This prototype demonstrates the database page working against a
hard-coded connection string to a PostgreSQL database. It lists
tables and their columns and their row count.,
Queries with reserved words or characters according to the SQLite
FTS5 query language could cause errors.
Queries are now escaped like so:
dog cat => "dog" "cat"
The index page now only shows row counts for immutable databases OR for
databases with less than 30 tables provided it could get a count for
each of those tables in less than 10ms.
Closes#467, Refs #460
If we have less than 5 tables we now also show one or more views in the
summary on the homepage.
Also corrected the logic for the row counts - we now count hidden and
visible tables separately.
Closes#373, Refs #460
I've run the black code formatting tool against everything:
black tests datasette setup.py
I also added a new unit test, in tests/test_black.py, which will fail if the code does not
conform to black's exacting standards.
This unit test only runs on Python 3.6 or higher, because black itself doesn't run on 3.5.
Binary columns (including spatialite geographies) get shown as ugly
binary strings in the HTML by default. Nobody wants to see that mess.
Show the size of the column in bytes instead. If you want to decode
the binary data, you can use a plugin to do it.
At the moment it's not easy to tell whether the hook is being called
in (for example) the row or table view, as in both cases the
`database` and `table` parameters are provided.
This passes the `view_name` added in #441 to the `extra_body_script`
hook.
Datasette previously only supported one type of faceting: exact column value counting.
With this change, faceting logic is extracted out into one or more separate classes which can implement other patterns of faceting - this is discussed in #427, but potential upcoming facet types include facet-by-date, facet-by-JSON-array, facet-by-many-2-many and more.
A new plugin hook, register_facet_classes, can be used by plugins to add in additional facet classes.
Each class must implement two methods: suggest(), which scans columns in the table to decide if they might be worth suggesting for faceting, and facet_results(), which executes the facet operation and returns results ready to be displayed in the UI.
