Related
I've been tasked with coming up with a recommendation of how to proceed with a EDW and am looking for clarification on what I'm seeing. Everything that I am learning about states that Kimball's approach will bring value quicker to business vs Inmon's. I get that Kimball's approach is a dimensional model from the getgo and different data marts (star schema) are integrated through conformed dimensions... thus the theory is I can simply come up with my immediate DM to solve business need and go on from there.
What I'm learning states that Inmon's model suggests that I have a EDW designed in 3NF. The EDW is not defined by source system but instead the structure of the business, Corporate Factory (Orders, HR, etc.). So data from disparate systems map into this structure. Once the data is in this form, ETLs are then created to produce DMs.
Personally I feel Inmon's approach is a better way. I believe this way is going to ensure that data is going to be consistent and it feels like you can do more with this data. What holds me back with this approach though is everything I'm reading says it's going to take much more time to deliver something but I'm not seeing how that is true. From my narrow view, it feels like no matter what the end result is we need a DM. Regardless of using Kimball's or Inmon's approach the end result is the same.
So then the question becomes how do we get there? In Kimballs approach we will create ETLs to some staging location and generally from there create a DM. In Inmon's approach I feel we just add in another layer... that is from the staging area we load this data into another database in 3NF organized by function. What I'm missing is how this step adds so much time.
I feel I can look at the end DM that needs to be made. Map those back to a DW in 3NF and then as more DMs are requested keep building up the DW in 3NF with more and more data. However if I create a DM in Kimballs model that DM is going to be built around the level of grain decided for that DM and what if the next DM requested wants reporting at even a deeper grain (to me it feels like Kimballs methodology would take more work) and with Inmon's it doesn't matter. I have everything at the transnational level so DMs of varying grains are requested, well I have the data, just ETL it to a DM and all DMs will report the same since they are sourced from the same data.
I dunno... just looking for others views. Everything I read says Kimball's is quicker... I say sure maybe a little bit but there is certainly a cost attributed by going to quicker route. And for sake of argument... let's say it takes a week to get a DM up and running through Kimballs methodology... to me it feels like it should only take 10% maybe 20% longer utilizing Inmon's.
If anyone has any real world experience with the different models and if one really takes so much longer then the other... please share. Or if I have this so backwards tell me that too!
For context; I look after a 3 billion record data warehouse, for a large multi-national. Our data makes its way from the various source systems through staging and into a 3NF db. From here our ELT processes move the data into a dimensionally modelled, star schema db.
If I could start again I would definitely drop the 3NF step. When I first built that layer I thought it would add real value. I felt sure that normalisation would protect the integrity of my data. I was equally confident the 3NF db would be the best place to run large/complex queries.
But in practice, it has slowed our development. Most changes require an update to the stage, 3NF and star schema db.
The extra layer also increases the amount of time it takes to publish our data. The additional transformations, checks and reconciliations all add up.
The promised improvement in integrity never materialised. I realise now that because I control the ETL, and the validation processes within, I can ensure my data is both denormalised and accurate. In reporting data we control every cell in every table. The more I think about that, the more I see it as a real opportunity.
Large and complex queries was another myth that has been busted by experience. I now see the need to write complex reporting queries as a failing of my star db. When this occurs I always ask myself: why isn't this question easy to answer? The answer is most often bad table design. The heavy lifting is best carried out when transforming the data.
Running a 3NF and star also creates an opportunity for the two systems to disagree. When this happens it is often a very subtle difference. Neither is wrong, per se. Instead, it is possible the 3NF and star query are asking slightly different questions, and therefore returning different results. Although technically correct, this can be hard to explain. Even minor and explainable differences can erode confidence, over time.
In defence of our 3NF db, it does make loading into the star easier. But I would happily trade more complex SSIS packages for one less layer.
Having said all of this; it is very hard to recommend an approach to anyone without a deep understanding of their systems, requirements, culture, skills, etc. Having read your question I am sure you have wrestled with all these issues, and many more no doubt! In the end, only you can decide what the best approach for your situation is. Once you've made your mind up, stick with it. Consistency, clarity and a well-defined methodology are more important that anything else.
