What is the best way or recommended best practice in the flow of database driven asp.net web application? I mean the database first or coding first or side by side?
Your data access code won't compile without an existing database - unless you stub (or Mock) it. So probably the database comes first.
But it is a bad idea to do whole chunks of the application in isolation. Ideally you should design and build slivers of the system - database and application - hand-in-hand. These slivers should be cohesive sub-sets of functionality, probably smaller than sub-systems. Inevitably, the act of coding screens and business rules will throw up problems in the data model. So it is good to have a data modeller or DBA who is happy to work incrementally alongside the developers.
edit
Stephanie makes an extremely pertinent point:
"the core tables which are persisting
your app's data really can't be
piecemealed. Most of the data is known
at project start. It has a form, you
need to find it."
I agree that the core entities are knowable at project start, and the physical data model can be derived from that logical data model. But I don't think it is ever possible to nail down completely the structure of any table, even a core table, at the start. This is because at the start of the design/build phase all we have to go on are the Requirements, and if there's one thing history tells us about the Requirements it is that they will change.
So, new tables will be needed and some existing tables will become obsolete. There will be columns which need to be added, columns which need to be modified, columns which need to be dropped. This is why Nature gave us the ALTER TABLE statement.
I am not suggesting that we don't design our tables, or assemble them piecemeal. I am merely suggesting that when we start designing the HR sub-system we need to worry about the EMPLOYEES table and the SALARIES table. We don't need to concern ourselves with INVENTORY or ORDERS until we commence work on Sales.
We personally start with the Domain and do things side-by-side. The important part is that we implement vertical slices of the application (fully working end-to-end features), not horizontal slices (e.g. first the whole database layer, then the data access, then the services, then the presentation): we build the application incrementally and demonstrate progress with working code after each iteration.
Applications are all about features.
You don't build apps to store data,
but to provide functionality. If we
can't agree on that, the discussion is
moot of course. Software should be
developed to satisfy the needs of its
users and not of its developers.
Well I have really no understanding of the second sentence. If you think my company pays me a good salary to write code that satisfies me and not my users you're crazy. So that argument is a strawman. Back to the first.
This is a common view point of application centric people (they), vs. database centric people (We). They see the entire point of the exercise to "provide features". Those are things the clients know they want and ask for them. To them, the database is just persistence required for these features. And when they are done, that's it, features delivered, database is sufficient for those features. Could be an entire Rube Goldberg inside the database with redundant data, severe violations of normal forms, constraints enforced by the application, what have you.
think overall usability alone outweighs database design
If the design of your database is affecting your usability than the design was bad. I have no doubt that one who strives for features will leave the database in such a state that it severely hampers usability.
Data Centric people, don't look at a system as a place to provide only what's been asked for, but a repository of Intellectual Capital that can be exploited by more than whatever the Application-du-jour is. I can't begin to describe the number of cases where one team has used the database of some other team's app to enhance their apps value. Just look at all the medical research that is nothing more that the meta-analysis of existing studies. None of that is possible if you believe that only the features of your app matter and subsequent uses of your apps data do not.
A good data model isn't inviolate. Sure you'll add to it, change it when requirements change. But if you don't completely understand your data, I don't know how anyone can begin to write code.
I guess you need first to define datamodel and only then going coding. You should plan everything carefully before actually writting the code.
First is a feature list.
Then, detailed spec.
Then test plan and design of all, including databases.
Then, it wouldn't matter which to implement first.
You'll probably end up doing it "side by side".
You need some data to be able to test the application, but you need the application to be able to verify that you're storing the correct data.
Do some modelling first and then build the minimum you can for one or two features. Then when these are working add the next feature and so on.
You'll need to write some database update procedures (both the code and the rules about what and when to update) as you will have to extend your tables, but you'll need those for the final system anyway as it will have to change as new requirements come along.
Having done it quite a few times, I find myself invariably doing it like so:
Define the problem I'm trying to solve.
Write out some use-cases.
Have my significant other or a friend tell me if this is even a problem.
Sketch out a few sample screens.
Write flow diagrams for the use cases.
Ask my Rubber-duck questions.
Use questions to refine 1-6.
Write out the 'nouns'. Those become my data Model.
Write out the actions. Those become application logic.
Code data Model.
Code Application Logic.
Realize I've gotten it a little wrong.
Repeat 10-12 as many times as needed.
Ask, "Have I solved the problem"?
If not, rinse, lather and repeat 1-15.
This is a trick question. IMO, they both come in parallel during your planning and design phase. They are so closely related that it make sense to do them together. Just keep in mind that your database design will be almost fully developed while your code is still in its infancy (though your application logic should be almost fully mapped out in you head or on paper)
The idea is that you're designing your solution in the context of the problem. When you're planning out your solution you will be (or should be) defining your application as a set of things and actions (nouns and verbs).
