OpenAI · 2026
Scaling Git at OpenAI
OpenAI built Pando, a Git-compatible backend for its monorepo after Codex pushed concurrent branches, merges and fetches past the old system (reliability sometimes below 90%). Pando stores each push as an immutable pack in blob storage and records per-branch ref versions in a horizontally scalable metadata store, giving serializability per ref and causal consistency across the repo so unrelated branches do not contend. Instead of building unique packs per client, it reuses stored packs and serves them via packfile-uri through regional Express caches (over 99% hit rate), with LSM-style background compaction so fetches plan in logarithmic time. Deployed first as a read/write-through proxy, then cut over with a Git CLI wrapper; it now powers all monorepo development, cut median push latency by nearly 5x, and can deliver nearly a petabyte of Git data per day (57+ TB in a single hour).
nearly a petabyte of Git data/daymedian push latency cut nearly 5x (~80%)per-ref causal consistencyreusable packfilesstorage/compute separationGitPandoRust
OpenAI · 2026
Rapidly scaling online storage to serve over 1 billion ChatGPT users (Part II)
OpenAI's Habitat storage platform went from one shared Azure Cosmos DB container, to dedicated single-tenant containers that pushed capacity planning onto every product team, and back to a shared container by default once Azure added hot-key throttling, request-priority QoS and a new serverless mode where each physical partition scales to 10K RU/s and splits on RU usage. After failover and failback outages in late 2024, OpenAI and Azure now co-own testing and monitoring. Replicating small latency-critical datasets widely, a Valkey lookaside cache invalidated through the Cosmos DB change feed, and a Rust rewrite of the proxy cut global read p50 from about 120 ms to about 10 ms.
1B+ users across 40 regions500+ PB served by Habitatmulti-tenancyhot key throttlingCDC cache invalidationAzure Cosmos DBValkeyKafka
Netflix · 2026
Ontology-Driven Observability: Building an End-to-End Knowledge Graph at Netflix Scale
Netflix found a typical regression investigation took about four hours, mostly spent reconstructing relationships between systems like Atlas, Radar, Spinnaker, PagerDuty, incident.io and Slack that each named the same thing differently. It built an Observability Ontology on the W3C stack (RDF, OWL, SHACL, SPARQL), stored as a knowledge graph in QuipuDB, its homegrown distributed quad store built on the Rust key-value store redb. Enrichers, some LLM-based, turn noisy inputs into typed triples, every application is validated against a catalog of about 49,000 entries, and dependency edges are capped. A correlation engine scores chains across 13 signal types within a ten-minute window, discarding anything under 0.6 confidence, and an agentic AutoRCA system reads the graph to hand engineers ranked, evidence-backed hypotheses.
~65M concurrent streams (Paul vs. Tyson, Nov 2024)2B+ requests/dayknowledge graphsobservabilityroot cause analysisRDFOWLSHACL