Dimensions and measures are a well proven method for presenting and simplifying data to end users.
If you present a schema based on the source system (3nf) to an end user, vs a dimensionally modelled star schema (Kimball) to an end user, they will be able to make much more sense of the dimensionally modelled one
I've never really looked into an Inmon decision support system but to me it seems to be just the ODS portion of a full datawarehouse.
You are right in saying "The EDW is not defined by source system but instead the structure of the business". A star schema reflects this but an ODS (a copy of the source system) doesn't
A star schema takes longer to build than just an ODS but gives many benefits including
Slowly changing dimensions can track changes over time
Denormalisation simplifies joins and improves performance
Surrogate keys allow you to disconnect from source systems
Conformed dimensions let you report across business units (i.e. Profit per headcount)
If your Inmon 3NF database is not just an ODS (replica of source systems), but some kind of actual business model then you have two layers to model: the 3NF layer and the star schema layer.
It's difficult nowadays to sell the benefit of even one layer of data modelling when everyone thinks they can just do it all in a 'self service' tool! (which I believe is a fallacy). Your system should be no more complicated than it needs to be because all that complexity adds up to maintenance and that's the real issue - introducing changes 12 months into the build when you have to change many layers
To paraphrase #destination-data: your source system to star schema transformation (and seperation) is already achieved through ETL so the 3nf seems redundant to me. You design your star schema to be independent from source systems by correctly implementing surrogate keys and business keys, and modelling it on the business, not on the source system
With ETL and back-end data wrangling taking up about 70% of the project time for this kind of endeavour, an extra layer makes a big difference. Its an extra layer of transforming from source to target, to agree with the business and to test. It all adds up.
Whilst I'm not saying that dimensional models (the Kimball kind) are always easy to change, you've got a whole lot more inflexibility should you have to always change lots of layers when you want to change your BI.
In fact, where I've been consulting in places that have data warehouses that are considered to be inflexible and expensive to develop for, and not keeping pace with changes to the business, they have without exception included the 3NF layer prior to the DMs. As Nick mentioned, it is hard nowadays to sell the idea of a 'proper' data warehouse as opposed to a Data Discovery Bi tool- and the appeal of these is often driven by DWs being seen to be slow and expensive to develop.
Kimball isn't against having a 3NF layer prior to his DW if it makes sense for a situation, he just doesn't agree with Inmon that there's a point.
One common misunderstanding is that Kimball proposes distinct data marts, so that you'd have to change it each time there is a different reporting request. Instead, Kimball's DMs are based on real life business processes and modelled accordingly. Although its true you will then try and make them suitable for reporting, you try and make them so they can answer forseaable queries. You don't aggregate and store just the aggregates: you work with the transactional data in a Kimball dimensional model.
So no need to be reluctant from that perspective.
If an ODS works for you, then go for it- but a Kimball DW will meet the majority of requirements.
I have the following models..
Provider
Patient
Clinic
Like an eco-system, all models should have many-to-many relationships with one another. It's very important that I am able to query data from all directions.
After intensive research on Active Record associations, I find many blogs warning against has_and_belongs_to_many and using has_many :through. Only issue is that requires a table to act as the "middle-man" for lack of better words but I'm unsure how that would work with a 3 models.
The other option is a polymorphic association but I'm unsure if I should invest the time in understanding that method if it's inapplicable for this particular situation.
Any advice on how to create these relationships for maximum flexibility and efficiency?
I have the following models
Tables, if they were modelled for a Relational database, files otherwise.
It's very important that I am able to query data from all directions.
Understood. That is simple and easy in a Relational Database.
Like an eco-system, all models should have many-to-many relationships with one another.
That is not correct.
If each of the three can be related to the other two, then your data is not modelled. There are basic dependencies that you have not identified. Eg: I can imagine that:
Providers provide services at Clinics
Clinics provide services to Patients
Patients visit Clinics to obtain services
Therefore, any relationship that a Patient may have with a Provider is via a Clinic, and not privately, without a Clinic.
