issue_comments: 1018086273
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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/1609#issuecomment-1018086273 | https://api.github.com/repos/simonw/datasette/issues/1609 | 1018086273 | IC_kwDOBm6k_c48rsOB | 9599 | 2022-01-21T01:45:46Z | 2022-01-21T01:45:46Z | OWNER | This whole thing reminds me of my ongoing internal debate about version pinning: should the Datasette package released to PyPI pin to the exact versions of the dependencies that are known to work, or should it allow a range of dependencies so users can pick other versions of the dependencies to use in their environment? As I understand it, the general rule is to use exact pinning for applications but use ranges for libraries. Datasette is almost entirely an application... but it can also be used as a library - and in fact I'm hoping to encourage that usage more in the future, see: - #1398 I'd also like to release a packaged version of Datasette that doesn't require Uvicorn, for running on AWS Lambda and other function-as-a-service platforms. Those platforms have their own HTTP layer and hence don't need the Uvicorn dependency. Maybe the answer is to have a `datasette-core` package which provides the core of Datasette with unpinned dependencies and no Uvicorn, and then have the existing `datasette` package provide the Datasette CLI tool with Uvicorn and pinned dependencies? | {"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | 1109884720 |