System Design Interview Questions and Answers

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1. Design a Parking Lot Management System

Features:

• Vehicle Management: Entry/exit logs, license plate recognition.
• Parking Slot Allocation: Dynamic slot assignment, floor preferences.
• Payment System: Hourly/daily rates, prepaid/postpaid options.
• Admin Panel: Slot overview, reports, maintenance scheduling.

Architecture:

Frontend:

• Mobile/web app for customers (book slots, view history).
• Admin dashboard (manage slots, generate reports).

Backend:

• REST API for slot booking, vehicle logs, and payments.
• Database for storing parking slots, vehicles, transactions (SQL or NoSQL based on scale).

IoT Integration:

• Sensors for slot occupancy.
• Cameras for license plate recognition.

Challenges & Solutions:

• Concurrency: Use locks or distributed transactions to prevent overbooking.
• Scalability: Implement horizontal scaling for high-traffic parking lots.
• Real-Time Updates: Use WebSockets or Server-Sent Events for live updates.

2. Design an API Rate Limiter

Features:

• Rate Limiting: Throttling requests based on user or IP.
• Burst Handling: Temporary allowance for bursts.
• Quota Management: Monthly/annual limits per user tier.

Implementation:

Algorithms:

• Token Bucket: Each request consumes a token; tokens refill at a fixed rate.
• Leaky Bucket: Requests are queued and processed at a fixed rate.

Storage:

• Use Redis to maintain counters for user requests due to its speed and atomic operations.

Middleware:

• Integrate with API Gateway or use libraries like ngx_http_limit_req_module (Nginx).

Challenges & Solutions:

• Distributed System: Use a consistent hashing mechanism for distributed rate-limiting across servers.
• User Differentiation: Assign different limits for free vs. premium users.

3. Handle Security in Distributed Systems

Key Aspects:

Authentication:

• OAuth 2.0 or OpenID Connect.
• Use short-lived JWTs for secure communication.

Encryption:

• TLS for communication.
• Encrypt sensitive data at rest (e.g., AES-256).

Access Control:

• Implement Role-Based Access Control (RBAC) or Attribute-Based Access Control (ABAC).

Auditing:

• Log all critical actions (e.g., admin changes, data access).

Example:

• For microservices: Use a service mesh (e.g., Istio) for encrypted communication between services.

4. Handle Millions of Events Per Second

Features:

• Ingest, process, and store large volumes of events in real time.
• Provide fault-tolerant and low-latency operations.

Architecture:

Data Ingestion:

• Use Kafka or RabbitMQ for high-throughput message queues.

Processing:

• Use Apache Flink or Apache Storm for real-time event processing.

Storage:

• Use columnar databases like Cassandra for scalable writes.

Visualization:

• Integrate with tools like Grafana for dashboards.

Challenges & Solutions:

• Backpressure: Use Flow Control mechanisms in Kafka.
• Latency: Ensure processing clusters are geographically close to data sources.

5. Design an Online Booking System like Airbnb

Key Features:

Search & Listings:

• ElasticSearch for fast query results.
• Ranking based on user preferences and ratings.

Availability & Booking:

• Lock slots in the database during the transaction.
• Use distributed locks for consistency.

Payment Integration:

• Secure payment gateways like Stripe or PayPal.

Notifications:

• Email/SMS alerts for booking confirmations.

Architecture:

• Frontend: Progressive Web App (PWA) for users and hosts.
• Backend:
• REST/GraphQL APIs.
• Database: Shard by geographic region for scalability.

6. High Availability in Critical Applications

Strategies:

Redundancy:

• Use Active-Active deployments for databases.
• Implement multi-region replication.

Load Balancing:

• Use tools like AWS Elastic Load Balancer.

Health Monitoring:

• Implement tools like Prometheus for alerting.

Data Backups:

• Frequent snapshots of databases for disaster recovery.

7. Photo-Sharing Service like Instagram

Features:

• Image upload, processing, and sharing.
• User feeds and notifications.

Architecture:

Frontend:

• Web/mobile app.
• Image optimization on the client side.

