2. Relational Query Languages
Query languages: Allow manipulation and retrieval
of data from a database.
Relational model supports simple, powerful QLs:
Strong formal foundation based on logic.
Allows for much optimization.
Query Languages != programming languages!
QLs not expected to be “Turing complete”.
QLs not intended to be used for complex calculations.
QLs support easy, efficient access to large data sets.
3. Formal Relational Query Languages
Two mathematical Query Languages form the
basis for “real” languages (e.g. SQL), and for
implementation:
Relational Algebra: More operational (procedural), very
useful for representing execution plans.
Relational Calculus: Lets users describe what they
want, rather than how to compute it: Non-operational,
declarative.
4. Preliminaries
A query is applied to relation instances, and the
result of a query is also a relation instance.
Schemas of input relations for a query are fixed.
The schema for the result of a given query is also
fixed! - determined by definition of query language
constructs.
Positional vs. named-field notation:
Positional notation easier for formal definitions,
named-field notation more readable.
Both used in SQL
5. Example Instances
sid sname rating age
22 dustin 7 45.0
31 lubber 8 55.5
58 rusty 10 35.0
sid sname rating age
28 yuppy 9 35.0
31 lubber 8 55.5
44 guppy 5 35.0
58 rusty 10 35.0
sid bid day
22 101 10/10/96
58 103 11/12/96
R1
S1
S2
“Sailors” and “Reserves”
relations for our examples.
We’ll use positional or
named field notation,
assume that names of fields
in query results are
`inherited’ from names of
fields in query input
relations.
6. Relational Algebra
Basic operations:
Selection ( ) Selects a subset of rows from relation.
Projection ( ) Deletes unwanted columns from relation.
Cross-product ( ) Allows us to combine two relations.
Set-difference ( ) Tuples in reln. 1, but not in reln. 2.
Union ( ) Tuples in reln. 1 and in reln. 2.
Additional operations:
Intersection, join, division, renaming: Not essential, but
(very!) useful.
Since each operation returns a relation, operations can
be composed: algebra is “closed”.
7. Projection
sname rating
yuppy 9
lubber 8
guppy 5
rusty 10
sname rating
S
,
( )
2
age
35.0
55.5
age S
( )
2
Deletes attributes that are not in
projection list.
Schema of result contains exactly
the fields in the projection list,
with the same names that they had
in the input relation.
Projection operator has to
eliminate duplicates! Why?
Note: real systems typically
don’t do duplicate elimination
unless the user explicitly asks
for it (by DISTINCT). Why not?
8. Selection
rating
S
8
2
( )
sid sname rating age
28 yuppy 9 35.0
58 rusty 10 35.0
sname rating
yuppy 9
rusty 10
sname rating rating
S
,
( ( ))
8
2
Selects rows that satisfy
selection condition.
No duplicates in result!
Why?
Schema of result identical
to schema of input
relation.
What is Operator
composition?
Selection is distributive
over binary operators
Selection is commutative
9. Union, Intersection, Set-Difference
All of these operations take
two input relations, which
must be union-compatible:
Same number of fields.
`Corresponding’ fields
have the same type.
What is the schema of result?
sid sname rating age
22 dustin 7 45.0
31 lubber 8 55.5
58 rusty 10 35.0
44 guppy 5 35.0
28 yuppy 9 35.0
sid sname rating age
31 lubber 8 55.5
58 rusty 10 35.0
S S
1 2
S S
1 2
sid sname rating age
22 dustin 7 45.0
S S
1 2
10. 1
Cross-Product (Cartesian Product)
Each row of S1 is paired with each row of R1.
Result schema has one field per field of S1 and R1,
with field names `inherited’ if possible.
Conflict: Both S1 and R1 have a field called sid.
( ( , ), )
C sid sid S R
1 1 5 2 1 1
(sid) sname rating age (sid) bid day
22 dustin 7 45.0 22 101 10/10/96
22 dustin 7 45.0 58 103 11/12/96
31 lubber 8 55.5 22 101 10/10/96
31 lubber 8 55.5 58 103 11/12/96
58 rusty 10 35.0 22 101 10/10/96
58 rusty 10 35.0 58 103 11/12/96
Renaming operator:
11. 1
Joins: used to combine relations
Condition Join:
Result schema same as that of cross-product.
Fewer tuples than cross-product, might be able to
compute more efficiently
Sometimes called a theta-join.
R c S c R S
( )
(sid) sname rating age (sid) bid day
22 dustin 7 45.0 58 103 11/12/96
31 lubber 8 55.5 58 103 11/12/96
S R
S sid R sid
1 1
1 1
. .
