Revised 3/20/21

I recently came across a review of Edsger Dijkstra's work, where the following comment on a book he co-authored (referred to as D&S) raised my debunking antennae:

“... in general computer people seem to have a penchant for whipping up homebrew logics ... D&S is not the only example ... See E.F. Codd’s Relational Calculus, an obvious mess.”
--Maarten van Emden, A Bridge too Far: E.W. Dijkstra and Logic 

Having recently argued that "Codd was wrong" and "You're teaching [his] gospel" Betray Lack of Foundation Knowledge, my suspicion should hardly surprise. Besides, criticism of Dijkstra is a very tall order in itself, particularly in the context of disputes among logicians). As a reader asked, "What’s so obviously messy in Codd’s Relational Calculus?". Answer:

In response to an online publication of a book appendix regurgitating Codd's 12 famous rules (some of which were, typically, incorrect[1]) I posted earlier a clarification of the rules. This is a revision thereof for better consistency with the new understanding of the RDM based on McGoveran's re-interpretation, extension and formalization[2] of Codd's work.

“...the theoretical awesomeness of relational algebra is kinda hard to intuitively relate back to your payroll or audit-log tables - the real power is the computed join ... it lets you dynamically fetch sets of data in the exact format that you need ... with any group of tables in the dataset. Unlike other data models, where the things you can fetch are typically fixed when you define your elements, and where relationships between data - if any - are statically defined in advance ... joins let you specify the relationships between objects (rows and tables in SQL-based relational databases) ... create queries and run them on your data without needing to write a lot of extra code beyond the SQL itself. This Ad Hoc Query capability ends up being hugely valuable when doing "secondary" business tasks in a big data world such as doing reporting and analytics, and is often hugely difficult to do in non-relational environments without a lot of extra code and often a specialized reporting schema.”

“Relational theory as applied to databases is that all data is connected to each other, keyed to each piece ... And with a SQL query [you can] create anything that can exist, as output.”

“...main so called power of RDBMs lies within ACID compliance. A transaction in a RDB is Atomic, Consistent, isolated, durable ... makes a database useful, unique, or I suppose powerful ... let's say you update or insert some new record, right in the middle power goes out, due to ACID compliance your transaction will not go through ... either the operation will complete or fail, nothing in between. [And RDBMSs are] tried, tested and true for almost 50 years.”

“Over the past decade, there have been a number of DBMSs introduced (typically labeled as NoSQL) which utilize a network or hierarchical data model. MongoDB and Cassandra come immediately to mind as examples. Some such systems support networks through the concepts of "links" and some support hierarchical data using a nested data model often utilizing JSON. In my opinion, these systems have not internalized lessons from history.

“At the SIGFIDET (now SIGMOD) annual conference in 1974, there was a "Great Debate" over the merits of the relational model versus the network and hierarchical models ... Basically, the argument was about which model [relational or network] was a better fit for structured data (as opposed to documents, e-mails, etc.) and boiled down to two questions:

Question 1: Are high-level data sublanguages a good idea?
Question 2: Are tables the best data structure or should one use a network or hierarchy?”

“The last 45 years have definitely affirmed Codd’s position on both issues ... The conclusion from the 1970s was that the relational model provides superior data independence, compared to the network and hierarchical [graph] models. Forty-five years later, this conclusion is still true. If you want to insulate yourself from the changes that business conditions dictate, use a relational DBMS. If you want the successor to the successor to your job to thank you for your wise decision, use a relational model.”

• A data sublanguage is short for data manipulation language (DML) that combines (1) a relationally complete retrieval component (i.e., that expresses the RA) with (2) a component that expresses updates as relation transformations;
• A DBMS language is a careful combination, for practical purposes, of the data sublanguage with several sublanguages, each of which expresses a data management function (e.g., data definition, transactions, concurrency, authorizations) -- that are not relational, but are consistent with the RDM, and must not include syntactic elements that are at odds with, or subvert those of the DML.

“Recently I have read that SQL is actually a data sublanguage and not a programming language like C++ or Java or C# ... The answers ... have the pattern of "No, it is not. Because it's not Turing complete.", etc, etc. ... I am a bit confused, because since you can develop things through SQL, I thought it is similar to other programming languages ... I am curious about knowing why exactly is SQL not a programming language? Which features does it lack? (I know it can't do loops, but what else more?)”

--StackOverflow.com

“The SQL operators were meant to implement the relational algebra as proposed by Dr. Ted Codd. Unfortunately Dr. Codd based some of his ideas on a "extended set theory", which was an idea formulated and described in a 1977 paper by D. L. Childs ... But Childs’ extensions were not ideally suited, which is explained in quite some detail in [a] book ... by Professor Gary Sherman & Robin Bloor [who] argue that mainstream Zermelo-Fraenkel set theory (Cantor), would have been a better starting point. One key issue is that sets should be able to be sets of sets.”

