Patterns
Harold
Patterns
Large File Processing
Patterns
Fanout (Read vs Write)
Patterns
Real-time Messaging
Patterns
Write-heavy Systems
A write-heavy system receives many writes, events, or updates. In interviews, the main challenge is protecting the system from overload while keeping writes durable and correct. What it means A system is write-heavy when new data arrives very frequently. Examples: * logging system * metrics platform * click analytics * chat messages * location updates
Patterns
Read-heavy Systems
A read-heavy system has far more reads than writes. In interviews, the main goal is to make the read path fast, cheap, and reliable. What it means A system is read-heavy when users read data much more often than they create or update it. Examples: * URL shortener redirects * product detail
Deep Dive Toolkit
TTL, Expiration & Cleanup
Deep Dive Toolkit
Backpressure & Circuit Breaker
Deep Dive Toolkit
Idempotency & Deduplication
Deep Dive Toolkit
Rate Limiting Algorithms
Deep Dive Toolkit
Consistent Hashing
Deep Dive Toolkit
Partitioning / Sharding Basics
Partitioning, or sharding, means splitting data across multiple machines. In interviews, use it when one database can no longer hold all the data or handle all the traffic. The basic idea A single database has limits. It can run out of: * storage, * CPU, * memory, * read capacity, * write capacity. Sharding solves