Skip to main content

MongoDB's unofficial Python implementation.

Project description


Monty, Mongo tinified. MongoDB implemented in Python !

Build Status Coverage Status Version Maintainability

Inspired by TinyDB and it's extension TinyMongo.

MontyDB is:

  • A tiny version of MongoDB, against to MongoDB 4.0.11
  • Written in pure Python, testing on Python 2.7, 3.6, 3.7, 3.8, PyPy*
  • Literally serverless.
  • Similar to mongomock, but a bit more than that.

All those implemented functions and operators, should behaved just like you were working with MongoDB. Even raising error for same cause.


pip install montydb

Optinal Requirements
  • pymongo (for bson)

    bson is opt-out by default even it's installed, set env var MONTY_ENABLE_BSON=1 to enable it.

Example Code

>>> from montydb import MontyClient
>>> col = MontyClient(":memory:").db.test
>>> col.insert_many([{"stock": "A", "qty": 6}, {"stock": "A", "qty": 2}])

>>> cur = col.find({"stock": "A", "qty": {"$gt": 4}})
>>> next(cur)
{'_id': ObjectId('5ad34e537e8dd45d9c61a456'), 'stock': 'A', 'qty': 6}


  • You may visit Projects' TODO to see what's going on.
  • You may visit This Issue to see what's been implemented and what's not.

Storage Engine Configurations

The configuration process only required on repository creation or modification.

Currently, one repository can only assign one storage engine.

  • Memory

Memory storage does not need nor have any configuration, nothing saved to disk.

>>> from montydb import MontyClient
>>> client = MontyClient(":memory:")
  • FlatFile

FlatFile is the default on-disk storage engine.

>>> from montydb import MontyClient
>>> client = MontyClient("/db/repo")

FlatFile config:

cache_modified: 0  # how many document CRUD cached before flush to disk.
  • SQLite

SQLite is NOT the default on-disk storage, need configuration first before get client.

Pre-existing sqlite storage file which saved by montydb<=1.3.0 is not read/writeable after montydb==2.0.0.

>>> from montydb import set_storage, MontyClient
>>> set_storage("/db/repo", storage="sqlite")
>>> client = MontyClient("/db/repo")

SQLite config:

journal_mode: WAL

SQLite write concern:

>>> client = MontyClient("/db/repo",
>>>                      synchronous=1,
>>>                      automatic_index=False,
>>>                      busy_timeout=5000)


You could prefix the repository path with montydb URI scheme.

  >>> client = MontyClient("montydb:///db/repo")


Pymongo bson may required.

  • montyimport

    Imports content from an Extended JSON file into a MontyCollection instance. The JSON file could be generated from montyexport or mongoexport.

    >>> from montydb import open_repo, utils
    >>> with open_repo("foo/bar"):
    >>>     utils.montyimport("db", "col", "/path/dump.json")
  • montyexport

    Produces a JSON export of data stored in a MontyCollection instance. The JSON file could be loaded by montyimport or mongoimport.

    >>> from montydb import open_repo, utils
    >>> with open_repo("foo/bar"):
    >>>     utils.montyexport("db", "col", "/data/dump.json")
  • montyrestore

    Loads a binary database dump into a MontyCollection instance. The BSON file could be generated from montydump or mongodump.

    >>> from montydb import open_repo, utils
    >>> with open_repo("foo/bar"):
    >>>     utils.montyrestore("db", "col", "/path/dump.bson")
  • montydump

    Creates a binary export from a MontyCollection instance. The BSON file could be loaded by montyrestore or mongorestore.

    >>> from montydb import open_repo, utils
    >>> with open_repo("foo/bar"):
    >>>     utils.montydump("db", "col", "/data/dump.bson")
  • MongoQueryRecorder

    Record MongoDB query results in a period of time. Requires to access databse profiler.

    This works via filtering the database profile data and reproduce the queries of find and distinct commands.

    >>> from pymongo import MongoClient
    >>> from montydb.utils import MongoQueryRecorder
    >>> client = MongoClient()
    >>> recorder = MongoQueryRecorder(client["mydb"])
    >>> recorder.start()
    >>> # Make some queries or run the App...
    >>> recorder.stop()
    >>> recorder.extract()
    {<collection_1>: [<doc_1>, <doc_2>, ...], ...}
  • MontyList

    Experimental, a subclass of list, combined the common CRUD methods from Mongo's Collection and Cursor.

    >>> from montydb.utils import MontyList
    >>> mtl = MontyList([1, 2, {"a": 1}, {"a": 5}, {"a": 8}])
    >>> mtl.find({"a": {"$gt": 3}})
    MontyList([{'a': 5}, {'a': 8}])

Why I did this ?

Mainly for personal skill practicing and fun. I work in VFX industry, some of my production needs (mostly edge-case) requires to run in a limited environment (e.g. outsourced render farms), which may have problem to run or connect a MongoDB instance. And I found this project really helps.


Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

montydb-2.0.1.tar.gz (53.5 kB view hashes)

Uploaded source

Built Distribution

montydb-2.0.1-py3-none-any.whl (65.5 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page