![]() ![]() There are various implementations of the connection pool pattern available on SQLAlchemy. Besides that, this pattern allows easier management of the number of connections that an application might use simultaneously. In the case of database connections, opening and maintaining new ones is expensive, time-consuming, and wastes resources. ![]() The main reason why programs take advantage of this design pattern is to improve performance. That is, instead of spending time to create objects that are frequently needed (like connections to databases) the program fetches an existing object from the pool, uses it as desired, and puts back when done. Object pools are used as caches of pre-initialized objects ready to use. SQLAlchemy Connection PoolsĬonnection pooling is one of the most traditional implementations of the object pool pattern. To learn more about the options available to create SQLAlchemy engines, take a look at the official documentation. PostgreSQL, MySQL, Oracle, Microsoft SQL Server, and SQLite are all examples of engines that we can use alongside with SQLAlchemy. Since SQLAlchemy relies on the DBAPI specification to interact with databases, the most common database management systems available are supported. This process is postponed to when it's needed (like when we submit a query, or when create/update a row in a table). Note that, creating an engine does not connect to the database instantly. It also defines that it will use usr and pass as the credentials to interact with the sqlalchemy database. This example creates a PostgreSQL engine to communicate with an instance running locally on port 5432 (the default one). To create an engine and start interacting with databases, we have to import the create_engine function from the sqlalchemy library and issue a call to it: from sqlalchemy import create_engine The following two sections will explain what these two concepts are, but for now it suffices to say that SQLAlchemy uses them to interact with DBAPI functions. Engines, on SQLAlchemy, are used to manage two crucial factors: Pools and Dialects. Whenever we want to use SQLAlchemy to interact with a database, we need to create an Engine. ![]() Also, to learn about what other database engines we can use (like MySQL or Oracle), take a look at the official list of database interfaces available. To better understand the DBAPI specification, what functions it requires, and how these functions behave, take a look into the Python Enhancement Proposal that introduced it. Other Python drivers communicate with PostgreSQL as well, but psycopg is the best candidate since it fully implements the DBAPI specification and has great support from the community. In this article, we are going to install and use the most popular PostgreSQL DBAPI implementation available: psycopg. Consequently, whenever we use a Python module that adheres to the specification, we can rest assured that we will find these functions and that they will behave as expected. Although we won't interact with this API directly-we will use SQLAlchemy as a facade to it-it's good to know that it defines how common functions like connect, close, commit, and rollback must behave. The Python DBAPI (an acronym for DataBase API) was created to specify how Python modules that integrate with databases should expose their interfaces. The following sections will introduce important concepts that every Python developer needs to understand before dealing with SQLAlchemy applications. ![]() Besides that, with SQLAlchemy, boilerplate code to handle tasks like database connections is abstracted away to let developers focus on business logic.īefore diving into the ORM features provided by SQLAlchemy, we need to learn how the core works. SQLAlchemy provides a standard interface that allows developers to create database-agnostic code to communicate with a wide variety of database engines.Īs we will see in this article, SQLAlchemy relies on common design patterns (like Object Pools) to allow developers to create and ship enterprise-grade, production-ready applications easily. Most of the times, this library is used as an Object Relational Mapper (ORM) tool that translates Python classes to tables on relational databases and automatically converts function calls to SQL statements. SQLAlchemy is a library that facilitates the communication between Python programs and databases. ![]()
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