THE DBDEBUNK GUIDE TO MISCONCEPTIONS OF DATA FUNDAMENTALS available to order here.
1. What's wrong with this picture?
"Our terminology is broken beyond repair. [Let me] point out some problems with Date's use of terminology, specifically in two cases:
- "type" = "domain": I fully understand why one might equate "type" and "domain", but ... in today's programming practice, "type" and "domain" are quite different. The word "type" is largely tied to system-level (or "physical"-level) definitions of data, while a "domain" is thought of as an abstract set of acceptable values.
- "class" != "relvar": In simple terms, the word "class" applies to a collection of values allowed by a predicate, regardless of whether such a collection could actually exist. Every set has a corresponding class, although a class may have no corresponding set ... in mathematical logic, a "relation" *is* a "class" (and trivially also a "set"), which contributes to confusion.
In modern programming parlance "class" is generally distinguished from "type" *only* in that "type" refers to "primitive" (system-defined) data definitions while "class" refers to higher-level (user-defined) data definitions. This distinction is almost arbitrary, and in some contexts, "type" and "class" are actually synonymous." --Comment @dbdebunk.com
Note: This is a 11/24/17 re-write of Part III of a three-part series that replaced several previous posts (the pages of which redirect here), to to bring it in line with the McGoveran formalization and interpretation [1] of Codd's true RDM.
(Continued from Part II)
POOD and SQL
As we have seen, if relations are uniquely constrained, with a true RDBMS supporting logical independence (LI) and constraint inheritance, database design can adhere to the POOD and enable DBMS-enforced consistency. A RDBMS can also support ESS explicitly.
Industry misconceptions notwithstanding, SQL DBMSs are, of course, not relational. They have weak declarative integrity support, which, coupled with bad database designs, makes adherence to the POOD (as well as the other design principles) difficult. While even its weak relational fidelity was sufficient to render SQL superior to what preceded it, this is but one example of the many advantages of the RDM that SQL has failed to concretize.
I have revised:
THE DBDEBUNK GUIDE TO MISCONCEPTIONS OF DATA FUNDAMENTALS available to order here.
1. Quotes of the Week
"Wow. Been using SQL for 15 years, and didn’t even know about Except and Intersect!" --Blog.Jooq.org
"I am looking for database schemas with many tables (>100 tables). Where can I find them? I am currently using mysql and haven't done serious database design. So interested in looking at samples with ER diagrams." --YCombinator.com
2. To Laugh or Cry?
Note: This is a 11/24/17 re-write of Part II of a three-part series that replaced several previous posts (the pages of which redirect here), to bring in line with the McGoveran formalization and interpretation [1] of Codd's true RDM.
(Continued from Part I)
To recall from Part I, adherence to the POOD means independent base relations (i.e., not derivable from other base relations), which the design example in Part I,
EMPS (EMP#,ENAME,HIREDATE)
SAL_EMPS (EMP#,ENAME,HIREDATE,SALARY)
COMM_EMPS (EMP#,ENAME,HIREDATE,COMMISSION)
violates: EMPS is derivable via union of projections of SAL_EMPS and COMM_EMPS. It requires at least:
- A disjunctive constraint on each of the SAL_EMPS and COMM_EMPS relations, to ensure mutual exclusivity;
- A redundancy control constraint to prevent inconsistency due to partial updates (can you formulate it?);
- Use of the transaction management component of the data language to ensure that each candidate tuple is properly inserted (1) into EMPS and (2) the correct subtype relation;
My October post @All Analytics.
Be that as it may, practitioners insist that performance improves when they denormalize databases, because "bundling" facts into less relations reduces joins.
But even if this were always true -- it is not -- performance gains, if
any, do not come from denormalization per se, but from trading off
integrity for performance.
What many data professionals miss is that the redundancy introduced by
denormalization must be controlled by the DBMS to ensure data integrity,
which requires special integrity constraints
that, it turns out, involve the very joins that denormalization is
intended to avoid, defeating its purpose. These constraints are
practically never declared and enforced, which creates the illusion that denormalization improves performance at no cost.
Read it all. (Please comment there, not here)
When I discussed with a book publisher the idea of a guide/reference to misconceptions about data fundamentals, whose objective -- distinct from the usual cookbooks -- is to help data professionals base their practice on understanding, rather than cookbooks, he said "they are not interested in understanding, only in succeeding in their jobs". Apparently, the former is no longer a factor in the latter. Given the increasingly deteriorating experiences I had with publishers, it was time to stop bothering with them -- they pay and do very little -- and self-publish.
THE DBDEBUNK GUIDE TO MISCONCEPTIONS ABOUT DATA FUNDAMENTALS - A DESK REFERENCE FOR THE THINKING DATA PROFESSIONAL AND USER is now available for purchase ($35, order via the BOOKS page (not to be confused with the RECOMMENDED BOOKS page); contact me by email for volume discounts).