For example, a very basic helpdesk program has people and tickets. People need to create tickets, update tickets, and close tickets. The nouns that require persistent storage will comprise your database, and the nouns + actions will be contained in your application.
Sometimes your table mappings and the relationship between tables will be obvious (IE people create tickets, ticket.creatorID = people.personID) and other times the relationship doesn't really click in your head until you start working through use cases or until you start writing your code (IE different ppl have different access levels defining what they can do. At a glance this would seem like a simple field in a table, but in practice it is better as a separate table).
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Whether we like it or not, many if not most of us developers either regularly work with databases or may have to work with one someday. And considering the amount of misuse and abuse in the wild, and the volume of database-related questions that come up every day, it's fair to say that there are certain concepts that developers should know - even if they don't design or work with databases today.
What is one important concept that developers and other software professionals ought to know about databases?
The very first thing developers should know about databases is this: what are databases for? Not how do they work, nor how do you build one, nor even how do you write code to retrieve or update the data in a database. But what are they for?
Unfortunately, the answer to this one is a moving target. In the heydey of databases, the 1970s through the early 1990s, databases were for the sharing of data. If you were using a database, and you weren't sharing data you were either involved in an academic project or you were wasting resources, including yourself. Setting up a database and taming a DBMS were such monumental tasks that the payback, in terms of data exploited multiple times, had to be huge to match the investment.
Over the last 15 years, databases have come to be used for storing the persistent data associated with just one application. Building a database for MySQL, or Access, or SQL Server has become so routine that databases have become almost a routine part of an ordinary application. Sometimes, that initial limited mission gets pushed upward by mission creep, as the real value of the data becomes apparent. Unfortunately, databases that were designed with a single purpose in mind often fail dramatically when they begin to be pushed into a role that's enterprise wide and mission critical.
The second thing developers need to learn about databases is the whole data centric view of the world. The data centric world view is more different from the process centric world view than anything most developers have ever learned. Compared to this gap, the gap between structured programming and object oriented programming is relatively small.
The third thing developers need to learn, at least in an overview, is data modeling, including conceptual data modeling, logical data modeling, and physical data modeling.
Conceptual data modeling is really requirements analysis from a data centric point of view.
Logical data modeling is generally the application of a specific data model to the requirements discovered in conceptual data modeling. The relational model is used far more than any other specific model, and developers need to learn the relational model for sure. Designing a powerful and relevant relational model for a nontrivial requirement is not a trivial task. You can't build good SQL tables if you misunderstand the relational model.
Physical data modeling is generally DBMS specific, and doesn't need to be learned in much detail, unless the developer is also the database builder or the DBA. What developers do need to understand is the extent to which physical database design can be separated from logical database design, and the extent to which producing a high speed database can be accomplished just by tweaking the physical design.
The next thing developers need to learn is that while speed (performance) is important, other measures of design goodness are even more important, such as the ability to revise and extend the scope of the database down the road, or simplicity of programming.
Finally, anybody who messes with databases needs to understand that the value of data often outlasts the system that captured it.
Whew!
Good question. The following are some thoughts in no particular order:
Normalization, to at least the second normal form, is essential.
Referential integrity is also essential, with proper cascading delete and update considerations.
Good and proper use of check constraints. Let the database do as much work as possible.
Don't scatter business logic in both the database and middle tier code. Pick one or the other, preferably in middle tier code.
Decide on a consistent approach for primary keys and clustered keys.
Don't over index. Choose your indexes wisely.
Consistent table and column naming. Pick a standard and stick to it.
Limit the number of columns in the database that will accept null values.
Don't get carried away with triggers. They have their use but can complicate things in a hurry.
Be careful with UDFs. They are great but can cause performance problems when you're not aware how often they might get called in a query.
Get Celko's book on database design. The man is arrogant but knows his stuff.
First, developers need to understand that there is something to know about databases. They're not just magic devices where you put in the SQL and get out result sets, but rather very complicated pieces of software with their own logic and quirks.
Second, that there are different database setups for different purposes. You do not want a developer making historical reports off an on-line transactional database if there's a data warehouse available.
Third, developers need to understand basic SQL, including joins.
Past this, it depends on how closely the developers are involved. I've worked in jobs where I was developer and de facto DBA, where the DBAs were just down the aisle, and where the DBAs are off in their own area. (I dislike the third.) Assuming the developers are involved in database design:
They need to understand basic normalization, at least the first three normal forms. Anything beyond that, get a DBA. For those with any experience with US courtrooms (and random television shows count here), there's the mnemonic "Depend on the key, the whole key, and nothing but the key, so help you Codd."
