I am a little bit confused about Dataset compared to ORM (NHibernate or Spring.Net). From my understanding the ORM sits between the application layer and the database layer. It will generate the SQL commands for the application layer. Is this the same as what Dataset does? What is the difference between the Dataset and ORM? What are the advantages and disadvantages for these two methods? Hope the experts in here can explain something.
Thanks,
Fakhrul
There is a BIG difference between them, first of all about the programming model they represent:
The Dataset is based on a Table Model
An ORM (without specify a particular product of framework) is based and tends to a Domain Model.
There is another kind of tool which could be used in data scenario, this kind of tool is a Data Mapper (eg. iBatis.NET)
As others answers before me, I think it's important to view what Microsoft says about Dataset and better what Wikipedia says about ORM, but I think (this was for me at beginning) it's more to understand the difference between them in terms of model. Understanding that will not only clarify the choises behind but better, will do too easy to approach and understand a tool itself.
As little explanation it's possible to say:
Table Model
is a model which tends to represent tabular data in a memory structure as close as possible (and even as needed). So it's easy to find implementations which implements concepts as Table, Columns, Relations in fact the model is concetrate on the table structure, so object orientation is based on that not on data itself. This model could has its own advantages, but in some case could be heavy to manage and difficult to apply concepts on contained data. As previous answers says, implementations like Dataset, let, or better, force you to prepare (even if with a tool) needed SQL instructions to perform actions over the data.
ORM
is a model which (as mendelt says before me..) where Objects are mapped directly to database objects, principally Tables and Views (even if it's possible to map even functions and procedures too). This is done in 2 ways generally, with a mapping file which describes the mapping, or with (in case of .NET or Java) code Attributes. This model is based on Objects which represents the data, so object orientation could be done on them as in normal programs, it's clear with more attention and caution in certain cases, but generally, when you are confident with ORM it could be a really powerfull tool! Even ORM could be heavy to manage if it's not managed and designed well, or better understood weel, so it's important to understand techniques, but I can say with my experience that ORM is a really powerfull tool. In ORM, the tool principally it's responsible to generate the SQL instructions needed as operations are done in code, and in more cases ORMs has a middle language (like HQL) to perform operations on Objects.
MAPPER
A mapper is a tool which doesn't makes things like an ORM, but, maps hand written SQL instructions to an Object Model. Thi kind of tool could be a better solution when it's needed to write by hand SQL instructions but It's wanted to designe an application Object model to represent data.
In this "model" objects are mapped to instruction and described in a mapping file (generally an Xml file as iBatis.Net or iBATIS (java) does). A mapper let you define granular rules in SQL instructions. In this scenario could be easy to find some ORM concepts as for example session management.
ORM and Mappers let to apply some very interesting Design Patterns, which could be not so easy to apply in the same way to a Table Model and in this case to a Dataset.
First of all excuse me for this long answer and about my poor english, but for me, an answer like this makes me in past to understand well the difference between this models and then between implementations.
the Dataset class is definitly not an ORM; an ORM maps relational data with an object oriented representation.
It can be regarded as some kind of 'unit of work' though, since it keeps track of the rows that have to be deleted/updated/inserted.
ADO.NET DataSet =
http://msdn.microsoft.com/en-us/library/zb0sdh0b(VS.80).aspx
ORM =
http://en.wikipedia.org/wiki/Object-relational_mapping
(Example Developer Express
XPO,DataObjects.NET)
ORM is based on mapping between objects and tables. Not the case for this dataset. Dataset is itself in a way directly to the table. ORM is based on a minimum of SQL script. But enough to use the dataset you write SQL clause. Dataset in this case is not an ORM.
Look at dataset and ORM.
No, Datasets are not ORM's. They may look like orms because datasets map tables to objects just like ORM's the main difference lies in what objects they map to.
Datasets have their own table and row object types that closely resemble the structure of the database. You're rebuilding part of the database's relational model in objects. Restricting these objects into something resembling a relational database gets around some of the problems inherent in mapping a database to an object model.
An ORM maps the tables and rows from the database into your own object model. The structure of your object model can be optimized for your application instead of resembling a relational database. The ORM takes care of the difficulties in transforming a relational model into an object model.
DataSet is a DTO, a data transfer object. DataSet itself can't do anything. You can use a DataAdapter (of the provider used) to produce sql or call predefined queries, though it still isn't doing anything.