12. 1
Join
Equi-Join: A special case of condition join where
the condition c contains only equalities.
Result schema similar to cross-product, but only
one copy of fields for which equality is specified.
Natural Join: Equijoin on all common fields.
sid sname rating age bid day
22 dustin 7 45.0 101 10/10/96
58 rusty 10 35.0 103 11/12/96
S R
sid
1 1
13. 1
Properties of join
Selecting power: can join be used for selection?
Is join commutative? = ?
Is join associative?
Join and projection perform complementary
functions
Lossless and lossy decomposition
1
1 R
S
1
1 S
R
?
1
)
1
1
(
)
1
1
(
1 C
R
S
C
R
S
14. 1
Division
Not supported as a primitive operator, but useful for
expressing queries like:
Find sailors who have reserved all
boats.
Let A have 2 fields, x and y; B have only field y:
A/B =
i.e., A/B contains all x tuples (sailors) such that for every y
tuple (boat) in B, there is an xy tuple in A.
Or: If the set of y values (boats) associated with an x value
(sailor) in A contains all y values in B, the x value is in A/B.
In general, x and y can be any lists of fields; y is the list of
fields in B, and x y is the list of fields of A.
x x y A y B
| ,
16. 1
Example of Division
Find all customers who have an account at all
branches located in Chville
Branch (bname, assets, bcity)
Account (bname, acct#, cname, balance)
17. 1
Example of Division
R1: Find all branches in Chville
R2: Find (bname, cname) pair from Account
R3: Customers in r2 with every branch name in r1
1
2
3
)
(
2
)
(
1
,
'
'
r
r
r
Account
r
r
cname
bname
Branch
Chville
bcity
bname
18. 1
Expressing A/B Using Basic Operators
Division is not essential op; just a useful shorthand.
Also true of joins, but joins are so common that systems
implement joins specially.
Idea: For A/B, compute all x values that are not
`disqualified’ by some y value in B.
x value is disqualified if by attaching y value from B, we
obtain an xy tuple that is not in A.
Disqualified x values:
A/B:
)
)
)
(
(( A
B
A
x
x
x A
( ) all disqualified tuples
19. 1
Exercises
Given relational schema:
Sailors (sid, sname, rating, age)
Reservation (sid, bid, date)
Boats (bid, bname, color)
1) Find names of sailors who’ve reserved boat #103
2) Find names of sailors who’ve reserved a red boat
3) Find sailors who’ve reserved a red or a green boat
4) Find sailors who’ve reserved a red and a green boat
5) Find the names of sailors who’ve reserved all boats
20. 2
Summary of Relational Algebra
The relational model has rigorously defined
query languages that are simple and
powerful.
Relational algebra is more operational; useful
as internal representation for query
evaluation plans.
Several ways of expressing a given query; a
query optimizer should choose the most
efficient version.
22. 2
Relational Calculus
Comes in two flavors: Tuple relational calculus (TRC)
and Domain relational calculus (DRC).
Calculus has variables, constants, comparison ops,
logical connectives and quantifiers.
TRC: Variables range over (i.e., get bound to) tuples.
DRC: Variables range over domain elements (= field values).
Both TRC and DRC are simple subsets of first-order logic.
Expressions in the calculus are called formulas. An
answer tuple is essentially an assignment of
constants to variables that make the formula
evaluate to true.
23. 2
Domain Relational Calculus
Query has the form:
x x xn p x x xn
1 2 1 2
, ,..., | , ,...,
Answer includes all tuples that
make the formula be true.
x x xn
1 2
, ,...,
p x x xn
1 2
, ,...,
Formula is recursively defined, starting with
simple atomic formulas (getting tuples from
relations or making comparisons of values),
and building bigger and better formulas using
the logical connectives.
24. 2
DRC Formulas
Atomic formula:
, or X op Y, or X op constant
op is one of
Formula:
an atomic formula, or
, where p and q are formulas, or
, where X is a domain variable or
, where X is a domain variable.
The use of quantifiers and is said to bind X.
x x xn Rname
1 2
, ,...,
, , , , ,
p p q p q
, ,
X p X
( ( ))
X p X
( ( ))
X X
25. 2
Free and Bound Variables
The use of quantifiers and in a formula is
said to bind X.
A variable that is not bound is free.
Let us revisit the definition of a query:
X X
x x xn p x x xn
1 2 1 2
, ,..., | , ,...,
There is an important restriction: the variables
x1, ..., xn that appear to the left of `|’ must be
the only free variables in the formula p(...).
