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I'm not quite sure stackoverflow is a place for such a general question, but let's give it a try.
Being exposed to the need of storing application data somewhere, I've always used MySQL or sqlite, just because it's always done like that. As it seems like the whole world is using these databases (most of all software products, frameworks, etc), it is rather hard for a beginning developer like me to start thinking about whether this is a good solution or not.
Ok, say we have some object-oriented logic in our application, and objects are related to each other somehow. We need to map this logic to the storage logic, so relations between database objects are required too. This leads us to using relational database, and I'm ok with that - to put it simple, our database table rows sometimes will need to have references to other tables' rows. But why use SQL language for interaction with such a database?
SQL query is a text message. I can understand this is cool for actually understanding what it does, but isn't it silly to use text table and column names for a part of application that no one ever seen after deploynment? If you had to write a data storage from scratch, you would have never used this kind of solution. Personally, I would have used some 'compiled db query' bytecode, that would be assembled once inside a client application and passed to the database. And it surely would name tables and colons by id numbers, not ascii-strings. In the case of changes in table structure those byte queries could be recompiled according to new db schema, stored in XML or something like that.
What are the problems of my idea? Is there any reason for me not to write it myself and to use SQL database instead?
EDIT To make my question more clear. Most of answers claim that SQL, being a text query, helps developers better understand the query itself and debug it more easily. Personally, I haven't seen people writing SQL queries by hand for a while. Everyone I know, including me, is using ORM. This situation, in which we build up a new level of abstraction to hide SQL, leads to thinking if we need SQL or not. I would be very grateful if you could give some examples in which SQL is used without ORM purposely, and why.
EDIT2 SQL is an interface between a human and a database. The question is why do we have to use it for application/database interaction? I still ask for examples of human beings writing/debugging SQL.
Everyone I know, including me, is using ORM
Strange. Everyone I know, including me, still writes most of the SQL by hand. You typically end up with tighter, more high performance queries than you do with a generated solution. And, depending on your industry and application, this speed does matter. Sometimes a lot. yeah, I'll sometimes use LINQ for a quick-n-dirty where I don't really care what the resulting SQL looks like, but thus far nothing automated beats hand-tuned SQL for when performance against a large database in a high-load environment really matters.
If all you need to do is store some application data somewhere, then a general purpose RDBMS or even SQLite might be overkill. Serializing your objects and writing them to a file might be simpler in some cases. An advantage to SQLite is that if you have a lot of this kind of information, it is all contained in one file. A disadvantage is that it is more difficult to read it. For example, if you serialize you data to YAML, you can read the file with any text editor or shell.
Personally, I would have used some
'compiled db query' bytecode, that
would be assembled once inside a
client application and passed to the
database.
This is how some database APIs work. Check out static SQL and prepared statements.
Is there any reason for me not to
write it myself and to use SQL
database instead?
If you need a lot of features, at some point it will be easier to use an existing RDMBS then to write your own database from scratch. If you don't need many features, a simpler solution may be wiser.
The whole point of database products is to avoid writing the database layer for every new program. Yes, a modern RDMBS might not always be a perfect fit for every project. This is because they were designed to be very general, so in practice, you will always get additional features you don't need. That doesn't mean it is better to have a custom solution. The glove doesn't always need to be a perfect fit.
UPDATE:
But why use SQL language for
interaction with such a database?
Good question.
The answer to that may be found in the original paper describing the relational model A Relational Model of Data for Large Shared Data Banks, by E. F. Codd, published by IBM in 1970. This paper describes the problems with the existing database technologies of the time, and explains why the relational model is superior.
The reason for using the relational model, and thus a logical query language like SQL, is data independence.
Data independence is defined in the paper as:
"... the independence of application programs and terminal activities from the growth in data types and changes in data representations."
Before the relational model, the dominate technology for databases was referred to as the network model. In this model, the programmer had to know the on-disk structure of the data and traverse the tree or graph manually. The relational model allows one to write a query against the conceptual or logical scheme that is independent of the physical representation of the data on disk. This separation of logical scheme from the physical schema is why we use the relational model. For a more on this issue, here are some slides from a database class. In the relational model, we use logic based query languages like SQL to retrieve data.
Codd's paper goes into more detail about the benefits of the relational model. Give it a read.
SQL is a query language that is easy to type into a computer in contrast with the query languages typically used in a research papers. Research papers generally use relation algebra or relational calculus to write queries.
In summary, we use SQL because we happen to use the relational model for our databases.
