We are in the early stages of design of a big business application that will have multiple modules. One of the requirements is that the application should be database independent, it should support SQL Server, Oracle, MySQL and DB2.
From what I have read on the web, database independence is a very bad idea: it would result in a hard-to-maintain code, database design with the least-common features in all supported DBMSs, bad performance and bad scalability. My personal gut feeling is that the complexity of this feature, more than any other feature, could increase the development cost and time exponentially. The code will be dreadful.
But I cannot persuade anybody to ignore this feature. The problem is that most data on this issue are empirical data, lacking numbers to support the case. If anyone can share any numbers-supported data on the issue I would appreciate it.
One of the possible design options is to use Entity framework for the database tier with provider for each DBMS. My personal feeling is that writing SQL statements manually without any ORM would be a "must" since you have no control on the SQL generated by the entity framework, and a database-independent scenario will need some SQL tweaking based on the DBMS the code is targeting, and I think that third-party entity framework providers will have a significant amount of bugs that only appear in the complex scenarios that the application will have. I would like to hear from anyone who has had an experience with using entity framework for database-independent scenario before.
Also, one of the possibilities discussed by the team is to support one DBMS (SQL Server, for example) in the first iteration and then add support for other DBMSs in successive iterations. I think that since we will need a database design with the least common features, this development strategy is bad, since we need to know all the features of all databases before we start writing code for the first DBMS. I need to hear from you about this possibility, too.
Have you looked at Comparison of different SQL implementations ?
This is an interesting comparison, I believe it is reasonably current.
Designing a good relational data model for your application should be database agnostic, for the simple reason that all RDBMSs are designed to support the features of relational data models.
On the other hand, implementation of the model is normally influenced by the personal preferences of the people specifying the implementation. Everybody has their own slant on doing things, for instance you mention autoincremented identity in a comment above. These personal preferences for implementation are the hurdles that can limit portability.
Reading between the lines, the requirement for database independence has been handed down from above, with the instruction to make it so. It also seems likely that the application is intended for sale rather than in-house use. In context, the database preference of potential clients is unkown at this stage.
Given such requirements, then the practical questions include:
who will champion each specific database for design and development ? This is important, inasmuch as the personal preferences for implementation of each of these people need to be reconciled to achieve a database-neutral solution. If a specific database has no champion, chances are that implementing the application on this database will be poorly done, if at all.
who has the depth of database experience to act as moderator for the champions ? This person will have to make some hard decisions at times, but horsetrading is part of the fun.
will the programming team be productive without all of their personal favourite features ? Stored procedures, triggers etc. are the least transportable features between RDBMs.
The specification of the application itself will also need to include a clear distinction between database-agnostic and database specific design elements/chapters/modules/whatever. Amongst other things, this allows implementation with one DBMS first, with a defined effort required to implement for each subsequent DBMS.
Database-agnostic parts should include all of the DML, or ORM if you use one.
Database-specific parts should be more-or-less limited to installation and drivers.
Believe it or not, vanilla-flavoured sql is still a very powerful programming language, and personally I find it unlikely that you cannot create a performant application without database-specific features, if you wish to.
In summary, designing database-agnostic applications is an extension of a simple precept:
Encapsulate what varies
I work with Hibernate which gives me the benefits of the ORM plus the database independence. Database specific features are out of the question and this usually improves my design. Everything (domain model, business logic and data access methods) are testable so development is not painful.
مرحبا , Muhammed!
Database independence is neither "good" nor "bad". It is a design decision; it is a trade-off.
Let's talk about the choices:
It would result in a hard to maintain code
This is the choice of your programmers. If you make your code database-independent, then you should use a layer between your code and the database. The best kind of layer is one that someone else has written.
...Database design with the least common features in all supported DBMSs
This is, by definition, true. Luckily, the common features in all supported databases are fairly broad; they should all implement the SQL-99 standard.
...bad performance and bad scalability
This should not be true. The layer should add minimal cost to the database.
...this is the most feature ever that could increase the development cost and time exponentially with complexity. The code will be dreadful.
