Peewee Cookbook

Below are outlined some of the ways to perform typical database-related tasks with peewee.

Examples will use the following models:

import peewee

class Blog(peewee.Model):
    creator = peewee.CharField()
    name = peewee.CharField()


class Entry(peewee.Model):
    blog = peewee.ForeignKeyField(Blog)
    title = peewee.CharField()
    body = peewee.TextField()
    pub_date = peewee.DateTimeField()
    published = peewee.BooleanField(default=True)

Database and Connection Recipes

Creating a database connection and tables

While it is not necessary to explicitly connect to the database before using it, managing connections explicitly is a good practice. This way if the connection fails, the exception can be caught during the “connect” step, rather than some arbitrary time later when a query is executed.

>>> database = SqliteDatabase('stats.db')
>>> database.connect()

To use this database with your models, specify it in an inner “Meta” class:

class MyModel(Model):
    some_field = CharField()

    class Meta:
        database = database

It is possible to use multiple databases (provided that you don’t try and mix models from each):

>>> custom_db = peewee.SqliteDatabase('custom.db')

>>> class CustomModel(peewee.Model):
...     whatev = peewee.CharField()
...
...     class Meta:
...         database = custom_db
...

>>> custom_db.connect()
>>> CustomModel.create_table()

Best practice: define a base model class that points at the database object you wish to use, and then all your models will extend it:

custom_db = peewee.SqliteDatabase('custom.db')

class CustomModel(peewee.Model):
    class Meta:
        database = custom_db

class Blog(CustomModel):
    creator = peewee.CharField()
    name = peewee.TextField()

class Entry(CustomModel):
    # etc, etc

Note

Remember to specify a database in a model class (or its parent class), otherwise peewee will fall back to a default sqlite database named “peewee.db”.

Using with Postgresql

Point models at an instance of PostgresqlDatabase.

psql_db = peewee.PostgresqlDatabase('my_database', user='code')


class PostgresqlModel(peewee.Model):
    """A base model that will use our Postgresql database"""
    class Meta:
        database = psql_db

class Blog(PostgresqlModel):
    creator = peewee.CharField()
    # etc, etc

Using with MySQL

Point models at an instance of MySQLDatabase.

mysql_db = peewee.MySQLDatabase('my_database', user='code')


class MySQLModel(peewee.Model):
    """A base model that will use our MySQL database"""
    class Meta:
        database = mysql_db

class Blog(MySQLModel):
    creator = peewee.CharField()
    # etc, etc


# when you're ready to start querying, remember to connect
mysql_db.connect()

Multi-threaded applications

Some database engines may not allow a connection to be shared across threads, notably sqlite. If you would like peewee to maintain a single connection per-thread, instantiate your database with threadlocals=True:

concurrent_db = SqliteDatabase('stats.db', threadlocals=True)

The above implementation stores connection state in a thread local and will only use that connection for a given thread. Sqlite can also share a connection across threads natively, so if you would prefer to use sqlite’s native support:

native_concurrent_db = SqliteDatabase('stats.db', check_same_thread=False)

Deferring initialization

Sometimes the database information is not known until run-time, when it might be loaded from a configuration file/etc. In this case, you can “defer” the initialization of the database by passing in None as the database_name.

deferred_db = peewee.SqliteDatabase(None)

class SomeModel(peewee.Model):
    class Meta:
        database = deferred_db

If you try to connect or issue any queries while your database is uninitialized you will get an exception:

>>> deferred_db.connect()
Exception: Error, database not properly initialized before opening connection

To initialize your database, you simply call the init method with the database_name and any additional kwargs:

database_name = raw_input('What is the name of the db? ')
deferred_db.init(database_name)

Creating, Reading, Updating and Deleting

Creating a new record

You can use the Model.create() method on the model:

>>> Blog.create(creator='Charlie', name='My Blog')
<__main__.Blog object at 0x2529350>

This will INSERT a new row into the database. The primary key will automatically be retrieved and stored on the model instance.

