Like most new ideas, the hard part of understanding what the relational
model is comes in un-learning what you know about file systems.
As Artemus Ward (William Graham Sumner, 1840-1910) put it, "It ain't so
much the things we don't know that get us into trouble. It's the things
we know that just ain't so."
If you already have a background in data processing with traditional
file systems, the first things to un-learn are:
(0) Databases are not file sets.
(1) Tables are not files.
(2) Rows are not records.
(3) Columns are not fields.
Modern data processing began with punch cards, or Hollerith cards used
by the Bureau of the Census. Their original size was that of a United
States Dollar bill. This was set by their inventor, Herman Hollerith,
because he could get furniture to store the cards from the United States
Treasury Department, just across the street. Likewise, physical
constraints limited each card to 80 columns of holes in which to record
a symbol.
The influence of the punch card lingered on long after the invention of
magnetic tapes and disk for data storage. This is why early video
display terminals were 80 columns across. Even today, files which were
migrated from cards to magnetic tape files or disk storage still use 80
column records.
But the influence was not just on the physical side of data processing.
The methods for handling data from the prior media were imitated in the
new media.
Data processing first consisted of sorting and merging decks of punch
cards (later, sequential magnetic tape files) in a series of distinct
steps. The result of each step feed into the next step in the process.
Relational databases do not work that way. Each user connects to the
entire database all at once, not to one file at time in a sequence of
steps. The users might not all have the same database access rights
once they are connected, however. Magnetic tapes could not be shared
among users at the same time, but shared data is the point of a
database.
Tables versus Files
A file is closely related to its physical storage media. A table may or
may not be a physical file. DB2 from IBM uses one file per table,
while Sybase puts several entire databases inside one file. A table is
a <i>set<i> of rows of the same kind of thing. A set has no ordering
and it makes no sense to ask for the first or last row.
A deck of punch cards is sequential, and so are magnetic tape files.
Therefore, a <i>physical<i> file of ordered sequential records also
became the <i>mental<i> model for data processing and it is still hard
to shake. Anytime you look at data, it is in some physical ordering.
The various access methods for disk storage system came later, but even
these access methods could not shake the mental model.
Another conceptual difference is that a file is usually data that deals
with a whole business process. A file has to have enough data in
itself to support applications for that business process. Files tend to
be "mixed" data which can be described by the name of the business
process, such as "The Payroll file" or something like that.
Tables can be either entities or relationships within a business
process. This means that the data which was held in one file is often
put into several tables. Tables tend to be "pure" data which can be
described by single words. The payroll would now have separate tables
for timecards, employees, projects and so forth.
Tables as Entities
An entity is physical or conceptual "thing" which has meaning be itself.
A person, a sale or a product would be an example. In a relational
database, an entity is defined by its attributes, which are shown as
values in columns in rows in a table.
To remind users that tables are sets of entities, I like to use plural
or collective nouns that describe the function of the entities within
the system for the names of tables. Thus "Employee" is a bad name
because it is singular; "Employees" is a better name because it is
plural; "Personnel" is best because it is collective and does not summon
up a mental picture of individual persons.
If you have tables with exactly the same structure, then they are sets
of the same kind of elements. But you should have only one set for each
kind of data element! Files, on the other hand, were PHYSICALLY
separate units of storage which could be alike -- each tape or disk file
represents a step in the PROCEDURE , such as moving from raw data, to
edited data, and finally to archived data.
Tables as Relationships
A relationship is shown in a table by columns which reference one or
more entity tables. Without the entities, the relationship has no
meaning, but the relationship can have attributes of its own. For
example, a show business contract might have an agent, an employer and a
talent. The method of payment is an attribute of the contract itself,
and not of any of the three parties.
Rows versus Records
Rows are not records. A record is defined in the application program
which reads it; a row is defined in the database schema and not by a
program at all. The name of the field in the READ or INPUT statements
of the application; a row is named in the database schema.
All empty files look alike; they are a directory entry in the operating
system with a name and a length of zero bytes of storage. Empty tables
still have columns, constraints, security privileges and other
structures, even tho they have no rows.
This is in keeping with the set theoretical model, in which the empty
set is a perfectly good set. The difference between SQL's set model and
standard mathematical set theory is that set theory has only one empty
set, but in SQL each table has a different structure, so they cannot be
used in places where non-empty versions of themselves could not be used.