Straighten those rules and dependencies out first, that will result in less than three Associative tables second. Something like this:
Clinic Provider Patient Table Relation Diagram
Response to Comment
Any advice on how to create these relationships for maximum flexibility and efficiency?
Well, Dr E F Codd's Relational Model is strongly established as the model, the method for organising data, such that it has (a) complete integrity (objects can't) (b) maximum flexibility (c) maximum speed. In the 45 years since its advent, there have been no other contenders. That is the model I am using. The principles that underpin that model are the principles that I rely upon, when I make my proposals.
All that has been, of course, confirmed and reinforced during my 34 years of database implementations. As well as thousands of other high-end implementations.
Data Independence
I've taken your post with great consideration and the difficulty is that there are many unique nuances with my app that won't allow me to simplify it to mirror the real world.
It is the other way around. The fact is, you are writing an app+db that deals with the real world. Therefore the db has to reflect that real world (limited to the scope of the enterprise) that you wish to engage with. Thousands of modellers have done that successfully (millions have done it incorrectly).
To the extent that you have "nuances" and complexities in the app, you have not modelled the data, as data, and the result will be a complex app that engages with an incorrectly modelled database, or worse, a non-database. All those "nuances" and complexities are in fact data; facts about data; rules about the data; and relationships between data. But you have an established view that the "nuances" and complexities are in the app, in your "models", not in the data.
Therefore your notion is false.
Every rule, constraint, control, nuance, complexity re the data, must be implemented in the data. That is simply data definition as per the RM. Otherwise you have no data independence, no database (just a bunch of files for data storage), no integrity, no relational power, no speed. And worse, you will be forever fixing up the "nuances" and complexities in the app layers, the object stack.
Data Definition
Let me start from first principles.
a databse is a collections of Facts, including relationships between those Facts
Assuming you understand that, there is an important next point.
There is no Fact that cannot be declared.
This is First order Logic, which the RM is based on. Therefore there is no such thing, as a "model" that is too complex (has "nuances" or too many "nuances") to be declared in terms of FOL. The scientific exercise that is called for, is to reduce that complexity to FOL Facts.
To the extent that those Facts are Facts about the real world, and they reflect the truth, your database will be isolated from the effect of change (you can extend the database and the app easily). Eg. Provider, Service, Specilisation are separate Facts.
To the extent that those "facts" are "facts" that you have chosen to store (as being relevant, from the perspective of your app, object design, etc), eg. Provider, Service and Specialisation as a single complex "fact", not discrete Facts, and not a reflection of the real world, your database and objects (a) will be hard to change, and (b) will keep changing, forever, until they are elevated, such that they do reflect the real world. You will have to "re-factor" the "database" every quarter.
Confidentiality
The data is very confidential so I'm reluctant to get to far into the matter.
We have been working with confidential data and/or systems for over 45 years without breaching confidentiality.
There is nothing new under the Sun.
We are dealing with structure, not content, it is not reasonable to suggest that your structure is so new and unique that it cannot be (i) discussed or (ii) modelled.
But most important, if you cannot describe it (in FOL terms), you cannot model it. If you cannot model it, you cannot write an app to engage with it (you can try, but as evidenced here, you will be stuck in that unresolved position).
Noting that the OO/ORM literature teaches people to obsess about the data content, in order to avoid dealing with the structure, meaning, relevance, etc, please note that I do not want to know. and the exercise does not need to know, the content. Describe only the data in terms of meaning, relevance, relationships, and we will model the required structure.
My question was more about how to create a cycle of many-to-many relationships with 3 models than whether my models "should" have them or not.
I think I understand that. That would be adding complexity to an article of which the complexity has been determined to be the fundamental problem. If you ask me to build an airplane without wings, and I tell you that your approach is incorrect, that you need wings, there is no point in telling me that you are seeking someone who can tell you HOW to build an airplane without wings, you have missed the point.
Reasoning
I would love to hear your reasoning if you believe there should never be a situation like this in any database.
Again, you have that the wrong way around. It is not that there should never be a situation like this in any database, it is that if there is a situation like this, it raises a red flag (to qualified and experienced modellers) that the data is not yet Normalised, not yet organised into discrete Facts. That means you need to take a step back and deal with the complexity in your "models", first. Then the relationships that are your current focus will be simplified.