Backend:

• Image Storage: Use a CDN (e.g., AWS S3 + CloudFront).
• Metadata: Store in relational DB (e.g., MySQL).
• Feed Generation: Precompute feeds for efficiency.

Search:

• Use ElasticSearch for tag-based image search.

8. CAP Theorem

Explanation:

• In a distributed system, you can only achieve two out of three:
• Consistency: All nodes see the same data at the same time.
• Availability: Every request receives a response (success/failure).
• Partition Tolerance: The system continues to operate despite network partitions.

Examples:

1. CP Systems (e.g., HBase): Prioritize consistency over availability.
2. AP Systems (e.g., DynamoDB): Prioritize availability over consistency.

9. Big Data in Real-Time

Architecture:

Data Ingestion:

• Kafka for event streaming.

Processing:

• Flink for event aggregation.

Storage:

• HDFS or S3 for archival.
• Cassandra for real-time analytics.

Challenges:

• Data Skew: Use key partitioning strategies to balance load.
• Latency: Use low-latency databases like Redis for temporary storage.

10. Distributed File Storage System

Features:

• High durability and availability.
• Efficient data retrieval.

Architecture:

• Metadata Server: Stores file system metadata (e.g., file locations).
• Chunk Servers: Store actual file data.
• Replication: Replicate data across multiple nodes.

Example:

• Google’s GFS: Divides files into chunks and stores them across nodes.

11. Design an Ad-Serving Platform

Key Features:

Real-Time Bidding (RTB):

• Advertisers bid on ad space in real-time.
• Use a demand-side platform (DSP) to manage bids.

Targeting:

• Behavioral targeting based on user history, preferences, and location.
• Contextual targeting based on content.

Ad Delivery:

• Optimize delivery to reduce latency using a Content Delivery Network (CDN).

Architecture:

Frontend:

• User-facing application to display ads.

Backend:

• Ad Server: Stores ad creatives and serves them based on rules.
• User Profile Store: Stores data for targeting (e.g., demographics, history).
• Tracking System: Tracks clicks, impressions, and conversions.

Storage:

• NoSQL database (e.g., MongoDB or DynamoDB) for storing ad metadata.

Real-Time Processing:

• Use Kafka for clickstream data ingestion.
• Spark for fraud detection and analytics.

Challenges:

• Fraud Detection: Use anomaly detection models to identify invalid clicks/impressions.
• Latency: Aim for response times <100ms for seamless user experience.

12. Strategies for Fraud Detection in Online Transactions

Techniques:

Rule-Based Systems:

• Define rules (e.g., transactions > $10,000 from a new IP).
• Quick to implement but lacks adaptability.

Machine Learning Models:

• Supervised learning models (e.g., Random Forest, XGBoost) for classification.
• Anomaly detection models for unsupervised fraud detection.

Behavioral Analysis:

• Monitor typical user behaviors (e.g., locations, transaction times).

Architecture:

Ingestion:

• Use Kafka to stream transaction events.

Processing:

• Real-time scoring using ML models deployed via Flask/FastAPI.

Storage:

• Store flagged transactions in a NoSQL database for further investigation.

Challenges:

• False Positives: Optimize models to reduce legitimate transaction rejections.
• Real-Time Analysis: Ensure decisions are made within milliseconds.

13. Real-Time Analytics System

Key Features:

• Event Ingestion: Handle high-volume data from multiple sources.
• Processing: Aggregate, transform, and analyze data in real time.
• Visualization: Present insights via dashboards.

Architecture:

Data Ingestion:

• Kafka for event streaming.

Real-Time Processing:

• Apache Flink or Apache Spark Streaming for aggregations.

Storage:

• Real-time data: Redis or Memcached.
• Historical data: Data lake (e.g., AWS S3) or Druid.

Visualization:

• Tools like Tableau, Grafana, or Power BI.

Challenges:

• High Throughput: Partition data streams to distribute the load.
• Fault Tolerance: Use checkpoints in Flink to recover from failures.

