PostgreSQL VS SQLAlchemy

While both Postgres and ClickHouse serve different purposes, the key distinction lies in how they handle replication and sharding. Postgres is primarily designed for transactional workloads (OLTP), where data consistency and durability are prioritized. On the other hand, ClickHouse is tailored for analytical workloads (OLAP), and optimized for high-speed querying and large-scale data analysis.

For your information, the tuplesort.c module flags when the Sort algorithm will use disk by setting the state to SORTEDONTAPE at this line. Disk interactions is handled by the logtape.c module.

This error happens when using a relational database, like PostgreSQL (one of the most popular, and advanced open-source SQL databases on the market).

This tutorial is going to use Postgres for its database because it is well battle-tested, but you're going to use the Prisma ORM to abstract away the database layer. This gives you the flexibility to use a variety of databases, and simplifies the API that you use to interact with it.

Yes, it's a balancing act. I think it's better to have safer defaults here, not prioritizing extra performance. People who are concerned with performance to the degree where checksums would matter need to consult configuration anyway (in many aspects, not only this one) and can disable this specific thing easily.

For the reference, change is in https://github.com/postgres/postgres/commit/04bec894a04c (so should land in PostgreSQL 18 in a year, unless it will get reverted for some reason).

PostgreSQL, commonly referred to as Postgres, is a powerful, open-source relational database management system (RDBMS) known for its robustness, extensibility, and standards compliance. It was introduced in 1986 by Michael Stonebraker and his team at the University of California, Berkeley, as part of the POSTGRES project, which aimed to address some of the limitations of existing database systems at the time.

PostgreSQL: An open-source object-relational database management system, used early on by Uber.

PostgreSQL

Link: https://www.postgresql.org/

When building web applications, authentication is almost always a core requirement. I needed something simple and secure that would let me get started quickly on new projects without over-complicating the setup. My goal was to have a solution that's easy to implement, handles email verification out of the box, and just works. In this post, I'm sharing how to build a straightforward magic link authentication system using Next.js, Auth.js, Prisma, PostgreSQL, and Resend. It's a powerful yet simple solution that accomplishes exactly what I needed, and I hope it'll be useful for your projects too.

Let’s start with a simple example of a data pipeline that reads data from a CSV file, processes it, and stores the result in a database. We’ll use Pandas for data manipulation and SQLAlchemy to interact with an SQL database.

We will define our database schema using SQLAlchemy. First, we will store a subset of the information in a single table called documents. This is to test that our database configuration is correct and avoid storing nested data now. The code is fairly simple to create a SQLite3 database with SQLAlchemy:

We've put a special focus on making sure Drizzle, Prisma, SQLAlchemy and Django ORMs work well with our platform. Common administrative and data exploration tools like DataGrip, pgAdmin and TablePlus have been put through the wringer to resolve any compatibility hiccups seen over the past few months.

SQLAlchemy: Python SQL toolkit and ORM (Object Relational Mapper). Use it to work with databases with Python objects.

We'll use the Flask-SQLAlchemy package to manage the database. This package provides SQLAlchemy support to Flask projects. That includes the Python SQL toolkit and the ORM.

SQLite for the database

I'm not familiar with the library either, but that seems to be a SQL expression executed on the database server. It's basically a copy-paste from the official documentation[0]. So no, not a lambda expression, because it's not computed in Python.

As to the extra parentheses: I bet that's a force-of-habit thing to prevent potential issues. For example, it seems Sqlite requires them for exactly this kind of default definition[1]. It could also read to nasty bugs when the lack of parentheses in the resulting SQL could result in a different parse than expected[2]. Adding them just-to-be-safe isn't the worst thing to do.

[0]: https://docs.sqlalchemy.org/en/13/core/metadata.html

[1]: https://github.com/sqlalchemy/sqlalchemy/issues/4474

[2]: https://github.com/sqlalchemy/sqlalchemy/issues/5344

Depending on the progress of your application, you may want to migrate to another framework faster than Django. Django’s ORM is not exactly the fastest out there, and, at the time of writing, it is not asynchronous. You might want to consider giving sqlalchemy, ponyorm a try.

SQLAlchemy is a widely used database toolkit for Python, providing a SQL abstraction layer covering most, if not all, your relational database needs. It's often used together with popular frameworks such as Flask and FastAPI, using either its Core library and/or its ORM components.

In SQLAlchemy, models representing database tables typically subclass sqlalchemy.orm.DeclarativeBase (this class supersedes the sqlalchemy.orm.declarative_base function). Accordingly, the abstract base class in this database wrapper component is a sqlalchemy.orm.DeclarativeBase subclass, accompanied by another custom base class providing additional dunder methods.