10 FAANG System Design Interview Questions and Answers

System design interviews are a critical component of the hiring process at FAANG companies. These interviews assess your ability to architect scalable, efficient, and maintainable systems, which are essential skills for handling the complex and high-traffic applications these companies manage. Mastery of system design principles is crucial for roles that require building robust and high-performance systems.

This article offers a curated selection of system design questions tailored to FAANG interviews. By working through these examples, you will gain a deeper understanding of how to approach and solve system design problems, enhancing your readiness for high-stakes technical interviews.

FAANG System Design Interview Questions and Answers

1. Design a Distributed Cache System.

Designing a distributed cache system involves several components to ensure scalability, reliability, and performance:

• Cache Nodes: Servers that store cached data, distributed across multiple machines for availability and fault tolerance.
• Data Partitioning: Techniques like consistent hashing distribute data evenly and minimize the impact of node changes.
• Replication: Replicating data across nodes ensures availability and fault tolerance.
• Eviction Policies: Implement policies like Least Recently Used (LRU) or Least Frequently Used (LFU) to manage limited cache storage.
• Cache Coherence: Maintain a consistent view of data across nodes using write-through, write-back, or invalidation protocols.
• Load Balancing: Distribute load evenly across nodes to prevent bottlenecks.
• Monitoring and Metrics: Track performance and health to identify bottlenecks and optimize the system.
• Security: Implement authentication, authorization, and encryption mechanisms.

2. Build a Notification System.

To build a notification system, consider these components for scalability and reliability:

1. Notification Types: Support various types like email, SMS, and push notifications, each with different requirements.

2. User Preferences: Allow users to set preferences for notification types and channels.

3. Message Queue: Use a message queue (e.g., RabbitMQ, Kafka) to decouple notification generation from delivery.

4. Notification Service: Process messages from the queue and send notifications via appropriate channels.

5. Template Management: Manage notification templates centrally for consistency.

6. Logging and Monitoring: Track delivery status and diagnose issues using tools like ELK stack or Prometheus.

7. Retry Mechanism: Implement retries for transient delivery failures.

8. APIs: Provide secure, rate-limited APIs for triggering notifications.

3. Design a Real-Time Chat Application.

Designing a real-time chat application involves several components for efficient communication:

The architecture typically follows a client-server model, using WebSocket protocol for real-time, bidirectional communication. The server handles message broadcasting using a publish-subscribe model, acting as a broker for clients subscribing to specific channels.

Data storage is crucial for message persistence and retrieval. NoSQL databases like MongoDB or distributed databases like Cassandra handle large data volumes and ensure availability.

Scalability is essential for growing user numbers. Load balancing distributes connections across server instances, and distributed messaging systems like Apache Kafka manage message queues.

Security involves end-to-end encryption for message privacy and authentication mechanisms for user identity verification.

4. Implement a Load Balancer.

A load balancer is essential for distributing network traffic across servers, improving performance and reliability. Strategies include:

• Round Robin: Sequentially distributes requests across servers.
• Least Connections: Directs traffic to the server with the fewest active connections.
• IP Hash: Uses the client’s IP address to determine the server handling the request.

Load balancers operate at different OSI model layers, such as transport (Layer 4) or application (Layer 7). Layer 4 uses IP and TCP/UDP ports, while Layer 7 uses application-level data like HTTP headers.

Example of a simple load balancer using Round Robin strategy:

class LoadBalancer:
    def __init__(self, servers):
        self.servers = servers
        self.index = 0

    def get_server(self):
        server = self.servers[self.index]
        self.index = (self.index + 1) % len(self.servers)
        return server

servers = ['Server1', 'Server2', 'Server3']
lb = LoadBalancer(servers)

for _ in range(6):
    print(lb.get_server())

5. Design a Search Autocomplete System.

Designing a search autocomplete system involves several components for efficient suggestions:

• Data Storage and Indexing: Use a trie or Ternary Search Tree (TST) for quick retrieval and prefix-based searches.
• Query Processing: Retrieve potential matches by traversing the data structure, optimized for large datasets.
• Ranking Algorithms: Rank suggestions based on factors like frequency, recency, and context. Machine learning models can enhance ranking.
• Caching: Cache frequently searched prefixes to improve performance.
• Scalability: Handle concurrent users and growing datasets with distributed systems and sharding.
• User Experience: Provide a responsive experience, handling typos with fuzzy matching and error-tolerant algorithms.

