\(\newcommand{\AntiJoin}{ \ \bar{\Join} \ } \)

Relational Algebra

  • Relational Algebra repesents the operations on relations for Relational Databases
  • Relational Algebra is algebra that consists of operations for constructing new relations from given relations
    • (it's closed, i.e. each operation always produces another relation)
  • RA is not used as a query language, but usually SQL is translated to it in RDBMS


Relational Algebra Operators

Operations of traditional RA:

  • usual set operations (union, intersection, difference)
  • operations that remove some parts of the relation
selections eliminate tuples (rows)
projections eliminate attributes (columns)
  • operations to combine tuples of two relations: Cartesian product, joins, etc
  • renaming

Binary

  • Union U
  • Intersection
  • Difference

Unary

  • Projection
  • Join

Extended RA

  • bags semantic
  • duplicate elimination: $d$
  • Grouping and aggregation: $\gamma$
  • Sorting: $t$


Traditional RA Operations

Set-Based Union

$R_1 \cup R_2$

  • Both $R_1$ and $R_2$ must have the same schema
  • And the result is a relation with the same schema
  • The result contains elements that are in $R_1$ or in $R_2$

SQL:

SELECT * FROM R1
UNION
SELECT * FROM R2

Example:

$\begin{array}{ c | c }

 A & B \\
 \hline
 \hline
 a_1 & b_1 \\ 
 a_2 & b_1 \\

\end{array} \cup \begin{array}{ c | c }

 A & B \\
 \hline
 \hline
 a_1 & b_1 \\ 
 a_3 & b_3 \\

\end{array} = \begin{array}{ c | c }

 A & B \\
 \hline
 \hline
 a_1 & b_1 \\ 
 a_2 & b_1 \\
 a_3 & b_3 \\

\end{array} $


Set-Based Difference

$R_1 - R_2$

  • Both $R_1$ and $R_2$ must have the same schema
  • And the result is a relation with the same schema
  • The result contains elements that are in $R_1$ but not in $R_2$


SELECT * FROM R1
EXCEPT 
SELECT * FROM R2


Example:

  • $\begin{array}{ c | c }
 A & B \\
 \hline \hline
 a_1 & b_1 \\ 
 a_2 & b_1 \\

\end{array} \cup \begin{array}{ c | c }

 A & B \\
 \hline \hline
 a_1 & b_1 \\
 a_3 & b_3 \\

\end{array} = \begin{array}{ c | c }

 A & B \\
 \hline \hline
 a_2 & b_1 \\

\end{array} $


Set-Based Intersection

$R_1 \cap R_2$

  • Both $R_1$ and $R_2$ must have the same schema
  • And the result is a relation with the same schema
  • The result contains elements that are in $R_1$ and in $R_2$


SQL

SELECT * FROM R1
INTERSECT 
SELECT * FROM R2


Example

$\begin{array}{ c | c }

 A & B \\
 \hline
 \hline
 a_1 & b_1 \\ 
 a_2 & b_1 \\

\end{array} \cup \begin{array}{ c | c }

 A & B \\
 \hline
 \hline
 a_1 & b_1 \\ 
 a_3 & b_3 \\

\end{array} = \begin{array}{ c | c }

 A & B \\
 \hline
 \hline
 a_1 & b_1 \\ 

\end{array} $


Can be expressed via Union and Difference

  • $R_1 \cap R_2 = R_1 - (R_1 - R_2)$
  • $R_1 \cap R_2 = R_1 \Join R_2$


Selection

A binary operator

  • Takes a relation $R$
  • Outputs all tuples of $R$ that satisfy a certain condition $\theta$
  • Attributes that take part in $\theta$ must be present in the relation $R$

in SQL:

SELECT ... FROM R WHERE {condition}

For example

  • $\sigma_\theta(\text{Relation})$
  • $\sigma_{A \geqslant 3} \left (

\begin{array}{ c | c }

 A & B \\
 \hline \hline
 1 & 2 \\ 
 3 & 4 \\

\end{array} \right) = \begin{array}{ c | c }

 A & B \\
 \hline \hline
 3 & 4 \\

\end{array} $


A condition can be anything

  • salary > 40000 (inequality)
  • name = "Smith" (equation)
  • etc


Set-Based Projection

$\pi_{A_1, ..., A_n}(R)$

A binary operator

  • Takes a relation $R$
  • Outputs only specified attributes $A_1, ..., A_n$ of $R$
  • All $A_1, ..., A_n$ must be present in $R$

SQL:

SELECT A1, ..., An FROM R

example

  • $\pi_{\text{SSN}, \text{name}}(\text{Employee})$


Note that for set-based projection there are no duplicated in the output:

  • $\pi_{A, C} \left(

\begin{array}{ c | c | c | c}

 A & B & C & D \\
 \hline \hline
 1 & 2 & 3 & 4 \\ 
 1 & 2 & 3 & 5 \\
 3 & 4 & 5 & 6 \\
 5 & 6 & 3 & 4 \\

\end{array} \right) = \begin{array}{ c | c}

 A & C \\
 \hline \hline
 1 & 3 \\ 
 3 & 5 \\
 5 & 3 \\

\end{array}$


Cartesian Product

Sometimes also called "Cross-Product"