They need to have a clue about indexes, by which I mean they should have some idea what indexes they need and how they're likely to affect performance. This means not having useless indices, but not being afraid to add them to assist queries. Anything further (like the balance) should be left for the DBA.
They need to understand the need for data integrity, and be able to point to where they're verifying the data and what they're doing if they find problems. This doesn't have to be in the database (where it will be difficult to issue a meaningful error message for the user), but has to be somewhere.
They should have the basic knowledge of how to get a plan, and how to read it in general (at least enough to tell whether the algorithms are efficient or not).
They should know vaguely what a trigger is, what a view is, and that it's possible to partition pieces of databases. They don't need any sort of details, but they need to know to ask the DBA about these things.
They should of course know not to meddle with production data, or production code, or anything like that, and they should know that all source code goes into a VCS.
I've doubtless forgotten something, but the average developer need not be a DBA, provided there is a real DBA at hand.
Basic Indexing
I'm always shocked to see a table or an entire database with no indexes, or arbitrary/useless indexes. Even if you're not designing the database and just have to write some queries, it's still vital to understand, at a minimum:
What's indexed in your database and what's not:
The difference between types of scans, how they're chosen, and how the way you write a query can influence that choice;
The concept of coverage (why you shouldn't just write SELECT *);
The difference between a clustered and non-clustered index;
Why more/bigger indexes are not necessarily better;
Why you should try to avoid wrapping filter columns in functions.
Designers should also be aware of common index anti-patterns, for example:
The Access anti-pattern (indexing every column, one by one)
The Catch-All anti-pattern (one massive index on all or most columns, apparently created under the mistaken impression that it would speed up every conceivable query involving any of those columns).
The quality of a database's indexing - and whether or not you take advantage of it with the queries you write - accounts for by far the most significant chunk of performance. 9 out of 10 questions posted on SO and other forums complaining about poor performance invariably turn out to be due to poor indexing or a non-sargable expression.
Normalization
It always depresses me to see somebody struggling to write an excessively complicated query that would have been completely straightforward with a normalized design ("Show me total sales per region.").
If you understand this at the outset and design accordingly, you'll save yourself a lot of pain later. It's easy to denormalize for performance after you've normalized; it's not so easy to normalize a database that wasn't designed that way from the start.
At the very least, you should know what 3NF is and how to get there. With most transactional databases, this is a very good balance between making queries easy to write and maintaining good performance.
How Indexes Work
It's probably not the most important, but for sure the most underestimated topic.
The problem with indexing is that SQL tutorials usually don't mention them at all and that all the toy examples work without any index.
Even more experienced developers can write fairly good (and complex) SQL without knowing more about indexes than "An index makes the query fast".
That's because SQL databases do a very good job working as black-box:
Tell me what you need (gimme SQL), I'll take care of it.
And that works perfectly to retrieve the correct results. The author of the SQL doesn't need to know what the system is doing behind the scenes--until everything becomes sooo slooooow.....
That's when indexing becomes a topic. But that's usually very late and somebody (some company?) is already suffering from a real problem.
That's why I believe indexing is the No. 1 topic not to forget when working with databases. Unfortunately, it is very easy to forget it.
Disclaimer
The arguments are borrowed from the preface of my free eBook "Use The Index, Luke". I am spending quite a lot of my time explaining how indexes work and how to use them properly.
I just want to point out an observation - that is that it seems that the majority of responses assume database is interchangeable with relational databases. There are also object databases, flat file databases. It is important to asses the needs of the of the software project at hand. From a programmer perspective the database decision can be delayed until later. Data modeling on the other hand can be achieved early on and lead to much success.
I think data modeling is a key component and is a relatively old concept yet it is one that has been forgotten by many in the software industry. Data modeling, especially conceptual modeling, can reveal the functional behavior of a system and can be relied on as a road map for development.
On the other hand, the type of database required can be determined based on many different factors to include environment, user volume, and available local hardware such as harddrive space.
Avoiding SQL injection and how to secure your database
Every developer should know that this is false: "Profiling a database operation is completely different from profiling code."
There is a clear Big-O in the traditional sense. When you do an EXPLAIN PLAN (or the equivalent) you're seeing the algorithm. Some algorithms involve nested loops and are O( n ^ 2 ). Other algorithms involve B-tree lookups and are O( n log n ).
This is very, very serious. It's central to understanding why indexes matter. It's central to understanding the speed-normalization-denormalization tradeoffs. It's central to understanding why a data warehouse uses a star-schema which is not normalized for transactional updates.