If you understand the relational model, it is not hard to see why SQL is the way it is. So basically, you need to study the relation model and database internals more in-depth to really understand why we use SQL. It may be a bit of a mystery otherwise.
UPDATE 2:
SQL is an interface between a human
and a database. The question is why do
we have to use it for
application/database interaction? I
still ask for examples of human beings
writing/debugging SQL.
Because the database is a relational database, it only understands relational query languages. Internally it uses a relational algebra like language for specifying queries which it then turns into a query plan. So, we write our query in a form we can understand (SQL), the DB takes our SQL query and turns it into its internal query language. Then it takes the query and tries to find a "query plan" for executing the query. Then it executes the query plan and returns the result.
At some point, we must encode our query in a format that the database understands. The database only knows how to convert SQL to its internal representation, that is why there is always SQL at some point in the chain. It cannot be avoided.
When you use ORM, your just adding a layer on top of the SQL. The SQL is still there, its just hidden. If you have a higher-level layer for translating your request into SQL, then you don't need to write SQL directly which is beneficial in some cases. Some times we do not have such a layer that is capable of doing the kinds of queries we need, so we must use SQL.
Given the fact that you used MySQL and SQLite, I understand your point of view completely. Most DBMS have features that would require some of the programming from your side, while you get it from database for free:
Indexes - you can store large amounts of data and still be able to filter and search very quickly because of indexes. Of course, you could implement you own indexing, but why reinvent the wheel
data integrity - using database features like cascading foreign keys can ensure data integrity across the system. You only need to declare relationship between data, and system takes care of the rest. Of course, once more, you could implement constraints in code, but it's more work. Consider, for example, deletion, where you would have to write code in object's destructor to track all dependent objects and act accordingly
ability to have multiple applications written in different programming languages, working on different operating systems, some even distributed across the network - all using the same data stored in a common database
dead easy implementation of observer pattern via triggers. There are many cases where only some data depends on some other data and it does not affect UI aspect of application. Ensuring consistency can be very tricky or require a lot of programming. Of course, you could implement trigger-like behavior with objects but it requires more programming than simple SQL definition
There are some good answers here. I'll attempt to add my two cents.
I like SQL, I can think in it pretty easily. The queries produced by layers on top of the DB (like ORM frameworks) are usually hideous. They'll select tons of extra stuff, join in things you don't need, etc.; all because they don't know that you only want a small part of the object in this code. When you need high performance, you'll often end up going in and using at least some custom SQL queries in an ORM system just to speed up a few bottlenecks.
Why SQL? As others have said, it's easy for humans. It makes a good lowest common denominator. Any language can make SQL and call command line clients if necessary, and they is pretty much always a good library.
Is parsing out the SQL inefficient? Somewhat. The grammar is pretty structured, so there aren't tons of ambiguities that would make the parser's job really hard. The real thing is that the overhead of parsing out SQL is basically nothing.
Let's say you run a query like "SELECT x FROM table WHERE id = 3", and then do it again with 4, then 5, over and over. In that case, the parsing overhead may exist. That's why you have prepared statements (as others have mentioned). The server parses the query once, and can swap in the 3 and 4 and 5 without having to reparse everything.
But that's the trivial case. In real life, your system may join 6 tables and have to pull hundreds of thousands of records (if not more). It may be a query that you let run on a database cluster for hours, because that's the best way to do things in your case. Even with a query that takes only a minute or two to execute, the time to parse the query is essentially free compared to pulling records off disk and doing sorting/aggregation/etc. The overhead of sending the ext "LEFT OUTER JOIN ON" is only a few bytes compared to sending special encoded byte 0x3F. But when your result set is 30 MB (let alone gigs+), those few extra bytes are worthless compared to not having to mess with some special query compiler object.
Many people use SQL on small databases. The biggest one I interact with is only a few dozen gigs. SQL is used on everything from tiny files (like little SQLite DBs may be) up to terabyte size Oracle clusters. Considering it's power, it's actually a surprisingly simple and small command set.
It's an ubiquitous standard. Pretty much every programming language out there has a way to access SQL databases. Try that with a proprietary binary protocol.
Everyone knows it. You can find experts easily, new developers will usually understand it to some degree without requiring training
SQL is very closely tied to the relational model, which has been thoroughly explored in regard to optimization and scalability. But it still frequently requires manual tweaking (index creation, query structure, etc.), which is relatively easy due to the textual interface.
But why use SQL language for interaction with such a database?
I think it's for the same reason that you use a human-readable (source code) language for interaction with the compiler.
Personally, I would have used some 'compiled db query' bytecode, that would be assembled once inside a client application and passed to the database.
