In Part 1 we showed (yet again) how even those with their heart in the right (relational) place can't help being affected by the common and entrenched industry misconceptions, in this case about relationships, relations and tables. More often than not authors exhibit the very misconceptions they try to debunk.
We left the author distinguishing sets (with unordered, unique elements) from tables (lists of ordered, possibly duplicate rows).
Note: To demonstrate the correctness and stability conferred by a sound theoretical foundation relative to the industry's fad-driven, ad-hoc "cookbook" practices, I am re-publishing as "Oldies But Goodies" material from the old DBDebunk.com (2000-06), so that you can judge for yourself how well my arguments of then hold up and whether the industry has progressed beyond the misconceptions those arguments were intended to dispel. I may revise, break into parts, and/or add comments and/or references.
The following is an email exchange from 2001 that I recommend reading jointly with my Data Meaning and Mining post (itself a revision of an article originally published at the old "All Analytics" website). I have slightly touched my replies for pedagogical purposes and clarity. You can substitute any data structure for XML hierarchy.
Note: Each "Test Your Foundation Knowledge" post presents one or more misconceptions about data fundamentals. To test your knowledge, first try to detect them, then proceed to read our debunking, reflecting the current understanding of the RDM, distinct from whatever has passed for it in the industry to date. If there isn't a match, you can review references -- reflecting the current understanding of the RDM, distinct from whatever has passed for it in the industry to date -- which explain and correct the misconceptions. You can acquire further knowledge by checking out our POSTS, BOOKS, PAPERS, LINKS (or, better, organize one of our on-site SEMINARS, which can be customized to specific needs).
“Doesn't the data model come before the schema? I was tasked to build a data model and one of the resources was a schema. Isn't the schema made from the data model?”
“A data model can be different things. A schema can be a data model. Before that, there's a conceptual model, derived from the problem domain, then a logical model, capturing the entities, attributes, and relationships. After that, a schema is implemented based on those two models.”
“Yes, but if the system evolved, in practice you will have the schema (the structure of physical tables) as the ground truth, and you need to extract the logical model from it. In teaching environment of we tend to begin with the logical model and then create tables based on that.”
“this makes a little more sense to me. i thought a default data model would be out there but i can't find one. so i'm basically "recreating" one from the schema. then i assume i'll be adding on to it with third party products.”
--Reddit.com
Note: Each "Test Your Foundation
Knowledge" post presents one or more misconceptions about data
fundamentals. To test your knowledge, first try to detect them, then proceed to
read our debunking, reflecting the current understanding of the RDM, distinct
from whatever has passed for it in the industry to date. If there isn't a
match, you can review references -- reflecting the current understanding of the
RDM, distinct from whatever has passed for it in the industry to date -- which
explain and correct the misconceptions. You can acquire further knowledge by
checking out our POSTS, BOOKS, PAPERS, LINKS (or, better,
organize one of our on-site SEMINARS, which can be
customized to specific needs).
“...a Data Domain refers to all the valid values which a data element (column) may contain. The rule for determining the domain boundary may be as simple as a data type with a list of possible values. For example, a database table that has information about people, with one record per person, might have an "age" column. This gender column might be declared as a SMALLINT data type, and allowed to have a value between 0 and 120. The data domain for the age column is hence 0 - 120. In a normalized data model, the reference domain is typically specified in a reference table. Following the previous example, the age reference table could have exactly 120 records, one per allowed value. Reference tables are formally related to other tables in a database by the use of foreign keys. A better way would be to enforce the data domain through a check constraint. For example, the age column would require positive numeric values between 0 and 120. I have found that the best way to figure out all of your data domains and constraints is to spend some time designing and normalizing all of your tables.”
--Quora.com
Note: The difference between relational domains and programming data types are specified in Codd's RM/V2 book. SQL tables are not relations and SQL data types are not relational domains.
Recommended reading
Domains: The Database Glue
Understanding Domains and Attributes
The Interpretation and Representation of Database Relations
Note: To demonstrate the correctness and stability due to a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, I am re-publishing as "Oldies But Goodies" material from the old DBDebunk.com (2000-06), so that you can judge for yourself how well my arguments hold up and whether the industry has progressed beyond the misconceptions those arguments were intended to dispel. I may revise, break into parts, and/or add comments and/or references.
This is a continuation of an email exchange with readers in response to my post Normalization and Performance: Never the Twain Shall Meet started in Part 1.
(originally posted 03/22/2001)
“I read your article on Normalization and database speed. The rules of normalization are used to provide a guide when designing a logical approach to managing information. If that information management plan (IMP) is to be implemented using a database, then the ultimate "test" of the IMP is its performance on the physical level. The IMP architecture is commonly expressed using an Entity Relationship Diagram or ERD. Sure CPU, RAM, and RAID all play an important role in database performance. But when a DBA changes the way tables, columns, or indexes are structured he is changing the IMP and corresponding ERD. Now there are two sides to this story.When the second rule is violated then data corruption must be safeguarded against. The safeguard must be implemented on the database level because that is the only way corrupt data can be prevented regardless of the source (i.e. data loaded from a flat file, DBAs and users logged onto SQL*Plus changing information, other application software that also has access to the database, and last but not least; application developers that are not aware of the duplicate data stored in more than one location). I usually implement the safeguard by using database triggers so that when a record is changes in one table a trigger will make the corresponding change in another table. I have found that most "performance gains" achieved by denormalization are eradicated by the use of the trigger needed to safeguard the integrity of the data. The bottom line is that De-Normalization can often increase database speed. But this is most often achieved at the expense of data integrity.”
- The Pro De-Normalization group viewpoint: If the above change results in a "faster" database then denormalization has been "proven" to be necessary.
- The Pro Normalization group viewpoint: A change is only valid if it maintains the integrity, and flexibility that are inherent in a fully Normalized database. For example, violation of the second rule "Eliminate Redundant data" can result in corrupt data when data in two or more locations is not properly update, changed, or inserted in all locations.
Amid the plethora of industry misconceptions, an article titled "What if I told you there are no tables in relational databases?" is surprising. That it starts with:
“I’ve seen one sentence about relational databases repeated on the Web many, many times. I’ve seen it in countless comments, I’ve seen it in few articles. Recently I’ve even seen it in one book — which finally made me write this article. The sentence in question goes like this: "Ironically, relational databases deal poorly with relationships". Read it carefully. Think about it for a moment. I’m sure it must sound perfectly reasonable — for anyone who doesn’t understand the meaning of both "relational" and "irony".”
is practically shocking. Be that as it may, my regular readers know that I refer to pronouncements on the RDM as "heart in the right place": correct statements are not a guarantee of full grasp thereof, or effective explanations to practitioners lacking the necessary foundation knowledge. Hence this two part debunking.
