How can ORM improve your SQL code writing?

The short summary is: ORM don't help you write SQL - they write SQL on your behalf. Most, if not all, ORM systems do well when the data you need to fetch is simple. But the moment you need to do complex queries - they typically end up generating bad SQL that is difficult to tune. So, don't use an ORM - just learn SQL instead. SQL is made for accessing databases - you don't need another layer of abstraction to do that.

Using an ObjectRelationalMapping offers simplicity and abstraction, allowing developers to interact with databases in their native language, which boosts productivity and is helpful for those with limited SQL skills. It automates routine tasks like CRUD operations. However, ORMs can lead to performance issues with inefficient SQL queries, especially in complex operations, causing bottlenecks. They also limit flexibility and control, as their abstraction can restrict access to advanced SQL features, sometimes necessitating raw SQL for complex queries. The choice between ORM and direct SQL depends on project needs: ORMs suit simple tasks and ease of development, while direct SQL is better for high performance and control in complex queries.

I was going to throw my 2 cents in about how an ORM doesn't actually "improve your SQL code writing" and, in fact, can and will cause resource usage and performance problems, especially when in the hands of the very people it's supposed to help, but Peter Heller did a really good job in his post in this very section. +1 million to Mr. Heller! Following this thread for a while, Thomas Kejser later added a comment to this very section that echoes my opinion even more succinctly. My summary of both would be that the use of an ORM can certainly help with the mundane task of Writing C.R.U.D. interfaces but, for everything else, use the wonderful abstraction of Stored Procedures, which also don't require a full "code release" for changes.

ORM (Object Relational Mapping) often comes with build-in security features ensuring security against attacks like SQL injection. It helps developers to write complex queries in a simple way. ORM enforces developers to follow best practices of writing the queries. Though it helps but in case of complex requirement ORM put limitations as it does not provide customization. For example, if we need to get a report from multiple table which requires complex query to be written then ORM might not be the good choice.

Na verdade usar ORM n?o ajuda a escrever SQLs melhores, ele apenas facilita a utiliza??o do banco de dados. Para melhorar nas consultas você precisa estudar como montar ela corretamente e entender as tabelas e seus relacionamentos.

As for laravel it provides simple and direct syntax so it can fetch direct results from table So it is easy to remember and optimise the code

ORM improves productivity in several ways. First, it eliminates the need to write complex SQL queries manually, saving time and reducing the chances of errors. With ORM, you can work with objects and classes, which are more intuitive and easier to understand than raw SQL code. This simplifies the development process and allows you to focus more on the application logic rather than database operations. Additionally, ORM provides features like automatic query generation, database agnosticism, and caching, which optimize performance and reduce the need for manual optimizations. Overall, ORM streamlines database interactions, enhances code readability, and boosts developer productivity.

ORM systems help developers by streamlining basic database tasks such as create, read, update, and delete (CRUD). They're particularly useful for simple operations. For complex queries, however, ORMs tend to generate inefficient and suboptimal queries, causing performance problems. The time saved on simple tasks is often negated by the effort required to fix these problems. In these cases, knowledge of SQL is critical. Developers can then manually create efficient views or stored procedures and integrate them into the application to improve performance and reliability.

Many great ORM frameworks include tools to automatically generate migrations and even generate code from schemas. So, you have a two-way automated bridge from the database specialist to the software engineer and back again. Generally, you spend less time crafting complex statements, less time debugging such statements and less time worrying about the major security concerns of such hand-crafted statements. However, this isn't a silver bullet that lets you be oblivious to the underlying driver. Your software engineers need to have a rough idea of what their code will be translated into, what is efficient and what is not, and how to performance profile the execution of generated statements.

In the data world there is a mismatch between how SQL returns data and how applications (Java/Python etc) use them. At application layer, the data is used an objects ( JSONs, hash maps, etc) but SQL mostly returns data in a more raw form (tuples). This mismatch (called impedance mismatch) is what ORM are typically used to overcome. They act like a middle “translation layer” between your database and application. Not only does it make readability better but also makes data traversal and access better in your application.

Thomas Kejser and Peter Heller described it well. I've seen so many companies that started fast thanks to ORM. Then database grew and queries become inefficient. They spent tons of money at super-big machines and licences until they hit a wall: it is so costly to optimize (rewrite all back to SQL) and pay all licences for big machines, that some went bankrupt, some were sold, and some are still stuggling. ORM is a "living mud" trap for tech companies. If database is small, you will live well. When DB grows - the company dies, slowly. Because ORM does NOT scale and is super inefficient for anything other than simplest commands (CRUD).

