![]() ![]() Thus, the wildcard import at the bottom ensures that all of our models are imported at runtime.ĭefining a model is easy. Until a model is imported, SQLAlchemy won't know that it exists, even though we created the definition. As you'll see soon, each of our models resides in its own file. You might also notice the seemingly odd import at the bottom of the file, but it serves an important purpose. We'll see a lot of the global db variable throughout the application, because we'll import it wherever we need to interact with the database. The SQLALCHEMY_ECHO value is useful when debugging – when set to true, every SQL statement is logged, so you can see what's happening at every step. This ultimately depends on the environment variables we saw in Part 2, such as POSTGRES_USER and POSTGRES_HOST. The config value SQLALCHEMY_DATABASE_URI tells Flask-SQLAlchemy how to connect to the database. This is a reduced version of the init file containing just the minimum needed to set up Flask-SQLAlchemy. The app/_init_.py file contains all of the configuration necessary to get started.Īpp.config = create_db_uri() Luckily, setting up SQLAlchemy to work with a Flask application is very straightforward, thanks to the Flask-SQLAlchemy package. If you haven't used one before, using an ORM will allow us to work in terms of objects, instead of working with messy raw SQL strings in the Python code. We're going to fix that problem now with the help of SQLAlchemy – by far the most popular ORM library for Python. Part III: Flask, SQLAlchemy, and Postgresīack in the first post, we built a working Google search scraper, but we didn't have anywhere to put the results.Testing the NGINX and Flask configuration.Understanding how NGINX and Flask work together.Part II: Production Ready Deployment with NGINX, Flask, and Postgres.Using a proxy network for scraper requests.Setting up Puppeteer on an AWS instance.Part I: Building the Google Search Scraper.You can find the complete code on GitHub. Then we'll move to setting up our first real route handler, so that the scraper we built in part one can report its results. In this post, we'll set up SQLAlchemy and explore a few of the performance pitfalls that lurk behind the scenes. In the last post, we set up NGINX and Flask using Docker, with both a local development version, as well as a version suitable for production deployment. The example project is a Google rank tracker that we'll build together piece by piece, but you can apply these lessons to any kind of SaaS app. If you haven't read the first post in the series, this is a step by step guide on building a SaaS app that goes beyond the basics, showing you how to do everything from accept payments to manage users. Once you've finished this post, you'll have a foundation on which to build the data model of your applications, using SQLAlchemy and Postgres. ![]()
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