This is an existing (optional) feature of databases, called "stored procedures".
Edit:
I would be very grateful if you could give some examples in which SQL is used without ORM purposely, and why
When I implemented my own ORM, I implemented the ORM framework using ADO.NET: and using ADO.NET includes using SQL statements in its implementation.
After all the edits and comments, the main point of your question appears to be : why is the nature of SQL closer to being a human/database interface than to being an application/database interface ?
And the very simple answer to that question is : because that is exactly what it was originally intended to be.
The predecessors of SQL (QUEL being presumably the most important one) were intended to be exactly that : a QUERY language, i.e. one that didn't have any of INSERT, UPDATE, DELETE.
Moreover, it was intended to be a query language that could be used by any user, provided that user was aware of the logical structure of the database, and obviously knew how to express that logical structure in the query language he was using.
The original ideas behind QUEL/SQL were that a database was built using "just any mechanism conceivable", that the "real" database could be really just anything (e.g. one single gigantic XML file - allthough 'XML' was not considered a valid option at the time), and that there would be "some kind of machinery" that understood how to transform the actual structure of that 'just anything' into the logical relational structure as it was perceived by the SQL user.
The fact that in order to actually achieve that, the underlying structures are required to lend themselves to "viewing them relationally", was not understood as well in those days as it is now.
Yes, it is annoying to have to write SQL statements to store and retrieve objects.
That's why Microsoft have added things like LINQ (language integrated query) into C# and VB.NET to make it possible to query databases using objects and methods instead of strings.
Most other languages have something similar with varying levels of success depending on the abilities of that language.
On the other hand, it is useful to know how SQL works and I think it is a mistake to shield yourself entirely from it. If you use the database without thinking you can write extremely inefficient queries and index the database incorrectly. But once you understand how to use SQL correctly and have tuned your database, you have a very powerful tried-and-tested tool available for finding exactly the data you need extremely quickly.
My biggest reason for SQL is Ad-hoc reporting. That report your business users want but don't know that they need it yet.
SQL is an interface between a human
and a database. The question is why do
we have to use it for
application/database interaction? I
still ask for examples of human beings
writing/debugging SQL.
I use sqlite a lot right from the simplest of tasks (like logging my firewall logs directly to a sqlite database) to more complex analytic and debugging tasks in my day-to-day research. Laying out my data in tables and writing SQL queries to munge them in interesting ways seems to be the most natural thing to me in these situations.
On your point about why it is still used as an interface between application/database, this is my simple reasoning:
There is about 3-4 decades of
serious research in that area
starting in 1970 with Codd's seminal
paper on Relational Algebra.
Relational Algebra forms the
mathematical basis to SQL (and other
QLs), although SQL does not
completely follow the relational
model.
The "text" form of the language
(aside from being easily
understandable to humans) is also
easily parsable by machines (say
using a grammar parser like like
lex) and is easily convertable to whatever "bytecode" using any number of optimizations.
I am not sure if doing this in any
other way would have yielded
compelling benefits in the generic cases. Otherwise it
would have been probably discovered
and adopted in the 3 decades of
research. SQL probably provides the
best tradeoffs when bridging the
divide between humans/databases and
applications/databases.
The question that then becomes interesting to ask is, "What are the real benefits of doing SQL in any other "non-text" way?" Will google for this now:)
SQL is a common interface used by the DBMS platform - the entire point of the interface is that all database operations can be specified in SQL without needing supplementary API calls. This means that there is a common interface across all clients of the system - application software, reports and ad-hoc query tools.
Secondly, SQL gets more and more useful as queries get more complex. Try using LINQ to specify a 12-way join a with three conditions based on existential predicates and a condition based on an aggregate calculated in a subquery. This sort of thing is fairly comprehensible in SQL but unlikely to be possible in an ORM.
In many cases an ORM will do 95% of what you want - most of the queries issued by applications are simple CRUD operations that an ORM or other generic database interface mechanism can handle easily. Some operations are best done using custom SQL code.
However, ORMs are not the be-all and end-all of database interfacing. Fowler's Patterns of Enterprise Application Architecture has quite a good section on other types of database access strategy with some discussion of the merits of each.
There are often good reasons not to use an ORM as the primary database interface layer. An example of a good one is that platform database libraries like ADO.Net often do a good enough job and integrate nicely with the rest of the environment. You might find that the gain from using some other interface doesn't really outweigh the benefits from the integration.
