montydb 2.3.8

Monty, Mongo tinified. MongoDB implemented in Python !

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 real bson in operation (pymongo will be installed)

  For minimum requirements, montydb ships with it's own fork of ObjectId in montydb.types, so you may ignore this option if ObjectId is all you need from bson

  pip install montydb[bson]
  

• optional, to use lightning memory-mapped db as storage engine

  pip install montydb[lmdb]
  

Storage

🦄 Available storage engines:

• 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 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 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 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

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 after montydb==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 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 database 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.

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This project is supported by JetBrains