Proof of concept: Cache Conda env and add ccache
Proof of concept to speed up GH actions builds by ~10 minutes.

- Cache Conda envs across builds (cache is invalided when deps change, and 1x per day)
- Add ccache (cache is invalidated daily)

I wonder what everyone thinks about this and whether you think this is worth spending more effort on (eg. porting it also to Azure).

- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry

> I wonder what everyone thinks about this and whether you think this is worth spending more effort on (eg. porting it also to Azure).

If this works and is robust it'll be great. It should be pretty robust. For the Conda caching part the key is to come up with a proper cache key, ie. something that changes whenever the deps are changed. (Strictly speaking it should be invalidated every time Anaconda/Conda-forge is updated but that would invalidate the cache every few minutes.) For ccache I don't think we'll have to take care of anything as ccache is an incredibly robust and widely-used tool. How are we gaining 10 minutes? The env setup takes around 4 By caching the C compilation using ccache, which takes around 6-8 minutes Does the compilation recognize, that it is already there when you try recompiling it?   Can you please clarify the question? > For the Conda caching part the key is to come up with a proper cache key, ie. something that changes whenever the deps are changed.

Maybe something like `conda env export > key.yml`, and then hash this `key.yml` instead of the environment file.

 > Maybe something like `conda env export > key.yml`, and then hash this `key.yml` instead of the environment file.

Can you elaborate please? How would you export an env before it's been installed? > > Maybe something like `conda env export > key.yml`, and then hash this `key.yml` instead of the environment file.
> 
> Can you elaborate please? How would you export an env before it's been installed?

Sorry I was wrong... It should be something like this `conda env create -f environment.yml --dry-run > key.txt`. Then we can hash this `ket.txt` file. > Sorry I was wrong... It should be something like this `conda env create -f environment.yml --dry-run > key.txt`. Then we can hash this `ket.txt` file.

I see, that makes more sense. But it would also invalidate the cache whenever there is a minor version update of any of the (transitive) dependencies, so the cache would likely not survive more than a couple of hours. Could you clarify which part of the setup this is caching? Package download and/or c compilation?

Does this skip the dependency solve step as well?

(Off-topic: I'd like to eventually see the CI use conda-lock files as I've been trying to align/share dependency files. Not sure how this intersects with this change. https://github.com/conda-incubator/conda-lock) > Could you clarify which part of the setup this is caching? Package download and/or c compilation?

This implements _env_ caching and C compilation caching. Env caching = caching the entire `envs/` Conda directory. C caching = Using ccache. I plan to make separate PRs for these things. Re conda-lock: that definitely makes sense and also makes env setup faster. > Does this skip the dependency solve step as well?
> 
> (Off-topic: I'd like to eventually see the CI use conda-lock files as I've been trying to align/share dependency files. Not sure how this intersects with this change. https://github.com/conda-incubator/conda-lock)

We could set up a daily CI job to generate a lock file for each build.
 @jonashaag @fangchenli 

see https://github.com/ibis-project/ibis/blob/master/poetry.lock

@cpcloud has spent a bit of time setting up using these lock file with github actions on ibis. maybe can offer a good roadmap here. The way this works for ibis is that whenever someone submits a PR that modifies files related to dependencies, we trigger a PR comment `/condalock` which kicks off another action to actually generate the lock files and push a new commit to the corresponding PR.

`poetry.lock` (the output of poetry's solver) doesn't come into play when generating conda lock files, because `conda-lock` uses `pyproject.toml` to generate dependency constraints and then lets conda solve the constraint problem. If `conda-lock` were to use `poetry.lock`, most of the solves would fail because many packages that are available through `poetry` (which generally pulls from PyPI) aren't available on `conda-forge`.

This setup was adapted from https://github.com/pangeo-data/pangeo-docker-images/blob/master/.github/workflows/ChatOpsDispatcher.yml. I've done some work on ccache (GHA only so far) here https://github.com/pandas-dev/pandas/pull/45701. Also here https://github.com/pandas-dev/pandas/pull/45902 I've  changed Conda for Mamba. Caching Conda environments should gain another ~4 minute cut (and more on Windows). Closing this (splitting work into multiple PRs)