However, the final reason that you can't really ignore SQL is that you are ultimately working with a database if you are doing a database application. There are many, many WTF stories about screw-ups in commercial application code done by people who didn't understand databases properly. Poorly thought-out database code can cause trouble in so many ways, and blithely thinking that you don't need to understand how the DBMS works is an act of Hubris that is bound to come and bite you some day. Worse yet, it will come and bite some other poor schmoe who inherits your code.
While I see your point, SQL's query language has a place, especially in large applications with a lot of data. And to point out the obvious, if the language wasn't there, you couldn't call it SQL (Structured Query Language). The benefit of having SQL over the method you described is SQL is generally very readable, though some really push the limits on their queries.
I whole heartly agree with Mark Byers, you should not shield yourself from SQL. Any developer can write SQL, but to really make your application perform well with SQL interaction, understanding the language is a must.
If everything was precompiled with bytecode as you described, I'd hate to be the one to have to debug the application after the original developer left (or even after not seeing the code for 6 months).
I think the premise of the question is incorrect. That SQL can be represented as text is immaterial. Most modern databases would only compile queries once and cache them anyway, so you already have effectively a 'compiled bytecode'. And there's no reason this couldn't happen client-wise though I'm not sure if anyone's done it.
You said SQL is a text message, well I think of him as a messenger, and, as we know, don't shoot the messenger. The real issue is that relations are not a good enough way of organising real world data. SQL is just lipstick on the pig.
If the first part you seem to refer to what is usually called the Object - relational mapping impedance. There are already a lot of frameworks to alleviate that problem. There are tradeofs as well. Some things will be easier, others will get more complex, but in the general case they work well if you can afford the extra layer.
In the second part you seem to complain about SQL being text (it uses strings instead of ids, etc)... SQL is a query language. Any language (computer or otherwise) that is meant to be read or written by humans is text oriented for that matter. Assembly, C, PHP, you name it. Why? Because, well... it does make sense, doesn't it?
If you want precompiled queries, you already have stored procedures. Prepared statements are also compiled once on the fly, IIRC. Most (if not all) db drivers talk to the database server using a binary protocol anyway.
yes, text is a bit inefficient. But actually getting the data is a lot more costly, so the text based sql is reasonably insignificant.
SQL was created to provide an interface to make ad hoc queries against a relational database.
Generally, most relational databases understand some form of SQL.
Object-oriented databases exist, and (presumably) use objects to do their querying... but as I understand it, OO databases have a lot more overheard, and relational databases work just fine.
Relational Databases also allow you to operate in a "disconnected" state. Once you have the information you asked for, you can close the database connection. With an OO database, you either need to return all objects related to the current one (and the ones they're related to... and the... etc...) or reopen the connection to retrieve new objects as they are accessed.
In addition to SQL, you also have ORMs (object-relational mappings) that map objects to SQL and back. There are quite a few of them, including LINQ (.NET), the MS Entity Framework (.NET), Hibernate (Java), SQLAlchemy (Python), ActiveRecord (Ruby), Class::DBI (Perl), etc...
A database language is useful because it provides a logical model for your data independent of any applications that use it. SQL has a lot of shortcomings however, not the least being that its integration with other languages is poor, type support is about 30 years behind the rest of the industry and it has never been a truly relational language anyway.
SQL has survived mostly because the database market has been and remains dominated by the three mega-vendors who have a vested interest in protecting their investment. That's changing and SQL's days are probably numbered but the model that will finally replace it probably hasn't arrived yet - although there are plenty of contenders around these days.
I don't think most people are getting your question, though I think it's very clear. Unfortunately I don't have the "correct" answer. I would guess it's a combination of several things:
Semi-arbitrary decisions when it was designed such as ease of use, not needing a SQL compiler (or IDE), portability, etc.
It happened to catch on well (probably due to similar reasons)
And now due to historical reasons (compatibility, well known, proven, etc.) continues to be used.
I don't think most companies have bothered with another solution because it works well, isn't much of a bottleneck, it's a standard, blah, blah..
One of the Unix design principles can be said thusly, "Write programs to handle text streams, because that is a universal interface.".
And that, I believe, is why we typically use SQL instead of some 'byte-SQL' that only has a compilation interface. Even if we did have a byte-SQL, someone would write a "Text SQL", and the loop would be complete.
Also, MySQL and SQLite are less full-featured than, say, MSSQL and Oracle SQL. So you're still in the low end of the SQL pool.
Actually there are a few non-SQL database (like Objectivity, Oracle Berkeley DB, etc.) products came but non of them succeeded. In future if someone finds intuitive alternative for SQL, that will answer your question.
