How we adapted our application storage platform, Habitat, in Python to manage unprecedented growth.
By Jon Lee, Chaomin Yu, and Ben Ries, Members of Technical Staff
Every OpenAI product depends on fast, reliable access to data, whether someone is logging in, checking their Codex settings, or starting a new conversation in ChatGPT. Each of those actions may require many separate data lookups before the product can respond. If those requests are slow, the product feels slow. If those requests fail, the product stops working entirely.
Habitat is the online storage platform we built so OpenAI products can quickly and reliably access needed information. Habitat now handles more than 70 million requests every second, supporting products used by over 1 billion people each week, across almost 40 geographic regions. Habitat first launched to support GPTs at DevDay 2023, starting as a simple Python client-side library connected to a single database. Today, it’s a complex distributed system that serves more than 500 petabytes of data.
Habitat is the online storage platform we built so OpenAI products can quickly and reliably access needed information.
Building and operating infrastructure at this scale is no easy feat, but also not particularly challenging. What made our situation unique is the unprecedented rate at which we’ve had to scale to support staggering user growth and product demand while simultaneously building out a mature platform. Often, system engineers build for 10x scale, and hope for it to hold for a few years while preparing for the next 10x.
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