github
id | node_id | number | title | user | state | locked | assignee | milestone | comments | created_at | updated_at | closed_at | author_association | pull_request | body | repo | type | active_lock_reason | performed_via_github_app | reactions | draft | state_reason |
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639993467 | MDU6SXNzdWU2Mzk5OTM0Njc= | 850 | Proof of concept for Datasette on AWS Lambda with EFS | 9599 | open | 0 | 25 | 2020-06-16T21:48:31Z | 2020-06-16T23:52:16Z | OWNER | https://aws.amazon.com/about-aws/whats-new/2020/06/aws-lambda-support-for-amazon-elastic-file-system-now-generally-/ If Datasette can run on Lambda with access to EFS it could both read AND write large databases there. | 107914493 | issue | { "url": "https://api.github.com/repos/simonw/datasette/issues/850/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
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813880401 | MDExOlB1bGxSZXF1ZXN0NTc3OTUzNzI3 | 5 | WIP: Add Gmail takeout mbox import | 306240 | open | 0 | 25 | 2021-02-22T21:30:40Z | 2021-07-28T07:18:56Z | FIRST_TIME_CONTRIBUTOR | dogsheep/google-takeout-to-sqlite/pulls/5 | WIP This PR adds the ability to import emails from a Gmail mbox export from Google Takeout. This is my first PR to a datasette/dogsheep repo. I've tested this on my personal Google Takeout mbox with ~520,000 emails going back to 2004. This took around ~20 minutes to process. To provide some feedback on the progress of the import I added the "rich" python module. I'm happy to remove that if adding a dependency is discouraged. However, I think it makes a nice addition to give feedback on the progress of a long import. Do we want to log emails that have errors when trying to import them? Dealing with encodings with emails is a bit tricky. I'm very open to feedback on how to deal with those better. As well as any other feedback for improvements. | 206649770 | pull | { "url": "https://api.github.com/repos/dogsheep/google-takeout-to-sqlite/issues/5/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
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1124731464 | I_kwDOCGYnMM5DCgpI | 399 | Make it easier to insert geometries, with documentation and maybe code | 9599 | open | 0 | 25 | 2022-02-05T00:11:26Z | 2023-05-16T03:11:52Z | OWNER | In playing with the new SpatiaLite helpers from #385 I noticed that actually populating geometry columns is still a little bit tricky. Here's what I ended up doing: ```python import httpx, sqlite_utils db = sqlite_utils.Database("/tmp/spatial.db") attractions = httpx.get("https://latest.datasette.io/fixtures/roadside_attractions.json?_shape=array").json() db["attractions"].insert_all(attractions, pk="pk") # Schema of that table is now: # CREATE TABLE [attractions] ( # [pk] INTEGER PRIMARY KEY, # [name] TEXT, # [address] TEXT, # [latitude] FLOAT, # [longitude] FLOAT # ) db.init_spatialite() db["attractions"].add_geometry_column("point", "POINT") db.execute(""" update attractions set point = GeomFromText( 'POINT(' || longitude || ' ' || latitude || ')', 4326 ) """) ``` That last line took some figuring out - especially the need for the SRID of `4326`, without which I got this error: > `IntegrityError: attractions.point violates Geometry constraint [geom-type or SRID not allowed]` It would be good to both document this in more detail, but ideally also to come up with a more obvious pattern for inserting common types of spatial data. Also related: - #398 - #79 | 140912432 | issue | { "url": "https://api.github.com/repos/simonw/sqlite-utils/issues/399/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |