system-design-questions-2

Topic	Key Considerations
Chat application	Communication protocols, real-time messaging, message storage, presence management
Social network	User profiles, news feed generation, data storage, search, recommendation engines
E-commerce	Product catalog, shopping cart, checkout flow, payment processing, inventory management
Video streaming	Video encoding, storage and delivery, video player, user analytics, content recommendation
Search engine	Web crawler, index creation and storage, query processing, ranking algorithm
Map/reduce	Distributed computing, task scheduling, fault tolerance, data partitioning
Load balancing	Distribution algorithms, server health monitoring, auto-scaling, failover
Caching	Cache eviction policies, cache coherence, cache miss handling
Rate limiting	Token bucket algorithm, sliding window log algorithm, load shedding
Database sharding	Data partitioning, shard management, consistency, replication
Content delivery network (CDN)	Content distribution, caching, server selection, edge location mapping
Message queue	Message durability, message ordering, message size, message processing
Real-time analytics	Data ingestion, stream processing, data storage, data visualization
IoT	Device communication protocols, data ingestion, event processing, command and control
Blockchain	Consensus algorithms, transaction processing, data privacy, smart contracts

Question	System Design
Design a scalable system to find the most popular search queries in the last hour.	Use a distributed architecture with multiple servers, each handling a subset of incoming search queries. Store the queries in a time-series database and use a sliding window algorithm to determine the most popular queries in the last hour.
Design a distributed system for recommendation engine.	Use a collaborative filtering approach, where user behavior data is used to recommend items. Store user behavior data and item data in a distributed database such as Cassandra or HBase. Use MapReduce or Spark to compute recommendations based on the stored data.
Design a system to store and serve billions of images.	Use a distributed storage system such as Hadoop HDFS or Amazon S3. Store the images as binary blobs and use a content-based addressing system such as MD5 or SHA-1 to ensure uniqueness. Serve the images through a content delivery network (CDN) to improve performance.
Design a system to predict stock prices.	Collect real-time stock market data from various sources such as Bloomberg or Yahoo Finance. Store the data in a distributed database such as Cassandra or HBase. Use machine learning algorithms such as linear regression or neural networks to predict future stock prices based on historical data.
Design a system to detect fraudulent transactions in real-time.	Collect transaction data in real-time and store it in a distributed database such as Cassandra or HBase. Use machine learning algorithms such as decision trees or random forests to detect fraudulent transactions based on patterns in the data.
Design a system to manage ride-hailing requests in a city.	Use a distributed system with multiple servers handling incoming ride requests. Use a location-based approach to match drivers with riders. Store user and driver data in a distributed database such as Cassandra or HBase. Use a payment gateway to handle payment transactions.
Design a system to monitor and analyze the performance of a large-scale web application.	Use a distributed logging system to collect logs from various servers in the application stack. Store the logs in a distributed database such as Elasticsearch or HBase. Use data visualization tools such as Kibana or Grafana to analyze the logs and identify performance issues.
Design a system to manage inventory and orders for an e-commerce website.	Store product information, user information, and order information in a distributed database such as Cassandra or HBase. Use a payment gateway to handle payment transactions. Use a queuing system such as Kafka to handle incoming orders and ensure scalability.
Design a system to handle real-time chat messages for millions of users.	Use a distributed messaging system such as Kafka or RabbitMQ to handle real-time chat messages. Store user and message data in a distributed database such as Cassandra or HBase. Use a web socket protocol to enable real-time communication between users.
Design a system to perform sentiment analysis on social media data in real-time.	Use a distributed system to collect social media data in real-time from various sources such as Twitter or Facebook. Store the data in a distributed database such as Cassandra or HBase. Use machine learning algorithms such as Naive Bayes or SVM to perform sentiment analysis on the data. Use a web interface to display the results in real-time.