Alternatively, you can build up a model instance programmatically and then save it:

>>> blog = Blog()
>>> blog.creator = 'Chuck'
>>> blog.name = 'Another blog'
>>> blog.save()
>>> blog.id
2

Updating existing records

Once a model instance has a primary key, any attempt to re-save it will result in an UPDATE rather than another INSERT:

>>> blog.save()
>>> blog.id
2
>>> blog.save()
>>> blog.id
2

If you want to update multiple records, issue an UPDATE query. The following example will update all Entry objects, marking them as “published”, if their pub_date is less than today’s date.

>>> update_query = Entry.update(published=True).where(pub_date__lt=datetime.today())
>>> update_query.execute()
4 # <--- number of rows updated

For more information, see the documentation on UpdateQuery.

Deleting a record

To delete a single model instance, you can use the Model.delete_instance() shortcut:

>>> blog = Blog.get(id=1)
>>> blog.delete_instance()
1 # <--- number of rows deleted
>>> Blog.get(id=1)
BlogDoesNotExist: instance matching query does not exist:
SQL: SELECT "id", "creator", "name" FROM "blog" WHERE "id" = ? LIMIT 1
PARAMS: [1]

To delete an arbitrary group of records, you can issue a DELETE query. The following will delete all Entry objects that are a year old.

>>> delete_query = Entry.delete().where(pub_date__lt=one_year_ago)
>>> delete_query.execute()
7 # <--- number of entries deleted

For more information, see the documentation on DeleteQuery.

Selecting a single record

You can use the Model.get() method to retrieve a single instance matching the given query (passed in as a mix of Q objects and keyword arguments).

This method is a shortcut that calls Model.select() with the given query, but limits the result set to 1. Additionally, if no model matches the given query, a DoesNotExist exception will be raised.

>>> Blog.get(id=1)
<__main__.Blog object at 0x25294d0>

>>> Blog.get(id=1).name
u'My Blog'

>>> Blog.get(creator='Chuck')
<__main__.Blog object at 0x2529410>

>>> Blog.get(id=1000)
BlogDoesNotExist: instance matching query does not exist:
SQL: SELECT "id", "creator", "name" FROM "blog" WHERE "id" = ? LIMIT 1
PARAMS: [1000]

For more information see notes on SelectQuery and Querying API in general.

Selecting multiple records

To simply get all instances in a table, call the Model.select() method:

>>> for blog in Blog.select():
...     print blog.name
...
My Blog
Another blog

When you iterate over a SelectQuery, it will automatically execute it and start returning results from the database cursor. Subsequent iterations of the same query will not hit the database as the results are cached.

Another useful note is that you can retrieve instances related by ForeignKeyField by iterating. To get all the related instances for an object, you can query the related name. Looking at the example models, we have Blogs and Entries. Entry has a foreign key to Blog, meaning that any given blog may have 0..n entries. A blog’s related entries are exposed using a SelectQuery, and can be iterated the same as any other SelectQuery:

>>> for entry in blog.entry_set:
...     print entry.title
...
entry 1
entry 2
entry 3
entry 4

The entry_set attribute is just another select query and any methods available to SelectQuery are available:

>>> for entry in blog.entry_set.order_by(('pub_date', 'desc')):
...     print entry.title
...
entry 4
entry 3
entry 2
entry 1

Filtering records

>>> for entry in Entry.select().where(blog=blog, published=True):
...     print '%s: %s (%s)' % (entry.blog.name, entry.title, entry.published)
...
My Blog: Some Entry (True)
My Blog: Another Entry (True)

>>> for entry in Entry.select().where(pub_date__lt=datetime.datetime(2011, 1, 1)):
...     print entry.title, entry.pub_date
...
Old entry 2010-01-01 00:00:00

You can also filter across joins:

>>> for entry in Entry.select().join(Blog).where(name='My Blog'):
...     print entry.title
Old entry
Some Entry
Another Entry

If you are already familiar with Django’s ORM, you can use the “double underscore” syntax:

>>> for entry in Entry.filter(blog__name='My Blog'):
...     print entry.title
Old entry
Some Entry
Another Entry

If you prefer, you can use python operators to query:

>>> for entry in Entry.select().join(Blog).where(Blog.name=='My Blog')
...     print entry.title

To perform OR lookups, use the special Q object. These work in both calls to filter() and where():

>>> User.filter(Q(staff=True) | Q(superuser=True)) # get staff or superusers

To perform lookups against another column in a given row, use the F object:

>>> Employee.filter(salary__lt=F('desired_salary'))

Check the docs for more examples of querying.

