/ THE FEED /

Read what ships.

Deep, recent reads ranked by depth and freshness.

Reliability Performance Machine Learning Data Engineering Testing Databases Cloudflare Depth 5+Clear all

01Cloudflare · architecture
Origin Range Requests: Serving large files without range-request tuning
Cloudflare replaced a legacy, fixed-size range caching design that suffered from bandwidth waste and memory pressure with a Pingora-based chunk coordinator. The new implementation dynamically combines cached chunks and aligned origin fetches to serve range requests accurately without manual tuning. This approach cut p99 time to first byte in half for R2 storage and reduced tiered cache traffic by six percent.
DEPTH ■■■■■8 min read4 days ago
Databases Performance Redis
Sign in to save and rate
02Cloudflare · performance
Cutting our inference bill by 68% by being obsessive about optimization
An engineering team investigated a steady rise in AI code review costs and found that cache misses and redundant model calls were driving up expenses. By restructuring system prompts to enable cross-repository prompt caching, eliminating wasted tool calls, and switching to more efficient models, the team cut inference costs by 68% while also significantly improving review latency.
DEPTH ■■■■■15 min read7 days ago
Machine Learning
Sign in to save and rate
03Cloudflare · architecture
Under the hood, off the hot path: How D1 scales to millions of databases
Cloudflare scaled its serverless SQLite database, D1, to support millions of databases by decoupling the control plane from the data plane. The team used distributed metadata caching via Workers KV, optimized database connections through Hyperdrive, and implemented a self-correcting heartbeat mechanism. This architecture prevents centralized bottlenecks and allows D1 to handle rapid on-demand database creation efficiently.
DEPTH ■■■■■22 min read2 weeks ago
Databases
Sign in to save and rate