SQLAlchemy in Tornado to be Async
Now that we have a foundational understanding of the basic concepts, let’s move on to the steps needed to get SQLAlchemy running asynchronously in a Tornado application.
Step 1: Set the environment
Make sure you have the following options installed before you begin.
- Python
- Tornado
- SQLAlchemy
- A compatible database engine (e.g., SQLite, MySQL, PostgreSQL)
You can install Tornado and SQLAlchemy with pip:
pip install tornado sqlalchemy
Step 2: Run the Tornado Application
To create a Tornado application Start by defining a basic Tornado web application. This will be the basis for integrating SQLAlchemy asynchronously.
Python3
import tornado.ioloopimport tornado.webclass MainHandler(tornado.web.RequestHandler): async def get(self): self.write("Hello, Tornado!")def make_app(): return tornado.web.Application([ (r"/", MainHandler), ])if __name__ == "__main__": app = make_app() app.listen(8888) tornado.ioloop.IOLoop.current().start() |
Step 3: Configure SQLAlchemy
Next, you will need to configure SQLAlchemy to work with your chosen database engine. SQLAlchemy supports multiple databases, so make sure you have the correct database driver installed. Here is an example of configuring SQLAlchemy for SQLite:
Python3
from sqlalchemy import create_enginefrom sqlalchemy.orm import sessionmaker# Create a SQLAlchemy engineengine = create_engine("sqlite:///mydatabase.db")# Create a session factorySessionFactory = sessionmaker(bind=engine) |
Step 4: Use Asynchronous SQLAlchemy Queries
To make SQLAlchemy asynchronous, you will need to use coroutines and Tornado asynchronous preparers. In this example, we use the @gen.coroutine decorator to tag the query_database. In the coroutine, we create a session, execute an asynchronous database query, and close the session on the finally block. Let’s create an asynchronous task for a simple database query:
Python3
from tornado import gen@gen.coroutinedef query_database(): # Create a session session = SessionFactory() try: # Asynchronously query the database result = session.query(User).filter(User.name == "Alice").first() # Process the result if result: print("User found:", result.name) else: print("User not found") finally: # Close the session session.close() |
Step 5: Add Asynchronous SQLAlchemy to Tornado Handlers
To use asynchronous SQLAlchemy queries in your Tornado handler, simply call the query_database function in the Tornado handler. Be sure to use the yield keyword when calling an asynchronous function to wait for the result:
Python3
class UserHandler(tornado.web.RequestHandler): async def get(self): # Call the asynchronous database query await query_database() self.write("User query complete")if __name__ == "__main__": app = make_app() app.listen(8888) tornado.ioloop.IOLoop.current().start() |
When you call await query_database(), you allow the Tornado event loop to continue its other tasks while it waits for the database query to complete.
How to make SQLAlchemy in Tornado to be async?
In the world of internet improvement, asynchronous programming has grown in importance to efficiently manage excessive degrees of concurrency. Tornado is a popular asynchronous web framework for Python, regarded for handling thousands of concurrent transactions easily. SQLAlchemy, alternatively, is an effective Object-Relational Mapping (ORM) library that simplifies database interactions in Python applications. Combining the power of Tornado and SQLAlchemy can result in sturdy and high-performance internet applications. In this article, we explore how to make SQLAlchemy work easily in a Tornado application, whilst leveraging the power of asynchronous programming.