Again, I recommend that you use a layer between your code and the database.
You didn't specify which language or platform you're writing for. Luckily, many languages have already abstracted out databases:
Java has JDBC drivers
Python has the Python Database API
.NET has ADO.NET
Good luck.
Database independence is an overrated application feature. In reality, it is very rare for a large business application to be moved onto a new database platform after it's built and deployed. You can also miss out on DBMS specific features and optimisations.
That said, if you really want to include database independence, you might be best to write all your database access code against interfaces or abstract classes, like those used in the .NET System.Data.Common namespace (DbConnection, DbCommand, etc.) or use an O/RM library that supports multiple databases like NHibernate.
Related
In past year I've made numerous projects with NoSQL json based databases - the rich kinds (not the key/value stores) - such as CouchDB, MongoDB, RavenDB. I talk to fellow programmers often about my adoption, I notice though I'm always quick to add "of course SQL RDBMS system still have a place, its always whats best for particular project/task" - as a little disclaimer so not be seen as kool aid drinker, however its pretty shallow statement. Outside of legacy projects that already have an investment in RDBMS, or corporate mandates insisting on Oracle, I struggling to think of any future green field project I'd opt for a SQL database. Its CouchDB all the way for me as far as I can see with rich map/reduce, changes feed, replication support, RESTFUL api (sorry thats starting to sound like a plug)
I'd like to hear those that do "get" (beyond screencasts) NoSQL Json M/R databases such as CouchDb, what type of projects do you think you'll use MS-SQL, Oracle, Postgresql etc.. in the future ?
Thanks
One of the biggest strengths of SQL is that there is a standard way of modelling just about anything - for any given project it may not provide an optimal solution, but it does provide a reasonable one. This means that in a corporate environment you can decide that everything will be stored in Oracle to get the maintenance benefits of having a single system without the risk that it will be completely inappropriate for future projects.
This ability to handle different requirements without needing a lot of planning is also relevant at the start of projects where the design doesn't get signed off until six months into development - again, something that applies mostly in corporate development.
Using NoSQL properly generally requires better developers than you need for SQL development. One of the many SQL code samples available can be edited into a working system by someone who barely knows what they are doing. NoSQL does things like eliminating integrity checks for performance - a good developer produces well tested code that doesn't insert invalid records or understands why a few invalid records don't matter for a particular app. A bad developer thinks anything without error messages is good code. The average developer at a successful web startup can probably handle it. The average developer maintaining internal corporate apps probably can't.
On my own projects where I have complete control of the platform choice and know both the requirements and who will do the development, NoSQL is as good a complete solution as you suggest. Nothing really matters except the technical advantages - easier to code, easier to maintain, better performance, horizontal scaling.
In the corporate environment it's the other realities that dominate - incompetent developers, unknown/changing requirements, long application life cycles, the need to minimize the number of systems - NoSQL wasn't designed for that problem set.
Off-hand, I'd simply say that if the data is inherently relational, an RDBMS is the optimal choice. For instance, an order management system is a good fit for an RDBMS. If the data is not inherently relational (like Google's search index, for example), than NoSQL is a better choice. They both have their place.
My latest application is massively hierarchical/relational with objects containing sub-objects containing sub-objects, all related by natural keys. It's a perfect fit for an RDBMS, and would have been much trickier in a NoSQL DB.
ORM seems to be a fast-growing model, with both pros and cons in their side. From Ultra-Fast ASP.NET of Richard Kiessig (http://www.amazon.com/Ultra-Fast-ASP-NET-Build-Ultra-Scalable-Server/dp/1430223839/ref=pd_bxgy_b_text_b):
"I love them because they allow me to develop small, proof-of-concept sites extremely quickly. I can side step much of the SQL and related complexity that I would otherwise need and focus on the objects, business logic and presentation. However, at the same time, I also don't care for them because, unfortunately, their performance and scalability is usually very poor, even when they're integrated with a comprehensive caching system (the reason for that becomes clear when you realize that when properly configured, SQL Server itself is really just a big data cache"
My questions are:
What is your comment about Richard's idea. Do you agree with him or not? If not, please tell why.