Related
I have read up on both, but it has just confused me more. I have tried to find the differences (and similarities), but am unable to convince myself. Both of them are an intermediate layer between the business logic and the database. Is there a difference or are they the same?
ORM (Object/Relational Mapper):
It is a library/tool that executes the input SQL query (some ORMs also generate the query for you) and converts (maps) output DataReader (or equivalent in other languages) to your strongly typed objects. This is basic feature of any ORM.
That said, it works as layer between your data storage and your application.
Core role of ORM is mapping; it always (mostly?) return strongly typed objects. In rare cases, it may return values in basic data types provided by language like int, string etc.
This is application agnostic; except that you have to configure this separately per application/data store.
This only deals with RDBMS.
Some advanced ORMs (full-ORM) can be used as DAL; this is mostly a design decision.
Being application agnostic, this cannot implement your specific persistence logic.
DAL (Data Access Layer):
It is a layer that handles all your data needs. But this is different from ORM.
Actually, this may use ORM internally for any RDBMS communication. Although DAL can be designed without using any ORM also.
DAL may return strongly typed objects but not always necessary.
DAL may communicate with any form of data store including Web API, XML, RDBMS.
Mapping may or may not be the part of DAL depending on how it is being designed.
This is application specific.
There are different patterns available to implement DAL as Data Access Object or Repository.
Being designed for specific application, this may include your specific persistence logic like data encryption.
ORM is a general programming approach centered around dealing with data in systems in a way that presents them (and lets you work with them) as objects in your programming language of choice. Even if the data comes from a source that has nothing to do with your chosen programming language. The abstract concept of interacting with data through an object "veneer" is ORM.
DAL on the other hand is simply the name for the entire collection of things that a programming language offers that makes working with stored data easier. It's effectively a convenient term for talking about "all the APIs for dealing with stored data".
And to tie it together: "The Data Access Layer for your chosen programming language may use Object-Relational mapping."
In Java, the most common scenario when one refers to Object Relational Mapping is the act of mapping tables into Java objects. One very popular Object Relational Mapper is Hibernate. Let's say you have a table row of a Car. Surely it has many columns such as Year, Make, Model, etc. An ORM would map this table row into a Car.java class with data members that correspond to the columns of the row.
The mapping occurs inside the Data Access Layer, which consists of class(es) that perform the conversion. Additionally, you may have service classes that provide certain Java objects dependent on certain criteria (queries). One example would be let's say you have an application with Users. You would likely have a UserService.java class with a getAllUsersSortedByLastName() class to perform that operation. This would involve utilizing the active db connection, performing the proper query to get the rows of users, put those users rows into User Java objects, put those User.java objects in a List, and finally process that list if needed before returning. The ability to do what I describe would all be in the Data Access Layer of your application. You would use an ORM to perform the mapping between table rows and Java objects and vice versa.
I like to use a GUI application to design databases using ERD. Currently I am using the EER Diagram of the free MySQLWorkbench.
Once I like the way the ERD looks, I Forward Engineer the ERD in MySQLWorkbench to create the actual database. Then I introspect the MySQL database with django-admin.py inspectdb to Reverse Engineer into an output of a Python snippet code for Django's models.py.
But then I have to take the inspectdb output and manually edit it to my liking. One particular part I really don't like to do is manually eliminating each join table from a many-to-many relationship.
Is there a good (and preferably free) GUI ERD design program out there specifically designed for Django?
If you wish to design your models at the database level, in the way that you're describing, you are going to need to do exactly that: design your SQL and then convert that into Django models.
This is not the normal way of designing a Django application: typically you would design the models as you wanted them to be, and only put a lot of effort into schema design if you need to resolve some performance problems. Django models aren't really meant to be an abstraction of a relational database: they're meant to be an abstraction of your application's persisted objects, which happens to be implemented on top of a relational database.
There is nothing wrong with wanting/needing to do an explicit schema design, but it makes you a bit of an outlier (most web devlelopers don't), hence the difficulty you're having finding tools suited to your needs.
The closest thing is the graph_models command which is part of Django command extensions. This lets you visualize your models (you still write them in python code, but the visual representation will help you iterate faster).
It is not usual to design a database layout for django apps as such, and doing so is likely to lead to a sub-optimal design.