Sorting records

>>> for e in Entry.select().order_by('pub_date'):
...     print e.pub_date
...
2010-01-01 00:00:00
2011-06-07 14:08:48
2011-06-07 14:12:57

>>> for e in Entry.select().order_by(peewee.desc('pub_date')):
...     print e.pub_date
...
2011-06-07 14:12:57
2011-06-07 14:08:48
2010-01-01 00:00:00

You can also order across joins. Assuming you want to order entries by the name of the blog, then by pubdate desc:

>>> qry = Entry.select().join(Blog).order_by(
...     (Blog, 'name'),
...     (Entry, 'pub_date', 'DESC'),
... )

>>> qry.sql()
('SELECT t1.* FROM entry AS t1 INNER JOIN blog AS t2 ON t1.blog_id = t2.id ORDER BY t2.name ASC, t1.pub_date DESC', [])

Paginating records

The paginate method makes it easy to grab a “page” or records – it takes two parameters, page_number, and items_per_page:

>>> for entry in Entry.select().order_by('id').paginate(2, 10):
...     print entry.title
...
entry 10
entry 11
entry 12
entry 13
entry 14
entry 15
entry 16
entry 17
entry 18
entry 19

Counting records

You can count the number of rows in any select query:

>>> Entry.select().count()
100
>>> Entry.select().where(id__gt=50).count()
50

Iterating over lots of rows

To limit the amount of memory used by peewee when iterating over a lot of rows (i.e. you may be dumping data to csv), use the iterator() method on the QueryResultWrapper. This method allows you to iterate without caching each model returned, using much less memory when iterating over large result sets:

# let's assume we've got 1M stat objects to dump to csv
stats_qr = Stat.select().execute()

# our imaginary serializer class
serializer = CSVSerializer()

# loop over all the stats and serialize
for stat in stats_qr.iterator():
    serializer.serialize_object(stat)

For simple queries you can see further speed improvements by using the SelectQuery.naive() query method. See the documentation for details on this optimization.

stats_query = Stat.select().naive() # note we are calling "naive()"
stats_qr = stats_query.execute()

for stat in stats_qr.iterator():
    serializer.serialize_object(stat)

Performing atomic updates

Use the special F object to perform an atomic update:

>>> MessageCount.update(count=F('count') + 1).where(user=some_user)

Aggregating records

Suppose you have some blogs and want to get a list of them along with the count of entries in each. First I will show you the shortcut:

query = Blog.select().annotate(Entry)

This is equivalent to the following:

query = Blog.select({
    Blog: ['*'],
    Entry: [Count('id')],
}).group_by(Blog).join(Entry)

The resulting query will return Blog objects with all their normal attributes plus an additional attribute ‘count’ which will contain the number of entries. By default it uses an inner join if the foreign key is not nullable, which means blogs without entries won’t appear in the list. To remedy this, manually specify the type of join to include blogs with 0 entries:

query = Blog.select().join(Entry, 'left outer').annotate(Entry)

You can also specify a custom aggregator:

query = Blog.select().annotate(Entry, peewee.Max('pub_date', 'max_pub_date'))

Let’s assume you have a tagging application and want to find tags that have a certain number of related objects. For this example we’ll use some different models in a Many-To-Many configuration:

class Photo(Model):
    image = CharField()

class Tag(Model):
    name = CharField()

class PhotoTag(Model):
    photo = ForeignKeyField(Photo)
    tag = ForeignKeyField(Tag)

Now say we want to find tags that have at least 5 photos associated with them:

>>> Tag.select().join(PhotoTag).join(Photo).group_by(Tag).having('count(*) > 5').sql()

