This document presents a brief, high-level overview of Peewee’s primary features. This guide will cover:
If you’d like something a bit more meaty, there is a thorough tutorial on
creating a “twitter”-style web app using peewee and the
Flask framework. In the projects
examples/ folder you can find more
self-contained Peewee examples, like a blog app.
I strongly recommend opening an interactive shell session and running the code. That way you can get a feel for typing in queries.
Model classes, fields and model instances all map to database concepts:
Column on a table
Row in a database table
When starting a project with peewee, it’s typically best to begin with your
data model, by defining one or more
from peewee import * db = SqliteDatabase('people.db') class Person(Model): name = CharField() birthday = DateField() class Meta: database = db # This model uses the "people.db" database.
Peewee will automatically infer the database table name from the name of
the class. You can override the default name by specifying a
attribute in the inner “Meta” class (alongside the
To learn more about how Peewee generates table names,
refer to the Table Names section.
Also note that we named our model
Person instead of
People. This is
a convention you should follow – even though the table will contain
multiple people, we always name the class using the singular form.
There are lots of field types suitable for storing various types of data. Peewee handles converting between pythonic values those used by the database, so you can use Python types in your code without having to worry.
Things get interesting when we set up relationships between models using foreign key relationships. This is simple with peewee:
class Pet(Model): owner = ForeignKeyField(Person, backref='pets') name = CharField() animal_type = CharField() class Meta: database = db # this model uses the "people.db" database
Now that we have our models, let’s connect to the database. Although it’s not necessary to open the connection explicitly, it is good practice since it will reveal any errors with your database connection immediately, as opposed to some arbitrary time later when the first query is executed. It is also good to close the connection when you are done – for instance, a web app might open a connection when it receives a request, and close the connection when it sends the response.
We’ll begin by creating the tables in the database that will store our data. This will create the tables with the appropriate columns, indexes, sequences, and foreign key constraints:
from datetime import date uncle_bob = Person(name='Bob', birthday=date(1960, 1, 15)) uncle_bob.save() # bob is now stored in the database # Returns: 1
When you call
save(), the number of rows modified is
You can also add a person by calling the
create() method, which
returns a model instance:
grandma = Person.create(name='Grandma', birthday=date(1935, 3, 1)) herb = Person.create(name='Herb', birthday=date(1950, 5, 5))
To update a row, modify the model instance and call
persist the changes. Here we will change Grandma’s name and then save the
changes in the database:
grandma.name = 'Grandma L.' grandma.save() # Update grandma's name in the database. # Returns: 1
Now we have stored 3 people in the database. Let’s give them some pets. Grandma doesn’t like animals in the house, so she won’t have any, but Herb is an animal lover:
bob_kitty = Pet.create(owner=uncle_bob, name='Kitty', animal_type='cat') herb_fido = Pet.create(owner=herb, name='Fido', animal_type='dog') herb_mittens = Pet.create(owner=herb, name='Mittens', animal_type='cat') herb_mittens_jr = Pet.create(owner=herb, name='Mittens Jr', animal_type='cat')
After a long full life, Mittens sickens and dies. We need to remove him from the database:
herb_mittens.delete_instance() # he had a great life # Returns: 1
The return value of
delete_instance() is the number of rows
removed from the database.
Uncle Bob decides that too many animals have been dying at Herb’s house, so he adopts Fido:
herb_fido.owner = uncle_bob herb_fido.save()
The real strength of our database is in how it allows us to retrieve data through queries. Relational databases are excellent for making ad-hoc queries.
Getting single records¶
Let’s retrieve Grandma’s record from the database. To get a single record from
the database, use
grandma = Person.select().where(Person.name == 'Grandma L.').get()
We can also use the equivalent shorthand
grandma = Person.get(Person.name == 'Grandma L.')
Lists of records¶
Let’s list all the people in the database:
for person in Person.select(): print(person.name) # prints: # Bob # Grandma L. # Herb
Let’s list all the cats and their owner’s name:
query = Pet.select().where(Pet.animal_type == 'cat') for pet in query: print(pet.name, pet.owner.name) # prints: # Kitty Bob # Mittens Jr Herb
There is a big problem with the previous query: because we are accessing
pet.owner.name and we did not select this relation in our original
query, peewee will have to perform an additional query to retrieve the
pet’s owner. This behavior is referred to as N+1 and it
should generally be avoided.
For an in-depth guide to working with relationships and joins, refer to the Relationships and Joins documentation.