Instead, just design your model classes, taking account of how you will query them, and the way that ForeignKey (and the other relations types) work. If you don't do this, you are likely to find that your app suffers from conceptual mismatch.
Consider a database(MSSQL 2005) that consists of 100+ tables which have primary keys defined to a certain degree. There are 'relationships' between tables, however these are not enforced with foreign key constraints.
Consider the following simplified example of typical types of tables I am dealing with. The are clear relations between the User and City and Province tables. However, they key issues is the inconsistent data types in the tables and naming conventions.
User:
UserRowId [int] PK
Name [varchar(50)]
CityId [smallint]
ProvinceRowId [bigint]
City:
CityRowId [bigint] PK
CityDescription [varchar(100)]
Province:
ProvinceId [int] PK
ProvinceDesc [varchar(50)]
I am considering a rewrite of the application (in ASP.net MVC) that uses this data source as is similar in design to MVC storefront. However I am going through a proof of concept phase and this is one of the stumbling blocks I have come across.
What are my options in terms of ORM choice that can be easily used and why?
Should I even be considering an ORM? (The reason I ask this is that most explanations and tutorials all work with relatively cleanly designed existing databases, or newly created ones when compared to mine. I am thus having a very hard time trying to find a way forward with this problem)
There is a huge amount of existing SQL queries, would a datamappper(eg IBatis.net) be more suitable since we could easily modify them to work and reuse the investment already made?
I have found this question on SO which indicates to me that an ORM can be used - however I get the impression that this a question of mapping?
Note: at the moment, the object model is not clearly defined as it was non-existent. The existing system pretty much did almost everything in SQL or consisted of overly complicated, and numerous queries to complete functionality. I am pretty much a noob and have zero experience around ORMs and MVC - so this an awesome learning curve I am on.
I agree with Ben.
I was in this situation with a LAMP stack. An old dirty, bady coded website needed bringing up to scratch. It was literally the worst database I have seen, coupled with line after line of blind SQL execution.
Job? Get rid of all that SQL very quickly and replace it with an abstraction. Which ORM? I found that using an existing ORM to fit over a bad database (most databases really) retrospectively is bad news. I think this is a problem with ORMs, they move database/storage concerns closer to the application ... not further away.
My Solution: A reflective ORM that used only the existing database state to work out what was going on. All selects, inserts, updates and what-not used views/stored proceedures to mask the cruddy database. It is powered by a linq-esque API just rewrite the grim SQL with. Boiled around 100klocs SQL statements down to less than 2klocs.
pros: I can gradually port the database to a better structure behind the views and proceedures. IMHO this is how all databases should be organised, taking full advantage of the abstraction that SPs and views provide. I never want to see a single SQL statement (or an ORM masquerading as SQL) directly against a table.
That's my story. An overengineered way to slot a nice abstraction above an existing and crap database, without rewriting the database first, and without crowbaring an ORM into the mix making things much more complex.
a hack, no doubt, but it works so well I am using it in projects where I can design the database from scratch anyway ;)
The amount of work involved in trying to keep the existing schema and then crowbaring it into a much more structured orm pattern would probably be large and complex. If you are rewriting the whole system and retiring the old one then i would devise my data model create a new db and set of classes,maybe using linq2sql, then write a data migration script to move the data from the old schema to the new one. That way your complex fiddly code is all in the migration and you don't have to deal with maintining and managing a complex mapping between a structured class model and a badly designed db.
We've just faced this problem with an awful schema design (randomly has primary keys, no foreign keys at all, badly designed tables - just a mess).
We had the luxury of technology choice, and went MVC2 front end (irrelevant to your question), and had 2 devs split off - one try to model using NHibernate, the other using Entity Framework 4.
I hasten to add that we had a strong idea of what we wanted from our domain model, and modelled that first (not wanting to be constrained by the database), so our 'User' object from a schema point of view actually spanned 5 tables, we encapsulated a lot of the business logic so that the domain model wasn't aneamic, and once we were happy with our User object, we started the process of trying to plugin the ORM.
I can say without hesitation in both cases (NH and EF4) the compromises we had to make on our model in order to shoe-horn the implementation in was phenomenal. I'll give you the examples from EF4 as that's the one I was most closely involved in, others may be able to relate these to other ORMs.
private setters
Nope - not on your life with EF4. Your properties must be public. There are workarounds (for example, creating wrappers around properties that were coming in from your DB)
enums
Again, no - there was a wrapper concept and a 'mapping' to try to get a lookup int out of the DB into the models enum types.
outcomes
We persevered for a while with both approaches to get to a point where we'd completed the mapping of a user, and the outcome was that we had to compromise our domain model in too many ways.
where did we go after that?
