fastlite
provides some little quality-of-life improvements for
interactive use of the wonderful
sqlite-utils library. It’s likely
to be particularly of interest to folks using Jupyter.
pip install fastlite
from sqlite_utils import Database
from fastlite import *
from fastcore.utils import *
We demonstrate fastlite
‘s features here using the ’chinook’ sample
database.
url = 'https://github.com/lerocha/chinook-database/raw/master/ChinookDatabase/DataSources/Chinook_Sqlite.sqlite'
path = Path('chinook.sqlite')
if not path.exists(): urlsave(url, path)
db = Database("chinook.sqlite")
Databases have a t
property that lists all tables:
dt = db.t
dt
Album, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track
You can use this to grab a single table…:
artist = dt.Artist
artist
<Table Artist (ArtistId, Name)>
…or multiple tables at once:
dt['Artist','Album','Track','Genre','MediaType']
[<Table Artist (ArtistId, Name)>,
<Table Album (AlbumId, Title, ArtistId)>,
<Table Track (TrackId, Name, AlbumId, MediaTypeId, GenreId, Composer, Milliseconds, Bytes, UnitPrice)>,
<Table Genre (GenreId, Name)>,
<Table MediaType (MediaTypeId, Name)>]
It also provides auto-complete in Jupyter, IPython, and nearly any other interactive Python environment:
Column work in a similar way to tables, using the c
property:
ac = artist.c
ac
ArtistId, Name
Auto-complete works for columns too:
Columns, tables, and view stringify in a format suitable for including in SQL statements. That means you can use auto-complete in f-strings.
qry = f"select * from {artist} where {ac.Name} like 'AC/%'"
print(qry)
select * from "Artist" where "Artist"."Name" like 'AC/%'
You can view the results of a select query using q
:
db.q(qry)
[{'ArtistId': 1, 'Name': 'AC/DC'}]
Views can be accessed through the v
property:
album = dt.Album
acca_sql = f"""select {album}.*
from {album} join {artist} using (ArtistId)
where {ac.Name} like 'AC/%'"""
db.create_view("AccaDaccaAlbums", acca_sql, replace=True)
acca_dacca = db.q(f"select * from {db.v.AccaDaccaAlbums}")
acca_dacca
[{'AlbumId': 1,
'Title': 'For Those About To Rock We Salute You',
'ArtistId': 1},
{'AlbumId': 4, 'Title': 'Let There Be Rock', 'ArtistId': 1}]
A dataclass
type with the names, types, and defaults of the tables is
created using dataclass()
:
album_dc = album.dataclass()
Let’s try it:
album_obj = album_dc(**acca_dacca[0])
album_obj
Album_cls(AlbumId=1, Title='For Those About To Rock We Salute You', ArtistId=1)
You can get the definition of the dataclass using fastcore’s
dataclass_src
– everything is treated as nullable, in order to handle
auto-generated database values:
src = dataclass_src(album_dc)
hl_md(src, 'python')
@dataclass
class Album_cls:
AlbumId: int | None = None
Title: str | None = None
ArtistId: int | None = None
There’s also a shortcut to select from a table – just call it as a
function. If you’ve previously called dataclass()
, returned iterms
will be constructed using that class by default. There’s lots of params
you can check out, such as limit
:
album(limit=2)
[Album_cls(AlbumId=1, Title='For Those About To Rock We Salute You', ArtistId=1),
Album_cls(AlbumId=2, Title='Balls to the Wall', ArtistId=2)]
Pass a truthy value as the first param and you’ll get tuples of primary keys and records:
album(1, limit=2)
[(1,
Album_cls(AlbumId=1, Title='For Those About To Rock We Salute You', ArtistId=1)),
(2, Album_cls(AlbumId=2, Title='Balls to the Wall', ArtistId=2))]
get
also uses the dataclass by default:
album.get(1)
Album_cls(AlbumId=1, Title='For Those About To Rock We Salute You', ArtistId=1)
If you import from fastlite.kw
then the following methods accept
**kwargs
, passing them along to the first dict
param:
create
transform
transform_sql
update
insert
upsert
lookup
from fastlite.kw import *
Without the above import, create
would require a dict
param, but
here we just pass keyword args directly:
cats = dt.cats
cats.create(id=int, name=str, weight=float, pk='id')
hl_md(cats.schema, 'sql')
CREATE TABLE [cats] (
[id] INTEGER PRIMARY KEY,
[name] TEXT,
[weight] FLOAT
)
…the same applies to insert
here:
cats.insert(name='meow', weight=6)
(idx,cat),*_ = cats(1)
idx,cat
(1, {'id': 1, 'name': 'meow', 'weight': 6.0})
Using **
in upsert here doesn’t actually achieve anything, since we
can just pass a dict
directly – it’s just to show that it works:
cat['name'] = "moo"
cats.upsert(**cat)
cats()
[{'id': 1, 'name': 'moo', 'weight': 6.0}]
cats.drop()
cats
<Table cats (does not exist yet)>
If you have graphviz installed, you can create database diagrams:
diagram(db.tables)
Pass a subset of columns to just diagram those. You can also adjust the size and aspect ratio.
diagram(db.t['Artist','Album','Track','Genre','MediaType'], size=8, ratio=0.4)