We can avoid the extra queries by selecting both Pet and Person, and adding a join.
query = (Pet .select(Pet, Person) .join(Person) .where(Pet.animal_type == 'cat')) for pet in query: print(pet.name, pet.owner.name) # prints: # Kitty Bob # Mittens Jr Herb
Let’s get all the pets owned by Bob:
for pet in Pet.select().join(Person).where(Person.name == 'Bob'): print(pet.name) # prints: # Kitty # Fido
We can do another cool thing here to get bob’s pets. Since we already have an object to represent Bob, we can do this instead:
for pet in Pet.select().where(Pet.owner == uncle_bob): print(pet.name)
Let’s make sure these are sorted alphabetically by adding an
for pet in Pet.select().where(Pet.owner == uncle_bob).order_by(Pet.name): print(pet.name) # prints: # Fido # Kitty
Let’s list all the people now, youngest to oldest:
for person in Person.select().order_by(Person.birthday.desc()): print(person.name, person.birthday) # prints: # Bob 1960-01-15 # Herb 1950-05-05 # Grandma L. 1935-03-01
Combining filter expressions¶
Peewee supports arbitrarily-nested expressions. Let’s get all the people whose birthday was either:
before 1940 (grandma)
after 1959 (bob)
d1940 = date(1940, 1, 1) d1960 = date(1960, 1, 1) query = (Person .select() .where((Person.birthday < d1940) | (Person.birthday > d1960))) for person in query: print(person.name, person.birthday) # prints: # Bob 1960-01-15 # Grandma L. 1935-03-01
Now let’s do the opposite. People whose birthday is between 1940 and 1960:
query = (Person .select() .where(Person.birthday.between(d1940, d1960))) for person in query: print(person.name, person.birthday) # prints: # Herb 1950-05-05
Aggregates and Prefetch¶
Now let’s list all the people and how many pets they have:
for person in Person.select(): print(person.name, person.pets.count(), 'pets') # prints: # Bob 2 pets # Grandma L. 0 pets # Herb 1 pets
Once again we’ve run into a classic example of N+1 query
behavior. In this case, we’re executing an additional query for every
Person returned by the original
SELECT! We can avoid this by performing
a JOIN and using a SQL function to aggregate the results.
query = (Person .select(Person, fn.COUNT(Pet.id).alias('pet_count')) .join(Pet, JOIN.LEFT_OUTER) # include people without pets. .group_by(Person) .order_by(Person.name)) for person in query: # "pet_count" becomes an attribute on the returned model instances. print(person.name, person.pet_count, 'pets') # prints: # Bob 2 pets # Grandma L. 0 pets # Herb 1 pets
Peewee provides a magical helper
fn(), which can be used to call
any SQL function. In the above example,
would be translated into
COUNT(pet.id) AS pet_count.
Now let’s list all the people and the names of all their pets. As you may have guessed, this could easily turn into another N+1 situation if we’re not careful.
Before diving into the code, consider how this example is different from the
earlier example where we listed all the pets and their owner’s name. A pet can
only have one owner, so when we performed the join from
there was always going to be a single match. The situation is different when we
are joining from
Pet because a person may have zero pets or
they may have several pets. Because we’re using a relational databases, if we
were to do a join from
Pet then every person with multiple
pets would be repeated, once for each pet.
It would look like this:
query = (Person .select(Person, Pet) .join(Pet, JOIN.LEFT_OUTER) .order_by(Person.name, Pet.name)) for person in query: # We need to check if they have a pet instance attached, since not all # people have pets. if hasattr(person, 'pet'): print(person.name, person.pet.name) else: print(person.name, 'no pets') # prints: # Bob Fido # Bob Kitty # Grandma L. no pets # Herb Mittens Jr
Usually this type of duplication is undesirable. To accommodate the more common
(and intuitive) workflow of listing a person and attaching a list of that
person’s pets, we can use a special method called
query = Person.select().order_by(Person.name).prefetch(Pet) for person in query: print(person.name) for pet in person.pets: print(' *', pet.name) # prints: # Bob # * Kitty # * Fido # Grandma L. # Herb # * Mittens Jr
One last query. This will use a SQL function to find all people whose names start with either an upper or lower-case G:
expression = fn.Lower(fn.Substr(Person.name, 1, 1)) == 'g' for person in Person.select().where(expression): print(person.name) # prints: # Grandma L.
This is just the basics! You can make your queries as complex as you like. Check the documentation on Querying for more info.
We’re done with our database, let’s close the connection:
In an actual application, there are some established patterns for how you would manage your database connection lifetime. For example, a web application will typically open a connection at start of request, and close the connection after generating the response. A connection pool can help eliminate latency associated with startup costs.
To learn about setting up your database, see the Database documentation, which provides many examples. Peewee also supports configuring the database at run-time as well as setting or changing the database at any time.