There are a lot of non relational database systems. Here are just a few:
Memcached
Tokyo Cabinet
As far as finding a relational database that doesn't use SQL as its primary interface, I think you won't find it. Reason: SQL is a great way to talk about relations. I can't figure out why that's a big deal to you: if you don't like SQL, put an abstraction over it (like an ORM) so you don't have to worry about it. Let the abstraction worry about it. It gets you to the same place.
However, the problem your'e really mentioning here is the object-relation disconnect - the problem is with the relation itself. Objects and relational-tuples don't always lend themselves to be a 1-1 relationship, which is the reason why a developer can frustrated with a database. The solution to that is to use a different database type.
Because often, you cannot be sure that (citing you) "no one ever seen after deployment". Knowing that there is an easy interface for reporting and for dataset level querying is a good path for evolution of your app.
You're right, that there are other solutions that may be valid in some situations: XML, plain text files, OODB...
But having a set of common interfaces (like ODBC) is a huge plus for the life of data.
I think the reason might be the search/find/grab algorithms the sql laungage is connected to do. Remember that sql has been developed for 40 years - and the goal has been both preformence wise and user firendly wise.
Ask yourself what the best way of finding 2 attibutes is. Now why investigating that each time you would want to do something that includes that each time you develope your application. Assuming the main goal is the developing of your application when developing an application.
An application has similarities with other applications, a database has similarities with other databases. So there should be a "best way" of these to interact, logically.
Also ask yourself how you would develop a better console only application that does not use sql laungage. If you cannot do that I think you need to develope a new kind of GUI that are even more fundamentally easier to use than with a console - to develope things from it. And that might actually be possible. But still most development of applications is based around console and typing.
Then when it comes to laungage I don´t think you can make a much more fundamentally easier text laungage than sql. And remember that each word of anything is inseperatly connected to its meaning - if you remove the meaning the word cannot be used - if you remove the word you cannot communicate the meaning. You have nothing to describe it with (And maybe you cannot even think it beacuse it woulden´t be connected to anything else you have thought before...).
So basically the best possible algorithms for database manipulation are assigned to words - if you remove these words you will have to assign these manipulations something else - and what would that be?
i think you can use ORM
if and only if you know the basic of sql.
else the result there isn't the best
I have read many strong views (both for and against) SPs or DS.
I am writing a query engine in C++ (mySQL backend for now, though I may decide to go with a C++ ORM). I cant decide whether to write a SP, or to dynamically creat the SQL and send the query to the db engine.#
Any tips on how to decide?
Here's the simple answer:
If your programmers do both database and coding work, keep the SQL with the app. It's easier to maintain that way. Otherwise, let the DB guys handle it in SPs.
You have more control over the mechanisms outside the database. The biggest win for taking care of this outside the database is simply maintenance (in my mind). It'd be slightly hard to version control the SP vs the code you generate outside the database. One more thing to keep track of.
While we're on the topic, it's similar to handling data/schema migrations. It's annoyingly complex to version/handle schema migrations, if you don't already have a mechanism for this, you will have yet another thing you'll need to manage. It comes down to simply being easier to manage/version these things outside the database.
Consider the scenario where you have a bug in your SP. Now it needs to be changed, but then you hop over to another developers database/sandbox. What version is the sandbox and the SP? Now you have to track multiple versions.
One of the main differentiators is whether you are writing the "one true front end" or whether the database is the central piece of your application.
If you are going to have multiple front ends stored procedures make a lot of sense because you reduce your maintenance overhead. If you are writing only one interface, stored procedures are a pain, because you lose a lot of flexibility in changing your data set as your front end needs change, plus you now have to do code maintenance, version control, etc. in two places. Databases are a real pain to keep in sync with code repositories.
Finally, if you are coding for multiple databases (Oracle and SQL compatible code, for example), I'd avoid stored procedures completely.
You may in certain rare circumstances, after profiling, determine that some limited stored procedures are useful to you. This situation comes up way less than people think it does.
The main scenarios when you MUST have the SP is:
1) When you have very complex set of queries with heavy compile overhead and data drift low enough that recompiling is not needed on a regular basis.
2) When the "Only True" logic for accessing the specific data set is VERY complicated, needs to be accessed from several different codebases on different platforms (so writing multiple APIs in code is much more expensive).
Any other scenario, it's debatable, and can be decided one way or another.
I must also say that the other posters' arguments about versioning are not really such a big deal in my experience - having your SPs in version control is as easy as creating a "sql/db_name" directory structure and having easy basic "database release" script which releases the SP code from the version control location to the database. Every company I worked for had some kind of setup like this, central one run by DBAs or departmental one run by developers.
