issue_comments: 1065929510

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html_url	issue_url	id	node_id	user	created_at	updated_at	author_association	body	reactions	issue	performed_via_github_app
https://github.com/simonw/datasette/issues/1384#issuecomment-1065929510	https://api.github.com/repos/simonw/datasette/issues/1384	1065929510	IC_kwDOBm6k_c4_iMsm	167160	2022-03-12T17:49:59Z	2022-03-12T17:49:59Z	NONE	Ok, I'm taking a slightly different approach, which I think is sort of close to the in-memory _metadata table idea. I'm using a startup hook to load metadata / other info from the database, which I store in the datasette object for later: ``` @hookimpl def startup(datasette): async def inner(): datasette._mypluginmetadata = # await db query return inner ``` Then, I can use this in other plugins: ``` @hookimpl def render_cell(value, column, table, database, datasette): # use datasette._mypluginmetadata ``` For my app I don't need anything to update dynamically so it's fine to pre-populate everything on startup. It's also good to have things precached especially for a hook like render_cell, which would otherwise require a ton of redundant db queries. Makes me wonder if we could take a sort of similar caching approach with the internal _metadata table. Like have a little watchdog that could query all of the attached dbs for their _metadata tables every 5min or so, which then could be merged into the in memory _metadata table which then could be accessed sync by the plugins, or something like that. For most the use cases I can think of, live updates don't need to take into effect immediately; refreshing a cache every 5min or on some other trigger (adjustable w a config setting) would be just fine.	{"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0}	930807135