Most popular Postgres ORMs
Tags: postgres, orm, api, database
Published: 2024-05-29 15:00:00

This blog post will discuss popular Postgres ORM tools and other alternatives.

Those who are not familiar with ORMs should read “What is an ORM and when developers should and shouldn't use it", but to recap Object-Relational Mappers (ORMs) abstract the database interactions in applications by converting database records into objects and vice versa.

This blog post includes:

* Introduction

* Postgres ORM tools

* Popular Postgres ORMs

- Sequelize
- SQLAlchemy
- Active Record
- Eloquent
- Hibernate
- GORM
* Postgres ORMs - Challenges and limitations

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## Introduction
[PostgreSQL](https://www.postgresql.org/) (commonly also referred to as Postgres) is one of the most popular open-source relational database management systems (RDBMS). Renowned for its robustness, extensibility, and adherence to SQL standards, PostgreSQL has positioned itself as one of the most reliable and feature-rich relational databases available. Its support for complex data types and advanced indexing methods makes it suitable for handling diverse data requirements. Postgres is also recognized for its extensibility through the use of custom functions, data types, and procedural languages.

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## Postgres ORM Tools
PostgreSQL supports various ORM tools for different programming languages, with some popular ones being SQLAlchemy for Python, Hibernate for Java, and Sequelize for TypeScript/Node.js. These tools provide a set of abstractions that allow developers to interact with the PostgreSQL database using object-oriented concepts.

This enables developers to model database entities as objects and manipulate them using familiar programming language constructs, reducing the need for raw SQL queries. It also enhances code readability and maintainability while also promoting a more natural development workflow.

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## Popular Postgres ORMs
Here are some popular PostgreSQL ORMs for specific programming languages:

- Sequelize (for TypeScript): Sequelize is a widely used ORM for TypeScript and JavaScript. It supports PostgreSQL and a few other databases, and provides a powerful set of features for working and querying databases.
- SQLAlchemy (for Python): SQLAlchemy is a robust ORM for Python, offering a flexible and expressive way to interact with PostgreSQL databases. It supports various PostgreSQL-specific features and provides a powerful SQL expression language.
- Active Record (for Ruby): Active Record, part of the Ruby on Rails framework, is a popular ORM for Ruby. It simplifies database interactions and supports PostgreSQL seamlessly, allowing developers to work with Ruby classes representing database tables.
- Eloquent (for PHP): Eloquent is the ORM included in the Laravel PHP framework. It simplifies database operations and supports PostgreSQL, offering an elegant syntax for querying databases using PHP.
- Hibernate (for Java): Hibernate is a widely used ORM for Java applications. It supports PostgreSQL and provides a robust framework for mapping Java objects to database tables, along with powerful querying capabilities.
- GORM (for Go): GORM is a popular ORM for the Go programming language. It offers support for PostgreSQL and provides a simple and concise syntax for defining and querying database models in Go.

## Postgres ORMs - Challenges and limitations

- Performance Optimization: ORM-generated queries may not always be optimized for specific use cases, requiring developers to fine-tune queries manually directly in their code for better performance. The performance problems tend to worsen as the application scales.‍
- Learning Curve: ORMs are specific to a particular programming language and working with an ORM introduces a learning curve. Developers need to understand the intricacies of the ORM tool, programming language framework, and PostgreSQL to use them effectively.
- Abstraction Leaks: Abstraction leaks often occur as applications move beyond basic CRUD queries. This leads to messy code, unexpected behavior and performance issues. Developers must be cautious and carefully consider their application requirements, architecture, and data access patterns.
- Schema Changes and Migrations: Managing schema changes and migrations can be challenging, particularly in large applications. Developers need to plan for seamless database evolution as the application evolves.
- Modern application architectures: ORM tools may not seamlessly align with modern cloud and serverless architectures. As applications scale, ORM's centralized management of database connections and transactions can become a bottleneck, leading to increased latency and decreased overall throughput. Similarly, in serverless setups, ORM's heavyweight abstraction may struggle with short-lived compute instances. Developers need to carefully evaluate the trade-offs between ORM convenience and cloud-native benefits when architecting applications. 


## Postgres ORM alternative with Cloud based Data APIs
Neurelo’s programming interface for databases addresses many of the challenges associated with Postgres ORMs such as N+1 queries and leaky abstractions.

## Full Transparency of Queries: 
Traditional ORMs obscure the SQL layer. Neurelo provides full visibility into the queries being executed. This makes debugging much easier but also offers developers choices to optimize database interactions.

## Query Strategies: 
Neurelo minimizes the number of queries made to the database using intelligent techniques like eager loading (while also offering the option of lazy loading, when needed) which retrieves the data in a single query using joins. This solves N+1 query issues associated with many traditional ORMs.

## Override Capability for Queries: 
Flexibility and control are key in application development, and hence Neurelo allows developers to override the default query generation behavior for APIs, when needed, as per the applications requirements. This feature is particularly useful for optimizing performance or handling edge cases as the application evolves over time, offering both short-term benefits – get started quickly with the queries that Neurelo has optimized to begin with, and long-term flexibility – debug, understand, and update these queries over time as your data, use cases, and applications demand. 

## Extending Neurelo APIs for Complex Queries: 
Neurelo’s [AI-powered custom APIs](https://docs.neurelo.com/definitions/custom-query-endpoints-as-apis/ai-assisted-query-generation?_gl=1*1waumcc*_gcl_au*NzIwNzAxNzY1LjE3MTQ0MzIxMzI.) go beyond the basic CRUD operations (typically offered by ORM frameworks) with auto-generated APIs that provide the ability to work on single entities (tables/collections) as well as advanced join read/write tasks that go across multiple entities. This means developers can craft bespoke solutions for specific requirements without compromising the integrity or efficiency of the application, thus solving “leaky abstractions”.

## Schema-as-code and alignment to software development lifecycle
By treating database schemas as code, Neurelo empowers developers to manage schema changes seamlessly within their existing codebase, enhancing collaboration, [version control](https://docs.neurelo.com/definitions/branches-and-commits?_gl=1*yawpku*_gcl_au*NzIwNzAxNzY1LjE3MTQ0MzIxMzI.), and automation. With [Schema as Code](https://www.neurelo.com/features/schema-as-code), developers can define database schemas using familiar language and version control systems, enabling them to track changes, review diffs, and rollback modifications with ease. This approach revolutionizes schema management by integrating it into the software development lifecycle, streamlining workflows, and ensuring consistency between application code and database schema. Neurelo's Schema as Code concept represents a paradigm shift in database development, offering a modern and agile approach to schema management for the next generation of applications.

[‍Try Neurelo](https://dashboard.neurelo.com/register) now and never experience ORM limitations.