Caching
caching
System Design
Blob Storage, Object Stores & CDNs
Databases are wrong for storing large unstructured files - photos, videos, backups, logs. Object stores like S3 give you cheap, durable, infinitely scalable storage for blobs, while CDNs cache that content at edges close to users. This lesson covers the object-storage data model, multi-part upload, storage classes, presigned URLs, and how a CDN turns a globally slow origin into a globally fast experience. By the end you can design the media layer for any social, video, or e-commerce system.
Caching Fundamentals (Write-Through, Write-Back, Write-Around)
A cache is a small, fast store that holds copies of data so the next request does not pay the cost of fetching it from the source of truth. This lesson covers what a cache is, where it lives in a stack, the four read and write patterns you will be asked about (cache-aside, read-through, write-through, write-back, write-around), eviction policies, and the failure modes (stampedes, hot keys, stale data) that bite real systems. By the end you can pick a caching strategy and defend it in an interview.
Distributed Caching (Redis, Memcached)
A single-node cache eventually runs out of RAM, CPU, or network. Distributed caching spreads keys across many nodes so total capacity and throughput scale horizontally. This lesson covers how Redis and Memcached partition data, replicate it for availability, fail over when nodes die, and how to choose between them. By the end you can design a multi-node cache layer for a real workload, defend the topology in an interview, and recognize the bug class behind 'why is one cache node maxed at 100% CPU while the others are idle?'.
Cache Invalidation Strategies & Consistency
There are only two hard problems in computer science: cache invalidation, naming things, and off-by-one errors. This lesson tackles the first one. We cover TTL-based, write-driven, and event-driven invalidation; the canonical race conditions (lost-update, double-write inconsistency, stale-after-failover); the consistency models a cache can offer; and the patterns that real systems (Facebook, Stripe, AWS) use to keep cached data trustworthy. By the end you can pick an invalidation strategy, defend it under interviewer pressure, and explain exactly why your cache will not silently serve yesterday's data.
Design a URL Shortener (TinyURL)
Design a URL shortening service like TinyURL or bit.ly that maps a long URL to a 7 character code, redirects clicks in under 50 ms, and survives a 100:1 read-to-write ratio. This lesson walks through capacity estimation, the choice between counter based and hash based key generation, the database split between a key store and an analytics store, and the caching strategy that lets a single mid-tier service handle 10K redirects per second on commodity hardware.
Community
Caching Strategies: Write-Through, Write-Behind, and When Each Fits
Write-through is the safe default. Write-behind is the option for write-heavy paths. Cache-aside is what most teams actually use, and that is fine.
An Embedding Cache With Content-Hash Keys
Re-embedding the same paragraphs on every deploy was costing us $400 a month. This is the SQLite-backed cache I shipped: the key is sha256(model + normalized text), TTL is per-row, and a single batch call backfills misses.
Interview Patterns Deep Dive
Monotonic stacks, binary tree serialization, and LRU cache design — three medium-difficulty patterns commonly asked at FAANG-tier interviews.
CDN 101: Edge Caches, Origin Shields, and Cache Keys
The cache key matters more than the TTL. Origin shield is a cheap config win. Most CDN incidents are key bugs, not capacity bugs.