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System Design

Practice requirements, topology, bottlenecks, tradeoffs, failure modes, and operational constraints as a design loop.

RequirementsTopologyBottlenecksTradeoffsFailure ModesSystem Design HLD Example

Begin with

System Design HLD Example gives you the cleanest entry point before branching into constraints, failures, and related systems.

12

Articles

10

Concepts

Start With System Design HLD Example

Grounding

Build the mental model.

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Shape

See how the pieces depend on each other.

See Context

Consequence

Compare what improves and what breaks.

Compare Tradeoffs

Stress

Change constraints and watch behavior.

Practice Reasoning

Next

Move to the next useful edge.

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Related systems

Follow the nearby ideas

Use the map as a quiet orientation layer, then move back into the articles for depth.

Guidance

Requirements

Continues from what you have already explored.

System behavior

Retrieval System Query Path

Query expansion, retrieval, reranking, and answer grounding.

Open
Step 1 / 2Normal flow
requestcommandpersistpublishconsumereqUClientActorGAPI GatewayBoundaryCCore ServiceCoordinatorDState StoreDurabilityQEvent StreamStreamWConsumerWorker

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1System Design HLD Example: URL Shortener (TinyURL and Bitly)TLDR: A URL shortener is a read-heavy system (100:1 ratio) that maps long URLs to short, unique aliases. The core scaling challenge is generating unique IDs without database contention—solved using a 19 min2System Design HLD Example: Video Streaming (YouTube/Netflix)TLDR: A video streaming platform is a two-sided architectural beast: a batch-oriented transcoding pipeline that converts raw uploads into multi-resolution segments, and a real-time global delivery net17 min3System Design HLD Example: Ride-Sharing (Uber/Lyft)TLDR: A ride-sharing platform is a high-velocity geospatial matching engine. Drivers stream GPS coordinates every 5 seconds into a Redis Geospatial Index. When a rider requests a trip, the Matching Se16 min4System Design HLD Example: Proximity Service (Yelp/Google Places)TLDR: A proximity service (Yelp/Google Places) solves the 2D search problem by encoding locations into Geohash strings, which are indexed in a standard B-tree. To guarantee results near grid boundarie17 min5System Design HLD Example: Real-Time LeaderboardTLDR: Real-time leaderboards for 10M+ active users require an in-memory ranking engine. Redis Sorted Sets (ZSET) are the industry standard, providing \(O(\log N)\) updates and rank lookups via an inte16 min6System Design HLD Example: Hotel Booking System (Airbnb)TLDR: A robust hotel booking system must guarantee atomicity in inventory subtraction. The core trade-off is Consistency vs. Availability: we prioritize strong consistency for the booking path (Postgr15 min7System Design HLD Example: Collaborative Document Editing (Google Docs)TLDR: Real-time collaborative editing relies on Operational Transformation (OT) or CRDTs to resolve concurrent edits without data loss. The core trade-off is Latency vs. Consistency: we use optimistic14 min8System Design HLD Example: Search Autocomplete (Google/Amazon)TLDR: Search autocomplete must respond in sub-10ms to feel "instant." The core trade-off is Latency vs. Data Freshness: we use an offline pipeline (Spark) to pre-calculate prefix-to-suggestion mapping15 min9System Design HLD Example: News Feed (Home Timeline)TLDR: A news feed system builds personalized timelines by combining content publishing, graph relationships, and ranking. The scalability crux is the fan-out amplified write path: a single celebrity p20 min10System Design HLD Example: API Gateway for MicroservicesTLDR: An API Gateway centralizes "cross-cutting concerns" like authentication, rate limiting, and routing at the edge of your infrastructure. The architectural crux is the separation of the Control Pl16 min11System Design HLD Example: Distributed Cache PlatformTLDR: Distributed caches trade strict consistency for sub-millisecond read latency, using consistent hashing to scale horizontally without causing database-shattering "cache stampedes" during cluster 15 min12Change Data Capture Pattern: Log-Based Data Movement Without Full ReloadsTLDR: Change data capture moves committed database changes into downstream systems without full reloads. It is most useful when freshness matters, replay matters, and the source database must remain t16 min

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