Project contains abstraction of pymongo driver for automatic reconnect on master switch in remote MongoDB cluster. It
also provides data definition layer.
Core of mongo_driver is the Engine class, handling queries reconnection with notification to registered reconnect
hooks.
Database creates Collections by their definitions. Database hooks itself to engine reconnect event, so it can switch
internal state of database's collections instances.
classDiagram
Executor <|-- Engine : implements
Database o-- Executor
Database *-- Collection
Database o-- CollectionDefinition
Collection o-- Executor
CollectionDefinition *-- Index
CollectionDefinition *-- Document
class Executor{
<<abstract>>
reconnect()
register_hook(reconnect_hook_func)
find_one(collection: pymongo.collection.Collection, ...)
other_collection_methods(collection: pymongo.collection.Collection, ...)
}
class Engine{
+client: MongoClient
-_retry_command(collection, command, ...)
dispose()
reconnect()
register_hook(reconnect_hook_func)
find_one(collection: pymongo.collection.Collection, ...)
other_collection_methods(collection: pymongo.collection.Collection, ...)
}
class Collection{
+dialect_entity: pymongo.collection.Collection
+engine: Executor
+find_one(...)
other_collection_methods()
}
class Document{
+unique_key: Optional[BsonDict]
+data: BsonDict
}
class CollectionDefinition{
+name: str
+indices: Sequence[Index]
+default_docs: Sequence[Document]
}
class Index{
+fields: OrderedDictType[str, Union[str, int]]
+name: Optional[str]
+unique: Optional[bool]
field_for_mongo() -> List[Tuple[str, Union[str, int]]]
}
class Database{
+engine: Executor
+topology: List[types.CollectionDefinition]
-_database: pymongo.database.Database
-_collections: Dict[str, Collection]
invalidate_cache_hook(source: Engine)
get_collection(name: str) -> Collection
extend(*new_definitions: types.CollectionDefinition)
create_all(skip_existing: bool)
ping() -> bool
}
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
import logging
from mongomancy import Engine, Database, CollectionDefinition, Index
engine = Engine("localhost", 27017)
logger = logging.getLogger(__name__)
db = Database(engine=engine, logger=logger)
game = CollectionDefinition(name="game", indices=[Index(fields={"genre": 1})])
player = CollectionDefinition(name="player", indices=[Index(fields={"player_id": 1}, unique=True)])
db.add_collection(game)
db.add_collection(player)
db.create_all()
db["game"].find({"genre": "adventure"})You can run tests with coverage tracing:
python -m coverage run -m unittest tests/test_* -v To generate coverage report:
python -m coverage html Clone repo and set up your pypi repo account credentials on build for build environment.
-
Move to package repo:
cd ~/git/mongomancy
-
Install requirements:
python -m pip install -Ur requirements.txt
-
Clean old build fragments:
rm -rf ./dist ./build ./mongomancy/mongomancy.egg-info
-
Build new package:
python -m build
-
Upload new package:
python -m twine upload dist/*