DynamoDB is a minimalistic NoSQL engine provided by Amazon as a part of their AWS product.
DynamoDB is great in production environement but sucks when testing your application. Tables needs roughtly 1 min to be created, deleted or updated. Items operation rates depends on how much you pay and tests will conflict if 2 developers run them at the same time.
ddbmock brings a tiny in-memory(tm) implementation of DynamoDB API. It can either be run as a stand alone server or as a regular library helping you to build lightning fast unit and functional tests :)
ddbmock is not intended for production use. It will lose your data. you’ve been warned! I currently recommend the “boto extension” mode for unit-tests and the “server” mode for functional tests.
$ pip install ddbmock
Ideal for test environment. For stage and production I highly recommend using DynamoDB servers. ddbmock comes with no warranty and will loose your data(tm).
Launch the server
$ pserve development.ini # launch the server on 0.0.0.0:6543
Start the client
import boto
from ddbmock import connect_boto_network
# Use the provided helper to connect your *own* endpoint
db = connect_boto_network()
# Done ! just use it wherever in your project as usual.
db.list_tables() # get list of tables (empty at this stage)
Note: if you do not want to import ddbmock only for the helper, here is a reference implementation:
def connect_boto_network(host='localhost', port=6543):
import boto
from boto.regioninfo import RegionInfo
endpoint = '{}:{}'.format(host, port)
region = RegionInfo(name='ddbmock', endpoint=endpoint)
return boto.connect_dynamodb(region=region, port=port, is_secure=False)
Ideal for unit testing or small scale automated functional tests. Nice to play around with boto DynamoDB API too :)
import boto
from ddbmock import connect_boto_patch
# Wire-up boto and ddbmock together
db = connect_boto_patch()
# Done ! just use it wherever in your project as usual.
db.list_tables() # get list of tables (empty at this stage)
Note, to clean patches made in boto.dynamodb.layer1, you can call clean_boto_patch() from the same module.
A significant part of ddbmock is now configurable through ddbmock.config parameters. This includes the storage backend.
By default, ddbmock has no persitence and stores everything in-memory. Alternatively, you can use the SQLite storage engine but be warned that it will be slower. To switch the backend, you will to change a configuration variable before creating the first table.
from ddbmock import config
# switch to sqlite backend
config.STORAGE_ENGINE_NAME = 'sqlite'
# define the database path. defaults to 'dynamo.db'
config.STORAGE_SQLITE_FILE = '/tmp/my_database.sqlite'
Please note that ddbmock does not persist table metadata currently. As a consequence, you will need to create the tables at each restart even with the SQLite backend. This is hoped to be improved in future releases.
See https://bitbucket.org/Ludia/dynamodb-mock/src/tip/ddbmock/config.py for a full list of parameters.