SELECT t1."id", t1."name"
FROM "tag" AS t1
INNER JOIN "phototag" AS t2
    ON t1."id" = t2."tag_id"
INNER JOIN "photo" AS t3
    ON t2."photo_id" = t3."id"
GROUP BY
    t1."id", t1."name"
HAVING count(*) > 5

Suppose we want to grab the associated count and store it on the tag:

>>> Tag.select({
...     Tag: ['*'],
...     Photo: [Count('id', 'count')]
... }).join(PhotoTag).join(Photo).group_by(Tag).having('count(*) > 5').sql()

SELECT t1."id", t1."name", COUNT(t3."id") AS count
FROM "tag" AS t1
INNER JOIN "phototag" AS t2
    ON t1."id" = t2."tag_id"
INNER JOIN "photo" AS t3
    ON t2."photo_id" = t3."id"
GROUP BY
    t1."id", t1."name"
HAVING count(*) > 5

SQL Functions, Subqueries and “Raw expressions”

Suppose you need to want to get a list of all users whose username begins with “a”. There are a couple ways to do this, but one method might be to use some SQL functions like LOWER and SUBSTR. To use arbitrary SQL functions, use the special R object to construct queries:

# select the users' id, username and the first letter of their username, lower-cased
query = User.select(['id', 'username', R('LOWER(SUBSTR(username, 1, 1))', 'first_letter')])

# now filter this list to include only users whose username begins with "a"
a_users = query.where(R('first_letter=%s', 'a'))

>>> for user in a_users:
...    print user.first_letter, user.username

This same functionality could be easily exposed as part of the where clause, the only difference being that the first letter is not selected and therefore not an attribute of the model instance:

a_users = User.filter(R('LOWER(SUBSTR(username, 1, 1)) = %s', 'a'))

We can write subqueries as part of a SelectQuery, for example counting the number of entries on a blog:

entry_query = R('(SELECT COUNT(*) FROM entry WHERE entry.blog_id=blog.id)', 'entry_count')
blogs = Blog.select(['id', 'name', entry_query]).order_by(('entry_count', 'desc'))

for blog in blogs:
    print blog.title, blog.entry_count

It is also possible to use subqueries as part of a where clause, for example finding blogs that have no entries:

no_entry_query = R('NOT EXISTS (SELECT * FROM entry WHERE entry.blog_id=blog.id)')
blogs = Blog.filter(no_entry_query)

for blog in blogs:
    print blog.name, ' has no entries'

Working with transactions

Context manager

You can execute queries within a transaction using the transaction context manager, which will issue a commit if all goes well, or a rollback if an exception is raised:

db = SqliteDatabase(':memory:')

with db.transaction():
    blog.delete_instance(recursive=True) # delete blog and associated entries

Decorator

Similar to the context manager, you can decorate functions with the commit_on_success decorator:

db = SqliteDatabase(':memory:')

@db.commit_on_success
def delete_blog(blog):
    blog.delete_instance(recursive=True)

Changing autocommit behavior

By default, databases are initialized with autocommit=True, you can turn this on and off at runtime if you like. The behavior below is roughly the same as the context manager and decorator:

db.set_autocommit(False)
try:
    blog.delete_instance(recursive=True)
except:
    db.rollback()
    raise
else:
    db.commit()
finally:
    db.set_autocommit(True)

If you would like to manually control every transaction, simply turn autocommit off when instantiating your database:

db = SqliteDatabase(':memory:', autocommit=False)

Blog.create(name='foo blog')
db.commit()

Introspecting databases

If you’d like to generate some models for an existing database, you can try out the database introspection tool “pwiz” that comes with peewee.

Usage:

python pwiz.py my_postgresql_database

It works with postgresql, mysql and sqlite:

python pwiz.py test.db --engine=sqlite

pwiz will generate code for:

  • database connection object
  • a base model class to use this connection
  • models that were introspected from the database tables

The generated code is written to stdout.

Schema migrations

Currently peewee does not have support for automatic schema migrations. Peewee does, however, come with a few helper functions:

Honestly, your best bet is to script any migrations and use plain ol’ SQL.