What is the best suitable fields for ORM and traditional database query? in other words, where you should use ORM and where you should use traditional database query :), which kind/size... of applications you should undoubtedly choose ORM/traditional database query
Thanks in advance
I can't agree to the common complain about ORMs that they perform bad. I've seen many plain-SQL applications until now. While it is theoretically possible to write optimized SQL, in reality, they ruin all the performance gain by writing not optimized business logic.
When using plain SQL, the business logic gets highly coupled to the db model and database operations and optimizations are up to the business logic. Because there is no oo model, you can't pass around whole object structures. I've seen many applications which pass around primary keys and retrieve the data from the database on each layer again and again. I've seen applications which access the database in loops. And so on. The problem is: because the business logic is already hardly maintainable, there is no space for any more optimizations. Often when you try to reuse at least some of your code, you accept that it is not optimized for each case. The performance gets bad by design.
An ORM usually doesn't require the business logic to care too much about data access. Some optimizations are implemented in the ORM. There are caches and the ability for batches. This automatic (and runtime-dynamic) optimizations are not perfect, but they decouple the business logic from it. For instance, if a piece of data is conditionally used, it loads it using lazy loading on request (exactly once). You don't need anything to do to make this happen.
On the other hand, ORM's have a steep learning curve. I wouldn't use an ORM for trivial applications, unless the ORM is already in use by the same team.
Another disadvantage of the ORM is (actually not of the ORM itself but of the fact that you'll work with a relational database an and object model), that the team needs to be strong in both worlds, the relational as well as the oo.
Conclusion:
ORMs are powerful for business-logic centric applications with data structures that are complex enough that having an OO model will advantageous.
ORMs have usually a (somehow) steep learning curve. For small applications, it could get too expensive.
Applications based on simple data structures, having not much logic to manage it, are most probably easier and straight forward to be written in plain sql.
Teams with a high level of database knowledge and not much experience in oo technologies will most probably be more efficient by using plain sql. (Of course, depending on the applications they write it could be recommendable for the team to switch the focus)
Teams with a high level of oo knowledge and only basic database experience are most probably more efficient by using an ORM. (same here, depending on the applications they write it could be recommendable for the team to switch the focus)
ORM is pretty old, at least in the Java world.
Major problems with ORM:
Object-Oriented model and Relational model are quite different.
SQL is a high level language to access data based on relational algebra, different from any OO language like C#, Java or Visual Basic.Net. Mixing those can you the worst of two worlds, instead of the best
For more information search the web on things like 'Object-relational impedance mismatch'
Either case, a good ORM framework saves you on quite some boiler-plate code. But you still need to have knowlegde of SQL, how to setup a good SQL databasemodel. Start with creating a good databasemodel using SQL, then base your OO model on that (not the other way around)
However, the above only holds if you really need to use a SQL database. I recommend looking into NoSQL movement as well. There's stuff like Cassandra, Couch-db. While google'ing for .net solutions I found this stackoverflow question: https://stackoverflow.com/questions/1777103/what-nosql-solutions-are-out-there-for-net
I'm the author of the book with the text quoted in the question.
Let me emphatically add that I am not arguing against using business objects or object oriented programming.
One issue I have with conventional ORM -- for example, LINQ to SQL or Entity Framework -- is that it often leads to developers making DB calls when they don't even realize that they're doing so. This, in turn, is a performance and scalability killer.
I review lots of websites for performance issues, and have found that DB chattiness is one of the most common causes of serious problems. Unfortunately, ORM tends to encourage chattiness, in spades.
The other complaints I have about ORM include:
No support for command batching
No support for multiple result sets
No support for table valued parameters
No support for native async calls (making them from a background thread doesn't count)
Support for SqlDependency and SqlCacheDependency is klunky if/when it works at all
I have no objection to using ORM tactically, to address specific business issues. But I do object to using it haphazardly, to the point where developers do things like make the exact same DB call dozens of time on the same page, or issue hugely expensive queries without considering caching and change notifications, or totally neglect async operations when scalability is a concern.