The one thing you want to avoid is to have your business logic spread across multiple tiers of your application. Database DDL and DML are difficult enough to keep in sync with an application code base as it is.
My recommendation is to create a good relational schema, but all your constraints and triggers so that the data retains integrity even if somebody goes to the database and tries to do something through some command line SQL.
Put all your business logic in an application or service that calls (static/dynamic) SQL then wraps the business functionality you are are trying to expose.
Stored-procedures have two purposes that I can think of.
An aid to simplifying data access.
The Stored Procedure does not have
any business logic in it, it just
knows about the structure of the
data and exposes an interface to
isolate accessing three tables and a
view just to get a single piece of
information.
Mapping the Domain Model to the Data
Model, Stored Procedures can assist
in making the Data Model look like a
given Domain Model.
After the program has been completed and has been profiled there are often performance issues with the pre 1.0 release. Stored procedures do offer batching of SQL without traffic needing to go back and forth between the DBMS and the Application. That being said in rare and extreme cases due to performance a few business rules might need to be migrated to the Stored-Procedure side. Make sure to document any exceptions to the architectural philosophy in multiple prominent places.
Stored Procedures are ideal for:
Creating reusable abstractions over complex queries;
Enforcing specific types of insertions/updates to tables (if you also deny permissions to the table);
Performing privileged operations that the logged-in user wouldn't normally be allowed to do;
Guaranteeing a consistent execution plan;
Extending the capabilities of an ORM (batch updates, hierarchy queries, etc.)
Dynamic SQL is ideal for:
Variable search arguments or output columns:
Optional search conditions
Pivot tables
IN clauses with user-specified values
ORM implementations (most can use SPs, but can't be built entirely on them);
DDL and administrative scripts.
They solve different problems, really. Use whichever one is more appropriate to the task at hand, and don't restrict yourself to just one or the other. After you work on database code for a while you'll start to get a more intuitive feel for these things; you'll find yourself banging together some rat's nest of strings for a query and think, "this should really go in a stored procedure."
Final note: Because this question implies a certain level of inexperience with SQL, I feel obliged to say, don't forget that you still need to parameterize your queries when you write dynamic SQL. Parameters aren't just for stored procedures.
DS is more flexible. SP approach makes your system more manageable.
I have been working with NHibernate, LINQ to SQL, and Entity Framework for quite some time. And while I see the benefits to using an ORM to keep the development effort moving quickly, the code simple, and the object relational impedance mismatch to a minimum, I still find it very difficult to convince a die hard SQL dba of an ORM's strengths. From my point of view an ORM can be used for at least 90-95% of all of your data access leaving those really hairy things to be done in procedures or functions where appropriate. I am by no means the guy that says we must do everything in the ORM!
Question: What are some of the better arguments for convincing an old school dba that the use of an ORM is not the absolute worst idea ever conceived by a programmer!
If you want to convince him, first you need to understand what his problem is with use of an ORM. Giving you a list of generic benefits is unlikely to help if it does not address the issues he has.
However, my first guess as to his issue would be that it prevents him from doing any optimisation because you're accessing tables directly so he has no layer of abstraction behind which to work, so if a table needs altering or (de)normalizing then he can't do it without breaking your application.
If you're wondering why a DBA would feel like this, and how to respond to it, then it's roughly the same as him coming up to you and saying he wants you to make all the private fields in your classes public, and that you can't change any of them without asking him first. Imagine what it would take for him to convince you that's a good idea, and then use the same argument on him.
Explain to them that creating a stored procedure for every action taken by an application is unmaintainable on several levels.
If the schema changes it's difficult
to track down all the stored
procedures that are affected.
It's impossible ensure that multiple
stored procedures aren't created to
do the same thing, or if slightly
altering an existing stored
procedure is going to have serious
ramifications.
It's difficult to make sure that the
application and database are in
sync after a deploy.
Dynamic SQL has all these issues and more.
I guess, my first question to "Convincing a die hard DBA to use an ORM" would be: Is the DBA also a programmer that also works outside the DB so that he/she would "use an ORM"? If not then why would the DBA give up a major part of their job to someone else and thereby significantly reduce their overall usefulness to the company? They wouldn't.
In any case, the best way to convince any engineer of anything is with empirical data. Setup a prototype with a few parts of the real application ported to ORM for the purpose of your demonstration and actually prove your points.
