.github/workflows | ||
docs | ||
moto | ||
other_langs | ||
scripts | ||
tests | ||
.coveragerc | ||
.gitignore | ||
.gitmodules | ||
.readthedocs.yaml | ||
AUTHORS.md | ||
CHANGELOG.md | ||
CODE_OF_CONDUCT.md | ||
codecov.yml | ||
CONFIG_README.md | ||
CONTRIBUTING.md | ||
Dockerfile | ||
IMPLEMENTATION_COVERAGE.md | ||
ISSUE_TEMPLATE.md | ||
LICENSE | ||
Makefile | ||
MANIFEST.in | ||
README.md | ||
requirements-dev.txt | ||
requirements-tests.txt | ||
requirements.txt | ||
setup.cfg | ||
setup.py | ||
update_version_from_git.py |
Moto - Mock AWS Services
Install
$ pip install 'moto[ec2,s3,all]'
In a nutshell
Moto is a library that allows your tests to easily mock out AWS Services.
Imagine you have the following python code that you want to test:
import boto3
class MyModel(object):
def __init__(self, name, value):
self.name = name
self.value = value
def save(self):
s3 = boto3.client('s3', region_name='us-east-1')
s3.put_object(Bucket='mybucket', Key=self.name, Body=self.value)
Take a minute to think how you would have tested that in the past.
Now see how you could test it with Moto:
import boto3
from moto import mock_s3
from mymodule import MyModel
@mock_s3
def test_my_model_save():
conn = boto3.resource('s3', region_name='us-east-1')
# We need to create the bucket since this is all in Moto's 'virtual' AWS account
conn.create_bucket(Bucket='mybucket')
model_instance = MyModel('steve', 'is awesome')
model_instance.save()
body = conn.Object('mybucket', 'steve').get()['Body'].read().decode("utf-8")
assert body == 'is awesome'
With the decorator wrapping the test, all the calls to s3 are automatically mocked out. The mock keeps the state of the buckets and keys.
For a full list of which services and features are covered, please see our implementation coverage.
Documentation
The full documentation can be found here: