Monty, Mongo tinified. MongoDB implemented in Python ! Was inspired by TinyDB and it's extension TinyMongo
What ?
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 operator has been implemented, you may visit this issue to see the full list.
And this project is testing against to:
- MongoDB 3.6, 4.0, 4.2 (4.4 on the way
💦 ) - Python 2.7, 3.6, 3.7, 3.8, 3.9
Install
pip install montydb
-
optional, to use
bsonin operation (pymongowill be installed)pip install montydb[bson] -
optional, to use lightning memory-mapped db as storage engine
pip install montydb[lmdb]
Storage
- in-memory
- flat-file
- sqlite
- lmdb (lightning memory-mapped db)
Depend on which one you use, may have to config the storage engine before 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 configurate a storage, take flat-file storage as 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 try importing pymongo if None
# 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 above examples.
Configuration
Now let's moving on to each storage engine's config settings.
🌟
In-Memory
memory storage does not need nor have any configuration, nothing saved to disk.
from montydb import MontyClient
client = MontyClient(":memory:")
# ready to go
🔰
Flat-File
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 curent 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
sqlite is NOT the default on-disk storage, need configuration first before getting client.
Pre-existing sqlite storage file which saved by
montydb<=1.3.0is 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)
🚀
LMDB (Lightning Memory-Mapped Database)
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.
URI
Optionally, You could prefix the repository path with montydb URI scheme.
client = MontyClient("montydb:///db/repo")
Utilities
Pymongo
bsonmay required.
-
montyimportImports content from an Extended JSON file into a MontyCollection instance. The JSON file could be generated from
montyexportormongoexport.from montydb import open_repo, utils with open_repo("foo/bar"): utils.montyimport("db", "col", "/path/dump.json")
-
montyexportProduces a JSON export of data stored in a MontyCollection instance. The JSON file could be loaded by
montyimportormongoimport.from montydb import open_repo, utils with open_repo("foo/bar"): utils.montyexport("db", "col", "/data/dump.json")
-
montyrestoreLoads a binary database dump into a MontyCollection instance. The BSON file could be generated from
montydumpormongodump.from montydb import open_repo, utils with open_repo("foo/bar"): utils.montyrestore("db", "col", "/path/dump.bson")
-
montydumpCreates a binary export from a MontyCollection instance. The BSON file could be loaded by
montyrestoreormongorestore.from montydb import open_repo, utils with open_repo("foo/bar"): utils.montydump("db", "col", "/data/dump.bson")
-
MongoQueryRecorderRecord 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
findanddistinctcommands.: [, , ...], ...} "> 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>, ...], ...}
-
MontyListExperimental, 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.
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