On another point I think you don't get the object relational impedance dilemma if you're trying to use that as an argument to use an Object-Relation-Mapper. The DBA could quote from that link you posted where where it says "Mapping such private object representation to database tables makes such databases fragile according to OOP philosophy" and that the issue is further pronounced "particularly when objects or class definitions are mapped (ORM) in a straightforward way to database tables or relational schemata" So according to your own link, by promoting ORM you are promoting the problem.
By using sprocs the DBA is free to make changes to the underlying schema, so long as the sproc still returns the same columns with the same types. Thusly with this abstraction that sprocs add, the direct schema mapping issues become nought. This does not mean however that you need to give up your beloved EF since EF can now be used quite happily with sprocs.
Procedures used to be more efficient because of predictable caching mechanisms. However, many DBA's overkill the procedures, introducing lots of branching logic with IF commands, resulting in an scenarios where they become uncacheable.
Next, procedures are only useful if you plan to span data logic across multiple platforms; a website and separate client application, for example. If you're only making a web application, the procedures introduce an unnecessary level of abstraction and more things to juggle. Having to adjust a table, then a procedure, then a data model is a lot of work when adjusting a single model via the ORM would suffice.
Lastly, procedures couple your code to your database very tightly. If you want to migrate to a different database you have to migrate all the procedures, some of which may need to be heavily rewritten. This sort of migration is significantly easier with an ORM since you can yank out the backend and install a new one without the frontend application knowing the difference.
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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 :) }
Which is better? Or use and OR mapper with SP's? If you have a system with SP's already, is an OR mapper worth it?
I like ORM's because you don't have to reinvent the wheel. That being said, it completely depends on your application needs, development style and that of the team.
This question has already been covered Why is parameterized SQL generated by NHibernate just as fast as a stored procedure?
There is nothing good to be said about stored procedures. There were a necessity 10 years ago but every single benefit of using sprocs is no longer valid. The two most common arguments are regarding security and performance. The "sending stuff over the wire" crap doesn't hold either, I can certainly create a query dynamically to do everything on the server too. One thing the sproc proponents won't tell you is that it makes updates impossible if you are using column conflict resolution on a merge publication. Only DBAs who think they are the database overlord insist on sprocs because it makes their job look more impressive than it really is.
This has been discussed at length on previous questions.
What are the pros and cons to keeping SQL in Stored Procs versus Code
At my work, we mostly do line of business apps - contract work.
For this type of business, I'm a huge fan of ORM. About four years ago (when the ORM tools were less mature) we studied up on CSLA and rolled our own simplified ORM tool that we use in most of our applications,including some enterprise-class systems that have 100+ tables.
We estimate that this approach (which of course includes a lot of code generation) creates a time savings of up to 30% in our projects. Seriously, it's rediculous.
There is a small performance trade-off, but it's insubstantial as long as you have a decent understanding of software development. There are always exceptions that require flexibility.
For instance, extremely data-intensive batch operations should still be handled in specialized sprocs if possible. You probably don't want to send 100,000 huge records over the wire if you could do it in a sproc right on the database.
This is the type of problem that newbie devs run into whether they're using ORM or not. They just have to see the results and if they're competent, they will get it.
What we've seen in our web apps is that usually the most difficult to solve performance bottlenecks are no longer database-related even with ORM. Rather, tey're on the front-end (browser) due to bandwidth, AJAX overhead, etc. Even mid-range database servers are incredibly powerful these days.
Of course, other shops who work on much larger high-demand systems may have different experiences there. :)
Stored procedures hands down. OR Mappers are language specific, and often add graphic slowdowns.
Stored procedures means you're not limited by the language interface, and you can merely tack on new interfaces to the database in forwards compatible ways.
My personal opinion of OR Mappers is their existence highlights a design flaw in the popular structure of databases. Database developers should realize the tasks people are trying to achieve with complicated OR-Mappers and create server-side utilities that assist in performing this task.
OR Mappers also are epic targets of the "leaky abstraction" syndrome ( Joel On Software: Leaky Abstractions )
Where its quite easy to find things it just cant handle because of the abstraction layer not being psychic.
Stored procedures are better, in my view, because they can have an independent security configuration from the underlying tables.
This means you can allow specific operations without out allowing writes/reads to specific tables. It also limits the damage that people can do if they discover a SQL injection exploit.
Definitely ORMs. More flexible, more portable (generally they tend to have portability built in). In case of slowness you may want to use caching or hand-tuned SQL in hot spots.
Generally stored procedures have several problems with maintainability.
separate from application (so many changes have now to be made in two places)
generally harder to change
harder to put under version control
harder to make sure they're updated (deployment issues)
portability (already mentioned)
I personally have found that SP's tend to be faster performance wise, at least for the large data items that I execute on a regular basis. But I know many people that swear by OR tools and wouldn't do ANYTHING else.
