Read what ships.

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 read2 days ago

DatabasesPerformanceRedis

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02Grab · architecture

App stability at scale with a layered defense

To maintain app stability amidst rapid weekly releases and AI-generated code, Grab uses a layered defense strategy covering prevention, release gates, and incident response. This pipeline includes static analysis via custom lint rules, multiple tiers of automated and manual testing, a pre-production early access build, staged rollouts, and feature flags with instant kill switches. As a result, critical defects are caught early in the development lifecycle before reaching the wider production user base.

DEPTH ■■■■■16 min read4 days ago

MobileReliability

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03Cloudflare · architecture

Bringing post-quantum cryptography to geographically fenced key management

Cloudflare introduced a post-quantum geographically restricted key manager in closed beta to enforce data sovereignty and protect customers against harvest-now-decrypt-later attacks. The team implemented a hybrid ML-KEM-768 and X25519 cryptographic design that separates globally distributed wrapped private keys from regionally restricted root keys. This ensures that decryption keys are only accessible within authorized geographical boundaries and fails closed if validation fails.

DEPTH ■■■■■12 min read2 days ago

Security

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04Cloudflare · 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 read6 days ago

Machine Learning

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05Grab · architecture

Powering AI-led research through simulation

The engineering team built a self-contained simulation platform called sim-rs to model marketplace dispatch dynamics and accelerate experimentation. By connecting automated software agents to this simulator with strict safety safeguards, the team successfully optimized strategy policies and simulator performance. This closed-loop lab environment allows agents to test ideas rapidly while ensuring that only valid, production-ready improvements are produced.

DEPTH ■■■■■10 min read2 weeks ago

Machine Learning

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06Cloudflare · performance

Yandex outage: Data shows that instead of going dark, malicious bots went on the move

Following physical strikes on Yandex data centers, overall HTTP traffic from the network dropped significantly, leading to speculation that malicious bot farms had been disabled. However, traffic analysis revealed that search crawling simply decreased while malicious automation quickly migrated to other hosting providers with identical fingerprints. Consequently, overall malicious bot traffic remained flat, demonstrating that network disruptions do not stop portable automated workloads.

DEPTH ■■■■■8 min read11 days ago

Incidents & Postmortems

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07Cloudflare · postmortem

Inside Hyperdrive: How we solved a rare Hyperdrive MySQL caching bug

During the beta of MySQL support for Hyperdrive, engineers investigated rare empty query responses and connection hangs occurring in 0.001% of requests. They discovered that sharing the data center cache between client and endpoint nodes caused protocol capability mismatches when translating MySQL wire messages. The team resolved the issue by standardizing the cache to always store responses encoded with canonical Hyperdrive capabilities.

DEPTH ■■■■■9 min read11 days ago

MySQL

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08Cloudflare · 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 read12 days ago

Databases

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09Cloudflare · scaling

Rewinding the exponential: How we keep hardware usage down

Cloudflare tackled exponential traffic growth against linear hardware growth by continuously evolving their data pipeline architecture over five years. Key optimizations included breaking up monolithic schemas, switching to zstd compression, decoupling pipeline transit from database replication, decentralizing sampling, and moving aggregations earlier in the pipeline. These strategies significantly reduced backbone bandwidth usage, network congestion, and storage requirements while preserving data quality.

DEPTH ■■■■■12 min read12 days ago

Infrastructure

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10Cloudflare · architecture

Filter, reshape, and redact logs before export with Transformers

Cloudflare integrated SQL-based log transformation directly into its Logpush export pipeline, allowing customers to filter, redact, and reshape logs before delivery. The team chose to embed the Apache DataFusion engine in-process using Rust and Go across an Arrow IPC boundary to ensure high throughput and avoid network latency. As a result, the system processes high-volume log streams efficiently while maintaining predictable resource usage and strict delivery reliability.

DEPTH ■■■■■8 min read13 days ago

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11Grab · architecture

Data Mesh at Grab (Part III): Operationalizing data reliability with automated DPIs

Grab addressed runtime data reliability issues in its data mesh by implementing automated Data Production Issues (DPIs). The system uses an incident orchestrator to detect contract breaches, a Data Health API for cross-platform root cause analysis, and Hugo for automated remediation. This approach resolved 86.9 percent of incidents automatically and made mean time to resolve six times faster.

DEPTH ■■■■■12 min read8 weeks ago

DatabasesObservability

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