This site uses Linq-to-SQL I believe, and it's 'fairly' high traffic... I think that the time you save from writing the boiler plate code to access/insert/update simple items is invaluable, but there is always the option to drop down to calling a SPROC if you have something more complex, where you know you can write some screaming fast SQL directly.
I don't think that these things have to be mutually exclusive - use the advantages of both, and if there are sections of your application that start to slow down, then you can optimise as you need to.
ORM is far older than both Java and .NET. The first one I knew about was TopLink for Smalltalk. It's an idea as old as persistent objects.
Every "CRUD on the web" framework like Ruby on Rails, Grails, Django, etc. uses ORM for persistence because they all presume that you are starting with a clean sheet object model: no legacy schema to bother with. You start with the objects to model your problem and generate the persistence from it.
It often works the other way with legacy systems: the schema is long-lived, and you may or may not have objects.
It's astonishing how quickly you can get a prototype up and running with "CRUD on the web" frameworks, but I don't see them being used to develop enterprise apps in large corporations. Maybe that's a Fortune 500 prejudice.
Database admins that I know tell me they don't like the SQL that ORMs generate because it's often inefficient. They all wish for a way to hand-tune it.
I agree with most points already made here.
ORM's are not new in .NET, LLBLGen has been around for a long time, I've been using them for >5 years now in .NET.
I've seen very bad performing code written without ORMs (in-efficient SQL queries, bad indexes, nested database calls - ouch!) and bad code written with ORMs - I'm sure I've contributed to some of the bad code too :)
What I would add is that an ORM is generally a powerful and productivity-enhancing tool that allows you to stop worrying about plumbing db code for most of your application and concentrate on the application itself. When you start trying to write complex code (for example reporting pages or complex UI's) you need to understand what is happening underneath the hood - ignorance can be very costly. But, used properly, they are immensely powerful, and IMO won't have a detrimental effect on your apps performance. I for one wouldn't be happy on a project that didn't use an ORM.
Programming is about writing software for business use. The more we can focus on business logic and presentation and less with technicalities that only matter at certain points in time (when software goes down, when software needs upgrading, etc), the better.
Recently I read about talks of scalability from a Reddit founder, from here, and one line of him that caught my attention was this:
"Having to deal with the complexities
of relational databases (relations,
joins, constraints) is a thing of the
past."
From what I have watched, maintaining a complex database schema, when it comes to scalability, becomes a major pain as the site grows (you add a field, you reassign constraints, re-map foreign keys...etc). It was not entirely clear to me as to why is that. They're not using a NOSQL database though, they're in Postgres.
Add to that, here comes ORM, another layer of abstraction. It simplifies code writing, but almost often at a performance penalty. For me, a simple database abstraction library will do, much like lightweight AR libs out there together with database-specific "plain text" queries. I can't show you any benchmark but with the ORMs I have seen, most of them say that "ORM can often be slow".
Richard covers both sides of the coin, so I agree with him.
As for the fields, I really don't quite get the context of the "fields" you are asking about.
As others have said, you can write underperforming ORM code, and you can also write underperforming SQL.
Using ORM doesn't excuse you from knowing your SQL, and understanding how a query fits together. If you can optimize a SQL query, you can usually optimize an ORM query. For example, hibernate's criteria and HQL queries let you control which associations are joined to improve performance and avoid additional select statements. Knowing how to create an index to improve your most common query can make or break your application performance.
What ORM buys you is uniform, maintainable database access. They provide an extra layer of verification to ensure that your OO code matches up as closely as possible with your database access, and prevent you from making certain classes of stupid mistake, like writing code that's vulnerable to SQL injection. Of course, you can parameterize your own queries, but ORM buys you that advantage without having to think about it.