Linq to SQL with our own mapping layer. And we've never looked back - absolutely fantastic - we've written the mapping layer ourselves once that takes the Dto object down at the Dal layer and maps it (as we specify it) into our Domain model.
Good luck with any investigation of ORMs, I'd certainly re-investigate them if I had a decent schema to base them off, but as it stood, with an awful schema, it was easier to roll our own.
Cheers,
Terry
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For some of the apps I've developed (then proceeded to forget about), I've been writing plain SQL, primarily for MySQL. Though I have used ORMs in python like SQLAlchemy, I didn't stick with them for long. Usually it was either the documentation or complexity (from my point of view) holding me back.
I see it like this: use an ORM for portability, plain SQL if it's just going to be using one type of database. I'm really looking for advice on when to use an ORM or SQL when developing an app that needs database support.
Thinking about it, it would be far better to just use a lightweight wrapper to handle database inconsistencies vs. using an ORM.
Speaking as someone who spent quite a bit of time working with JPA (Java Persistence API, basically the standardized ORM API for Java/J2EE/EJB), which includes Hibernate, EclipseLink, Toplink, OpenJPA and others, I'll share some of my observations.
ORMs are not fast. They can be adequate and most of the time adequate is OK but in a high-volume low-latency environment they're a no-no;
In general purpose programming languages like Java and C# you need an awful lot of magic to make them work (eg load-time weaving in Java, instrumentation, etc);
When using an ORM, rather than getting further from SQL (which seems to be the intent), you'll be amazed how much time you spend tweaking XML and/or annotations/attributes to get your ORM to generate performant SQL;
For complex queries, there really is no substitute. Like in JPA there are some queries that simply aren't possible that are in raw SQL and when you have to use raw SQL in JPA it's not pretty (C#/.Net at least has dynamic types--var--which is a lot nicer than an Object array);
There are an awful lot of "gotchas" when using ORMs. This includes unintended or unexpected behavior, the fact that you have to build in the capability to do SQL updates to your database (by using refresh() in JPA or similar methods because JPA by default caches everything so it won't catch a direct database update--running direct SQL updates is a common production support activity);
The object-relational mismatch is always going to cause problems. With any such problem there is a tradeoff between complexity and completeness of the abstraction. At times I felt JPA went too far and hit a real law of diminishing returns where the complexity hit wasn't justified by the abstraction.
There's another problem which takes a bit more explanation.
The traditional model for a Web application is to have a persistence layer and a presentation layer (possibly with a services or other layers in between but these are the important two for this discussion). ORMs force a rigid view from your persistence layer up to the presentation layer (ie your entities).
One of the criticisms of more raw SQL methods is that you end up with all these VOs (value objects) or DTOs (data transfer objects) that are used by simply one query. This is touted as an advantage of ORMs because you get rid of that.
Thing is those problems don't go away with ORMs, they simply move up to the presentation layer. Instead of creating VOs/DTOs for queries, you create custom presentation objects, typically one for every view. How is this better? IMHO it isn't.
I've written about this in ORM or SQL: Are we there yet?.
My persistence technology of choice (in Java) these days is ibatis. It's a pretty thin wrapper around SQL that does 90%+ of what JPA can do (it can even do lazy-loading of relationships although its not well-documented) but with far less overhead (in terms of complexity and actual code).
This came up last year in a GWT application I was writing. Lots of translation from EclipseLink to presentation objects in the service implementation. If we were using ibatis it would've been far simpler to create the appropriate objects with ibatis and then pass them all the way up and down the stack. Some purists might argue this is Bad™. Maybe so (in theory) but I tell you what: it would've led to simpler code, a simpler stack and more productivity.
ORMs have some nice features. They can handle much of the dog-work of copying database columns to object fields. They usually handle converting the language's date and time types to the appropriate database type. They generally handle one-to-many relationships pretty elegantly as well by instantiating nested objects. I've found if you design your database with the strengths and weaknesses of the ORM in mind, it saves a lot of work in getting data in and out of the database. (You'll want to know how it handles polymorphism and many-to-many relationships if you need to map those. It's these two domains that provide most of the 'impedance mismatch' that makes some call ORM the 'vietnam of computer science'.)
