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)
• tons of extensions
• use with flask, fastapi, pydantic, and more.

New to peewee? These may help:

• Quickstart
• Example twitter app
• Using peewee interactively
• Models and fields
• Querying
• Relationships and joins
• Extensive library of SQL / Peewee examples
• Flask setup or FastAPI setup

Installation:

pip install peewee

Sqlite 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:

• Building a note-taking app with Flask and Peewee as well as Part 2 and Part 3.
• Analytics web service built with Flask and Peewee.
• Personalized news digest (with a boolean query parser!).
• Structuring Flask apps with Peewee.
• Creating a lastpass clone with Flask and Peewee.
• Creating a bookmarking web-service that takes screenshots of your bookmarks.
• Building a pastebin, wiki and a bookmarking service using Flask and Peewee.
• Encrypted databases with Python and SQLCipher.
• Dear Diary: An Encrypted, Command-Line Diary with Peewee.