10 Interview Prep Projects (2026)

FAANG and top-tier tech interviews test your ability to design, build, and scale real systems. These 10 projects go beyond LeetCode — they teach you system design, concurrency, data structure choices, and the trade-offs engineers make daily. Each project includes scaling considerations and algorithm decisions to discuss in interviews.

1. URL Shortener (System Design Practice)

What you’ll build: A service like TinyURL that maps long URLs to short codes and redirects visitors.

Scaling considerations: Use base62 encoding for short codes (7 chars = 62⁷ = 3.5 trillion combinations). Pre-generate codes in a batch to avoid DB locking under high write load. Redirects must be fast — use Redis cache with 301/302 decision (301 for permanent caching, 302 for analytics count). Database sharding on short code prefix for horizontal scaling.

Algorithm choices: Consistent hashing for cache distribution. Bloom filter to check short code availability before DB query.

Interview talking points: Read-heavy vs write-heavy trade-off, cache invalidation for analytics, choosing hash function for uniform distribution.

2. Rate Limiter (Concurrency + Design)

What you’ll build: A middleware that limits API requests per user/IP using sliding window or token bucket algorithms.

Scaling considerations: Sliding window uses Redis sorted sets (O(log n) per check). Token bucket is more memory-efficient (store last refill + tokens). For distributed rate limiting, use Redis cluster with Lua scripting for atomic operations. Move to client-side rate limiting hints (X-RateLimit-Remaining headers) for scale.

Algorithm choices: Sliding window log (accurate but memory heavy), sliding window counter (approximate but efficient), token bucket (good for burst handling).

Interview talking points: Distributed rate limiting challenges, clock skew in token bucket, consistency vs availability trade-off (CAP theorem relevance).

3. Web Crawler (BFS/DFS + Scale)

What you’ll build: A web crawler that discovers pages, respects robots.txt, and extracts content.

Scaling considerations: BFS for politeness (breadth-first = less likely to overload one domain). URL frontier with priority queue (important pages first). Distributed crawling with worker queues (RabbitMQ/Kafka). Deduplication via Bloom filter (reduces memory by 10x vs hash set). Respect robots.txt with Crawl-Delay. Rate limit per domain to avoid bans.

Algorithm choices: BFS for domain-polite crawling, priority queue for freshness scoring, Bloom filter for visited URL deduplication.

Interview talking points: Choosing BFS vs DFS for crawling, single-host politeness, detecting infinite loops (spider traps), URL normalization canonicalization.

4. Key-Value Store (Data Structures)

What you’ll build: A simple distributed key-value store with get/put/delete, replication, and failure handling.

Scaling considerations: Consistent hashing for partition assignment (virtual nodes for even distribution). Write-ahead log (WAL) for durability before in-memory store. Replication factor of 3 (quorum: W=R=2). Hinted handoff for temporary failures. Merkle trees for anti-entropy / data reconciliation.

Algorithm choices: Consistent hashing with virtual nodes, merge trees (LSM-tree vs B-tree), Merkle tree for replica sync.

Interview talking points: Dynamo vs Bigtable architecture, CAP theorem trade-offs (AP vs CP), gossip protocol for membership, read repair vs hinted handoff.

5. Chat System (WebSocket + Design)

What you’ll build: A real-time messaging system supporting one-on-one and group chats with online status.

Scaling considerations: WebSocket connections stick to specific server instances — use Redis pub/sub to broadcast across servers. Messages persisted in database (Cassandra for high write throughput). Last-seen timestamp and typing indicators via ephemeral state. Offline messages queued and delivered on reconnect. For group chats with 10k+ members, fan-out on write (write once to group inbox) vs fan-out on read (write to each member).

Algorithm choices: WebSocket reconnection with exponential backoff, last-write-wins for status updates, consistent hashing for WebSocket server assignment.

Interview talking points: Connection management at scale, presence detection (heartbeat intervals), handling reconnection + message ordering, group chat scaling strategies.

6. Notification Service (Queue + Scaling)

What you’ll build: A notification system that sends push, email, and SMS notifications with delivery guarantees.

Scaling considerations: Use message queues (RabbitMQ, Kafka) to decouple notification generation from delivery. Separate queues per channel (push, email, SMS) since they have different throughput and failure modes. Rate limit per user (max 10 push/hr) and per channel (email provider sends 14 emails/sec). Retry with exponential backoff for failed deliveries. Deduplicate identical notifications within a time window.

