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Master High-Level Design (HLD) and Low-Level Design (LLD) from single-server monoliths to global-scale distributed systems handling billions of users.
Step-by-step scaling patterns from real-world products.
Showing 30 of 30 case studies — numbered badges below mark the curated 1-12 starter path
Key generation, redirection, caching, and database scaling.
Real-world parallel: Google, Bitly
Token bucket in Redis for fair, distributed API request limiting.
Real-world parallel: Cloudflare, Stripe
Consistent hashing, replication, and failover like Redis or Memcached.
Real-world parallel: Redis, Meta
WebSocket servers, message queuing, delivery status checks, and presence store.
Real-world parallel: Meta, WhatsApp
Fan-out write/read paths, feed caching, media storage, and scaling read paths.
Real-world parallel: Meta, Twitter/X
Frontier, politeness, deduplication, and storage at Googlebot scale.
Real-world parallel: Google
Geohashing and quadtrees for fast nearby-places search at scale.
Real-world parallel: Yelp, Google Maps
Seat-hold locking to prevent double-booking and flash-sale traffic spikes.
Real-world parallel: BookMyShow, Ticketmaster
Quorum reads/writes, vector clocks, hinted handoff, and LSM-trees.
Real-world parallel: Amazon DynamoDB
Leases, fencing tokens, and consensus-backed mutual exclusion.
Real-world parallel: ZooKeeper, Google Chubby
Idempotent transactions, reconciliation loops, and ledger architecture.
Real-world parallel: Stripe, PayPal
Operational Transformation, CRDTs, and live multi-user presence.
Real-world parallel: Google Docs, Figma
Leader election, exactly-once triggers, and DAG dependencies.
Real-world parallel: Airbnb, LinkedIn
Batching, delivery, and multi-channel (push, email, SMS) fan-out.
Real-world parallel: Amazon, Uber
Routing, auth, request aggregation, and per-dependency circuit breaking.
Real-world parallel: Netflix, Kong
Async image processing pipeline, TTL-based ephemeral Stories, and embedding-based discovery feed.
Real-world parallel: Instagram, Meta
Real-time typeahead suggestions like Google Search autocomplete.
Real-world parallel: Google, Amazon
Sharded inverted index, scatter-gather queries, and BM25 relevance ranking.
Real-world parallel: Elastic, Google
Streaming aggregation, HyperLogLog counting, and fraud dedup.
Real-world parallel: Google Ads, Meta Ads
Inventory reservation, order state machine, and saga-coordinated checkout.
Real-world parallel: Amazon, Walmart
Chunking uploads, sync conflict resolution, and metadata sharding.
Real-world parallel: Dropbox, Google
Partitioned commit logs, consumer groups, OS zero-copy (sendfile), and ISR replicas.
Real-world parallel: LinkedIn, Confluent
Pull metrics scraper, Gorilla XOR TSDB compression, and rollup downsampling.
Real-world parallel: Datadog, Prometheus
365-day availability bitmaps, 15-min SETNX reservation holds, and Quadtree search.
Real-world parallel: Airbnb, Booking.com
Elixir WS gateway, ScyllaDB channel logs, WebRTC SFUs, and presence aggregation.
Real-world parallel: Discord, Slack
Geospatial indexing (Geohash), WebSocket connections, and matching service.
Real-world parallel: Uber, Lyft
Parallel chunk uploads, HLS transcoding, recommendation feed ranker, and Flink analytics.
Real-world parallel: TikTok, ByteDance
Retweet fan-out amplification and real-time trending topics via Count-Min Sketch.
Real-world parallel: Twitter/X
VPA resolution, switch-coordinated interbank debit/credit, and deferred net settlement.
Real-world parallel: NPCI, Google Pay, PhonePe
Transcoding pipelines, CDN delivery, view counters, and upload flow.
Real-world parallel: YouTube, Netflix
Master the building blocks of distributed systems before diving into case studies.