For applications that are transactional, i.e. you make a request, get some objects, traverse them to get some data and render it on a Web page, the performance tax is small, and in many cases ORM can be faster because it will cache objects it's seen before, that otherwise would have queried the database multiple times.
For applications that are reporting-heavy, or deal with a large number of database rows per request, the ORM tax is much heavier, and the caching that they do turns into a big, useless memory-hogging burden. In that case, simple SQL mapping (LinQ or iBatis) or hand-coded SQL queries in a thin DAL is the way to go.
I've found for any large-scale application you'll find yourself using both approaches. (ORM for straightforward CRUD and SQL/thin DAL for reporting).
I say plain SQL for Reads, ORM for CUD.
Performance is something I'm always concerned about, specially in web applications, but also code maintainability and readability. To address these issues I wrote SqlBuilder.
ORM is not just portability (which is kinda hard to achieve even with ORMs, for that matter). What it gives you is basically a layer of abstraction over a persistent store, when a ORM tool frees you from writing boilerplate SQL queries (selects by PK or by predicates, inserts, updates and deletes) and lets you concentrate on the problem domain.
Any respectable design will need some abstraction for the database, just to handle the impedance mismatch. But the simplest first step (and adequate for most cases) I would expect would be a DAL, not a heavyweight ORM. Your only options aren't those at the ends of the spectrum.
EDIT in response to a comment requesting me to describe how I distinguish DAL from ORM:
A DAL is what you write yourself, maybe starting from a class that simply encapsulates a table and maps its fields to properties. An ORM is code you don't write for abstraction mechanisms inferred from other properties of your dbms schema, mostly PKs and FKs. (This is where you find out if the automatic abstractions start getting leaky or not. I prefer to inform them intentionally, but that may just be my personal preference).
The key that made my ORM use really fly was code generation. I agree that the ORM route isn't the fastest, in code performance terms. But when you have a medium to large team, the DB is changing rapidly the ability to regenerate classes and mappings from the DB as part of the build process is something brilliant to behold, especially when you use CI. So your code may not be the fastest, but your coding will be - I know which I'd take in most projects.
My recommendation is to develop using an ORM while the Schema is still fluid, use profiling to find bottlenecks, then tune those areas which need it using raw Sql.
Another thought, the caching built into Hibernate can often make massive performance improvements if used in the right way. No more going back to the DB to read reference data.
Dilemma whether to use a framework or not is quite common in modern day software development scenario.
What is important to understand is that every framework or approach has its pros and cons - for example in our experience we have found that ORM is useful when dealing with transactions i.e. insert/update/delete operations - but when it comes to fetch data with complex results it becomes important to evaluate the performance and effectiveness of the ORM tool.
Also it is important to understand that it is not compulsory to select a framework or an approach and implement everything in that. What we mean by that is we can have mix of ORM and native query language. Many ORM frameworks give extension points to plugin in native SQL. We should try not to over use a framework or an approach. We can combine certain frameworks or approaches and come with an appropriate solution.
You can use ORM when it comes to insertion, updation, deletion, versioning with high level of concurrency and you can use Native SQL for report generation and long listing
There's no 'one-tool-fits-all' solution, and this is also true for the question 'should i use an or/m or not ? '.
I would say: if you have to write an application/tool which is very 'data' focused, without much other logic, then I 'd use plain SQL, since SQL is the domain-specific language for this kind of applications.
On the other hand, if I was to write a business/enterprise application which contains a lot of 'domain' logic, then I'd write a rich class model which could express this domain in code. In such case, an OR/M mapper might be very helpfull to successfully do so, as it takes a lot of plumbing code out of your hands.
One of the apps I've developed was an IRC bot written in python. The modules it uses run in separate threads, but I haven't figured out a way to handle threading when using sqlite. Though, that might be better for a separate question.
I really should have just reworded both the title and the actual question. I've never actually used a DAL before, in any language.
Use an ORM that works like SQL, but provides compile-time checks and type safety. Like my favorite: Data Knowledge Objects (disclosure: I wrote it)
For example:
for (Bug bug : Bug.ALL.limit(100)) {
int id = bug.getId();
String title = bug.getTitle();
System.out.println(id +" "+ title);
}
Fully streaming. Easy to set up (no mappings to define - reads your existing schemas). Supports joins, transactions, inner queries, aggregation, etc. Pretty much anything you can do in SQL. And has been proven from giant datasets (financial time series) all the way down to trivial (Android).