ORM improves readability by allowing you to work with objects and classes instead of writing raw SQL queries. Objects and classes use familiar programming concepts that are easier to understand and maintain. With ORM, you can express database operations in a more intuitive and natural way, making your code more readable and self-explanatory. The mapping between objects and database tables provided by ORM also helps in understanding the data model and relationships. Overall, ORM simplifies the code structure and makes it easier for developers to comprehend and collaborate on database-related tasks.

Hibernate, a Java ORM, mitigates SQL injection risks using parameterized queries. In traditional SQL, a query like "SELECT * FROM users WHERE id = '" + userInput + "'"; can be exploited if 'userInput' is malicious. In contrast, Hibernate uses a safer approach: String hql = "FROM User WHERE id = :userId"; Query query = session.createQuery(hql); query.setParameter("userId", userInput); Here, ':userId' acts as a placeholder. Regardless of the input's content, it's treated as a value, not SQL code. This prevents alterations to the query's structure, significantly reducing injection vulnerabilities. Other ORMs also have security mechanisms in place to avoid injection attacks or at least make it tough to exploit.

ORM can improve security in several ways. Firstly, ORM frameworks often provide built-in security features such as parameterized queries and input sanitization, which help prevent SQL injection attacks. By automatically handling the generation of SQL queries, ORM frameworks can also ensure that best practices for secure coding are followed consistently. Additionally, ORM frameworks often include features like authentication and authorization mechanisms, which can help enforce access controls and protect sensitive data. By abstracting away the low-level database interactions, ORM can help developers focus on security measures and reduce the likelihood of introducing vulnerabilities.

ORM improves portability by abstracting away the specific details of the underlying database system. With ORM you can write your code using a consistent set of object-oriented concepts and APIs, regardless of the specific database being used. This means that you can easily switch between different database systems without having to rewrite or modify your code extensively. ORM frameworks typically provide support for multiple database systems, allowing you to seamlessly transition between them. This portability is particularly useful when developing applications that may need to be deployed on different platforms or environments. So, ORM simplifies the process of adapting your code to different databases making it more portable and flexible.

By abstracting away the driver, you make your code portable to any driver, but not always. Most ORMs let you reach into the code generation for things such as type overriding. Once you start doing this, your code portability becomes more and more diminished. Additionally, generally once you begin a deployment, you're generating and executing migrations. Migrations are tracked in source and their execution is tracked in the target database they're executed against. This violates stricter definitions of portability. In this case, you'd have to purge the existing migrations from source, regenerate your initial migration on the new driver and then figure out a way to migrate your existing data, which won't be trivial.

ORM provides code reusability and ease of maintenance of code in order to adapt to changes. Moreover, ORM can automatically generate queries at an abstract level to perform different database operations.

ORMs can be incredibly powerful, and eliminate a lot of the up front work when creating a new product. This being said, you should be careful when blindly making the decision to adopt an ORM for your application. Abstractions like ORMs have a purpose and can be incredibly useful, but they aren't a one-size-fits-all solution for every application. What I find is that ORMs often simplify your codebase for simple tasks. For CRUD applications, they're extremely useful. However, chances are if you need to perform a complex SQL query, it'll be even more difficult to do the same with an ORM. SQL gives you full control of your data, and in some applications where this is important it makes more sense to use a SQL generator than an ORM.

Understanding Object-Relational Mapping (ORM) tools can significantly enhance your SQL code writing. ORMs abstract database interactions, streamlining code, reducing errors, and accelerating development by providing a higher-level interface that handles SQL operations behind the scenes. They enable you to focus more on application logic, improving overall efficiency within your development environment.

As a person that makes money from performance tuning SQL, anything that makes SQL slower means more money for me, so yes ORM is great. Also AWS and Azure will love you spending money on much bigger servers than you need.

Using ORMs presents a nuanced set of advantages and drawbacks, which requires you to make a decision whether or not to use them based on the specific context of your project. In my experience, ORMs excel in facilitating straightforward data retrieval tasks. However, when confronted with complex and resource-intensive queries, opting for manual SQL composition often proves to be a more efficacious approach. In such instances, the precision and optimization achievable through direct SQL composition outweigh the convenience offered by the ORM, necessitating a discerning evaluation of the trade-offs involved.