April 2019 Summaries

In this post, the author presents benchmarks comparing the performance of different data access methods in Python applications. The methods tested include Django ORM, SQLAlchemy, MongoDB, PostgreSQL with psycopg2 and asyncpg, and EdgeDB. The results show that while some methods perform better than others, the overall performance is comparable across all tested methods. The author also provides a detailed walkthrough of how to use each method in practice, including sample code snippets for each one. This makes it easier for readers to understand how these different data access methods work and how they can be used effectively in their own Python applications. Finally, the author discusses some potential improvements that could be made to further optimize performance with certain methods, such as using more efficient data encodings or fine-tuning query execution plans. This suggests that there is still room for improvement when it comes to maximizing the efficiency of data access operations in Python applications. Overall, this post provides valuable insights into the strengths and weaknesses of various data access methods in Python, which can help developers make more informed decisions about which method to use in their own projects.

EdgeDB 1.0 alpha 1 introduces a powerful query language, EdgeQL, designed for performance and usability, with notable features like stable syntax, enhanced JSON support, and a robust type system that avoids NULL values by treating missing data as empty sets. The release comes after incorporating user feedback from its Technology Preview and is now ready for evaluation. EdgeDB aims to simplify data access and manipulation by offering a modern database solution that supports meta-programming, a new binary protocol, and native Python client support, with future plans to extend to NodeJS and Golang. The blog post outlines a comparison of EdgeDB with other databases and ORMs, showcasing its efficiency in building a simple movie catalog app. Benchmarks reveal that EdgeDB performs competitively, requiring less code to set up schemas and queries compared to traditional methods, while supporting both HTTP and GraphQL interfaces. Although PostgreSQL with asyncpg currently shows the best performance, EdgeDB's unique capabilities, such as handling deep relation hierarchies more easily, highlight its potential for future enhancements and optimizations in upcoming releases.