Never got anything but pain and frustration from ORM packages. If I'd write my SQL the way they autogen it - yeah I'd claim to be fast while my code would be slow :-) Have you ever seen SQL generated by an ORM ? Barely has PK-s, uses FK-s only for misguided interpretation of "inheritance" and if it wants to do paging it dumps the whole recordset on you and then discards 90% of it :-))) Then it locks everything in sight since it has to take in a load of records like it went back to 50 yr old IBM's batch processing.
For a while I thought that the biggest problem with ORM was splintering (not going to have a standard in 50 yrs - every year different API, pardon "model" :-) and ideologizing (everyone selling you a big philosophy - always better than everyone else's of course :-) Then I realized that it was really the total amateurism that's the root cause of the mess and everything else is just the consequence.
Then it all started to make sense. ORM was never meant to be performant or reliable - that wasn't even on the list :-) It was academic, "conceptual" toy from the day one, the consolation prize for professors pissed off that all their "relational" research papers in Prolog went down the drain when IBM and Oracle started selling that terrible SQL thing and making a buck :-)
The closest I came to trusting one was LINQ but only because it's possible and quite easy to kick out all "tracking" and use is just as deserialization layer for normal SQL code. Then I read how the object that's managing connection can develop spontaneous failures that sounded like premature GC while it still had some dangling stuff around. No way I was going to risk my neck with it after that - nope, not my head :-)
So, let me make a list:
Totally sloppy code - not going to suffer bugs and poor perf
Not going to take deadlocks from ORM's 10-100 times longer "transactions"
Drastic reduction of capabilities - SQL has huge expressive power these days
Tying you up into fringe and sloppy API (every ORM aims to hijack your codebase)
SQL queries are highly portable and SQL knowledge is totally portable
I still have to know SQL just to clean up ORM's mess anyway
For "proof-of-concept" I can just serialize to binary or XML files
not much slower, zero bug libraries and one XPath can select better anyway
I've actually done heavy traffic web sites all from XML files
if I actually need real graph then I have no use for DB - nothing real to query
I can serialize a blob and dump into SQL in like 3 lines of code
If someone claims that he does it all from DB to UI - keep your codebase locked :-)
and backup your payroll DB - you'll thank me latter :-)))
NoSQL bases are more honest than ORM - "we specialize in persistence"
and have better code quality - not surprised at all
That would be the short list :-) BTW, modern SQL engines these days do trees and spatial indexing, not to mention paging without a single record wasted. ORM-s are actually "solving" problems of 10yrs ago and promoting amateurism. To that extent NoSQL, also known as document
Is LINQ a kind of Object-Relational Mapper?
LINQ in itself is a set of language extensions to aid querying, readability and reduce code. LINQ to SQL is a kind of OR Mapper, but it isn't particularly powerful. The Entity Framework is often referred to as an OR Mapper, but it does quite a lot more.
There are several other LINQ to X implementations around, including LINQ to NHibernate and LINQ to LLBLGenPro that offer OR Mapping and supporting frameworks in a broadly similar fashion to the Entity Framework.
If you are just learning LINQ though, I'd recommend you stick to LINQ to Objects to get a feel for it, rather than diving into one of the more complicated flavours :-)
LINQ is not an ORM at all. LINQ is a way of querying "stuff", and can be more or less seen as a SQL-like language extension for different things (IEnumerables).
There are various types of "stuff" that can be queried, among them SQL Server databases. This is called LINQ-to-SQL. The way it works is that it generates (implicit) classes based on the structure of the DB and your query. In this sense it works much more like a code generator.
LINQ-to-SQL is not an ORM because it doesn't try at all to solve the object-relational impedance mismatch. In an ORM you design the classes and then either map them manually to tables or let the ORM generate the database. If you then change the database for whatever reason (typically refactoring, renormalization, denormalization), many times you are able to keep the classes as they are by changing the mapping.
LINQ-to-SQL does nothing of the sort. Your LINQ queries will be tightly coupled to the database structure. If you change the DB, you will probably have to change the LINQ as well.
LINQ to SQL (part of Visual Studio 2008) is an OR Mapper.
LINQ is a new query language that can be used to query many different types of sources.
LINQ itself is not a ORM. LINQ is the language features and methods that exist in allowing you to query objects like SQL.
