* Startup and shutdown events.
* Test client built on HTTPX.
* CORS, GZip, Static Files, Streaming responses.
* Session and Cookie support.
* 100% test coverage.
* 100% type annotated codebase.
* Few hard dependencies.

Starlette is currently the fastest Python framework tested. Only surpassed by Uvicorn, which is not a framework, but a server.

Starlette provides all the basic web microframework functionality.

* 🌐 Add French translation for FastAPI People. PR [#2232](https://github.com/tiangolo/fastapi/pull/2232) by [@JulianMaurin](https://github.com/JulianMaurin).
* 🌐 Add Korean translation for Tutorial - Path Parameters. PR [#2355](https://github.com/tiangolo/fastapi/pull/2355) by [@hard-coders](https://github.com/hard-coders).

- login: Smlep
  count: 11
  avatarUrl: https://avatars.githubusercontent.com/u/16785985?v=4
  url: https://github.com/Smlep
- login: hard-coders
  count: 10
  avatarUrl: https://avatars.githubusercontent.com/u/9651103?u=95db33927bbff1ed1c07efddeb97ac2ff33068ed&v=4
  url: https://github.com/hard-coders
- login: alejsdev
  count: 10
  avatarUrl: https://avatars.githubusercontent.com/u/90076947?u=1ee3a9fbef27abc9448ef5951350f99c7d76f7af&v=4

Then, when it's your turn, you do actual "productive" work, you process the menu, decide what you want, get your crush's choice, pay, check that you give the correct bill or card, check that you are charged correctly, check that the order has the correct items, etc.

  - login: DMantis
    avatarUrl: https://avatars.githubusercontent.com/u/9536869?v=4
    url: https://github.com/DMantis
  - login: hard-coders
    avatarUrl: https://avatars.githubusercontent.com/u/9651103?u=95db33927bbff1ed1c07efddeb97ac2ff33068ed&v=4
    url: https://github.com/hard-coders
  - login: supdann
    avatarUrl: https://avatars.githubusercontent.com/u/9986994?u=9671810f4ae9504c063227fee34fd47567ff6954&v=4

**available machines** taking into account the amount of memory needed by them, and the amount available in the machines in the cluster.

If your application is **simple**, this will probably **not be a problem**, and you might not need to specify hard memory limits. But if you are **using a lot of memory** (for example with **machine learning** models), you should check how much memory you are consuming and adjust the **number of containers** that runs in **each machine** (and maybe add more machines...