If you're unclear on the algorithm being used do the following. Stop. Explain the Query Execution plan. Adjust indexes accordingly.
Also, the corollary: More Indexes are Not Better.
Sometimes an index focused on one operation will slow other operations down. Depending on the ratio of the two operations, adding an index may have good effects, no overall impact, or be detrimental to overall performance.
I think every developer should understand that databases require a different paradigm.
When writing a query to get at your data, a set-based approach is needed. Many people with an interative background struggle with this. And yet, when they embrace it, they can achieve far better results, even though the solution may not be the one that first presented itself in their iterative-focussed minds.
Excellent question. Let's see, first no one should consider querying a datbase who does not thoroughly understand joins. That's like driving a car without knowing where the steering wheel and brakes are. You also need to know datatypes and how to choose the best one.
Another thing that developers should understand is that there are three things you should have in mind when designing a database:
Data integrity - if the data can't be relied on you essentially have no data - this means do not put required logic in the application as many other sources may touch the database. Constraints, foreign keys and sometimes triggers are necessary to data integrity. Don't fail to use them because you don't like them or don't want to be bothered to understand them.
Performance - it is very hard to refactor a poorly performing database and performance should be considered from the start. There are many ways to do the same query and some are known to be faster almost always, it is short-sighted not to learn and use these ways. Read some books on performance tuning before designing queries or database structures.
Security - this data is the life-blood of your company, it also frequently contains personal information that can be stolen. Learn to protect your data from SQL injection attacks and fraud and identity theft.
When querying a database, it is easy to get the wrong answer. Make sure you understand your data model thoroughly. Remember often actual decisions are made based on the data your query returns. When it is wrong, the wrong business decisions are made. You can kill a company from bad queries or loose a big customer. Data has meaning, developers often seem to forget that.
Data almost never goes away, think in terms of storing data over time instead of just how to get it in today. That database that worked fine when it had a hundred thousand records, may not be so nice in ten years. Applications rarely last as long as data. This is one reason why designing for performance is critical.
Your database will probaly need fields that the application doesn't need to see. Things like GUIDs for replication, date inserted fields. etc. You also may need to store history of changes and who made them when and be able to restore bad changes from this storehouse. Think about how you intend to do this before you come ask a web site how to fix the problem where you forgot to put a where clause on an update and updated the whole table.
Never develop in a newer version of a database than the production version. Never, never, never develop directly against a production database.
If you don't have a database administrator, make sure someone is making backups and knows how to restore them and has tested restoring them.
Database code is code, there is no excuse for not keeping it in source control just like the rest of your code.
Evolutionary Database Design. http://martinfowler.com/articles/evodb.html
These agile methodologies make database change process manageable, predictable and testable.
Developers should know, what it takes to refactor a production database in terms of version control, continious integration and automated testing.
Evolutionary Database Design process has administrative aspects, for example a column is to be dropped after some life time period in all databases of this codebase.
At least know, that Database Refactoring concept and methodologies exist.
http://www.agiledata.org/essays/databaseRefactoringCatalog.html
Classification and process description makes it possible to implement tooling for these refactorings too.
About the following comment to Walter M.'s answer:
"Very well written! And the historical perspective is great for people who weren't doing database work at that time (i.e. me)".
The historical perspective is in a certain sense absolutely crucial. "Those who forget history, are doomed to repeat it.". Cfr XML repeating the hierarchical mistakes of the past, graph databases repeating the network mistakes of the past, OO systems forcing the hierarchical model upon users while everybody with even just a tenth of a brain should know that the hierarchical model is not suitable for general-purpose representation of the real world, etcetera, etcetera.
As for the question itself:
Every database developer should know that "Relational" is not equal to "SQL". Then they would understand why they are being let down so abysmally by the DBMS vendors, and why they should be telling those same vendors to come up with better stuff (e.g. DBMS's that are truly relational) if they want to go on sucking hilarious amounts of money out of their customers for such crappy software).
And every database developer should know everything about the relational algebra. Then there would no longer be a single developer left who had to post these stupid "I don't know how to do my job and want someone else to do it for me" questions on Stack Overflow anymore.
From my experience with relational databases, every developer should know:
- The different data types:
Using the correct type for the correct job will make your DB design more robust, your queries faster and your life easier.
- Learn about 1xM and MxM:
This is the bread and butter for relational databases. You need to understand one-to-many and many-to-many relations and apply then when appropriate.
- "K.I.S.S." principle applies to the DB as well:
Simplicity always works best. Provided you have studied how DB work, you will avoid unnecessary complexity which will lead to maintenance and speed problems.
