peewee
Peewee is a simple and small ORM. It has few (but expressive) concepts, making it easy to learn and intuitive to use.
Peewee is a single module with no required dependencies and has been running production workloads of all sizes since 2010.
a small, expressive ORM
flexible query-builder that exposes full power of SQL
supports sqlite, mysql, mariadb, postgresql
asyncio support built on the standard async drivers (aiosqlite, asyncpg, aiomysql)
schema migrations with diff-based generation (pwmigrate)
tons of extensions
New to peewee? These may help:
Installation:
pip install peeweeSqlite comes built-in provided by the standard-lib sqlite3 module. Other backends can be installed using the following instead:
pip install peewee[mysql] # Install peewee with pymysql.
pip install peewee[postgres] # Install peewee with psycopg2.
pip install peewee[psycopg3] # Install peewee with psycopg3.
# AsyncIO implementations.
pip install peewee[aiosqlite] # Install peewee with aiosqlite.
pip install peewee[aiomysql] # Install peewee with aiomysql.
pip install peewee[asyncpg] # Install peewee with asyncpg.Examples
Defining models is similar to Django or SQLAlchemy:
from peewee import *
import datetime
db = SqliteDatabase('my_database.db')
class BaseModel(Model):
class Meta:
database = db
class User(BaseModel):
username = CharField(unique=True)
class Tweet(BaseModel):
user = ForeignKeyField(User, backref='tweets')
message = TextField()
created_date = DateTimeField(default=datetime.datetime.now)
is_published = BooleanField(default=True)Connect to the database and create tables:
db.connect()
db.create_tables([User, Tweet])Create a few rows:
charlie = User.create(username='charlie')
huey = User(username='huey')
huey.save()
# No need to set `is_published` or `created_date` since they
# will just use the default values we specified.
Tweet.create(user=charlie, message='My first tweet')Queries are expressive and composable:
# A simple query selecting a user.
User.get(User.username == 'charlie')
# Get tweets created by one of several users.
usernames = ['charlie', 'huey', 'mickey']
users = User.select().where(User.username.in_(usernames))
tweets = Tweet.select().where(Tweet.user.in_(users))
# We could accomplish the same using a JOIN:
tweets = (Tweet
.select()
.join(User)
.where(User.username.in_(usernames)))
# How many tweets were published today?
tweets_today = (Tweet
.select()
.where(
(Tweet.created_date >= datetime.date.today()) &
(Tweet.is_published == True))
.count())
# Paginate the user table and show me page 3 (users 41-60).
User.select().order_by(User.username).paginate(3, 20)
# Order users by the number of tweets they've created:
tweet_ct = fn.Count(Tweet.id)
users = (User
.select(User, tweet_ct.alias('ct'))
.join(Tweet, JOIN.LEFT_OUTER)
.group_by(User)
.order_by(tweet_ct.desc()))
# Do an atomic update (for illustrative purposes only, imagine a simple
# table for tracking a "count" associated with each URL). We don't want to
# naively get the save in two separate steps since this is prone to race
# conditions.
Counter.update(count=Counter.count + 1).where(Counter.url == request.url).execute()Check out the example twitter app.
Asyncio
import asyncio
from peewee import *
from playhouse.pwasyncio import AsyncPostgresqlDatabase
db = AsyncPostgresqlDatabase('my_app')
class User(db.Model):
username = CharField(unique=True)
class Tweet(db.Model):
user = ForeignKeyField(User, backref='tweets')
message = TextField()
async def main():
async with db:
await db.acreate_tables([User, Tweet])
# Queries are awaited on the event loop using asyncpg.
huey = await User.acreate(username='huey')
tweet = await Tweet.acreate(user=huey, message='meow')
async with db.atomic():
tweet.message = 'purr'
await tweet.asave()
# Create a query - nothing is executed yet.
query = Tweet.select(Tweet, User).join(User)
# Execute and buffer the results.
tweets = await query.aexecute() # Or: await db.list(query)
for tweet in tweets:
print(tweet.user.username, '->', tweet.message)
# Streaming results via server-side cursor.
async for tweet in db.iterate(query):
print(tweet.user.username, '->', tweet.message)
await db.close_pool()
asyncio.run(main())See the asyncio docs for details.
Learning more
Check the documentation for more examples.
Specific question? Come hang out in the #peewee channel on irc.libera.chat, or post to the mailing list, http://groups.google.com/group/peewee-orm . If you would like to report a bug, create a new issue on GitHub.
Still want more info?
I’ve written a number of blog posts about building applications and web-services with peewee (and usually Flask). If you’d like to see some real-life applications that use peewee, the following resources may be useful:
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file peewee-4.4.0.tar.gz.
File metadata
- Download URL: peewee-4.4.0.tar.gz
- Upload date:
- Size: 817.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/7.0.0 CPython/3.13.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
ea3b013d2366d09bb0bd7b6223f0e3e34125caad5d4dfc23525c668c8aad7026
|
|
| MD5 |
593d8f3954c04cbe060f7d08ff308bb9
|
|
| BLAKE2b-256 |
4698919cd14a0115c4e81093d723faff2ed03145966c61c5c8dac13104d4d94f
|
File details
Details for the file peewee-4.4.0-py3-none-any.whl.
File metadata
- Download URL: peewee-4.4.0-py3-none-any.whl
- Upload date:
- Size: 191.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/7.0.0 CPython/3.13.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c4d6bc13d9a9b22fe691f695f5a5d716645b4bd5387fef27b9cb53ef14fae1e1
|
|
| MD5 |
ac3f92b0801deafa25b30917caa2a5c9
|
|
| BLAKE2b-256 |
69281ed7ca6ec0b45d0209346fb82d7145a5abf8fd9bdca6bcb75cb868601f43
|