Algorithm choices: Priority queue for urgent notifications (password reset > marketing). Retry queue with dead-letter for permanent failures.

Interview talking points: Exactly-once vs at-least-once delivery trade-offs, idempotency keys for deduplication, backpressure handling when downstream is slow.

7. Distributed Cache (LRU + Sharding)

What you’ll build: A distributed LRU cache with sharding, replication, and failure handling.

Scaling considerations: Implement LRU eviction with doubly-linked list + hash map (O(1) operations). Shard across nodes using consistent hashing. Replication factor of 2 for availability. TTL expiry with lazy deletion (on access) + periodic cleanup. Cache stampede prevention with mutex per key (first request triggers recompute, subsequent requests wait).

Algorithm choices: LRU vs LFU vs FIFO for different access patterns. Consistent hashing for minimal reshuffling on node changes.

Interview talking points: Cache-aside vs read-through vs write-through patterns, cache invalidation strategies, thundering herd problem, when to use Redis vs Memcached.

8. API Gateway (Routing + Auth)

What you’ll build: A gateway that routes requests to microservices, handles auth, rate limiting, and request transformation.

Scaling considerations: Stateless gateways behind a load balancer for horizontal scaling. Route matching via prefix tree (trie) for O(k) lookup (k = path length). Auth token validation with JWKs cache (public keys fetched and cached). Rate limiting per client (sliding window in Redis). Request aggregation (GraphQL-style) for composite responses. Circuit breaker for downstream service failures.

Algorithm choices: Trie-based routing, token bucket rate limiting, circuit breaker (closed/open/half-open states).

Interview talking points: Synchronous vs asynchronous composition, service discovery integration (DNS vs registry), handling partial failures (timeouts, circuit breakers), security (JWT validation, rate limiting per API key).

9. Search Autocomplete (Trie + Ranking)

What you’ll build: A typeahead / autocomplete service that suggests search queries as the user types.

Scaling considerations: Store top N suggestions per prefix in a trie. Each node in the trie maintains a min-heap of top N completions (by frequency or freshness). For scale, shard the trie by prefix (a-m on one server, n-z on another). Add trie compression (radix tree) to reduce memory. Personalize suggestions via user-specific trie branches. Update frequency counts asynchronously with a log-based aggregation (MapReduce / Spark).

Algorithm choices: Trie vs ternary search tree for prefix matching. Min-heap for maintaining top results. Levenshtein distance for fuzzy matching and typo tolerance.

Interview talking points: Memory vs speed trade-off (trie vs hashmap), handling real-time trending queries, personalization without excessive memory, stale vs fresh trade-off in suggestions.

10. Real-Time Leaderboard (Sorted Sets + Streaming)

What you’ll build: A real-time leaderboard that ranks players by score, updates instantly, and supports tie-breaking.

Scaling considerations: Redis sorted sets (ZADD, ZRANK, ZRANGE) for O(log n) operations. For very large leaderboards (millions of players), shard by score range. Stream score updates via Kafka for persistence + replay (in case Redis fails). Periodic snapshot to PostgreSQL for historical queries. Handle ties by adding timestamp as secondary sort (score + timestamp binary encoding).

Algorithm choices: Sorted set with lexicographic tie-breaking score (score * 2⁶⁴ + MAX_TS - timestamp). Skip list vs balanced BST for sorted data.

Interview talking points: Real-time consistency vs eventual consistency, handling tie scores at scale, streaming architecture for live updates, leaderboard percentile calculation.

How to Use These Projects for Interview Prep

System Design Interviews

Each project directly maps to system design interview questions:

• URL Shortener → “Design TinyURL”
• Rate Limiter → “Design a rate limiter”
• Chat System → “Design WhatsApp / Messenger”
• Notification Service → “Design a notification system”
• Distributed Cache → “Design a caching layer like Redis”
• API Gateway → “Design an API gateway”

Coding Interviews

The data structures and algorithms in these projects appear in coding interviews:

• LRU Cache → Frequent medium/hard question
• Trie with autocomplete → Common Google question
• Consistent hashing → System design discussion
• Sliding window rate limiter → Algorithm + design combined

Behavioral Interview Talking Points

These projects demonstrate exactly what FAANG interviewers look for:

• Ownership: You designed, built, and deployed the system.
• Trade-off awareness: You chose Redis over PostgreSQL for a reason.
• Scale thinking: You considered what happens at 1M vs 100M users.
• Resilience: You planned for failure (circuit breakers, retries).

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