Then, yes, there will not be a situation like this in the database.
My reasoning is Codd's RM, and the principles behind it. It has been the subject of many papers. (As well as many "papers" that are non-relational or anti-relational, such as those that support the OO/ORM "model".)
Specifically, here, that if you have a n-ary relation (technical term for the three-way relationship that you are seeking) that that can be, and should be, resolved into [multiple] binary relations (two-way relationships). Eg. the TRD I have suggested.
OO/ORM Mythology
In the context of "love to hear your reasoning", there are two sides, I have given the what you should be doing side, above. This is the what you should not be doing or why your method is broken side. Where do I start.
The OO/ORM model is that the "database" is merely a storage location to make the objects "persistent", a slave of the objects, and that constructing a monolith, layers of object classifiers, complexity, is the way to solve any problem.
The OO/ORM model is a total, abject failure. It has no scientific basis whatsoever.
(Noting that due to the destruction of the education system, these days "mathematicians" and "theoreticians" write "papers" that "prove" complete and utter nonsense. It is nonsense because it contradicts established science. They are not the class of mathematicians and theoreticians of the old school, who reject contradiction; non-science; nonsensical proofs. The only way that they can write such absurd "proofs", in to maintain a state of ignorance, a state of pathological denial, of other sciences.)
Specifically, they are in denial of the Relational Model (whilst referring to it, to give their papers some credibility, which is a fraud); its prescriptions (such as Data Independence, FOL); its prohibitions, they are in denial of Relational data models (UML cannot model data like IDEF1X can), thus they produce non-relational files, which have no Relational integrity, power, or speed.
They employ the Hammer phylosophy (ie. if one only knows a hammer, then every problem looks like a nai), in staggering denial that Maslow destroyed it, scientifically, over a century ago. Which leads them to pile more layers, more complexity, into the monolith. The converse is of course, to use the right tool for the job, which means define data according to the standard for data, the RM, and separately, objects according to whatever OO philosophy you choose.
They attempt to do everything in objects, to model everything in UML (which is not a standard by any means; nor adequate [one symbol plus a million notations], it has no decomposition, it is in fact a free-for-all in which everyone does their own thing).
The model exists is denial of the fact that since 1980's, in the software industry, we architect, write, and deploy components. Database components in the database, program components in the objects. Not monolithic Towers of Babel, that is pre-1970's technology.
Since 1984, we have had Client/Server and Open Architecture Standards. We have had OLTP Transaction Standards since 1960, restated in the C/S context in 1984. The OO/ORM crowd are in flagrant rebellion of each and every one of those Standards, they build monolithic object stacks sans architecture, sans components, sans Transactions, sans everything. (Apologies to the Bard.)
You might consider what everyone (even cartoonists) knows about the OO/ORM stack, the monolith, the non-architecture and compare it with the deployment of components in the Open Architecture diagram, given above.
Further, they are in denial that every implementation of the "model" is a massive failure, they deny the evidenced facts, and keep adding complexity to the already complex and unmanageable object layers. A "model" that has failed due to its non-architecture, part of which is precisely that failed complexity.
In case their evidenced pathological denial of the reality does not stand, in and of itself, as evidence of insanity, there is more, much more. The Twelve Steppers have an interesting definition of insanity: doing the same thing over and over again, being aware that it produces the same result, every time, but expecting a different result the next time. That doesn't stop them from adding more complexity to the complex model, or from marketing their pre-1970's technology, as "modern" "science".
But that doesn't stop them from writing yet more books and marketing their failed "model".
The OO/ORM crowd exists in isolation from, in pathological denial of, reality
Put another way, it is insane, in 2015, to implement software, in a un-architected monolith, that "does everything", rather than to architect; design; build; and deploy software components in the correct architectural position.
OO/ORM "Model" as Data
The fact that you keep calling your tables "model" is another red flag. That confirms that you have way too much complexity in them. A database consists of simple tables, each reflecting a discrete Fact, not models. To the extent that you consider them "models", you have (usually due to fixed notions re complex objects and classifiers) objects+data+complexity combined, not discrete data and discrete objects. That is the precise problem that will cause the app+db to fail.