6. Design a Video Streaming Service.

Designing a video streaming service involves several components:

1. Content Delivery Network (CDN): Distribute video content efficiently to reduce latency and improve quality.

2. Storage: Use scalable cloud storage solutions like Amazon S3 or Google Cloud Storage.

3. Encoding and Transcoding: Convert videos into various formats and resolutions for compatibility and quality.

4. Streaming Protocols: Use protocols like HLS or DASH for adaptive bitrate streaming.

5. User Authentication and Authorization: Manage access and subscriptions with OAuth and JWT.

6. Database: Store metadata and user information in NoSQL databases like MongoDB or DynamoDB.

7. Load Balancing: Distribute traffic across servers for availability and reliability.

8. Monitoring and Analytics: Use tools like Prometheus and Grafana for performance monitoring and Google Analytics for user insights.

7. Design a Recommendation Engine.

Designing a recommendation engine involves several steps:

1. Data Collection: Gather data from user interactions, purchase history, and browsing behavior.

2. Data Processing: Clean and preprocess data, handling missing values and normalizing.

3. Feature Engineering: Extract features like user demographics and item attributes.

4. Model Selection: Choose algorithms like collaborative filtering, content-based filtering, or hybrid methods.

5. Model Training: Train the model using historical data, tuning hyperparameters and evaluating performance.

6. Serving Recommendations: Deploy the model for real-time recommendations via a REST API or application integration.

7. Feedback Loop: Collect feedback to retrain and improve the model.

8. Design a Content Delivery Network (CDN).

A Content Delivery Network (CDN) is a system of distributed servers that deliver web content based on geographic location, origin, and server availability. The goal is to reduce latency and improve performance.

Key components include:

• Edge Servers: Cache content closer to users, reducing data travel distance.
• Origin Server: Central server storing original content, fetched by edge servers when needed.
• Load Balancers: Distribute traffic across servers for reliability and performance.
• DNS Routing: Routes requests to the nearest edge server based on location and load.
• Content Replication: Copy content from origin to edge servers, done proactively or reactively.
• Monitoring and Analytics: Track performance and optimize efficiency.

CDN workflow:

• User requests content from a website.
• DNS routes the request to the nearest edge server.
• If content is cached, it’s delivered to the user.
• If not, the edge server fetches it from the origin, caches it, and delivers it.

9. Design a Social Media Feed.

Designing a social media feed involves several components for efficiency and scalability:

1. Data Storage: Use a combination of SQL and NoSQL databases for structured and unstructured data.

2. Feed Generation: Choose between pull or push models for feed generation based on system requirements.

3. Ranking Algorithms: Use algorithms to rank posts based on interactions, recency, and content type. Machine learning models can personalize feeds.

4. Scalability: Use sharding, load balancing, and caching to manage growth. Distributed systems and microservices architecture enhance scalability.

5. Real-time Updates: Implement real-time updates with WebSockets or long polling, using message queues for data flow.

6. User Privacy and Security: Implement authentication, authorization, and data encryption, and regularly audit for vulnerabilities.

10. Design a Data Pipeline for Real-Time Analytics.

Designing a data pipeline for real-time analytics involves several components:

1. Data Ingestion: Collect data from sources like IoT devices or web applications using tools like Apache Kafka or Amazon Kinesis.

2. Data Processing: Use frameworks like Apache Flink or Spark Streaming for real-time processing, including event processing and transformations.

3. Data Storage: Store processed data in NoSQL or time-series databases like Cassandra or InfluxDB for real-time querying.

4. Data Visualization: Present data in dashboards or reports using tools like Grafana or Kibana.

5. Monitoring and Alerting: Use tools like Prometheus or ELK Stack to monitor the pipeline and set up alerts for anomalies.