PynamoDB was written from scratch to be Pythonic, and supports the entire DynamoDB API.

Creating a model

Let’s create a simple model to describe users.

from pynamodb.models import Model
from pynamodb.attributes import UnicodeAttribute

class UserModel(Model):
    A DynamoDB User
    class Meta:
        table_name = 'dynamodb-user'
        region = 'us-west-1'
    email = UnicodeAttribute(hash_key=True)
    first_name = UnicodeAttribute()
    last_name = UnicodeAttribute()

Models are backed by DynamoDB tables. In this example, the model has a hash key attribute that stores the user’s email address. Any attribute can be set as a hash key by including the argument hash_key=True. The region attribute is not required, and will default to us-east-1 if not provided.

PynamoDB allows you to create the table:

>>> UserModel.create_table(read_capacity_units=1, write_capacity_units=1)

Now you can create a user in local memory:

>>> user = UserModel('', first_name='Samuel', last_name='Adams')

To write the user to DynamoDB, just call save:


You can see that the table count has changed:

>>> UserModel.count()

Attributes can be accessed and set normally:

>>> = 'foo-bar'

Did another process update the user? We can refresh the user with data from DynamoDB:

>>> user.refresh()

Ready to delete the user?

>>> user.delete()

Changing items

Changing existing items in the database can be done using either update() or save(). There are important differences between the two.

Use of save() looks like this:

user = UserModel.get('')
user.first_name = 'Robert'

Use of update() (in its simplest form) looks like this:

user = UserModel.get('')

save() will entirely replace an object (it internally uses PutItem). As a consequence, even if you modify only one attribute prior to calling save(), the entire object is re-written. Any modifications done to the same user by other processes will be lost, even if made to other attributes that you did not change. To avoid this, use update() to perform more fine grained updates or see the Conditional Operations for how to avoid race conditions entirely.

Additionally, PynamoDB ignores attributes it does not know about when reading an object from the database. As a result, if the item in DynamoDB contains attributes not declared in your model, save() will cause those attributes to be deleted.

In particular, performing a rolling upgrade of your application after having added an attribute is an example of such a situation. To avoid data loss, either avoid using save() or perform a multi-step update with the first step is to upgrade to a version that merely declares the attribute on the model without ever setting it to any value.


PynamoDB provides an intuitive abstraction over the DynamoDB Query API. All of the Query API comparison operators are supported.

Suppose you had a table with both a hash key that is the user’s last name and a range key that is the user’s first name:

class UserModel(Model):
        A DynamoDB User
        class Meta:
            table_name = 'dynamodb-user'
        email = UnicodeAttribute()
        first_name = UnicodeAttribute(range_key=True)
        last_name = UnicodeAttribute(hash_key=True)

Now, suppose that you want to search the table for users with a last name ‘Smith’, and first name that begins with the letter ‘J’:

for user in UserModel.query('Smith', UserModel.first_name.startswith('J')):

You can combine query terms:

for user in UserModel.query('Smith', UserModel.first_name.startswith('J') |'')):

Counting Items

You can retrieve the count for queries by using the count method:

print(UserModel.count('Smith', UserModel.first_name.startswith('J'))

Counts also work for indexes:


Alternatively, you can retrieve the table item count by calling the count method without filters:


Note that the first positional argument to count() is a hash_key. Although this argument can be None, filters must not be used when hash_key is None:

# raises a ValueError
print(UserModel.count(UserModel.first_name == 'John'))

# returns count of only the matching users
print(UserModel.count('my_hash_key', UserModel.first_name == 'John'))

Batch Operations

PynamoDB provides context managers for batch operations.


DynamoDB limits batch write operations to 25 PutRequests and DeleteRequests combined. PynamoDB automatically groups your writes 25 at a time for you.

Let’s create a whole bunch of users:

with UserModel.batch_write() as batch:
    for i in range(100):'user-{0}'.format(i), first_name='Samuel', last_name='Adams'))

Now, suppose you want to retrieve all those users:

user_keys = [('user-{0}'.format(i)) for i in range(100)]
for item in UserModel.batch_get(user_keys):

Perhaps you want to delete all these users:

with UserModel.batch_write() as batch:
    items = [UserModel('user-{0}'.format(x)) for x in range(100)]
    for item in items: