you approach this?

A financial system requires strong consistency and fault tolerance. The architecture should use distributed databases supporting consensus protocols such as Raft. Writes should go through a leader node to maintain consistency.

Multi-region deployment should use active-passive or carefully managed active-active setups with strong replication controls. Idempotency keys must prevent duplicate transactions.

Security must include encryption, authentication, audit logging, and fraud detection services. The design must carefully balance latency and consistency, prioritizing correctness over availability when necessary.

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System Design MCQ Questions and Answers

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Preparing for objective-based rounds is also important in system design interviews, especially for written technical assessments. System design MCQs help you test your understanding of scalability, CAP theorem, caching, load balancing, database design, distributed systems, and architectural trade-offs.

Practicing structured system design MCQs improves conceptual clarity and helps reinforce important principles such as consistency models, partitioning strategies, and fault tolerance. You can regularly practice System Design MCQs on PlacementPreparation.io to strengthen your architectural thinking and interview readiness.

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Final Words

System design interviews evaluate not just knowledge, but structured thinking, trade-off analysis, and real-world architectural reasoning. Understanding scalability, reliability, consistency, and performance trade-offs is essential for designing modern distributed systems.

Focus on clear communication, structured problem breakdown, and explaining why you choose certain design decisions. With consistent practice and exposure to real-world scenarios, you can confidently approach system design interviews at any level.

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