Moto is a library that allows your python tests to easily mock out the boto library.
Imagine you have the following code that you want to test:
import boto
from boto.s3.key import Key
class MyModel(object):
def __init__(self, name, value):
self.name = name
self.value = value
def save(self):
conn = boto.connect_s3()
bucket = conn.get_bucket('mybucket')
k = Key(bucket)
k.key = self.name
k.set_contents_from_string(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 boto
from moto import mock_s3
from mymodule import MyModel
@mock_s3
def test_my_model_save():
model_instance = MyModel('steve', 'is awesome')
model_instance.save()
conn = boto.connect_s3()
assert conn.get_bucket('mybucket').get_key('steve') == '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.
It gets even better! Moto isn't just S3. Here's the status of the other AWS services implemented.
|---------------------------------------------------------------------------|
| Service Name | Decorator | Development Status |
|---------------------------------------------------------------------------|
| DynamoDB | @mock_dynamodb | core endpoints done |
|---------------------------------------------------------------------------|
| EC2 | @mock_ec2 | core endpoints done |
| - AMI | | core endpoints done |
| - EBS | | core endpoints done |
| - Instances | | all endpoints done |
| - Security Groups | | core endpoints done |
| - Tags | | all endpoints done |
|---------------------------------------------------------------------------|
| S3 | @mock_s3 | core endpoints done |
|---------------------------------------------------------------------------|
| SES | @mock_ses | core endpoints done |
|---------------------------------------------------------------------------|
| SQS | @mock_sqs | core endpoints done |
|---------------------------------------------------------------------------|
Imagine you have a function that you use to launch new ec2 instances:
import boto
def add_servers(ami_id, count):
conn = boto.connect_ec2('the_key', 'the_secret')
for index in range(count):
conn.run_instances(ami_id)
To test it:
from . import add_servers
@mock_ec2
def test_add_servers():
add_servers('ami-1234abcd', 2)
conn = boto.connect_ec2('the_key', 'the_secret')
reservations = conn.get_all_instances()
assert len(reservations) == 2
instance1 = reservations[0].instances[0]
assert instance1.image_id == 'ami-1234abcd'
All of the services can be used as a decorator, context manager, or in a raw form.
@mock_s3
def test_my_model_save():
model_instance = MyModel('steve', 'is awesome')
model_instance.save()
conn = boto.connect_s3()
assert conn.get_bucket('mybucket').get_key('steve') == 'is awesome'
def test_my_model_save():
with mock_s3():
model_instance = MyModel('steve', 'is awesome')
model_instance.save()
conn = boto.connect_s3()
assert conn.get_bucket('mybucket').get_key('steve') == 'is awesome'
def test_my_model_save():
mock = mock_s3()
mock.start()
model_instance = MyModel('steve', 'is awesome')
model_instance.save()
conn = boto.connect_s3()
assert conn.get_bucket('mybucket').get_key('steve') == 'is awesome'
mock.stop()
Moto also comes with a stand-alone server mode. This allows you to utilize the backend structure of Moto even if you don't use Python.
To run a service:
$ moto_server ec2
* Running on https://127.0.0.1:5000/
Then go to localhost to see a list of running instances (it will be empty since you haven't added any yet).
$ pip install moto
This library has been tested on boto v2.5+.
A huge thanks to Gabriel Falcão and his HTTPretty library. Moto would not exist without it.