Monty, Mongo tinified. MongoDB implemented in Python!
Inspired by TinyDB and it's extension TinyMongo
A pure Python-implemented database that looks and works like MongoDB.
>>> 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}
Most of the CRUD operators have been implemented. You can visit issue #14 to see the full list.
This project is tested against:
- MongoDB: 3.6, 4.0, 4.2 (4.4 on the way💦)
- Python: 3.7, 3.8, 3.9, 3.10, 3.11
pip install montydb
-
optional, to use real
bson
in operation (pymongo
will be installed) For minimum requirements,montydb
ships with it's own fork ofObjectId
inmontydb.types
, so you may ignore this option ifObjectId
is all you need frombson
pip install montydb[bson]
-
optional, to use lightning memory-mapped db as storage engine
pip install montydb[lmdb]
🦄 Available storage engines:
- in-memory
- flat-file
- sqlite
- lmdb (lightning memory-mapped db)
Depending on which one you use, you may have to configure the storage engine before you start.
⚠️ The configuration process only required on repository creation or modification. And, one repository (the parent level of databases) can only assign one storage engine.
To configure a storage, see flat-file storage for example:
from montydb import set_storage, MontyClient
set_storage(
# general settings
repository="/db/repo", # dir path for database to live on disk, default is {cwd}
storage="flatfile", # storage name, default "flatfile"
mongo_version="4.0", # try matching behavior with this mongodb version
use_bson=False, # default None, and will import pymongo's bson if None or True
# any other kwargs are storage engine settings.
cache_modified=10, # the only setting that flat-file have
)
# ready to go
Once that done, there should be a file named monty.storage.cfg
saved in your db repository path. It would be /db/repo
for the above examples.
Now let's moving on to each storage engine's config settings.
memory
storage does not need nor have any configuration, nothing saved to disk.
from montydb import MontyClient
client = MontyClient(":memory:")
# ready to go
flatfile
is the default on-disk storage engine.
from montydb import set_storage, MontyClient
set_storage("/db/repo", cache_modified=5) # optional step
client = MontyClient("/db/repo") # use current working dir if no path given
# ready to go
FlatFile config:
[flatfile]
cache_modified: 0 # how many document CRUD cached before flush to disk.
sqlite
is NOT the default on-disk storage, need configuration first before getting client.
Pre-existing sqlite storage file which saved by
montydb<=1.3.0
is not read/writeable aftermontydb==2.0.0
.
from montydb import set_storage, MontyClient
set_storage("/db/repo", storage="sqlite") # required, to set sqlite as engine
client = MontyClient("/db/repo")
# ready to go
SQLite config:
[sqlite]
journal_mode: WAL
SQLite write concern:
client = MontyClient("/db/repo",
synchronous=1,
automatic_index=False,
busy_timeout=5000)
lightning
is NOT the default on-disk storage, need configuration first before get client.
Newly implemented.
from montydb import set_storage, MontyClient
set_storage("/db/repo", storage="lightning") # required, to set lightning as engine
client = MontyClient("/db/repo")
# ready to go
LMDB config:
[lightning]
map_size: 10485760 # Maximum size database may grow to.
Optionally, You could prefix the repository path with montydb URI scheme.
client = MontyClient("montydb:///db/repo")
Pymongo
bson
may required.
-
Imports content from an Extended JSON file into a MontyCollection instance. The JSON file could be generated from
montyexport
ormongoexport
.from montydb import open_repo, utils with open_repo("foo/bar"): utils.montyimport("db", "col", "/path/dump.json")
-
Produces a JSON export of data stored in a MontyCollection instance. The JSON file could be loaded by
montyimport
ormongoimport
.from montydb import open_repo, utils with open_repo("foo/bar"): utils.montyexport("db", "col", "/data/dump.json")
-
Loads a binary database dump into a MontyCollection instance. The BSON file could be generated from
montydump
ormongodump
.from montydb import open_repo, utils with open_repo("foo/bar"): utils.montyrestore("db", "col", "/path/dump.bson")
-
Creates a binary export from a MontyCollection instance. The BSON file could be loaded by
montyrestore
ormongorestore
.from montydb import open_repo, utils with open_repo("foo/bar"): utils.montydump("db", "col", "/data/dump.bson")
-
Record MongoDB query results in a period of time. Requires to access database profiler.
This works via filtering the database profile data and reproduce the queries of
find
anddistinct
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>, ...], ...}
-
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}])
montydb uses Poetry to make it easy manage dependencies and set up the development environment.
After cloning the repository, you need to run the following commands to set up the development environment:
make install
This will create a virtual environment and download the required dependencies.
To keep dependencies updated after git operations such as local updates or merging changes into local dev branch
make update
A makefile is used to simplify common operations such as updating, testing, and deploying etc.
make or make help
install install all dependencies locally
update update project dependencies locally (run after git update)
ci Run all checks (codespell, lint, bandit, test)
test Run tests
lint Run linting with flake8
codespell Find typos with codespell
bandit Run static security analysis with bandit
build Build project using poetry
clean Clean project
Most of our tests compare montydb CRUD results against real mongodb instance, therefore we must have a running mongodb before testing.
For example, if we want to test against mongo 4.4:
docker run --name monty-4.4 -p 30044:27017 -d mongo:4.4
poetry run pytest --storage {storage engin name} --mongodb {mongo instance url} [--use-bson]
Example:
poetry run pytest --storage memory --mongodb localhost:30044 --use-bson
Mainly for personal skill practicing and fun.
I work in the VFX industry and 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.