14. Design a Trending Topics Feature for a Platform Like Twitter

Key Features:

Topic Detection:

• Use NLP techniques like Named Entity Recognition (NER).
• Hashtag frequency analysis.

Trend Ranking:

• Rank by tweet velocity, user engagement, and geographic location.

Localization:

• Tailor trends to specific regions or languages.

Architecture:

Data Ingestion:

• Stream tweets using Kafka.

Processing:

• Use Spark Streaming for calculating tweet frequencies.
• ML models for sentiment analysis and topic classification.

Storage:

• Store trending topics in a cache (e.g., Redis) for low-latency reads.

Challenges:

• Real-Time Updates: Use sliding window aggregations to compute trends.
• Spam Detection: Filter out bots and fake trends using behavioral analytics.

15. Design an Email Sending Service

Key Features:

Email Queueing:

• Queue emails for asynchronous sending.
• Retry mechanism for failures.

Spam Prevention:

• Validate email addresses and enforce SPF/DKIM/DMARC.

Tracking:

• Open rates, click-through rates (CTR), and delivery statuses.

Architecture:

Frontend:

• Interface for users to compose and schedule emails.

Backend:

• Email Sending Service: Use SMTP libraries like SendGrid or AWS SES.
• Queueing: Use RabbitMQ or Kafka for email queueing.

Storage:

• Relational DB for email logs and tracking data.

Challenges:

• Rate Limiting: Use a rate limiter to prevent spamming.
• Deliverability: Use domain reputation management tools.

16. Ensure Data Consistency in Microservices Architecture

Strategies:

Eventual Consistency:

• Accept that different services may temporarily hold different states.
• Use event-driven communication (e.g., Kafka).

Distributed Transactions:

• Implement the Saga pattern to manage transactions.
• Compensating Transactions: Roll back changes if a failure occurs.

Data Validation:

• Use data reconciliation jobs to identify and fix inconsistencies.

Example:

• For an e-commerce platform, ensure order, inventory, and payment services are consistent by publishing events to a central event bus.

17. Design a Calendar System

Key Features:

Event Creation:

• Single and recurring events.
• Notifications and reminders.

Time Zone Management:

• Handle users in different time zones.

Sharing:

• Allow sharing and collaboration on events.

Architecture:

Frontend:

• Calendar UI with drag-and-drop functionality.

Backend:

• Store events in a relational database (e.g., PostgreSQL).
• Use a job scheduler (e.g., Quartz) for reminders.

Challenges:

• Manage conflicts for shared events.
• Efficiently query events for a specific time range.

18. Zero-Downtime Deployments

Strategies:

Blue-Green Deployment:

• Deploy the new version to a staging environment.
• Switch traffic to the new version after validation.

Canary Deployment:

• Gradually release updates to a small subset of users.
• Rollback quickly if issues are detected.

Rolling Updates:

• Deploy updates incrementally across servers.

Example:

• Use Kubernetes rolling updates with health checks to ensure no downtime.

19. Track User Actions on a Website

Features:

Action Logging:

• Log page views, clicks, and interactions.

Storage:

• Store raw events for analytics and debugging.

Architecture:

Ingestion:

• Use JavaScript trackers to send events to Kafka.

Processing:

• Aggregate events using Spark/Flink.

Storage:

• Use ClickHouse for scalable event storage.

Visualization:

• Build dashboards in Grafana or Tableau.

Challenges:

• Data Volume: Partition data by user ID for efficient storage and querying.
• GDPR Compliance: Anonymize user data for privacy.

20. Optimize for Read-Heavy vs. Write-Heavy Systems

Read-Heavy:

Caching:

• Use Redis/Memcached for frequently accessed data.

Read Replicas:

• Add database replicas to distribute read queries.

Denormalization:

• Pre-compute and store aggregated data for faster reads.

Write-Heavy:

Efficient Schema Design:

• Use append-only logs for writes.

Event Sourcing:

• Write events instead of updating the state directly.

Partitioning:

• Partition data by write-intensive keys to balance load.