Practice scalability, distributed systems, and architecture design with real-world scenarios
Start Practicing NowLearn horizontal scaling, load balancing, and caching strategies
Understand CAP theorem, consistency, and distributed patterns
Design real systems like URL shorteners, chat apps, and feeds
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Answer realistic System Design interview questions in a simulated environment
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Load balancing and horizontal scaling
Caching strategies (CDN, Redis, memcached)
Database sharding and replication
Microservices vs monolithic architecture
Message queues and event-driven design
CAP theorem and consistency models
A: Requirements: generate short URLs, redirect to original. Design: generate unique hash (base62 encode auto-increment ID or hash function), store mapping in database (key-value store like Redis/DynamoDB), redirect service looks up and redirects. Scale: cache popular URLs, use CDN, database sharding by hash, handle concurrent writes with distributed ID generation.
A: CAP: Consistency (all nodes see same data), Availability (system responds to requests), Partition Tolerance (system works despite network splits). Can only have 2 of 3. In practice, partition tolerance required (networks fail), so choose CP (consistent but unavailable during partition) or AP (available but inconsistent). Examples: MongoDB (CP), Cassandra (AP).
A: Algorithms: Token bucket (most flexible), Leaky bucket (smooth rate), Fixed window (simple but edge cases), Sliding window log. Implementation: Redis with expiring keys, counters with timestamps. Distributed: centralized counter (Redis Cluster) or local counters with eventual consistency. Return 429 when limit exceeded, include rate limit headers.
A: Cache-aside: app reads from cache, fetches from DB on miss, writes to DB. Write-through: app writes to cache and DB synchronously. Write-behind: write to cache, async write to DB. Read-through: cache loads data on miss. Use cache-aside for read-heavy, write-through for consistency, write-behind for write-heavy, read-through with cache-aside.
Always clarify requirements and constraints before designing
Understand trade-offs between different approaches
Know how to calculate rough capacity estimates (QPS, storage, bandwidth)
Master common components: load balancers, CDNs, databases, caches, queues
Practice drawing system diagrams and explaining data flow
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