Another characteristic of rows in a table is that they are all alike in
structure and they are all the "same kind of thing" in the model. In a
file system, records can vary in size, datatypes and structure by having
flags in the data stream that tell the program reading the data how to
interpret it. The most common examples are Pascal's variant record, C's
struct syntax and Cobol's OCCURS clause.
The OCCURS keyword in Cobol and the Variant records in Pascal have a
number which tells the program how many time a record structure is to be
repeated in the current record.
Unions in 'C' are not variant records, but variant mappings for the same
physical memory. For example:
union x {int ival; char j[4];} myStuff;
defines myStuff to be either an integer (which are 4 bytes on most
modern C compilers, but this code is non-portable) or an array of 4
bytes, depending on whether you say myStuff.ival or myStuff.j[0];
But even more than that, files often contained records which were
summaries of subsets of the other records -- so called control break
reports. There is no requirement that the records in a file be related
in any way -- they are literally a stream of binary data whose meaning
is assigned by the program reading them.
Columns versus Fields
A field within a record is defined by the application program that reads
it. A column in a row in a table is defined by the database schema.
The datatypes in a column are always scalar.
The order of the application program variables in the READ or INPUT
statements is important because the values are read into the program
variables in that order. In SQL, columns are referenced only by their
names. Yes, there are shorthands like the SELECT * clause and INSERT
INTO <table name> statements which expand into a list of column names in
the physical order in which the column names appear within their table
declaration, but these are shorthands which resolve to named lists.
The use of NULLs in SQL is also unique to the language. Fields do not
support a missing data marker as part of the field, record or file
itself. Nor do fields have constraints which can be added to them in
the record, like the DEFAULT and CHECK() clauses in SQL.
Relationships among tables within a database
Files are pretty passive creatures and will take whatever an application
program throws at them without much objection. Files are also
independent of each other simply because they are connected to one
application program at a time and therefore have no idea what other
files looks like.
A database actively seeks to maintain the correctness of all its data.
The methods used are triggers, constraints and declarative referential
integrity.
Declarative referential integrity (DRI) says, in effect, that data in
one table has a particular relationship with data in a second (possibly
the same) table. It is also possible to have the database change itself
via referential actions associated with the DRI.
For example, a business rule might be that we do not sell products which
are not in inventory. This rule would be enforce by a REFERENCES clause
on the Orders table which references the Inventory table and a
referential action of ON DELETE CASCADE
Triggers are a more general way of doing much the same thing as DRI. A
trigger is a block of procedural code which is executed before, after or
instead of an INSERT INTO or UPDATE statement. You can do anything with
a trigger that you can do with DRI and more.
However, there are problems with TRIGGERs. While there is a standard
syntax for them in the SQL-92 standard, most vendors have not
implemented it. What they have is very proprietary syntax instead.
Secondly, a trigger cannot pass information to the optimizer like DRI.
In the example in this section, I know that for every product number in
the Orders table, I have that same product number in the Inventory
table. The optimizer can use that information in setting up EXISTS()
predicates and JOINs in the queries. There is no reasonable way to
parse procedural trigger code to determine this relationship.
The CREATE ASSERTION statement in SQL-92 will allow the database to
enforce conditions on the entire database as a whole. An ASSERTION is
not like a CHECK() clause, but the difference is subtle. A CHECK()
clause is executed when there are rows in the table to which it is
attached. If the table is empty then all CHECK() clauses are
effectively TRUE. Thus, if we wanted to be sure that the Inventory
table is never empty, and we wrote:
CREATE TABLE Inventory
( ...
CONSTRAINT inventory_not_empty
CHECK ((SELECT COUNT(*) FROM Inventory) > 0), ... );
it would not work. However, we could write:
CREATE ASSERTION Inventory_not_empty
CHECK ((SELECT COUNT(*) FROM Inventory) > 0);
and we would get the desired results. The assertion is checked at the
schema level and not at the table level.
--CELKO--
Please post DDL, so that people do not have to guess what the keys,
constraints, Declarative Referential Integrity, datatypes, etc. in your
schema are. Sample data is also a good idea, along with clear
specifications.
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