- Indices:
It's not enough if you know what they are. You need to understand when to used them and when not to.
also:
Boolean algebra is your friend
Images: Don't store them on the DB. Don't ask why.
Test DELETE with SELECT
I would like everyone, both DBAs and developer/designer/architects, to better understand how to properly model a business domain, and how to map/translate that business domain model into both a normalized database logical model, an optimized physical model, and an appropriate object oriented class model, each one of which is (can be) different, for various reasons, and understand when, why, and how they are (or should be) different from one another.
I would say strong basic SQL skills. I've seen a lot of developers so far who know a little about databases but are always asking for tips about how to formulate a quite simple query. Queries are not always that easy and simple. You do have to use multiple joins (inner, left, etc.) when querying a well normalized database.
I think a lot of the technical details have been covered here and I don't want to add to them. The one thing I want to say is more social than technical, don't fall for the "DBA knowing the best" trap as an application developer.
If you are having performance issues with query take ownership of the problem too. Do your own research and push for the DBAs to explain what's happening and how their solutions are addressing the problem.
Come up with your own suggestions too after you have done the research. That is, I try to find a cooperative solution to the problem rather than leaving database issues to the DBAs.
Simple respect.
It's not just a repository
You probably don't know better than the vendor or the DBAs
You won't support it at 3 a.m. with senior managers shouting at you
Consider Denormalization as a possible angel, not the devil, and also consider NoSQL databases as an alternative to relational databases.
Also, I think the Entity-Relation model is a must-know for every developper even if you don't design databases. It'll let you understand thoroughly what's your database all about.
Never insert data with the wrong text encoding.
Once your database becomes polluted with multiple encodings, the best you can do is apply some kind combination of heuristics and manual labor.
Aside from syntax and conceptual options they employ (such as joins, triggers, and stored procedures), one thing that will be critical for every developer employing a database is this:
Know how your engine is going to perform the query you are writing with specificity.
The reason I think this is so important is simply production stability. You should know how your code performs so you're not stopping all execution in your thread while you wait for a long function to complete, so why would you not want to know how your query will affect the database, your program, and perhaps even the server?
This is actually something that has hit my R&D team more times than missing semicolons or the like. The presumtion is the query will execute quickly because it does on their development system with only a few thousand rows in the tables. Even if the production database is the same size, it is more than likely going to be used a lot more, and thus suffer from other constraints like multiple users accessing it at the same time, or something going wrong with another query elsewhere, thus delaying the result of this query.
Even simple things like how joins affect performance of a query are invaluable in production. There are many features of many database engines that make things easier conceptually, but may introduce gotchas in performance if not thought of clearly.
Know your database engine execution process and plan for it.
For a middle-of-the-road professional developer who uses databases a lot (writing/maintaining queries daily or almost daily), I think the expectation should be the same as any other field: You wrote one in college.
Every C++ geek wrote a string class in college. Every graphics geek wrote a raytracer in college. Every web geek wrote interactive websites (usually before we had "web frameworks") in college. Every hardware nerd (and even software nerds) built a CPU in college. Every physician dissected an entire cadaver in college, even if she's only going to take my blood pressure and tell me my cholesterol is too high today. Why would databases be any different?
Unfortunately, they do seem different, today, for some reason. People want .NET programmers to know how strings work in C, but the internals of your RDBMS shouldn't concern you too much.
It's virtually impossible to get the same level of understanding from just reading about them, or even working your way down from the top. But if you start at the bottom and understand each piece, then it's relatively easy to figure out the specifics for your database. Even things that lots of database geeks can't seem to grok, like when to use a non-relational database.
Maybe that's a bit strict, especially if you didn't study computer science in college. I'll tone it down some: You could write one today, completely, from scratch. I don't care if you know the specifics of how the PostgreSQL query optimizer works, but if you know enough to write one yourself, it probably won't be too different from what they did. And you know, it's really not that hard to write a basic one.
The order of columns in a non-unique index is important.
The first column should be the column that has the most variability in its content (i.e. cardinality).
This is to aid SQL Server ability to create useful statistics in how to use the index at runtime.
Understand the tools that you use to program the database!!!
I wasted so much time trying to understand why my code was mysteriously failing.
If you're using .NET, for example, you need to know how to properly use the objects in the System.Data.SqlClient namespace. You need to know how to manage your SqlConnection objects to make sure they are opened, closed, and when necessary, disposed properly.
You need to know that when you use a SqlDataReader, it is necessary to close it separately from your SqlConnection. You need to understand how to keep connections open when appropriate to how to minimize the number of hits to the database (because they are relatively expensive in terms of computing time).
Basic SQL skills.
Indexing.
Deal with different incarnations of DATE/ TIME/ TIMESTAMP.
