Flask with SQLAlchemyįlask is a lightweight micro-framework that is used to build minimal web applications and through third-party libraries, we can tap into its flexibility to build robust and feature-rich web applications. Having introduced the ORM, let us build a simple Flask API that interacts with a Postgres database. Some big names in the industry that rely on SQLAlchemy include Reddit, Yelp, DropBox and Survey Monkey.
SQLAlchemy can also be used with Oracle and the Microsoft SQL Server. One good example is Pyscopg which is a PostgreSQL implementation of the DBAPI which when used in conjunction with SQLAlchemy allows us to interact with Postgres databases.įor MySQL databases, the PyMySQL library offers the DBAPI implementation require to interact with them.
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While SQLAlchemy ORM makes our applications database-agnostic, it is important to note that specific databases will require specific drivers to connect to them. In other words, SQLAlchemy Core provides the means to generate SQL queries. It is also important to note that the SQLAlchemy ORM is built on top of SQLAlchemy Core - which handles the DBAPI integration and implements SQL. The DBAPI was created to establish consistency and portability when it came to database management though we will not need to interact with it directly as SQLAlchemy will be our point of contact. SQLAlchemy wraps around the Python Database API (Python DBAPI) which ships with Python and was created to facilitate the interaction between Python modules and databases. SQLAlchemy is an ORM written in Python to give developers the power and flexibility of SQL, without the hassle of really using it. We also need to be familiar with ORMs and learn them in order to use them more efficiently and this introduces a bit of a learning curve. Through an ORM, the changes we would need to make would be limited to just changing a couple of configuration parameters.Įven though ORMs make our life easier by abstracting the database operations, we need to be careful to not forget what is happening under the hood as this will also guide how we use ORMs. If our SQL was integrated at multiple points in our application, this will prove to be quite the hassle.
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Say, if we used SQL in our application to connect to a MySQL database, we would need to modify our code if we were to switch to an MSSQL database since they differ in syntax. ORMs abstract databases from our application, enabling us to use multiple or switch databases with ease.
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This eases the process of developing software and reduces the chances of making manual mistakes when writing plain SQL code.Īnother advantage of using ORMs is that they help us write code that adheres to the DRY ( Don't Repeat Yourself) principles by allowing us to use our models to manipulate data instead of writing SQL code every time we need to access the database. That being said, as it's fairly easy to map an object to a database, the reverse is also very simple. In object-oriented programming languages, objects aren't that different from relational entities - they have certain fields/attributes that can be mapped interchangeably. Object-relational mapping, as the name suggests, maps objects to relational entities. In this post, we will delve deeper into ORMs and specifically SQLAlchemy, then use it to build a database-driven web application using the Flask framework. Naturally, as time passed, Object Relational Mappers (ORMs) came to be - which enable us to safely, easily and conveniently connect to our database programmatically without needing to actually run queries to manipulate the data.
Though initially done via dedicated SQL tools, we've quickly moved to using SQL from within applications to perform queries.
Generally, we use the Structured Query Language (SQL) to perform queries on the database and manipulate the data inside of it. Databases are a crucial part of modern applications since they store the data used to power them.
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