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System Design Interview Practice Questions for Technical Architects ​

Below are real-world system design interview questions commonly asked for technical architect roles, with detailed sample answers.

1. Design a Scalable URL Shortener (e.g., bit.ly) ​

How would you handle billions of URLs and high read/write throughput?

  • Use a distributed, horizontally scalable database (e.g., Cassandra, DynamoDB, or sharded MySQL/Postgres).
  • Employ caching (e.g., Redis, Memcached) for frequently accessed URLs.
  • Use load balancers and stateless application servers for scaling.

What database(s) would you use and why?

  • NoSQL databases like DynamoDB or Cassandra for high write throughput and scalability.
  • RDBMS with sharding if strong consistency is needed.

How would you prevent collisions and ensure short URL uniqueness?

  • Use auto-incrementing IDs, UUIDs, or hash functions (e.g., base62 encoding of unique IDs).
  • Check for collisions before assigning a new short URL.

How would you handle analytics and abuse prevention?

  • Store analytics in a separate data store (e.g., time-series DB).
  • Rate-limit API usage and monitor for suspicious activity.
  • Use CAPTCHAs or authentication for bulk/automated requests.

2. Design a Global E-Commerce Platform ​

How would you architect a system to support millions of users, global inventory, and multi-currency transactions?

  • Use microservices for modularity (user, product, order, payment, etc.).
  • Deploy services in multiple regions for low latency.
  • Use a global CDN for static assets.
  • Support multi-currency via currency conversion microservice.

How would you ensure high availability and disaster recovery?

  • Multi-region deployments with failover.
  • Regular backups and automated disaster recovery drills.
  • Use managed services with built-in HA (e.g., managed DBs, queues).

How would you handle catalog search and recommendations at scale?

  • Use search engines like Elasticsearch or Solr for catalog search.
  • Use recommendation engines (collaborative filtering, ML models) with batch and real-time processing.

3. Design a Real-Time Chat Application (e.g., WhatsApp, Slack) ​

How would you support millions of concurrent users and message delivery guarantees?

  • Use WebSockets for real-time communication.
  • Employ message queues (e.g., Kafka, RabbitMQ) for reliable delivery.
  • Scale horizontally with stateless chat servers.

How would you design for message ordering, delivery, and offline support?

  • Use message sequence numbers and persistent storage.
  • Store undelivered messages and deliver when users reconnect.

How would you handle group chats and media sharing?

  • Use group IDs and broadcast messages to group members.
  • Store media in object storage (e.g., S3, GCS) and share links.

4. Design a Video Streaming Platform (e.g., YouTube, Netflix) ​

How would you handle video upload, encoding, storage, and global delivery?

  • Use a microservice for uploads, trigger encoding jobs (e.g., via a queue).
  • Store videos in object storage (S3, GCS, Azure Blob).
  • Use a CDN for global delivery.

How would you design for adaptive bitrate streaming and CDN integration?

  • Encode videos in multiple bitrates and formats (HLS, DASH).
  • Use manifest files to allow clients to switch streams based on bandwidth.
  • Integrate with CDN for edge caching.

How would you support recommendations and personalized feeds?

  • Use ML models for recommendations (collaborative filtering, content-based).
  • Store user activity and preferences for personalization.

5. Design a Ride-Sharing Service (e.g., Uber, Lyft) ​

How would you match riders and drivers in real time?

  • Use geospatial indexing (e.g., QuadTree, Geohash) to find nearby drivers.
  • Use real-time messaging (WebSockets, push notifications) for updates.

How would you handle surge pricing, location tracking, and trip histories?

  • Calculate surge pricing based on demand/supply metrics.
  • Track locations using GPS updates and store in a time-series DB.
  • Store trip histories in a relational or NoSQL DB.

How would you ensure data consistency and low-latency updates?

  • Use eventual consistency for non-critical data, strong consistency for payments/trips.
  • Use in-memory data stores for fast lookups.

6. Design a Distributed Logging and Monitoring System ​

How would you collect, store, and analyze logs from thousands of servers?

  • Use log shippers (e.g., Fluentd, Logstash) to collect logs.
  • Store logs in a scalable store (e.g., Elasticsearch, S3, BigQuery).
  • Use a centralized logging service with search and analytics.

How would you design for alerting, dashboards, and scalability?

  • Use monitoring tools (e.g., Prometheus, Grafana) for metrics and dashboards.
  • Set up alerting rules for anomalies and thresholds.
  • Partition logs by time and source for scalability.

How would you ensure data privacy and retention policies?

  • Encrypt logs at rest and in transit.
  • Implement log retention and deletion policies.
  • Mask or redact sensitive data before storage.

7. Design a Multi-Tenant SaaS Platform ​

How would you isolate data and resources between tenants?

  • Use separate databases or schemas per tenant, or row-level security.
  • Isolate compute resources using containers or VMs.

How would you handle onboarding, billing, and tenant-specific customizations?

  • Automate onboarding with self-service portals.
  • Integrate with payment gateways for billing.
  • Use feature flags/configurations for customizations.

How would you design for extensibility and plugin support?

  • Provide APIs and webhooks for integrations.
  • Use a plugin architecture (e.g., via microservices or serverless functions).