JDBC driver documentation for the platform you are using.
Deal with binary data types (CLOB, BLOB, etc.)
For some projects, and Object-Oriented model is better.
For other projects, a Relational model is better.
The impedance mismatch problem, and know the common deficiencies or ORMs.
RDBMS Compatibility
Look if it is needed to run the application in more than one RDBMS. If yes, it might be necessary to:
avoid RDBMS SQL extensions
eliminate triggers and store procedures
follow strict SQL standards
convert field data types
change transaction isolation levels
Otherwise, these questions should be treated separately and different versions (or configurations) of the application would be developed.
Don't depend on the order of rows returned by an SQL query.
Three (things) is the magic number:
Your database needs version control too.
Cursors are slow and you probably don't need them.
Triggers are evil*
*almost always
At linkedin, when you visit someones profile you can see how you are connected to them. I believe that linkedin shows upto 3rd level connections if not more, something like
shabda -> Foo user, bar user, baz user -> Joel's connection -> Joel
How can I represent this in the database.
If I model as,
User
Id PK
Name Char
Connection
User1 FK
User2 FK
Then to find the network, three levels deep, I need to get all my connection, their connections, and their connections, and then see if the current user is there. This obviously would be very inefficient with DB of any size, and probably clunky to work with as well.
Since, on linked in I can see this network, on any profile I visit, I don't think this is precalculated either.
The other thing which comes to my mind is probably this is best not stored in a relational DB, but then what would be the best way to store and retrieve it?
My recommendation would be to use a graph database. There seems to be only one implementation currently available, and that's Neo4j. It's written in Java, but has bindings to Ruby and Scala (Python in progress).
If you don't know Java, you probably won't be able to find anything similar on any other platform (yet), unfortunately. However, if you do know Java (or are at least willing to learn), it's way worth it. (Technically you don't even need to learn Java because of the Ruby/Python bindings.) Neo4j was built for exactly what you're trying to do. You'd go through a ton of trouble trying to implement this in a relational database, when you'd be able to do the exact same thing in only a few lines of Java code, and also much more efficiently.
If that's not an option, I'd still recommend looking at other database types such as object databases. Relational databases weren't built for this kind of thing, and you'd go through more pain by trying to do it in an RDBMS than by switching to a different kind of database and learning it.
I don't see why there's anything wrong with using a relational database for this. The tables defined in the question are an excellent start. With proper optimization you'll be able to keep your performance well in hand. I personally think you would need something serious to justify shifting away from such a versatile mainstream product. You'll probably need an RBDMS in the project anyway and there are an unmatchable amount of legitimate choices in many price ranges (even free). You'll get quality documentation, support will be available, and you'll have a large supply of highly trained developers available in the job pool.
Regarding this model of self-relationships (users joined to other users), I recommend looking into recursive queries. That will keep you from performing a cascade of individual queries to find 3 levels of relationships. Consider the following SQL Server method for performing recursive queries with CTEs.
http://msdn.microsoft.com/en-us/library/ms186243.aspx
It allows you to specify how deep you want to go with the MAXRECURSION hint.
Next, you need to start thinking of ways to optimize. That starts with standard best-practices for setting up your tables with proper indexes and maintenance, etc. It inevitably ends with denormalization. That's one of those things you only do once you've already tried everything else, but if you know what you're doing and use good practices then your performance gain will be significant. There are many resources on the internet to help you learn about denormalization, just look it up.
I am in the early stages of developing a database-driven system and the largest part of the system revolves around an inheritance type of relationship. There is a parent entity with about 10 columns and there will be about 10 child entities inheriting from the parent. Each child entity will have about 10 columns. I thought it made sense to give the parent entity its own table and give each of the children their own tables - a table-per-subclass structure.
Today, my users requested to see the structure of the system I created. They balked at the idea of the table-per-subclass structure. They would prefer one big ~100 column table because it would be easier for them to perform their own custom queries.
Should I consider denormalizing the database for the sake of the users?
Absolutely not. You can always create a view later to show them what they want to see.
They are effectively asking for a report.
You could give them access to a view containing all the fields they require... that way you don't mess up your data model.
No. Structure the data properly and if the users need the a denormalized view of the data create it as a VIEW in the database.
Alternatively, consider that perhaps an RDBMS is not the appropriate storage tool for this project.
They are the users and not the programmers of the system for a reason. Provide a separate interface for their queries. Power users like this can both be helpful and a pain to deal with. Just explain you need the database designed a certain way so you can do your job, period. Once that is accomplished you and provide other means to make querying easier.
What do they know!? You could argue that users shouldn't even be having direct access to a database in the first place.
