Performance improvements come in different forms. Some ORM frameworks support two different modes of data fetching, eager and lazy. This allows the application to define whether all data for an object graph need to be fetched immediately or pieces of data can be fetched incrementally as they are used by the application.
Some frameworks provide optimisations for generating identities via bulk reservation of identifiers from the database thus reducing the number of round trips. Surely, there are pitfalls, right? Here are some of them: As explained above, you can get much more productive by letting the framework write all the boilerplate code around SQL query generation and result parsing. However, there are cases where this might be done in a suboptimal way, which means you will have to investigate what the framework really does under the hood.
Many frameworks allow you to instruct them to log all the queries they generate. So, if you want to be really cautious and avoid performance issues in production, you might end up sanity checking all the generated queries as you add new functionality.
This can reduce the productivity benefits from code generation. However, there is a bigger more fundamental risk here. People that make use of an ORM framework might start building an application from their domain model leaving the modelling of the persistence layer as the last step, trusting that the framework will do the mapping properly. This can be a recipe for disaster especially for applications that are performance sensitive, since it might not be easy to create a mapping that will give satisfactory performance.
There is also a discussion to be made on whether some of these things are really meant to be mapped or they are best left separate.
For example, do you really want to map inheritance relationships to the persistence layer? The topic of migrating between different databases is one that comes up quite often.
Data have some form of gravity, which makes it quite hard to migrate from one database system to another one. This becomes harder the bigger the datasets are and the most critical an application is for a business, since migrations naturally come with availability risks. For example, if you stick to using the standard SQL APIs most relational databases support, the code changes needed to migrate between them will be minimal.
In many cases, these APIs are perfectly sufficient, so this is not a purely theoretical example. Object Query Languages: New query languages called Object Query Languages are provided to perform queries on the object model.
They automatically generate SQL queries against the databse and the user is abstracted from the process. To Object Oriented developers this may seem like a benefit since they feel the problem of writing SQL is solved.
The problem in practicality is that these query languages cannot support some of the intermediate to advanced SQL constructs required by most real world applications. They also prevent developers from tweaking the SQL queries if necessary. Performance: The ORM layers use reflection and introspection to instantiate and populate the objects with data from the database. These are costly operations in terms of processing and add to the performance degradation of the mapping operations.
The Object Queries that are translated to produce unoptimized queries without the option of tuning them causing significant performance losses and overloading of the database management systems. Performance tuning the SQL is almost impossible since the frameworks provide little flexiblity over controlling the SQL that gets autogenerated. Tight coupling: This approach creates a tight dependancy between model objects and database schemas.
Developers don't want a one-to-one correlation between database fields and class fields. Changing the database schema has rippling affects in the object model and mapping configuration and vice versa.
Caches: This approach also requires the use of object caches and contexts that are necessary to maintian and track the state of the object and reduce database roundtrips for the cached data. These caches if not maintained and synchrnonized in a multi-tiered implementation can have significant ramifications in terms of data-accuracy and concurrency. Often third party caches or external caches have to be plugged in to solve this problem, adding extensive burden to the data-access layer.
For starters it helps you stay DRY. Either you schema or you model classes are authoritative and the other is automatically generated which reduces the number of bugs and amount of boiler plate code.
It helps with marshaling. Furthermore, it allows you to retrieve fully formed object from the DB rather than simply row objects that you have to wrap your self. Since your queries will return objects rather then just rows, you will be able to access related objects using attribute access rather than creating a new query. Such injected SQL you are responsible for sanitizing yourself, but, if you stay away from using such features then the ORM should take care of sanitizing user data automatically.
Many ORMs come with tools that will inspect a schema and build up a set of model classes that allow you to interact with the objects in the database. If you write your data access layer by hand, you are essentially writing your own feature poor ORM. Dealing with object mapping is only one facet of ORMs. The Active Record pattern is a good example of how ORMs are still useful in scenarios where objects map to tables. I have to say, working with an ORM is really the evolution of database-driven applications.
You worry less about the boilerplate SQL you always write, and more on how the interfaces can work together to make a very straightforward system. I just work in my high level abstraction, and I've taken care of database abstraction at the same time.
Having said that, I never have really run into an issue where I needed to run the same code on more than one database system at a time realistically. However, that's not to say that case doesn't exist, its a very real problem for some developers.
You can reverse-engineer a database to create the hibernate schema haven't tried this myself or you can create the schema from scratch. Most databases used are relational databases which does not directly translate to objects. What an Object-Relational Mapper does is take the data, create a shell around it with utility functions for updating, removing, inserting, and other operations that can be performed.
So instead of thinking of it as an array of rows, you now have a list of objets that you can manipulate as you would any other and simply call obj. Save when you're done.
I suggest you take a look at some of the ORM's that are in use, a favourite of mine is the ORM used in the python framework, django. The idea is that you write a definition of how your data looks in the database and the ORM takes care of validation, checks and any mechanics that need to run before the data is inserted.
Most frameworks try to adhere to db best practices where applicable, such as parametrized SQL and such. Because the implementation detail is coded in the framework, you don't have to worry about it. For this reason, however, it's also important to understand the framework you're using, and be aware of any design flaws or bugs that may open unexpected holes.
You provide the connection string as always. The framework providers e. Personally I've not had a great experience with using ORM technology to date. I'm currently working for a company that uses nHibernate and I really can't get on with it. Give me a stored proc and DAL any day! More code sure Instead of directly interacting with the database you'll be interacting with an abstraction layer that provides insulation between your code and the database implementation.
Granted you could do this yourself, but it's nice to have the framework guarantee. In both cases, once the schema has been mapped, the ORM may be able to create recreate your database structure for you. DB permissions probably still need to be applied by hand or via custom SQL. Having built-in support for migrating and seeding a database has made it much easier to prototype more quickly, which I wrote about here. Overall, I prefer working with an ORM to not. I hope that you have learned something new today!
I am a passionate pharmacist-turned-web-developer who wants to help others make the career change. The best of web and frontend development articles, tutorials, and news. Love JS? Follow to get the best stories. Sign in. An introduction to Object-Relational-Mappers. Mario Hoyos Follow. What are some pros of using an ORM? We do, however, tend to be much more fluent in one language or another and being able to leverage that fluency is awesome!
Depending on the ORM you get a lot of advanced features out of the box, such as support for transactions, connection pooling, migrations, seeds, streams, and all sorts of other goodies.
Many of the queries you write will perform better than if you wrote them yourself. What are some cons of using an ORM? There is overhead involved in learning how to use any given ORM. The initial configuration of an ORM can be a headache.
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