So the next step is to (a) shelve the current focus re HOW to relate un-normalised complex "models", and to (b) normalise those "models" such that they are defined in terms of a Relational Database, such that they are discrete Facts. Following which, (c) the relating of the then Normalised tables will be straight-forward.
In one instance, each model acts as a type of 'User' but in another, they don't.
That is exactly the type of mashed-up concept that has to be rationalised, Normalised, such that it is (a) absolutely clear [when it is an user, and when it isn't] (b) defined in data, in FOL terms, as discrete Facts (c) such that you can confidently write code against it, build objects from it. Conversely, the absence of that clarity, the retention of complexity in the object layers, will result in complex objects that fail, and more important, data stored in files that have no integrity, power, or speed.
Self-Contradiction
Consider this. Since you are seeking Relational integrity, power, and speed, you cannot at the same time be
seeking to retain unresolved complexity that is well-known to destroy integrity, power, and speed, or
refusing to implement the requirements the host integrity, power, and speed, that you seek.
It is a massive, and double, contradiction, on your side. It is a philosophical and reasoning issue, that you have to consider and resolve for yourself. The OO/ORM seduces people into believing in magicke, into such crazy self-contradictions.
Regardless, I was very impressed with your answer and really appreciate the time you took to make the diagram.
Thank you. You are most welcome.
That took me all of five minutes. Because I have clarity. Because I follow scientific principles and standards. Because we have had a science and a methodology since 1970. Because we have had a modelling methodology and full notation for modelling Relational Data since 1987 (as a standard, IDEF1X, since 1993). The point is, it is nothing special, the sad fact is, it is not common, and it should be. The second sad fact is, it is unknown in the OO/ORM world.
Further Reading
You may be interested in this Question and Answer. The Answer covers many aspects of Relational Databases, that you will most certainly have to deal with, if not now, then at some point in the future. The minimum reading I draw your attention to right now is:
- Response to Update 3, pages 1, 2 and 6,
- specifically including the embedded link to Predicates
That might give you an idea of the reasoning; the depth of data definition that the RM affords; that all Facts can be declared in terms of FOL Predicates, that the OO/ORM crowd is totally ignorant of.
You may choose to add an Update to your question and ask how to declare one or the other "model", in terms of the RM, as discrete Facts, or open a new Question (and ping me).
Conversely, if you choose to stick to your position, the original question, then I think I have answered it (but the answer raises issues that you must address and resolve).
Please study carefully and comment or ask questions.
I'm working on a project with developers who have not worked with Ruby OR Rails before.
They have created a schema that is too complicated, in my opinion. The schema has 117 tables, and obtaining the simplest piece of information would require traversing/joining 7 tabels...and of course, there's no "main" table that serves as a sort of key between them. The schema renders many of the rails tools like 'find' method, and many of the has_many/belongs to relationships almost useless. And coding for all of these relationships will likely be more time-consuming than we have the money to code for.
THE QUESTION:
Assuming you are VERY convinced (IMHO...hehe) that the schema is not ideal, and there are multiple ways to represent the domain, how would you argue FOR simplifying the schema (aside from what I've already said)?
I'll stand up in 2 roles here
DBA: Database admin/designer.
Dev: Application developer.
I assume the DBA is a person who really know all the Database tricks. Reaallyy Knows.
DBA:
Database is the key of the application and should have predefined structure in order to serve its purpose well and with best performance.
If you cannot use random schema (which is reasonably normalised and good) then the tools are wrong.
Dev:
The database is just a data store, so we need to keep it simple and concentrate on the application.
DBA:
Database is not a store it is the core of the application. There is no application without database.
Dev:
No. The application is the core. There is no application without the front-end and the business logic applied to it.
And the war begins...
Both points are valid and it is always trade off.
If the database will ONLY be used by RoR, then you can use it more like a simple store.
If the DB can be used by other application OR it will be used with large amount of data and high traffic it must enforce some best practices.
Generally there is no way you can disagree with DBA.