"LINQ to SQL" is a provider that allows us to use LINQ against SQL strongly-typed objects.
I think a good test to ascertain whether a platform or code block displays the characteristics of an O/R-M is simply:
With his solution hat on, does the developer(s) (or his/her code generator) have any direct, unabstracted knowledge of what's inside the database?
With this criterion, the answer for differing LINQ implementations can be
Yes, knowledge of the database schema is entirely contained within the roll-your-own, LINQ utilizing O/R-M code layerorNo, knowledge of the database schema is scattered throughout the application
Further, I'd extend this characterization to three simple levels of O/R-M.
1. Abandonment.
It's a small app w/ a couple of developers and the object/data model isn't that complex and doesn't change very often. The small dev team can stay on top of it.
2. Roll your own in the data access layer.
With some managable refactoring in a data access layer, the desired O/R-M functionality can be effected in an intermediate layer by the relatively small dev team. Enough to keep the entire team on the same page.
3. Enterprise-level O/R-M specification defining/overhead introducing tools.
At some level of complexity, the need to keep all devs on the same page just swamps any overhead introduced by the formality. No need to reinvent the wheel at this level of complexity. N-hibernate or the (rough) V1.0 Entity Framework are examples of this scale.
For a richer classification, from which I borrowed and simplified, see Ted Neward's classic post at
http://blogs.tedneward.com/2006/06/26/The+Vietnam+Of+Computer+Science.aspx
where he classifies O/R-M treatments (or abdications) as
1. Abandonment. Developers simply give up on objects entirely, and return to a programming model that doesn't create the object/relational impedance mismatch. While distasteful, in certain scenarios an object-oriented approach creates more overhead than it saves, and the ROI simply isn't there to justify the cost of creating a rich domain model. ([Fowler] talks about this to some depth.) This eliminates the problem quite neatly, because if there are no objects, there is no impedance mismatch.
2. Wholehearted acceptance. Developers simply give up on relational storage entirely, and use a storage model that fits the way their languages of choice look at the world. Object-storage systems, such as the db4o project, solve the problem neatly by storing objects directly to disk, eliminating many (but not all) of the aforementioned issues; there is no "second schema", for example, because the only schema used is that of the object definitions themselves. While many DBAs will faint dead away at the thought, in an increasingly service-oriented world, which eschews the idea of direct data access but instead requires all access go through the service gateway thus encapsulating the storage mechanism away from prying eyes, it becomes entirely feasible to imagine developers storing data in a form that's much easier for them to use, rather than DBAs.
3. Manual mapping. Developers simply accept that it's not such a hard problem to solve manually after all, and write straight relational-access code to return relations to the language, access the tuples, and populate objects as necessary. In many cases, this code might even be automatically generated by a tool examining database metadata, eliminating some of the principal criticism of this approach (that being, "It's too much code to write and maintain").
4. Acceptance of O/R-M limitations. Developers simply accept that there is no way to efficiently and easily close the loop on the O/R mismatch, and use an O/R-M to solve 80% (or 50% or 95%, or whatever percentage seems appropriate) of the problem and make use of SQL and relational-based access (such as "raw" JDBC or ADO.NET) to carry them past those areas where an O/R-M would create problems. Doing so carries its own fair share of risks, however, as developers using an O/R-M must be aware of any caching the O/R-M solution does within it, because the "raw" relational access will clearly not be able to take advantage of that caching layer.
5. Integration of relational concepts into the languages. Developers simply accept that this is a problem that should be solved by the language, not by a library or framework. For the last decade or more, the emphasis on solutions to the O/R problem have focused on trying to bring objects closer to the database, so that developers can focus exclusively on programming in a single paradigm (that paradigm being, of course, objects). Over the last several years, however, interest in "scripting" languages with far stronger set and list support, like Ruby, has sparked the idea that perhaps another solution is appropriate: bring relational concepts (which, at heart, are set-based) into mainstream programming languages, making it easier to bridge the gap between "sets" and "objects". Work in this space has thus far been limited, constrained mostly to research projects and/or "fringe" languages, but several interesting efforts are gaining visibility within the community, such as functional/object hybrid languages like Scala or F#, as well as direct integration into traditional O-O languages, such as the LINQ project from Microsoft for C# and Visual Basic. One such effort that failed, unfortunately, was the SQL/J strategy; even there, the approach was limited, not seeking to incorporate sets into Java, but simply allow for embedded SQL calls to be preprocessed and translated into JDBC code by a translator.
