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 :).

Updated documentation: https://github.com/simonw/datasette/blob/20a2ed6bec367d2f6759be4a879364a72780b59d/docs/facets.rst#facets-in-metadatajson

... actually no, I WILL document this, because not documenting this is what got us to this point in the first place!

I'm going to ship your fix now, but I'm not going to add this to the documentation yet because I hope to improve the design prior to Datasette 1.0.

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?

Part of the problem here is that facets were designed to accept optional extra configuration - partly to support m2m facets in #495 - but I haven't actually shipped any facets that use that ability.

Facet by delimiter would be a good one to exercise that ability: - #510

I tried that fix you suggested and now this metadata.json does the right thing: json { "databases": { "fixtures": { "tables": { "facetable": { "facets": [ { "array": "tags" } ] } } } } } It does further highlight the bug in #625 though - since then if you try to add ?_facet=tags to facet by tags treating them NOT as an array your request to do so is ignored.

I think you're right! I had completely forgotten that piece of code.

This just turned into a bug fix and a documentation update. Thanks for the research!

@simonw thank you for your fast answer and your guidance!

While digging into the code, I found an undocumented way of doing it:

yaml facets: ["Facet for a column", {"array": "Facet for an array"}]

The only remaining problem with that solution is here: https://github.com/simonw/datasette/blob/250db8192cb8aba5eb8cd301ccc2a49525bc3d24/datasette/facets.py#L33

We have:

python type, metadata_config = metadata_config.items()[0]

But it requires to cast the dict_items as a list prior to access the first element:

python type, metadata_config = list(metadata_config.items())[0]

I guess it's an unspotted bug? (I mean, independently of the facets-with-arrays issue.)

This is definitely a missing feature. The "different types of facet" stuff feels incomplete to me generally - this is one issue, but this one as well:

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