--Dataversity.net

Object Orientation and Unified Modeling Language

“A "counter revolution" against the relational movement was attempted in the 90’s. Graphical user interfaces came to dominate and they required advanced programming environments. Functionality like inheritance, sub-typing and instantiation helped programmers combat the complexities of highly interactive user dialogs. The corresponding Data Modeling tool is the Unified Modeling Language ...”

“The relational databases that emerged in the ’80s are efficient at storing and analyzing tabular data but their underlying data model makes it difficult to connect data scattered across multiple tables. The graph databases we’ve seen emerge in the recent years are designed for this purpose. Their data model is particularly well-suited to store and to organize data where connections are as important as individual data points. Connections are stored and indexed as first-class citizens, making it an interesting model for investigations in which you need to connect the dots. In this post, we review three common fraud schemes and see how a graph approach can help investigators defeat them.”

--AnalyticBridge.DataScienceCentral.com

• There is no "tabular data" (the relational data structure is the relation, which can be visualized as a table on a physical medium[2], and SQL tables are not relations);

• Analysis is not a DBMS, but an application function (while database queries, as deductions, are an important aspect of analysis, and computational functions can be added to the data sublanguage (as in SQL), the primary function of a DBMS is data management)[3];

• A data model has nothing to do with storage (storage and access methods are part of physical implementation, which determines efficiency/performance[4]).

“[According to Date] relvar ≠ class. [But i]n simple terms, 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 the latter 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.”

“I don't understand the concepts of domain and attribute in relational database modeling. Can someone give me an effective example?”

“Domain is an overloaded word in the DB lexicon. It probably should also be avoided. When one refers to an attribute domain in practice it is only referring to columns that have a check constraint on them that limit the values. Reference tables with foreign key constraints in general also fulfill the spirit of what domain attributes do outside of an RDBMS.”

“A domain in most SQL usage is essentially an alias name for an existing type + restrictions on an existing type that can be used in a column. As for an attribute, it's essentially a COLUMN in SQL, a field in other types of databases, etc.”

"To the question “How relevant is data modeling in the world of NoSQL?” I give the following answer.

The main purpose of data modeling is to understand the business, some application domain, some users world. The model becomes a representation of that world -- the "things" in it, the relationships among those things and any constraints on those things or relationships. A secondary purpose is to build a database to contain information which pertains to and describes that domain."

"Generally we speak of the model coming first, then the implementation, and finally, the data gets collected and stored according to the model. Hence, the business data model should not be concerned with issues of physical stored representation, or the transformations/manipulations/constraints which are imposed to facilitate implementation in some data (storage) management system. That could be a relational DBMS, or a NoSQL tool".

" ... increasingly the data already exists in some form. Which leaves us with the task of figuring out what it means, what it represents -- that is, understanding the data as it represents some user domain. NoSQL tools are often designed to deal with existing data and to process it more efficiently (that may be an oversimplification!). Either way, you must understand the business in order to make sense of the data." 

--Gordon Everest, LinkedIn.com

"You constantly remind us that the relational model is a logical model having no connection to any physical model (so I infer). You also indicate how no commercial product fully implements the relational model. Therefore, how do we make use of the relational model when dealing with the physical constructs of a commercial database program (Oracle, Access, DB2, etc.)?" --DBDebunk.com query

"Per Date’s AN INTRODUCTION TO DATABASE SYSTEMS, Date & Darwen’s DATABASES, TYPES, AND THE RELATIONAL MODEL, and related references, the features of a relational database are values, types, attributes, tuples, relations, relation-valued variables, operators, and constraints.

• A type is a set of values and related operators.
• An attribute is a name, value, type triple.
• A tuple is a set of attributes.
• A relation is a set of tuples with a given heading.
• A relation-valued variable (known as a relvar) is a persistent variable whose time-varying value is a relation." --Dave Voorhis, Computer scientist; lead developer of Rel, a true relational database system, Quora.com

"In my experience, using an object model in both the application layer and in the database layer results in an inefficient system. This are my personal design goals:
- Use a relational data model for storage
- Design the database tables using relational rules including 3rd normal form
- Tables should mirror logical objects, but any object may encompass multiple tables- Application objects, whether you are using an OO language or a traditional language using structured programming techniques should parallel application needs which most closely correspond to individual SQL statements than to tables or "objects". --LinkedIn.com

Understanding Codd's 12 Rules for RDBMS