But they can understand your situation and might allow you to loose the standards a bit so you could be more productive.
So you need to work closely, together.
And you need to talk to each other to explain and prove the point why database should be like this or that.
Otherwise, the team is broken and project can be failure with hight probability.
ActiveRecord is a very handy tool. But it cannot do everything for you. It does not provide Database structure by default that you expect exactly. So it should be tuned.
On the other side. If DBA can accept that all PKs are Auto incremented integers that would make Developer's life easier (ActiveRecord does it by default).
On the other side, if developers would accept some of DBA constraints it would make DBA's life easier.
Now to answer your question:
how would you argue FOR simplifying the schema
Do not argue. Meet the team and deliver the message and point on WHY it should be done.
Maybe it really shouldn't and you don't know all the things, maybe they are not aware of something.
You could agree on the general structure of the database AND try to describe it using RoR migrations as a meta language.
This way they would see the general picture, and you would use your great ActiveRecords.
And also everybody would be on the same page.
Your DB schema should reflect the domain and its relationships.
De-normalisation should only be done when you have measured that there is a performance problem.
7 joins is not excessive or bad, provided you have good indexes in place.
The general way to make this argument up the chain is based on cost. If you do things simply, there will be less code and fewer bugs. The system will be able to be built more quickly, or with more features, and thus will create more ROI. If you can get the money manager on board with that approach, he or she may let you dictate terms to the team. There is the counterargument that extreme over-normalization prevents bad data, but I have found that this is not the case, as the complexity it engenders tends to lead to more errors and more database code in general.
The architectural and technical argument here is simple. You have decided to use Ruby on Rails. Therefore you have decided to use the ActiveRecord pattern. The ActiveRecord pattern is driven by having the database tables match the object model. That's the pattern in use here, and in many other places, so the best practices they are trying to apply for extreme data normalization simply do not apply. Buy a copy of Patterns of Enterprise Application Architecture and put the little red bookmark at page 160 so they can understand how the pattern works from the architecture perspective.
What the DBA types tend to be unaware of is how much work ActiveRecord does for you, from query generation, cascading deletes, optimistic locking, auto populated columns, versioning (with acts_as_versioned), soft deletes (with acts_as_paranoid), etc. There is a strong argument to use well tested, community supported library functions to perform these operations versus custom code that must be maintained by a DBA.
The real issue with DBAs is then that they need some work to do. Let them focus on monitoring performance, finding slow queries in the code, creating indexes and doing backups.
If you end up losing the political battle for a sane schema, you may want to consider switching to DataMapper. It's the next pattern in PoEAA. The other thing you may be able to get them to do is to create views in the database that correspond to the object model. This way, you could use many of the finding capabilities in the ActiveRecord model based on the views, but have custom insert, update, and delete methods.
After much reading on ruby on rails and multiple database connections, it seems that I have found something that not that many folks do, at least not with ror. I am used to querying many different databases and schemas and pulling back the information either for a report or for one seamless page. So, a user doesn't have to log on to several different systems. I can create a page that has all the systems on one or two web pages.
Is that not a normal occurrence in the web and database driven design?
EDIT: Is this because most all my original code is in classic asp?
I really honestly think that most ORM designers don't seem to take the thought that users may want to access more than one database into account. This seems to be a pretty common limitation in the ORM universe.
Our client website runs across 3 databases, so I do this to. Actually, I'm condensing everything into views off of one central database which then connects to the others.
I never considered this to be "normal" behavior though. I would guess that most of the time you would be designing for one system and working against that.
EDIT: Just to elaborate, we use Linq to SQL for our data layer and we define the objects against the database views. This way we keep reports and application code working off the same data model. There is some extra work setting up the Linq entities, because you have to manually define primary keys and set up associations... however so far it has definitely proven worthwhile. We tried to do so with Entity Framework, but had a lot of trouble getting the relationships set up appropriately and had to give up. The funny thing is I had thought Entity Framework was supposed to be designed for more advanced scenarios like ours...
It is not uncommon to hit multiple databases during a single part of an application's workflow. However, in every instance that I have done it, this has been performed through several web service calls, which among other things wrap the databases in question.