6. Integration of relational concepts into frameworks. Developers simply accept that this problem is solvable, but only with a change of perspective. Instead of relying on language or library designers to solve this problem, developers take a different view of "objects" that is more relational in nature, building domain frameworks that are more directly built around relational constructs. For example, instead of creating a Person class that holds its instance data directly in fields inside the object, developers create a Person class that holds its instance data in a RowSet (Java) or DataSet (C#) instance, which can be assembled with other RowSets/DataSets into an easy-to-ship block of data for update against the database, or unpacked from the database into the individual objects.
Linq To SQL using the dbml designer yes, otherwise Linq is just a set of extension methods for Enumerables.
My boss asks me to write only ANSI SQL to make it database independent. But I learned that it is not that easy as no database fully ANSI SQL compatible. SQL code can rarely be ported between database systems without modifications.
I saw people do different way to make their program database independent. For example:
Externalize SQL statements to resource files.
Write many providers class to support different database.
Write only simple SQL, and keep away from advance functions/joins.
Do you always write your code "any database ready"? Or do it only if needed?
If yes, how do you achieve it?
You could use one of the many Object/Relational Mapper tools, like Hibernate/NHibernate, LLBLGen, and so forth. That can get you a long way to database portability. No matter what you do, you need to have some abstraction layer between your model and the rest of your code. This doesn't mean you need some sort of dependency injection infrastructure, but good OO design can get you pretty far. Also, sticking with plain SQL and thinking that will get you portability is rather naive. That would be true if your application was trivial and only used very trivial queries.
As for always writing an application to be "any database ready," I usually use some sort of abstraction layer so it is not hard to move from one database system to another. However, in many circumstances, this is not required, you are developing for the Oracle platform or SQL Server or MySQL whatever so you shouldn't sacrifice the benefits of your chosen RDBMS just for the possibility of an entirely seamless transition. Nevertheless, if you build a good abstraction layer, even targeting a specific RDBMS won't necessarily be too difficult to migrate to a different RDBMS.
To decouple the database engine from your application, use a database abstraction layer (also data access layer, or DAL). You didn't mention what language you use, but there are good database abstraction libraries for all the major languages.
However, by avoiding database-specific optimizations you will be missing out on the advantages of your particular brand. I usually abstract what's possible and use what's available. Changing database engines is a major decision and doesn't happen often, and it's best to use the tools you have available to the max.
Tell your boss to mind his own business. No, of course one can't say such things to one's boss, but stay tuned.
What's interesting is what business value is supposed to be supported by this requirement. One obvious candidate seems to be that the database code should be ready for working on other database engines than the current. If that's the case then that's what should be stated in the requirement.
From there it's up to you as an engineer to figure out the different ways to achieve that. One might be writing ANSI SQL. One might be using a database abstraction layer.
Further it's your responsibility to inform your boss what the costs of the different alternatives are (in terms of performance, speed of development, etcetera).
"Write ANSI SQL"... gah!
Just for the record. There is a similar question here on Stackoverflow:
Database design for database-agnostic applications
Being 100% compliant to ANSI SQL is a difficult goal to meet, and yet it doesn't guarantee portability anyway. So it's an artificial goal.
Presumably your boss is asking for this in order to make it easy and quick to switch database brands for some hypothetical purpose in the future (which actually may never come). But he's trading that future efficiency for a greater amount of work now, since it's harder to make the code database-neutral.*
So if you can phrase the problem in terms of the goals your manager should be focusing on, like finishing the current project phase on time and on budget, it may be more effective than just telling him it's too difficult to make the code database-neutral.