I know this question is very old, but I thought that I would post an answer in case anyone comes across it like me. ORMs have come a long way. Some of them actually give you the best of both worlds: making development more productive and maintaining performance.
Take a look at SQL Data (http://sqldata.codeplex.com). It is a very light weight ORM for c# that covers all the bases.
FYI, I am the author of SQL Data.
I'd like to add my voice to the chorus of replies that say "There's a middle ground!".
To an application programmer, SQL is a mixture of things you might want to control and things you almost certainly don't want to be bothered controlling.
What I've always wanted is a layer (call it DAL, ORM, or micro-ORM, I don't mind which) that will take charge of the completely predictable decisions (how to spell SQL keywords, where the parentheses go, when to invent column aliases, what columns to create for a class that holds two floats and an int ...), while leaving me in charge of the higher-level aspects of the SQL, i.e. how to arrange JOINs, server-side computations, DISTINCTs, GROUP BYs, scalar subqueries, etc.
So I wrote something that does this: http://quince-lib.com/
It's for C++: I don't know whether that's the language you're using, but all the same it might be interesting to see this take on what a "middle ground" could look like.
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As a web developer looking to move from hand-coded PHP sites to framework-based sites, I have seen a lot of discussion about the advantages of one ORM over another. It seems to be useful for projects of a certain (?) size, and even more important for enterprise-level applications.
What does it give me as a developer? How will my code differ from the individual SELECT statements that I use now? How will it help with DB access and security? How does it find out about the DB schema and user credentials?
Edit: #duffymo pointed out what should have been obvious to me: ORM is only useful for OOP code. My code is not OO, so I haven't run into the problems that ORM solves.
I'd say that if you aren't dealing with objects there's little point in using an ORM.
If your relational tables/columns map 1:1 with objects/attributes, there's not much point in using an ORM.
If your objects don't have any 1:1, 1:m or m:n relationships with other objects, there's not much point in using an ORM.
If you have complex, hand-tuned SQL, there's not much point in using an ORM.
If you've decided that your database will have stored procedures as its interface, there's not much point in using an ORM.
If you have a complex legacy schema that can't be refactored, there's not much point in using an ORM.
So here's the converse:
If you have a solid object model, with relationships between objects that are 1:1, 1:m, and m:n, don't have stored procedures, and like the dynamic SQL that an ORM solution will give you, by all means use an ORM.
Decisions like these are always a choice. Choose, implement, measure, evaluate.
ORMs are being hyped for being the solution to Data Access problems. Personally, after having used them in an Enterprise Project, they are far from being the solution for Enterprise Application Development. Maybe they work in small projects. Here are the problems we have experienced with them specifically nHibernate:
Configuration: ORM technologies require configuration files to map table schemas into object structures. In large enterprise systems the configuration grows very quickly and becomes extremely difficult to create and manage. Maintaining the configuration also gets tedious and unmaintainable as business requirements and models constantly change and evolve in an agile environment.
Custom Queries: The ability to map custom queries that do not fit into any defined object is either not supported or not recommended by the framework providers. Developers are forced to find work-arounds by writing adhoc objects and queries, or writing custom code to get the data they need. They may have to use Stored Procedures on a regular basis for anything more complex than a simple Select.
Proprietery binding: These frameworks require the use of proprietary libraries and proprietary object query languages that are not standardized in the computer science industry. These proprietary libraries and query languages bind the application to the specific implementation of the provider with little or no flexibility to change if required and no interoperability to collaborate with each other.
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 more information on our analysis you can read:
http://www.orasissoftware.com/driver.aspx?topic=whitepaper
At a very high level: ORMs help to reduce the Object-Relational impedance mismatch. They allow you to store and retrieve full live objects from a relational database without doing a lot of parsing/serialization yourself.
What does it give me as a developer?
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. ORMs generally handle marshaling the values of individual columns into the appropriate types so that you don't have to parse/serialize them yourself. Furthermore, it allows you to retrieve fully formed object from the DB rather than simply row objects that you have to wrap your self.
How will my code differ from the individual SELECT statements that I use now?
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. You are generally able to write SQL directly when you need to, but for most operations (CRUD) the ORM will make the code for interacting with persistent objects simpler.
