Note: About TYFK posts (Test Your Foundation Knowledge) see the post insert below.
“...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.”
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LATEST UPDATES
-12/24/20: Added 2021 to the POSTS page
-12/26/20: Added “Mathematics, machine learning and Wittgenstein to LINKS page
LATEST PUBLICATIONS (order from PAPERS and BOOKS pages)
- 08/19 Logical Symmetric Access, Data Sub-language, Kinds of Relations,
Database Redundancy and Consistency, paper #2 in the new UNDERSTANDING THE
REAL RDM series.
- 02/18 The Key to Relational Keys: A New Understanding, a new edition
of paper #4 in the PRACTICAL DATABASE FOUNDATIONS series.
- 04/17 Interpretation and Representation of Database Relations, paper
#1 in the new UNDERSTANDING THE REAL RDM series.
- 10/16 THE DBDEBUNK GUIDE TO MISCONCEPTIONS ABOUT DATA FUNDAMENTALS, my
latest book (reviewed by Craig Mullins, Todd Everett, Toon Koppelaars, Davide
Mauri).
USING THIS SITE
- To work around Blogger limitations, the labels are mostly abbreviations or
acronyms of the terms listed on the FUNDAMENTALS
page. For detailed
instructions on how to understand and use the labels in conjunction with the
that page, see the ABOUT
page. The 2017 and 2016 posts,
including earlier posts rewritten in 2017 were relabeled accordingly. As other
older posts are rewritten, they will also be relabeled. For all other older
posts use Blogger search.
- The links to my columns there no longer work. I moved only the 2017 columns
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#Antisemitism/#AntiZionism and the Arab-Israeli conflict.
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The Misconceptions
- Databases have no power, DBMSs that manage them do.
- The RDM is an adaptation and application of mathematical relation theory to database management -- its applicability to database practice is intuitive only with proper education on data fundamentals, but practitioners learn relational jargon from tool experience and without understanding of what it means and the practical implications.
- The power of the relational algebra (RA) comes from its set operations -- not just join -- and their nestability due to relational closure.
- A relational database consists of relations, R-tables are just visualizations of relations on a physical medium and play no part in the RDM (SQL tables are not relations, one reason SQL DBMSs and databases are not relational).
- To the extent that the only other model claimed to be a data model in the sense in which the RDM is one -- the "graph data model" (GDM) -- can be considered such, it is a database solution for conceptual models focused on relationships individual entities (e.g., social networks).
Note: The GDM (hierarchic is a special case of network) has not yet been fully formalized as the RDM -- what are (precisely, please!) its structural-integrity components? (there is no such thing as a NoSQL data model).
- Be that as it may, in these "data models the things you can fetch" are not "fixed when you define your elements", and "relationships between data, if any" are not "statically defined in advance"; rather, there is complexity and rigidity to change due to procedurality and lack of data independence.
- "Without needing to write a lot of extra code beyond the SQL itself" is a poor expression of a critical aspect of the RDM: declarative relationally complete data sublanguages hosted by computationally complete programming languages; SQL was intended as such, but is not exactly that.
- Relational database objects are relations (with attributes defined on simple domains) and tuples, not "tables and rows".
- Relationships in a conceptual model understood by users are represented by constraints in relational databases and enforced by the DBMS in the database.
- There are several types of relationships and, hence, of constraints "understood" algorithmically and enforced by the DBMS, which guarantees correctness of the data and query results - logical validity and semantic consistency ("all data is connected to each other, keyed to each piece" is a poor expression of this, and not very enlightening).
- Transaction management with ACID compliance is a DBMS function that is not part of, and is orthogonal to the RDM -- witness support by non-relational SQL DBMSs.
Note: There are reasons for which true RDBMSs are more amenable transaction support that are beyond the scope of this discussion.
The Answer
RDBMS power comes from the dual formal theoretical foundation (SST/FOPL) of the RDM, and specifically:
- Nestable set operations of the RA [1];
- Declarativity of relational data sublanguages [1];
- Data independence [2,3];
- System-guaranteed provable correctness [4].
References
[1] Pascal, F., Data Sublanguage Parts 1-4
[2] Pascal, F., Physical Independence Parts 1-4
[3] Pascal, F., On View Updating
[4] Pascal, F., Logical Validity and Semantic Consistency
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