26. 2
Find all sailors with a rating above 7
The condition ensures that
the domain variables I, N, T and A are bound to
fields of the same Sailors tuple.
The term to the left of `|’ (which should
be read as such that) says that every tuple
that satisfies T>7 is in the answer.
Modify this query to answer:
Find sailors who are older than 18 or have a rating under
9, and are called ‘Joe’.
I N T A I N T A Sailors T
, , , | , , ,
7
I N T A Sailors
, , ,
I N T A
, , ,
I N T A
, , ,
27. 2
Find sailors rated > 7 who have reserved
boat #103
We have used as a shorthand
for
Note the use of to find a tuple in Reserves that
`joins with’ the Sailors tuple under consideration.
I N T A I N T A Sailors T
, , , | , , ,
7
Ir Br D Ir Br D serves Ir I Br
, , , , Re 103
Ir Br D
, , ...
Ir Br D ...
28. 2
Find sailors rated > 7 who’ve reserved a
red boat
Observe how the parentheses control the scope of
each quantifier’s binding.
This may look cumbersome, but with a good user
interface, it could be intuitive. (MS Access, QBE)
I N T A I N T A Sailors T
, , , | , , ,
7
Ir Br D Ir Br D serves Ir I
, , , , Re
B BN C B BN C Boats B Br C red
, , , , ' '
29. 2
Find sailors who’ve reserved all boats
Find all sailors I such that for each 3-tuple
either it is not a tuple in Boats or there is a tuple
in Reserves showing that sailor I has reserved it.
I N T A I N T A Sailors
, , , | , , ,
B BN C B BN C Boats
, , , ,
Ir Br D Ir Br D serves I Ir Br B
, , , , Re
B BN C
, ,
30. 3
Find sailors who’ve reserved all
boats (again!)
Simpler notation, same query. (Much clearer!)
To find sailors who’ve reserved all red boats:
I N T A I N T A Sailors
, , , | , , ,
B BN C Boats
, ,
Ir Br D serves I Ir Br B
, , Re
C red Ir Br D serves I Ir Br B
' ' , , Re
.....
Any other way to specify it? Equivalence in logic
31. 3
Unsafe Queries, Expressive Power
It is possible to write syntactically correct calculus
queries that have an infinite number of answers!
Such queries are called unsafe.
e.g.,
It is known that every query that can be expressed in
relational algebra can be expressed as a safe query in
DRC / TRC; the converse is also true.
Relational Completeness: Query language (e.g., SQL)
can express every query that is expressible in
relational algebra/calculus.
S S Sailors
|
32. 3
Exercise of tuple calculus
Given relational schema:
Sailors (sid, sname, rating, age)
Reservation (sid, bid, date)
Boats (bid, bname, color)
1) Find all sialors with a rating above 7.
2) Find the names and ages of sailors with a rating
above 7
3) Find the sailor name, boal id, and reservation
date for each reservation
4) Find the names of the sailors who reserved all
boats.
33. 3
Summary of Relational Calculus
Relational calculus is non-operational, and
users define queries in terms of what they
want, not in terms of how to compute it.
(Declarativeness.)
Algebra and safe calculus have same
expressive power, leading to the notion of
relational completeness.
Editor's Notes
#1:The slides for this text are organized into chapters. This lecture covers relational algebra, from Chapter 4. The relational calculus part can be found in Chapter 4, Part B.
Examples are used extensively. The text and chapter exercises contain numerous additional examples, and I often ask students to do several of these as assignments.
This material is important for two reasons:
It is a foundation for SQL. This is an important reason to cover algebra in detail, but the discussion of SQL in the book does NOT depend on a detailed understanding of algebra. Instructors who prefer to explain the basic algebra operators and skip the discussion of how to write a wide range of queries in algebra can safely do so.
It is a foundation for query processing. Students need to know what each operator does, and how they can be composed in queries. Again, it is possible to skip a detailed discussion of how to write complex queries in algebra, and this is left to the instructor’s discretion.
#21:The slides for this text are organized into chapters. This lecture covers relational calculus from Chapter 4. The relational algebra part can be found in Chapter 4, Part A.
We only discuss Domain Relational Calculus (DRC).; the slides must be adapted to cover TRC (which is discussed in the text).
Relational calculus is part of the foundation for SQL, and a good grasp of this material is helpful when studying SQL. However, the coverage of SQL in the text does not rely upon a knowledge of calculus, and instructors who choose to omit coverage of calculus can do so safely.
