QUEL query languages

QUEL is a relational database query language, based on tuple relational calculus, with some similarities to SQL. It was created as a part of the Ingres DBMS effort at University of California, Berkeley, based on Codd's earlier suggested but not implemented Data Sub-Language ALPHA. QUEL was used for a short time in most products based on the freely available Ingres source code, most notably in an implementation called POSTQUEL supported by POSTGRES.[1] As Oracle and DB2 gained market share in the early 1980s, most companies then supporting QUEL moved to SQL instead. QUEL continues to be available as a part of the Ingres DBMS, although no QUEL-specific language enhancements have been added for many years.

QUEL
FamilyQuery language
Designed byMichael Stonebraker
First appeared1976 (1976)
Major implementations
Ingres, POSTQUEL
Influenced by
Alpha

Usage

QUEL statements are always defined by tuple variables, which can be used to limit queries or return result sets. Consider this example, taken from one of the first original Ingres papers:[2]

Example 1.1. Compute salary divided by age-18 for employee Jones.

 range of E is EMPLOYEE
 retrieve into W
 (COMP = E.Salary / (E.Age - 18))
 where E.Name = "Jones"

Here E is a tuple variable which ranges over the EMPLOYEE relation, and all tuples in that relation are found which satisfy the qualification E.Name = “Jones.” The result of the query is a new relation W, which has a single domain COMP that has been calculated for each qualifying tuple.

An equivalent SQL statement is:

 select (e.salary / (e.age - 18)) as comp
 from employee as e
 where e.name = "Jones"

Here is a sample of a simple session that creates a table, inserts a row into it, and then retrieves and modifies the data inside it and finally deletes the row that was added (assuming that name is a unique field).

QUELSQL
create student(name = c10, age = i4, sex = c1, state = c2)

range of s is student
append to s (name = "philip", age = 17, sex = "m", state = "FL")

retrieve (s.all) where s.state = "FL"

replace s (age=s.age+1)

retrieve (s.all)

delete s where s.name="philip"

create table student(name char(10), age int, sex char(1), state char(2));

insert into student (name, age, sex, state) values ("philip", 17, "m", "FL");


select * from student where state = "FL";

update student set age=age+1;

select * from student;

delete from student where name="philip";


Another feature of QUEL was a built-in system for moving records en-masse into and out of the system. Consider this command:

 copy student(name=c0, comma=d1, age=c0, comma=d1, sex=c0, comma=d1, address=c0, nl=d1)
 into "/student.txt"

which creates a comma-delimited file of all the records in the student table. The d1 indicates a delimiter, as opposed to a data type. Changing the into to a from reverses the process. Similar commands are available in many SQL systems, but usually as external tools, as opposed to being internal to the SQL language. This makes them unavailable to stored procedures.

QUEL has an extremely powerful aggregation capability. Aggregates can be nested, and different aggregates can have independent by-lists and/or restriction clauses. For example:

  retrieve (a=count(y.i by y.d where y.str = "ii*" or y.str = "foo"),b=max(count(y.i by y.d)))

This example illustrates one of the arguably less desirable quirks of QUEL, namely that all string comparisons are potentially pattern matches. y.str = "ii*" matches all y.str values starting with ii.

gollark: There are also a lot of things it can't do, like many other reasoning tasks, anything not expressible as text, and a lot of things requiring world modeling. But I don't know if that means it isn't "thinking".
gollark: I don't know if it can "think" because that's quite poorly defined. I do know that it can do some amount of logical and common-sense reasoning and has very good language abilities.
gollark: <@267332760048238593> "Manages charts"?
gollark: This is also "behavior".
gollark: What?

See also

References
  1. Stonebraker, M; Rowe, LA (May 1986). The design of POSTGRES (PDF). Proc. 1986 ACM SIGMOD Conference on Management of Data. Washington, DC.
  2. Stonebraker, Michael; Wong, Eugene; Kreps, Peter; Held, Gerald (1976). "The Design and Implementation of INGRES". ACM Transactions on Database Systems. 1 (3): 191. CiteSeerX 10.1.1.109.957. doi:10.1145/320473.320476.

Further reading
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