A Python library to help build things the way we want them built
Project description
rococo
A Python library to help build things the way we want them built.
Basic Usage
Installation
Install using pip:
pip install rococo
Example
Models
from rococo.models import Person
# Initialize a Person object from rococo's built-in models.
someone = Person(first_name="John", last_name="Doe")
# Prepare to save the object in the database adding/updating attributes for the object.
someone.prepare_for_save(changed_by_id="jane_doe")
someone.as_dict()
{
'active': True,
'changed_by_id': 'jane_doe',
'changed_on': datetime.datetime(2023, 9, 20, 19, 50, 23, 532875),
'entity_id': 'e06876705b364640a20efc165f6ffb76',
'first_name': 'John',
'last_name': 'Doe',
'previous_version': '7e63a5d0aa0f43b5aa9c8cc0634c41f2',
'version': '08489d2bc5d74f78b7af0f2c1d9c5498'
}
Messaging
RabbitMQ
# Producer
from rococo.messaging import RabbitMqConnection
with RabbitMqConnection('host', 'port', 'username', 'password', 'virtual_host') as conn:
conn.send_message('queue_name', {'message': 'data'})
# Consumer
from rococo.messaging import RabbitMqConnection
def process_message(message_data: dict):
print(f"Processing message {message_data}...")
with RabbitMqConnection('host', 'port', 'username', 'password', 'virtual_host') as conn:
conn.consume_messages('queue_name', process_message)
SQS
# Producer
from rococo.messaging import SqsConnection
with SqsConnection(region_name='us-east-1') as conn:
conn.send_message('queue_name', {'message': 'data'})
# Consumer
from rococo.messaging import SqsConnection
def process_message(message_data: dict):
print(f"Processing message {message_data}...")
with SqsConnection(region_name='us-east-1') as conn:
conn.consume_messages('queue_name', process_message)
# Note: since cleanup is not required for SQS connections, you can also do:
conn = SqsConnection(region_name='us-east-1')
conn.send_message('queue_name', {'message': 'data'})
conn.consume_messages('queue_name', process_message)
Processing
Processing data from messages can be achieved by implementing the abstract class BaseServiceProcessor within messaging/base.py
Data
SurrealDB
from rococo.data import SurrealDbAdapter
def get_db_connection():
endpoint = "ws://localhost:8000/rpc"
username = "myuser"
password = "mypassword"
namespace = "test"
database = "test"
return SurrealDbAdapter(endpoint, username, password, namespace, database)
with get_db_connection() as db:
db.execute_query("""insert into person {
user: 'me',
pass: 'very_safe',
tags: ['python', 'documentation']
};""")
print(db.execute_query("SELECT * FROM person;", {}))
How to use the adapter and base Repository in another projects
class LoginMethodRepository(BaseRepository):
def __init__(self, adapter, message_adapter, queue_name):
super().__init__(adapter, LoginMethod, message_adapter, queue_name)
def save(self, login_method: LoginMethod, send_message: bool = False):
with self.adapter:
return super().save(login_method,send_message)
def get_one(self, conditions: Dict[str, Any]):
with self.adapter:
return super().get_one(conditions)
def get_many(self, conditions: Dict[str, Any]):
with self.adapter:
return super().get_many(conditions)
-
The LoginMethodRepository class is a concrete implementation of the BaseRepository class. It is responsible for managing LoginMethod objects in the database.
The init() method takes an adapter object as input. This adapter object is responsible for communicating with the database. The adapter object is passed to the super().init() method, which initializes the base repository class. It also takes in a message adapter and queue name for RabbitMQ and SQS messaging which can later be used in the save() method by passing a boolean.
The save() method takes a LoginMethod object as input and saves it to the database. The get_one() method takes a dictionary of conditions as input and returns a single LoginMethod object that matches those conditions. The get_many() method takes a dictionary of conditions as input and returns a list of LoginMethod objects that match those conditions.
RepositoryFactory
class RepositoryFactory:
_repositories = {}
@classmethod
def _get_db_connection(cls):
endpoint = "ws://localhost:8000/rpc"
username = "myuser"
password = "mypassword"
namespace = "hell"
db_name = "abclolo"
return SurrealDbAdapter(endpoint, username, password, namespace, db_name)
@classmethod
def get_repository(cls, repo_class: Type[BaseRepository]):
if repo_class not in cls._repositories:
adapter = cls._get_db_connection()
cls._repositories[repo_class] = repo_class(adapter)
return cls._repositories[repo_class]
-
The RepositoryFactory class is a singleton class that is responsible for creating and managing repositories. It uses a cache to store the repositories that it has already created. This allows it to avoid creating the same repository multiple times.
The _get_db_connection() method creates a new database connection using the specified endpoint, username, password, namespace, and database name. The get_repository() method takes a repository class as input and returns the corresponding repository object. If the repository object does not already exist in the cache, then the factory will create a new one and add it to the cache.
Sample usage
sample_data = LoginMethod(
person_id="asd123123",
method_type="email",
method_data={},
email="user@example.com",
password="hashed_password",
)
repo = RepositoryFactory.get_repository(LoginMethodRepository)
result = repo.save(sample_data)
print("Done", repo.get_one({}))
-
The above code creates a new LoginMethod object and saves it to the database using the LoginMethodRepository object. It then retrieves the saved object from the database and prints it to the console.
This is just a simple example of how to use the LoginMethodRepository and RepositoryFactory classes. You can use these classes to manage any type of object in a database.
Deployment
The process described is a Continuous Integration (CI) and Continuous Deployment (CD) pipeline for a Python package using Github Actions. Here's the breakdown:
Development Phase:
Developers push their changes directly to the main branch. This branch is likely used for ongoing development work. Staging/Testing Phase:
When the team is ready to test a potential release, they push the code to a staging branch. Once code is pushed to this branch, Github Actions automatically publishes the package to the test PyPi server. The package can then be reviewed and tested by visiting https://test.pypi.org/project/rococo/. This step ensures that the package works as expected on the PyPi platform without affecting the live package. Release/Publish Phase:
When the team is satisfied with the testing and wants to release the package to the public, they create and publish a release on the Github repository. Following this action, Github Actions takes over and automatically publishes the package to the official PyPi server. The package can then be accessed and downloaded by the public at https://pypi.org/project/rococo/. In essence, there are three primary phases:
Development (main branch) Testing (staging branch with test PyPi server) Release (triggered by a Github release and publishes to official PyPi server).
Project details
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