How will it help with DB access and security?
Generally speaking, ORMs have their own API for building queries (eg. attribute access) and so are less vulnerable to SQL injection attacks; however, they often allow you to inject your own SQL into the generated queries so that you can do strange things if you need to. 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.
How does it find out about the DB schema and user credentials?
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. [Database] user credentials are generally stored in a settings file.
If you write your data access layer by hand, you are essentially writing your own feature poor ORM.
Oren Eini has a nice blog which sums up what essential features you may need in your DAL/ORM and why it writing your own becomes a bad idea after time:
http://ayende.com/Blog/archive/2006/05/12/25ReasonsNotToWriteYourOwnObjectRelationalMapper.aspx
EDIT: The OP has commented in other answers that his code base isn't very object oriented. 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 1:1 to tables.
Top Benefits:
Database Abstraction
API-centric design mentality
High Level == Less to worry about at the fundamental level (its been thought of for you)
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 love not having to worry about INNER JOIN and SELECT COUNT(*). 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.
I can't speak for other ORM's, just Hibernate (for Java).
Hibernate gives me the following:
Automatically updates schema for tables on production system at run-time. Sometimes you still have to update some things manually yourself.
Automatically creates foreign keys which keeps you from writing bad code that is creating orphaned data.
Implements connection pooling. Multiple connection pooling providers are available.
Caches data for faster access. Multiple caching providers are available. This also allows you to cluster together many servers to help you scale.
Makes database access more transparent so that you can easily port your application to another database.
Make queries easier to write. The following query that would normally require you to write 'join' three times can be written like this:
"from Invoice i where i.customer.address.city = ?" this retrieves all invoices with a specific city
a list of Invoice objects are returned. I can then call invoice.getCustomer().getCompanyName(); if the data is not already in the cache the database is queried automatically in the background
You can reverse-engineer a database to create the hibernate schema (haven't tried this myself) or you can create the schema from scratch.
There is of course a learning curve as with any new technology but I think it's well worth it.
When needed you can still drop down to the lower SQL level to write an optimized query.
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.
What does it give me as a developer?
Saves you time, since you don't have to code the db access portion.
How will my code differ from the individual SELECT statements that I use now?
You will use either attributes or xml files to define the class mapping to the database tables.
How will it help with DB access and security?
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.
How does it find out about the DB schema and user credentials?
You provide the connection string as always. The framework providers (e.g. SQL, Oracle, MySQL specific classes) provide the implementation that queries the db schema, processes the class mappings, and renders / executes the db access code as necessary.
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 ... but also more control and code that's easier to debug - from my experience using an early version of nHibernate it has to be added.
Using an ORM will remove dependencies from your code on a particular SQL dialect. 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. Additionally, ORMs typically provide protection from SQL injection by constructing parameterized queries. Granted you could do this yourself, but it's nice to have the framework guarantee.
ORMs work in one of two ways: some discover the schema from an existing database -- the LINQToSQL designer does this --, others require you to map your class onto a table. 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.
Typically, the credentials supplied programatically via the API or using a configuration file -- or both, defaults coming from a configuration file, but able to be override in code.
While I agree with the accepted answer almost completely, I think it can be amended with lightweight alternatives in mind.
If you have complex, hand-tuned SQL
If your objects don't have any 1:1, 1:m or m:n relationships with other objects
If you have a complex legacy schema that can't be refactored
...then you might benefit from a lightweight ORM where SQL is is not
obscured or abstracted to the point where it is easier to write your
own database integration.
These are a few of the many reasons why the developer team at my company decided that we needed to make a more flexible abstraction to reside on top of the JDBC.
There are many open source alternatives around that accomplish similar things, and jORM is our proposed solution.
I would recommend to evaluate a few of the strongest candidates before choosing a lightweight ORM. They are slightly different in their approach to abstract databases, but might look similar from a top down view.
jORM
ActiveJDBC
ORMLite
my concern with ORM frameworks is probably the very thing that makes it attractive to lots of developers.
nameley that it obviates the need to 'care' about what's going on at the DB level. Most of the problems that we see during the day to day running of our apps are related to database problems. I worry slightly about a world that is 100% ORM that people won't know about what queries are hitting the database, or if they do, they are unsure about how to change them or optimize them.
{I realize this may be a contraversial answer :) }