ORM (Object-Relational Mapping)
Choose to use an ORM, like Sequelize, Django ORM, Hibernate, or Entity Framework if:
- You need Rapid Development: ORM allows you to generate database code more efficiently.
- Your Data is Structured and Relational: Perfect for applications with clearly defined data models.
- You Prefer to Focus on Business Logic: The ORM handles database queries, letting you concentrate on the application.
- You're Looking for Ease of Maintenance: Updating data models is simpler, which makes maintenance easier.
- You Want Database Portability: It facilitates switching between different database systems without altering the application code.
Ideal for: Enterprise applications, inventory management, customer relationship management (CRM) systems, fast web applications.
No ORM (Pure SQL or Direct Queries)
Opt for not using an ORM and prefer pure SQL or direct queries if:
- You Require Total Control and Optimization: Direct database access allows for specific optimizations.
- Your Queries are Highly Complex and Customized: Ideal when ORM abstractions limit your querying capability.
- Performance is Critical: In high-volume applications where every millisecond of latency matters.
- You Have an Experienced SQL Team: That can handle complexities and optimize data access directly.
- You Need Maximum Flexibility in Data Handling: Particularly in systems with non-traditional or highly dynamic data structures.
Perfect for: High-load systems like online games, financial applications, big data analytics, high-performance platforms.
In summary, choose an ORM for rapid development, easy maintenance, and database portability. Opt for pure SQL if you need total control, query optimization, and are working on high-performance projects with complex or dynamic data. The choice depends on your project's needs, team skills, and performance goals.
Top comments (18)
The trouble is I've never met an application that didn't require at least a little SQL query optimization. The risk of producing extremely long-running queries is very high with ORMs. We're not talking about rare corner cases here.
You're right that ORMs can lead to inefficient queries. It's key to use profiling tools to identify and optimize these. Modern ORMs allow custom SQL for specific needs, balancing ease and control.
The key is to know what you're doing. And know your tool. You don't need to profile. I've been using them for decades
ORM may initially provide a comfortable development experience, but its efficiency diminishes when dealing with a user base as small as 10,000. The slowdown becomes noticeable, especially with multiple joins or lengthy logic. In my personal experience with Laravel PHP projects, I opted to move away from ORM.
While direct SQL introduces challenges, such as data marshaling between JSON and objects or the serialization and de-serialization process, using query builders like Medoo PHP proved to be a wise choice. These tools are lightweight and alleviate some of the burdens, allowing for a smoother development process.
However, in situations where extensive processing is required, like generating invoices for over 10,000 users, employing stored procedures becomes a preferable solution.
Your experience highlights that there's no one-size-fits-all solution. Choosing between ORM and direct SQL depends on your project's specific needs and performance requirements. Query builders and stored procedures can be effective alternatives for certain situations.
You are right.
And can I use both methods in the same project?
Yes, if you wish, you can combine ORM and pure SQL in a project to maximize efficiency: use ORM for rapid development and simple maintenance, and pure SQL for complex queries and performance optimization. This gives you the best of both worlds: productivity and detailed control as needed.
Some ORMs have entity tracking and let's say you update via raw query an entity that's already tracked, your tracking object won't be updated and this cause a lot of confusion and debugging pain. That's why I would avoid combining both.
I understand your concerns, GosuTeacher. However, many modern ORMs allow for the execution of custom SQL queries while still maintaining entity tracking and management. This offers us flexibility for complex cases without losing the advantages of the ORM. The key is to use this feature wisely, ensuring that complex operations integrate well with the overall data management. This way, we can enjoy the best of both worlds: the simplicity of the ORM and the precision of pure SQL for specific needs.
In my experience, ORMs are great until they're not, and then you're usually stuck with a very painful migration.
For example, at some point in my career I joined a team that modeled their data (highly normalizable, low cardinality) using an ORM.
That was pretty awesome, as we defined the model, added annotations and let the magic happen.
At some point we needed to introduce a fundamental requirement, we needed the database to be a time machine, meaning, You should be able to go to any point in time and get the system model at that point in time.
That simple requirement, threw ORM out the window, it actually threw referential integrity on the DB out the window.
Point of the story,
If you're using a well defined schema (and you always should) and a highly structured architecture,
Don't use an ORM.
Your anecdote emphasizes the importance of considering future requirements when choosing technology tools. While ORMs simplify the start, they may limit in complex scenarios. Keeping system design flexible to integrate different approaches as needed is important.
Ripped out more orm than written and have been using them since the heyday of toplink…. Yes rapid prototyping, but you pay in pounds of flesh as complexity creepes in as idea matures. Mitigation by maintenance of separation of concerns allows replacement with sql. If you are writing JQL (or equivalent), then you are probably on the edge of a maintenance headache for your replacement.
Scott, you're spot on. While ORMs make for a quick start, they often complicate the project as it grows. The solution is to plan for a flexible architecture in advance that allows for strategy shifts without effort. Designing with modularity from the start is important, preparing for the possibility of integrating direct SQL when needed, ensuring the project remains scalable and maintainable.
Anyone had any negative experiences with a hybrid approach? ORM for simple CRUD operations and the rest with SQL? My only concern is that if the database would change, hand written queries would have to be adapted.
Boris, for the hybrid approach, organizing SQL in specific modules and using migration tools can significantly ease adapting to database changes. This strategy helps balance ORM simplicity for CRUD with SQL's flexibility for complex tasks.
You can use pure SQL with any good orm
Absolutely, Mark. A good ORM should allow the flexibility to execute pure SQL when needed, offering the best of both worlds: ORM's convenience for standard operations and SQL's power for complex queries.