8. Design a Secure Online Banking System ​

How would you ensure transaction security, auditability, and compliance?

  • Use end-to-end encryption and secure authentication (MFA, OAuth2).
  • Maintain audit logs for all transactions and access.
  • Comply with standards (PCI DSS, GDPR, etc.).

How would you handle fraud detection and prevention?

  • Use ML models to detect anomalies and flag suspicious transactions.
  • Implement real-time monitoring and alerts.
  • Use device fingerprinting and behavioral analytics.

How would you design for high availability and regulatory requirements?

  • Deploy in multiple regions with failover.
  • Use redundant infrastructure and regular DR testing.
  • Ensure data residency and compliance with local regulations.

Tip: For each question, discuss trade-offs, scalability, reliability, security, and cost considerations. Draw diagrams and justify your technology choices.

Glossary ​

  • API: Application Programming Interface
  • API Gateway: A server that acts as an API front-end, receiving API requests and routing them to the appropriate backend service.
  • Anomalies: Deviations from the expected behavior, often used in fraud detection
  • Audit Logs: Records that provide a chronological sequence of events related to system activity
  • Auto-Incrementing ID: A database-generated unique identifier that increases automatically with each new record
  • Base62 Encoding: A method for encoding numeric values using 62 alphanumeric characters, often used for URL shorteners
  • Behavioral Analytics: The analysis of user behavior patterns to detect anomalies or predict future actions
  • CAPTCHA: Completely Automated Public Turing test to tell Computers and Humans Apart
  • CDN: Content Delivery Network
  • CI/CD: Continuous Integration/Continuous Deployment
  • Clustering: Grouping multiple servers or nodes to work together for scalability and redundancy
  • Compliance: Adherence to laws, regulations, and standards governing data security and privacy
  • Container Orchestration: Automated management of containerized applications (e.g., Kubernetes)
  • Data Residency: The requirement that data be stored within a specific geographic location
  • Device Fingerprinting: A technique used to identify devices based on their unique characteristics
  • Disaster Recovery (DR): Strategies and processes for restoring systems after a catastrophic failure
  • Domain-Driven Design (DDD): An approach to software development that emphasizes collaboration between technical and domain experts
  • End-to-End Encryption: A method of data transmission where only the communicating users can read the messages
  • Eventual Consistency: A consistency model used in distributed systems where updates propagate over time
  • Feature Flags: A technique to enable or disable features in a software application without deploying new code
  • Geohash: A system for encoding latitude/longitude coordinates into a compact string representation
  • Geospatial Indexing: Techniques for efficiently querying spatial data
  • Group ID: An identifier used to represent a group in messaging or chat systems
  • HA: High Availability
  • HLS: HTTP Live Streaming, a protocol for streaming media over the internet
  • Kafka: A distributed event streaming platform used for building real-time data pipelines
  • Kubernetes: An open-source system for automating deployment, scaling, and management of containerized applications
  • Load Balancer: A device or software that distributes network or application traffic across multiple servers
  • Manifest File: A file that describes the structure and metadata of media streams (e.g., for adaptive streaming)
  • MFA: Multi-Factor Authentication
  • Microservices: An architectural style that structures an application as a collection of loosely coupled services
  • ML: Machine Learning
  • NoSQL: A class of databases that provide flexible schemas and scalability (e.g., DynamoDB, Cassandra)
  • OAuth2: Open standard for access delegation commonly used for token-based authentication
  • Object Storage: A storage architecture that manages data as objects (e.g., S3, GCS, Azure Blob)
  • PCI DSS: Payment Card Industry Data Security Standard
  • Plugin Architecture: A design pattern that allows for extensibility by enabling third-party developers to add functionality
  • Prometheus: An open-source monitoring and alerting toolkit
  • QPS: Queries Per Second
  • QuadTree: A tree data structure which divides a two-dimensional space into four quadrants or regions
  • RabbitMQ: An open-source message broker software
  • Rate Limiting: A technique to control the rate of requests sent or received by a system
  • Real-Time Messaging: Communication where messages are delivered instantly as they are sent
  • Redundant Infrastructure: Systems designed to provide backup and failover capabilities in case of failure
  • Redis: An in-memory data structure store, used as a database, cache, and message broker
  • Recommendation Engine: A system that suggests products or content to users based on data analysis
  • Row-Level Security: A database feature that restricts data access at the row level based on user permissions
  • S3: Amazon Simple Storage Service, an object storage service
  • SaaS: Software as a Service
  • Scalability: The ability of a system to handle increased load by adding resources
  • Search Engine: A system that indexes and retrieves data efficiently (e.g., Elasticsearch, Solr)
  • Service Level Agreement (SLA): A contract that defines the level of service expected from a service provider
  • Sharding: Partitioning data across multiple databases or servers to improve scalability
  • Solr: An open-source search platform
  • Stateless Application Server: A server that does not store client session data between requests
  • Surge Pricing: Dynamic pricing strategy based on supply and demand
  • Time-Series DB: A database optimized for time-stamped data, often used for monitoring and analytics
  • UUID: Universally Unique Identifier
  • WebSocket: A protocol providing full-duplex communication channels over a single TCP connection