.. image:: https://media.charlesleifer.com/blog/photos/peewee4-logo.png
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 <https://docs.peewee-orm.com/en/latest/peewee/asyncio.html>__
built on the standard async drivers (aiosqlite, asyncpg, aiomysql)schema migrations <https://docs.peewee-orm.com/en/latest/peewee/db_tools.html#migration-runner>__
with diff-based generation (pwmigrate)- tons of extensions
- use with
flask <https://docs.peewee-orm.com/en/latest/peewee/framework_integration.html#flask>,
fastapi <https://docs.peewee-orm.com/en/latest/peewee/framework_integration.html#fastapi>,
pydantic <https://docs.peewee-orm.com/en/latest/peewee/orm_utils.html#module-playhouse.pydantic_utils>, and
more <https://docs.peewee-orm.com/en/latest/peewee/framework_integration.html>.
New to peewee? These may help:
Quickstart <https://docs.peewee-orm.com/en/latest/peewee/quickstart.html#quickstart>_Example twitter app <https://docs.peewee-orm.com/en/latest/peewee/example.html#example>_Using peewee interactively <https://docs.peewee-orm.com/en/latest/peewee/interactive.html#interactive>_Models and fields <http://docs.peewee-orm.com/en/latest/peewee/models.html>_Querying <http://docs.peewee-orm.com/en/latest/peewee/querying.html>_Relationships and joins <http://docs.peewee-orm.com/en/latest/peewee/relationships.html>_Extensive library of SQL / Peewee examples <https://docs.peewee-orm.com/en/latest/peewee/query_library.html#query-library>_Flask setup <https://docs.peewee-orm.com/en/latest/peewee/framework_integration.html#flask>_
or FastAPI setup <https://docs.peewee-orm.com/en/latest/peewee/framework_integration.html#fastapi>_
Installation:
.. code-block:: console
pip install peewee
Sqlite comes built-in provided by the standard-lib sqlite3 module. Other
backends can be installed using the following instead:
.. code-block:: console
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:
.. code-block:: python
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:
.. code-block:: python
db.connect()
db.create_tables([User, Tweet])
Create a few rows:
.. code-block:: python
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:
.. code-block:: python
# 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 <http://docs.peewee-orm.com/en/latest/peewee/example.html>_.
Asyncio
.. code-block:: python
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 <https://docs.peewee-orm.com/en/latest/peewee/asyncio.html>_
for details.
Learning more
Check the documentation <http://docs.peewee-orm.com/>_ 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 <https://github.com/coleifer/peewee/issues/new>_ on GitHub.
Still want more info?
.. image:: https://media.charlesleifer.com/blog/photos/wat.jpg
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 <https://charlesleifer.com/blog/saturday-morning-hack-a-little-note-taking-app-with-flask/>_ as well as Part 2 <https://charlesleifer.com/blog/saturday-morning-hacks-revisiting-the-notes-app/>_ and Part 3 <https://charlesleifer.com/blog/saturday-morning-hacks-adding-full-text-search-to-the-flask-note-taking-app/>_.Analytics web service built with Flask and Peewee <https://charlesleifer.com/blog/saturday-morning-hacks-building-an-analytics-app-with-flask/>_.Personalized news digest (with a boolean query parser!) <https://charlesleifer.com/blog/saturday-morning-hack-personalized-news-digest-with-boolean-query-parser/>_.Structuring Flask apps with Peewee <https://charlesleifer.com/blog/structuring-flask-apps-a-how-to-for-those-coming-from-django/>_.Creating a lastpass clone with Flask and Peewee <https://charlesleifer.com/blog/creating-a-personal-password-manager/>_.Creating a bookmarking web-service that takes screenshots of your bookmarks <https://charlesleifer.com/blog/building-bookmarking-service-python-and-phantomjs/>_.Building a pastebin, wiki and a bookmarking service using Flask and Peewee <https://charlesleifer.com/blog/dont-sweat-small-stuff-use-flask-blueprints/>_.Encrypted databases with Python and SQLCipher <https://charlesleifer.com/blog/encrypted-sqlite-databases-with-python-and-sqlcipher/>_.Dear Diary: An Encrypted, Command-Line Diary with Peewee <https://charlesleifer.com/blog/dear-diary-an-encrypted-command-line-diary-with-python/>_.
Peewee — это небольшая и выразительная ORM для Python, которая позволяет работать с SQL-базами данных через объекты Python и гибкий конструктор запросов.