html_url,issue_url,id,node_id,user,user_label,created_at,updated_at,author_association,body,reactions,issue,issue_label,performed_via_github_app
https://github.com/simonw/datasette/issues/417#issuecomment-474280581,https://api.github.com/repos/simonw/datasette/issues/417,474280581,MDEyOklzc3VlQ29tbWVudDQ3NDI4MDU4MQ==,82988,psychemedia,2019-03-19T10:06:42Z,2019-03-19T10:06:42Z,CONTRIBUTOR,"This would be really interesting but several possibilities in use arise, I think?
For example:
- I put a new CSV file into the import dir and a new table is created therefrom
- I put a CSV file into the import dir that replaces a previous file / table of the same name as a pre-existing table (eg files that contain monthly data in year to date). The data may also patch previous months, so a full replace / DROP on the original table may well be in order.
- I put a CSV file into the import dir that updates a table of the same name as a pre-existing table (eg files that contain last month's data)
CSV files may also have messy names compared to the table you want. Or for an update CSV, may have the form `MYTABLENAME-February2019.csv` etc","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",421546944,Datasette Library,
https://github.com/simonw/datasette/issues/412#issuecomment-474282321,https://api.github.com/repos/simonw/datasette/issues/412,474282321,MDEyOklzc3VlQ29tbWVudDQ3NDI4MjMyMQ==,82988,psychemedia,2019-03-19T10:09:46Z,2019-03-19T10:09:46Z,CONTRIBUTOR,Does this also relate to https://github.com/simonw/datasette/issues/283 and the ability to `ATTACH DATABASE`?,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",411257981,Linked Data(sette),
https://github.com/simonw/datasette/issues/420#issuecomment-474398127,https://api.github.com/repos/simonw/datasette/issues/420,474398127,MDEyOklzc3VlQ29tbWVudDQ3NDM5ODEyNw==,9599,simonw,2019-03-19T14:34:55Z,2019-03-19T14:34:55Z,OWNER,"I systematically reviewed the codebase for things that `.inspect()` is used for:
In `app.py`:
* `table_exists()` uses `table in self.inspect().get(database, {}).get(""tables"")`
* `.execute()` looks up the database name to get the `info[""file""]` (the correct filename with the `.db` extension)
In `cli.py`:
* The `datasette inspect` command dumps it to JSON
* `datasette skeleton` iterates over it
* `datasette serve` calls it on startup (to populate static cache of inspect data)
In `base.py`:
* `.database_url(database)` calls it to lookup the hash (if `hash_urls` config turned on)
* `.resolve_db_name()` uses it to lookup the hash
In `database.py`:
* `DatabaseView` uses it to find up the list of tables and views to display, plus the size of the DB file in bytes
* `DatabaseDownload` uses it to get the filepath for download
In `index.py`:
* `IndexView` uses it _extensively_ - to loop through every database and every table. This would make a good starting point for the refactor.
In `table.py`:
* `sortable_columns_for_table()` uses it to find the columns in a table
* `expandable_columns()` uses it to find foreign keys
* `expand_foreign_keys()` uses it to find foreign keys
* `display_columns_and_rows()` uses it to find primary keys and foreign keys... but also has access to a `cursor.description` which it uses to list the columns
* `TableView.data` uses it to lookup columns and primary keys and the `table_rows_count` (used if the thing isn't a view) and probably a few more things, this method is huge!
* `RowView.data` uses it for primary keys
* `foreign_key_tables()` uses it for foreign keys
In the tests it's used by `test_api.test_inspect_json()` and by a couple of tests in `test_inspect`.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",421971339,Fix all the places that currently use .inspect() data,
https://github.com/simonw/datasette/issues/420#issuecomment-474399630,https://api.github.com/repos/simonw/datasette/issues/420,474399630,MDEyOklzc3VlQ29tbWVudDQ3NDM5OTYzMA==,9599,simonw,2019-03-19T14:38:14Z,2019-03-19T14:38:14Z,OWNER,"Most of these can be replaced with relatively straight-forward direct introspection of the SQLite table.
The one exception is the incoming foreign keys: these can only be found by inspecting ALL of the other tables.
This requires running `PRAGMA foreign_key_list([table_name])` against every other table in the database. How expensive is doing this on a database with hundreds of tables?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",421971339,Fix all the places that currently use .inspect() data,
https://github.com/simonw/datasette/issues/420#issuecomment-474407617,https://api.github.com/repos/simonw/datasette/issues/420,474407617,MDEyOklzc3VlQ29tbWVudDQ3NDQwNzYxNw==,9599,simonw,2019-03-19T14:55:51Z,2019-03-19T14:55:51Z,OWNER,"A microbenchmark against `fivethirtyeight.db` (415 tables):
In [1]: import sqlite3
In [2]: c = sqlite3.connect(""fivethirtyeight.db"")
In [3]: %timeit c.execute(""select name from sqlite_master where type = 'table'"").fetchall()
283 µs ± 12.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [4]: tables = [r[0] for r in c.execute(""select name from sqlite_master where type = 'table'"").fetchall()]
In [5]: len(tables)
Out[5]: 415
In [6]: %timeit [c.execute(""pragma foreign_keys([{}])"".format(t)).fetchall() for t in tables]
1.81 ms ± 161 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
So running `pragma foreign_keys()` against 415 tables only takes 1.81ms. This is going to be fine.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",421971339,Fix all the places that currently use .inspect() data,