I have not, to my knowledge, ever had a need to hit multiple databases directly at once and merge results into a single report.
I've seen this kind of architecture in corporate Portals- where lots of data is pulled in via different data sources. The whole point of a portal is to bring silo'd systems together- users might not want to be using lots of systems in isolation (especially if they have to sign into each one). In that sort of scenario it is normal, particularly if it is a large company that has expanded rapidly and has a large number of heterogenous systems.
In your case whether this is the right thing to do depends on why you have these seperate DBs.
With ORM's it may be a little difficult. However, it can be done. Pull the objects as needed from the various databases, then use them as a composite to create a new object that is the actual one that is desired. If you can skip the ORM part of the process, then you can directly query the databases and build your object directly.
Pulling data from two databases and compiling a report is not uncommon, but because cross-database queries cannot be optimized by the query engine of either database, OLTP systems typically use a single database, to keep the application performant.
If you build the system from the ground up, it is not advisable to do it this way. If you are working with a system you didn't design, there is no much choice and it is not uncommon (that is the difference between "organic" and "planned" grow).
Not counting master and various test instances, I hit nine databases on a regular basis. Yes, I inherited it, and yes, "Classic" ASP figures prominently. Of course, all the "brillant" designers of this mess are long gone. We're replacing it with things more sane as quickly as we safely can.
I would think that if you're building a new system, and keep adding databases and get to the point of two or three databases, it's probably time to re-think your design. OTOH, if you're aggregating data from multiple, disparate systems, then, no, it's not that strange. Depending on the timliness you need, and your budget for throwing hardware at the problem, and if your data is mostly static, this would be a good scenario for a "reporting server" that pulls the data down from the Live server periodically.
Given that database is generally the least scalable component (of a web application), are there any situations where one would put logic in procedures/triggers over keeping it in his favorite programming language (ruby...) or her favorite web framework (...rails!).
Server-side logic is often much faster, even with procedural approach.
You can fine-tune your grant options and hide the data you don't want to show
All queries in one places are more convenient than if they were scattered all around the code.
And here's a (very subjective) article in my blog on the reason I prefer stored procedures:
Schema Junk
BTW, triggers (as opposed to functions / stored procedures / packages) I generally dislike.
They are completely other story.
You're keeping the processing in the database, along with the data.
If you process on the server side, then you have to transfer the data out to a server process across the network, process it, and (optionally) send it back. You have the network bandwidth/latency issues, plus memory overheads.
To clarify - if I have 10m rows of data, my two extreme scenarios are to a) pull those 10m rows across the network and process on the server side, or b) process in place in the database using the server and language (SQL) optimised for this purpose. Note that this is a generalisation and not a hard-and-fast rule, but it's the one I follow for most scenarios.
When many heterogeneous applications and various other systems need to access your single database and be sure through their operations data stays consistent without integrity conflicts. So you put your logic into triggers and stored procedures that will offer an interface to external clients.
Maybe not for most web-based systems, but certainly for enterprise databases. Stored procedures and the like allow you much greater control over security and performance, as well as offering a bit of encapsulation for the database itself. You can change the schema all you want as long as the stored procedure interface remains the same.
In (almost) every situation you would keep the processing that is part of the database in the database. Application code cannot substitute for triggers, you won't get very far before you have updated the database and failed to fire the application's equivalent of the triggers (the first time you use the DBMS's management console, for instance).
Let the database do the database work and let the application to the application's work. If you have a specific performance problem with the database, and that performance problem can be addressed by moving processing from the database, in that case you might want to consider doing so.
But worrying about database performance without a database performance problem existing (which is what you seem to be doing here) is both silly and, sadly, apparently a pre-occupation of many Stackoverlow posters.
Least scalable? SQL???
Look up, "federating."
If the database is shared, having logic in the database is better in order to control everything that happens. If it's not it might just make the system overly complicated.
If you have multiple applications that talk to your database, stored procedures and triggers can enforce correctness more pervasively. Accordingly, if correctness is more important than convenience, putting logic in the database is sensible.
