Category
caching
6 articles across 4 sub-topics
Redis Sorted Sets Explained: Skip Lists, Scores, and Real-World Use Cases
TLDR: Redis Sorted Sets (ZSETs) store unique members each paired with a floating-point score, kept in sorted order at all times. Internally they use a skip list for O(log N) range queries and a hash table for O(1) score lookup — giving you the best o...
LLD for LRU Cache: Designing a High-Performance Cache
TLDR TLDR: An LRU (Least Recently Used) Cache evicts the item that hasn't been accessed the longest when it's full. The classic implementation combines a HashMap (O(1) lookup) with a Doubly Linked List (O(1) move-to-front) for overall O(1) get and p...
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. Read-time fan-out (pull) computes feeds on demand...
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 ARC), cache invalidation pitfalls, thundering her...
How Bloom Filters Work: The Probabilistic Set
TLDR TLDR: A Bloom Filter is a bit array + multiple hash functions that answers "Is X in the set?" in $O(1)$ constant space. It can return false positives (say "yes" when the answer is "no") but never false negatives (never says "no" when the answer...