Topic
scalability
17 articles across 6 sub-topics
Sub-topic
9 articles
ID Generation Strategies in System Design: Base62, UUID, Snowflake, and Beyond
TLDR: Short shareable IDs need Base62 (URL shorteners). Database primary keys at scale need time-ordered IDs (Snowflake, UUID v7). Security tokens need random IDs (UUID v4, NanoID). Picking the wrong
Write-Time vs Read-Time Fan-Out: How Social Feeds Scale
TLDR: Fan-out is the act of distributing one post to many followers' feeds. Write-time fan-out (push) pre-computes feeds at post time — fast reads but catastrophic write amplification for celebrities.
System 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 (Postgr
System Design HLD Example: E-Commerce Platform (Amazon)
TLDR: A large-scale e-commerce platform separates catalog, cart, inventory, orders, and payments into independent microservices. The core architectural challenge is Inventory Correctness during flash
System 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 optimistic
System 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
Sub-topic
3 articles
Partitioning Approaches in SQL and NoSQL: Horizontal, Vertical, Range, Hash, and List Partitioning
TLDR: Partitioning splits one logical table into smaller physical pieces. The database skips irrelevant pieces entirely — turning a 30-second full-table scan into a sub-second single-partition read. S
Sharding Approaches in SQL and NoSQL: Range, Hash, and Directory-Based Strategies Compared
TLDR: Sharding splits your database across multiple physical nodes so no single machine carries all the data or absorbs all the writes. The strategy you choose — range, hash, consistent hashing, or di
System Design Sharding Strategy: Choosing Keys, Avoiding Hot Spots, and Resharding Safely
TLDR: Sharding means splitting one logical dataset across multiple physical databases so no single node carries all the data and traffic. The hard part is not adding more nodes. The hard part is choos
Sub-topic
2 articles
Serverless Architecture Pattern: Event-Driven Scale with Operational Guardrails
TLDR: Serverless is strongest for spiky asynchronous workloads when cold-start, observability, and state boundaries are intentionally designed. TLDR: Serverless works best for spiky, event-driven wo
Little's Law: The Secret Formula for System Performance
TLDR: Little's Law (\(L = \lambda W\)) connects three metrics every system designer measures: \(L\) = concurrent requests in flight, \(\lambda\) = throughput (RPS), \(W\) = average response time. If l
Sub-topic
1 article
The Role of Data in Precise Capacity Estimations for System Design
TLDR: Capacity estimation is the skill of back-of-the-envelope math that tells you whether your system design will survive its traffic before you write a line of code. Four numbers do most of the work
Sub-topic
1 article
System Design: Complete Guide to Caching — Patterns, Eviction, and Distributed Strategies
TLDR: Caching is the single highest-leverage performance tool in distributed systems. This guide covers every read/write pattern (Cache-Aside through Refresh-Ahead), every eviction policy (LRU through