issue_comments: 996229007

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https://github.com/simonw/datasette/issues/1552#issuecomment-996229007 https://api.github.com/repos/simonw/datasette/issues/1552 996229007 IC_kwDOBm6k_c47YT-P 3556 2021-12-16T22:04:39Z 2021-12-16T22:04:39Z CONTRIBUTOR

Wow, that was fast, thank you so much @simonw !

I'm also not convinced that this configuration syntax is right. It's a bit weird having a "facets" list that can either by column-name-strings or {"type-of-facet": "column-name"} objects. Maybe there's a better design for this?

I agree that it's not ideal, my initial naive approach was to detect if it's an array, like what is done here:

https://github.com/simonw/datasette/blob/2c07327d23d9c5cf939ada9ba4091c1b8b2ba42d/datasette/facets.py#L312-L313

But it requires an extra query to determine the type, which is a bit problematic, especially for big tables I guess.

Taking a look at #510, I wonder if a facet_delimiter should be defined for that kind of columns (that would help our team not to have an intermediary conversion step from foo|bar to ["foo","bar"] for instance).

To be consistent with the --extract-column parameter, maybe an explicit casting/delimiter would be useful: --set-column 'Foo:Array:|'.

Throwing a lot of ideas without knowing the big picture… but sometimes newcomers have superpowers :).

 
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