Doing that leaves you open to massive performance issues, just because a couple of users are running ridiculous queries.
How about if you created a VIEW in the format your users wanted while still maintaining a properly normalized table?
Aside from a lot of the technical reasons for or against your users' proposition, you need to be on same page in communicating the consequences of various scenarious and (more importantly) the costs of those consequences. If the users are your clients and they are paying you to do a job, explain that their awful "proposed" ideas may cost them more money in development time, additional hardware resources, etc.
Hopefully you can explain it in such a way that shows your expertise and why your idea is a much better value to your users in the long run.
As everyone more or less mentioned, that way lies madness, and you can always build a view.
If you just can't get them to come around on this point, consider showing them this thread and the number of pros who weighed in saying that the users are meddling with things that they don't fully understand, and the impact will be an undermined foundation.
A big part of the developer's craft is the feel for what won't work out long term, and the rules of normalization are almost canonical in that respect. There are situations where you need to denormalize (data warehouses, etc) but this doesn't sound like one of them!
It also sounds as though you may have a particularly troubling brand of user on your hand -- the amatuer developer who thinks they could do your job better themselves if only they had the time. This may or may not help, but I've found that those types respond well to presentation -- a few times now I've found that if I dress sharp and show a little bit of force in my personality, it helps them feel like I'm an expert and prevents a bunch of problems before they start.
I would strongly recommend coming up with an answer that doesn't involve someone running direct reports against your database. The moment that happens, your DB structure is set in stone and you can basically consider it legacy.
A view is a good start, but later on you'll probably want to structure this as an export, to decouple further. Of course, then you'll encounter someone who wants "real time" data. Proper business analysis usually reveals this to be unnecessary. Actual real time requirements are not best handled through reporting systems.
Just to be clear: I'd personally favour the table per subclass approach, but I don't think it's actually as big an issue as the direct reporting off transaction tables is going to be.
I would opt for a view (as others have suggested) or an inline table-valued function (the benefits of this is you require parameters - like an date range or a customer account - which can help to stop users from querying without any limits on the problem space) first. An inline TVF is really a parametrized view and is far closer to a view in terms of how the engine treats them than it is to a multi-statement table valued function or a scalar function, which can perform incredibly poorly.
However, in some cases, this can impact production performance if the view is complex or intensive. With poorly written ad hoc user queries, it can also cause locks to persist longer or be escalated further than they would on a better built query. It is also possible for users to misinterpret an E-R data model and produce multiplied numbers in cases where there are many-to-one or many-to-many relationships. The next option might be to materialize these views with indexes or make tables and keep them updated, which gets us closer to my next option...
So, given those drawbacks of the view option and already thinking of mitigating it by starting to make copies of data, the next option I would consider is to have a separate read-only (for these users) version of the data which is structured differently. Typically, I would first look at a Kimball-style star schema. You do not need to have a full-fledged time-consistent data warehouse. Of course, that's an option, but you could simply keep a reporting model up to date with data. Star-schemas are a special form of denormalization and are particularly good for numerical reporting, and a given star should not be able to be abused by users accidentally. You can keep the star up to date in a number of ways, including triggers, scheduled jobs, etc. They can be very fast for reporting needs and run on the same production installation - perhaps on a separate instance if not just a separate database.
Although such a solution may require you to effectively more than double your storage requirements, when compared with other practices it might be a really good option if you understand your data well and don't mind having two models - one for transactions and one for analysis (note that you will already start to have this logical separation anyway with the use of a the simplest first option of view).
Some architects will often double their servers and use the SAME model with some kind of replication in order to provide a reporting server which is indexed more heavily or differently. Such a second server doesn't impact production transactions with reporting requirements and can be kept up to date fairly easily. There will only be one model, but of course, this has the same usability problems with allowing users ad hoc access to the underlying model only, without the performance affects, since they get their own playground.
There are a lot of ways to skin these cats. Good luck.
The customer is always right. However, the customer is likely to back down when you convert their requirement into dollars and cents. A 100 column table will require extra dev time to write the code that does what the database would do automatically with the proper implementation. Further, their support costs will be higher since more code means more problems and lower ease of debugging.
I'm going to play devil's advocate here and say that both solutions sound like poor approximations of the actual data. There's a reason that object-oriented programming languages don't tend to be implemented with either of these data models, and it's not because Codd's 1970 ideas about relations were the ideal system for storing and querying object-oriented data structures. :-)
Remember that SQL was originally designed as a user interface language (that's why it looks vaguely like English and not at all like other languages of that era: Algol, C, APL, Prolog). The only reasons I've heard for not exposing a SQL database to users today are security (they could take down the server!) and usability (who wants to write SQL when you can clicky clicky?), but if it's their server and they want to, then why not let them?