I would argue that using an OR mapper will increase readability and maintainability of your applications source code, while using SP will increase the performance of the application.
They are not actually mutually exclusive, though to your point they usually are so.
The advantage of using Object Relational mapping is that you can swap out data sources. Not only database structure, but you could use any data source. With advent web services / Service-oriented architecture / ESB's, in a larger corporation, it would be wise to consider having a higher level separation of concerns than what you could get in stored procedures. However, in smaller companies and in application that will never use a different data source, then SP's can fit the bill fine. And one last point, it is not necessary to use an OR mapper to get the abstraction. My former team had great success by simply using an adapter model using Spring.NET to plug-in the data source.
# Kent Fredrick
My personal opinion of OR Mappers is their existence highlights a design flaw in the popular structure of databases"
I think you're talking about the difference between the relational model and object-oriented model. This is actually why we need ORMs, but the implementations of these models were done on purpose - it is not a design flow - it is just how things turned out to be historically.
Use stored procedures where you have identified a performance bottleneck. if you haven't identified a bottleneck, what are you doing with premature optimisation?
Use stored procedures where you are concerned about security access to a particular table.
Use stored procs when you have a SQL wizard who is prepared to sit and write complex queries that join together loads of tables in a legacy database- to do the things that are hard in an OR mapper.
Use the OR mapper for the other (at least) 80% of your database: where the selects and updates are so routine as to make access through stored procedures alone a pointless exercise in manual coding, and where updates are so infrequent that there is no performance cost. Use an OR mapper to automate the easy stuff.
Most OR mappers can talk to stored procs for the rest.
You should not use stored procs assuming that they're faster than a sql statement in a string, this is not necessarily the case in the last few versions of MS SQL server.
You do not need to use stored procs to thwart SQL injection attacks, there are other ways to do make sure that your query parameters are strongly typed and not just string-concatenated.
You don't need to use an OR mapper to get a POCO domain model, but it does help.
If you already have a data API that's exposed as sprocs, you'd need to justify a major architectural overhaul to go to ORM.
For a green-fields build, I'd evaluate several things:
If there's a dedicated DBA on the team, I'd lean to sprocs
If there's more than one application touching the same DB I'd lean to sprocs
If there's no possibility of database migration ever, I'd lean to sprocs
If I'm trying to implement MVCC in the DB, I'd lean to sprocs
If I'm deploying this as a product with potentially multiple backend dbs (MySql, MSSql, Oracle), I'd lean to ORM
If I'm on a tight deadline, I'd lean to ORM, since it's a faster way to create my domain model and keep it in sync with the data model (with appropriate tooling).
If I'm exposing the same domain model in multiple ways (web app, web service, RIA client), I'll lean to ORM as then data model is then hidden behind my ORM facade, making a robust domain model is more valuable to me.
I think performance is a bit of a red herring; hibernate seems to perform nearly as well or better than hand-coded SQL (due to it's caching tiers), and it's easy to write a bad query in your sproc either way.
The most important criteria are probably the team's skillset and long-term database portability needs.
Well the SP's are already there. It doesn't make sense to can them really. I guess does it make sense to use a mapper with SP's?
"I'm trying to drive in a nail. Should I use the heel of my shoe or a glass bottle?"
Both Stored Procedures and ORMs are difficult and annoying to use for a developer (though not necessarily for a DBA or architect, respectively), because they incur a start-up cost and higher maintenance cost that doesn't guarantee a pay-off.
Both will pay off well if the requirements aren't expected to change much over the lifespan of the system, but they will get in your way if you're building the system to discover the requirements in the first place.
Straight-coded SQL or quasi-ORM like LINQ and ActiveRecord is better for build-to-discover projects (which happen in the enterprise a lot more than the PR wants you to think).
Stored Procedures are better in a language-agnostic environment, or where fine-grained control over permissions is required. They're also better if your DBA has a better grasp of the requirements than your programmers.
Full-blown ORMs are better if you do Big Design Up Front, use lots of UML, want to abstract the database back-end, and your architect has a better grasp of the requirements than either your DBA or programmers.
And then there's option #4: Use all of them. A whole system is not usually just one program, and while many programs may talk to the same database, they could each use whatever method is appropriate both for the program's specific task, and for its level of maturity. That is: you start with straight-coded SQL or LINQ, then mature the program by refactoring in ORM and Stored Procedures where you see they make sense.