Scalability may be a red herring, though. Sometimes it's easier to express the behavior you want in the domain layer of an OO language, but it can be actually more expensive than doing the idiomatic SQL way.
The security mechanism at a previous company was first built in the service layer, then pushed to the db side. The motivation was actually due to some limitations in a data access framework we were using. The solution turned out to be a bit buggy because our security model was complicated, but the upside was that bugs only had to be fixed in the database; we didn't have to worry about different clients following different rules.
Triggers mean 3rd-party apps can modify the database without creating logical inconsistencies.
If you do that, you are tying your business logic to your model. If you code all your business logic in T-SQL, you aren't going to have a lot of fun if later you need to use Oracle or what have you as your database server. Actually, I'm not sure I understand this question exactly. How do you think this would improve scalability? It really shouldn't.
Personally, I'm really not a fan of triggers, particularly in a database dedicated to a single application. I hate trying to track down why some data is inconsistent, to find it's down to a poorly written trigger (and they can be tricky to get exactly correct).
Security is another advantage of using stored procs. You do not have to set the security at the table level if you don't use dynamic code (Including ithe stored proc). This means your users cannot do anything unless they have a proc to to it. This is one way of reducing the possibility of fraud.
Further procs are easier to performance tune than most application code and even better, when one needs to change, that is all you have to put on production, not recomplie the whole application.
Data integrity must be maintained at the database level. That means constraints, defaults values, foreign keys, possibly triggers (if you have very complex rules or ones involving multiple tables). If you do not do this at the database level, you will eventually have integrity issues. Peolpe will write a quick fix for a problem and run the code in the query window and the required rules are missed creating a larger problem. A millino new records will have to be imported through an ETL program that doesn't access the application because going through the application code would take too long running one record at a time.
If you think you are building an application where scalibility will be an issue, you need to hire a database professional and follow his or her suggestions for design based on performance. Databases can scale to terrabytes of data but only if they are originally designed by someone is a specialist in this kind of thing. When you wait until the while application is runnning slower than dirt and you havea new large client coming on board, it is too late. Database design must consider performance from the beginning as it is very hard to redesign when you already have millions of records.
A good way to reduce scalability of your data tier is to interact with it on a procedural basis. (Fetch row..process... update a row, repeat)
This can be done within a stored procedure by use of cursors or within an application (fetch a row, process, update a row) .. The result (poor performance) is the same.
When people say they want to do processing in their application it sometimes implies a procedural interaction.
Sometimes its necessary to treat data procedurally however from my experience developers with limited database experience will tend to design systems in a way that do not leverage the strenght of the platform because they are not comfortable thinking in terms of set based solutions. This can lead to severe performance issues.
For example to add 1 to a count field of all rows in a table the following is all thats needed:
UPDATE table SET cnt = cnt + 1
The procedural treatment of the same is likely to be orders of magnitude slower in execution and developers can easily overlook concurrency issues that make their process inconsistant. For example this kind of code is inconsistant given the avaliable read isolation levels of many RDMBS platforms.
SELECT id,cnt FROM table
...
foreach row
...
UPDATE table SET cnt = row.cnt+1 WHERE id=row.id
...
I think just in terms of abstraction and ease of servicing a running environment utilizing stored procedures can be a useful tool.
Procedure plan cache and reduced number of network round trips in high latency environments can also have significant performance advantages.
It is also true that trying to be too clever or work very complex problems in the RDBMS's half-baked procedural language can easily become a recipe for disaster.
"Given that database is generally the least scalable component (of a web application), are there any situations where one would put logic in procedures/triggers over keeping it in his favorite programming language (ruby...) or her favorite web framework (...rails!)."
What makes you think that "scalability" is the only relevant concern in a system design ? I agree with rexem where he commented that it is very obvious that you are "not" biased ...
Databases are sets of assertions of fact. Those sets become more valuable if they can also be guaranteed to conform to certain integrity rules. Those guarantees are not worth a dime if it is the applications that are expected to enforce such integrity. Triggers and sprocs are the only way SQL systems have to allow such guarantees to be offered by the DBMS itself.
That aspect outweighs "scalability" anytime, anywhere, anyhow.