Given that "the largest part of the system revolves around an inheritance type of relationship", then I'd seriously consider a database that lets me represent that natively, either Postgres (if SQL is important) or a native object database (which are awesome to work with, if you don't need SQL compatibility).
Finally, remember that every engineering decision is a tradeoff. By "sticking to your guns" (as somebody else proposed), you're implicitly saying the value of your users' desires are zero. Don't ask SO for a correct answer to this, because we don't know what your users want to do with your data (or even what your data is, or who your users are). Go tell them why you want a many-tables solution, and then work out a solution with them that's acceptable to both of you.
You've implemented Class Table Inheritance and they're asking for Single Table Inheritance. Both designs are valid in certain situations.
You might want to get a copy of Martin Fowler's Patterns of Enterprise Application Architecture to read more about the advantages and disadvantages of each design. That book is a classic reference to have on your bookshelf, in any case.
I know that the title might sound a little contradictory, but what I'm asking is with regards to ORM frameworks (SQLAlchemy in this case, but I suppose this would apply to any of them) that allow you to define your schema within your application.
Is it better to change the database schema directly and then update the column types in your program manually, or does it make more sense to define the tables in your application and then use the ORM framework's table generation functions to make the schema and then build the tables on the database side for you?
Bear in mind that applications and databases tend to live in a M:M relationship in any but the most trivial cases. If your application is at all likely to have interfaces to other systems, reports, data extracts or loads, or data migrated onto or off it from another system then the database has more than one stakeholder.
Be nice to the other stakeholders in your application. Take the time and get the schema right and put some thought into data quality in the design of your application. Keep an eye on anyone else using the application and make sure you don't break bits of the schema that they depend on without telling them. This means that the database has a life of its own to a greater or lesser extent. The more integration, the more independent the database.
Of course, if nobody else uses or cares about the data, feel free to ignore my advice.
My personal belief is that you should design the database on its own merits. The database is the best place to handle things modeling your Domain data. The database is also the biggest source of slow down in applications and letting your ORM design your database seems like a bad idea to me. :)
Of course, I've only got a couple of big projects behind me. I'm still learning daily. :)
The best way to define your database schema is to start with modeling your application domain (domain driven design anyone?) and seeing what tables take shape based on the domain objects you define.
I think this is the best way because really the database is simply a place to persist information from the application, it should never lead the design. It's not the only place to persist information as well. We have users that want to work from flat files or the database for instance. They could also use XML files. So by starting with your domain objects and then generating tables (or flat file or XML schema or whatever) from there will lead to a much better design in the end.
While this may depend on you using an object-oriented language, using an ORM tool like Hibernate/NHibernate, SubSonic, etc. can really make this transition easy for you up to, and including generating the database creation scripts.
In reference to performance, performance should be one of the last things you look at in an application, it should never drive the design. After you get a good schema up and running based on your domain you can always make tweaks to improve its performance.
Alot depends on your skill level with the specific database product that you're going to use. Think of it as the difference between a "manual" and "automatic" transmission car. ORMs provide you with that "automatic" transmission, just start designing your classes, and let the ORM worry about getting it stored into the database somehow.
Sounds good. The problem with most ORMs is that in their quest to be PI "persistence ignorant", they often don't take advantage of specific database features that can provide elegant solutions for a given task. Notice, I didn't say ALL ORMs, just most.
My take is to design the conceptual data model first yourself. Then you can go in either direction, up towards the application space, or down towards the physical database. But remember, only YOU know if it's more advantageous to use a view instead of a table, should you normalize or de-normalize a table, what non-clustered index(es) make sense with this table, is a natural or surrogate key more appropriate for this table, etc... Of course, if you feel that these questions are beyond your grasp, then let the ORM help you out.
One more thing, you really need to seperate the application design from the database design. They are almost never the same. How important is that data? Could another application be designed to use that data? It's a lot easier to refactor an application than it is to refactor a database with a billion rows of data spread across thousands of tables.
Well, if you can get away with it, doing it in the application is probably the best way. Since it's a perfect example of the DRY principle.
Having said that however, getting away with it is always going to be hard to pull off since you're practically choosing to give up most database specific optimizations. (more so, with querying, but it still applies to schemas (indexes, etc)).
You'll probably end up changing the schema by hand anyway, and then you'll be stuck with a brittle database schema that's going to be the source of your worst nightmares :)
My 2 Cents
Design each based on their own requirements as much as possible. Trying to keep them in too rigid sync is a good illustration of increased coupling/decreased cohesion.
Come to think of it, ORMs can easily be used to spread coupling (even though it can be avoided to some degree).