There is a scenario when you need to make truly database-neutral code, that is when you are developing a shrink-wrap application that is required to support multiple brands of database.
Anyway, even if you currently support only one brand, there are certainly cases where you have a choice to code some SQL using proprietary features, but there also exists a more portable way to achieve the same result. You can treat these as "low-hanging fruit" cases, and you can make it easier to port the code in the future if the need arises. But don't limit yourself either, use proprietary solutions if they give good value. Perhaps add a note in the comments that this deserves review if/when you need to make a port.
* I prefer the word "neutral" instead of "agnostic" when talking about platform-independence. It avoids the religious overtone. :-)
There is a lot of information out there on object-relational mappers and how to best avoid impedance mismatch, all of which seem to be moot points if one were to use an object database. My question is why isn't this used more frequently? Is it because of performance reasons or because object databases cause your data to become proprietary to your application or is it due to something else?
Familiarity. The administrators of databases know relational concepts; object ones, not so much.
Performance. Relational databases have been proven to scale far better.
Maturity. SQL is a powerful, long-developed language.
Vendor support. You can pick between many more first-party (SQL servers) and third-party (administrative interfaces, mappings and other kinds of integration) tools than is the case with OODBMSs.
Naturally, the object-oriented model is more familiar to the developer, and, as you point out, would spare one of ORM. But thus far, the relational model has proven to be the more workable option.
See also the recent question, Object Orientated vs Relational Databases.
I've been using db4o which is an OODB and it solves most of the cons listed:
Familiarity - Programmers know their language better then SQL (see Native queries)
Performance - this one is highly subjective but you can take a look at PolePosition
Vendor support and maturity - can change over time
Cannot be used by programs that don't also use the same framework - There are OODB standards and you can use different frameworks
Versioning is probably a bit of a bitch - Versioning is actually easier!
The pros I'm interested in are:
Native queries - Db4o lets you write queries in your static typed language so you don't have to worry about mistyping a string and finding data missing at runtime,
Ease of use - Defining buissiness logic in the domain layer, persistence layer (mapping) and finally the SQL database is certainly violation of DRY. With OODB you define your domain where it belongs.
I agree - OODB have a long way to go but they are going. And there are domain problems out there that are better solved by OODB,
One objection to object databases is that it creates a tight coupling between the data and your code. For certain apps this may be OK, but not for others. One nice thing that a relational database gives you is the possibility to put many views on your data.
Ted Neward explains this and a lot more about OODBMSs a lot better than this.
It has nothing to do with performance. That is to say, basically all applications would perform better with an OODB. But that would also put lots of DBA's out of work/having to learn a new technology. Even more people would be out of work correcting errors in the data. That's unlikely to make OODBs popular with established companies. Gavin seems to be totally clueless, a better link would be Kirk
Cons:
Cannot be used by programs that
don't also use the same framework
for accessing the data store, making
it more difficult to use across the
enterprise.
Less resources available online for
non SQL-based database
No compatibility across database
types (can't swap to a different db
provider without changing all the
code)
Versioning is probably a bit of a
bitch. I'd guess adding a new
property to an object isn't quite as
easy as adding a new column to a
table.
Sören
All of the reasons you stated are valid, but I see the problem with OODBMS is the logical data model. The object-model (or rather the network model of the 70s) is not as simple as the relational one, and is therefore inferior.
jodonnel, i dont' see how use of object databases couples application code to the data. You can still abstract your application from the OODB through using a Repository pattern and replace with an ORM backed SQL database if you design things properly.
For an OO application, an OO database will provide a more natural fit for persisting objects.
What's probably true is that you tie your data to your domain model, but then that's the crux!
Wouldn't it be good to have a single way of looking at both data, business rules and processes using a domain centric view?
So, a big pro is that an OODB matches how most modern, enterprise level object orientated software applications are designed, there is no extra effort to design a data layer using a different (relational) design. Cheaper to build and maintain, and in many cases general higher performance.
Cons, just general lack of maturity and adoption i reckon...