$R_1 \times R_2$

  • Result of $R_1 \times R_2$ is a new relation
  • in which each tuple in $R_1$ concatenated with each tuple in $R_2$
  • i.e. it outputs all possible combinations of tuples
  • $R_1$ and $R_2$ must have disjoint schema

SQL

SELECT * FROM R1, R2

Example:

  • $\begin{array}{ c | c}
 A & B \\
 \hline \hline
 1 & 2 \\ 
 3 & 4 \\

\end{array} \times \begin{array}{ c | c}

 C & D \\
 \hline \hline
 2 & 6 \\ 
 3 & 7 \\
 4 & 9 \\

\end{array} = \begin{array}{ c | c | c | c}

 A & B & C & D \\
 \hline \hline
 1 & 2 & 2 & 6 \\
 1 & 2 & 3 & 7 \\
 1 & 2 & 4 & 9 \\
 3 & 4 & 2 & 6 \\
 3 & 4 & 3 & 7 \\
 3 & 4 & 4 & 9 \\

\end{array} $


Natural Join

Or "Equi-Join"

$R \Join S$

  • no requirements for schema for $R$ and $S$
  • if they have one or more attributes in common, in the output tuples with same values will be matched
  • if they don't have attributes in common - the result is the same as $R \times S$ (Cartesian Product)

Example:

  • $\begin{array}{ c | c}
 A & B \\
 \hline \hline
 1 & 2 \\ 
 3 & 4 \\

\end{array} \Join \begin{array}{ c | c}

 B & D \\
 \hline \hline
 2 & 6 \\ 
 3 & 7 \\
 4 & 9 \\

\end{array} = \begin{array}{ c | c | c}

 A & B & D \\
 \hline \hline
 1 & 2 & 6 \\
 3 & 4 & 9 \\

\end{array} $

  • $\begin{array}{ c | c}
 A & B \\
 \hline \hline
 1 & 2 \\ 
 3 & 4 \\

\end{array} \Join \begin{array}{ c | c}

 C & D \\
 \hline \hline
 2 & 6 \\ 
 3 & 7 \\
 4 & 9 \\

\end{array} = \begin{array}{ c | c | c | c}

 A & B & C & D \\
 \hline \hline
 1 & 2 & 2 & 6 \\
 1 & 2 & 3 & 7 \\
 1 & 2 & 4 & 9 \\
 3 & 4 & 2 & 6 \\
 3 & 4 & 3 & 7 \\
 3 & 4 & 4 & 9 \\

\end{array} $

Theta Join

$R_1 \Join_{\theta} R_2$

  • A join that involves some predicate $\theta$
  • For all combinations of tuples from $R_1 \Join_{\theta} R_2$, a tuple is output if $\theta$ holds for the combination
  • essentially is the same as Cartesian Product plus Selection

Examples

  • $R_1 \Join_{\theta} R_2 = \sigma_{\Theta}{R_1 \times R_2}$
  • $\begin{array}{ c | c}
 A & B \\
 \hline \hline
 1 & 2 \\ 
 3 & 4 \\

\end{array} \Join_{B = C} \begin{array}{ c | c}

 C & D \\
 \hline \hline
 2 & 6 \\ 
 3 & 7 \\
 4 & 9 \\

\end{array} = \begin{array}{ c | c | c | c}

 A & B & C & D \\
 \hline \hline
 1 & 2 & 2 & 6 \\
 3 & 4 & 4 & 9 \\

\end{array} $


Anti-Join

$R \AntiJoin S$

  • if for a tuple in $R$ there is a match in $S$, we do not output this tuple from $R$
  • $R \AntiJoin S \equiv R - (R \Join S)$
  • The result is only tuples from $R$ (the resulting schema is also the same as in $R$)


Difference between $R \AntiJoin S$ and $R - S$ (Difference):

  • for Difference $R - S$ both $R$ and $S$ need to have the same schema
  • for $R \AntiJoin S$ - any schema
  • if $R$ and $S$ have the same schema, then $R \AntiJoin S \equiv R - S$

Example

  • $\begin{array}{ c | c}
 A & B \\
 \hline \hline
 1 & 2 \\ 
 3 & 4 \\
 5 & 6 \\
 6 & 7 \\

\end{array} \AntiJoin \begin{array}{ c | c | c }

 B & C & D \\
 \hline \hline
 2 & 5 & 6 \\ 
 6 & 4 & 2 \\

\end{array} = \begin{array}{ c | c }

 A & B \\
 \hline \hline
 3 & 4 \\
 6 & 7 \\

\end{array} $


Renaming

$\rho_{\text{prefix}}(R)$

  • takes all attributes of $R$ and
  • produces a new relation with $\text{prefix.}$ appended to all of them
  • so the resulting relation is the same, but with changed schema

SQL

SELECT * FROM Relation R

Example

  • $\rho_T \left(

\begin{array}{ c | c}

 A & B \\
 \hline \hline
 1 & 2 \\ 
 3 & 4 \\

\end{array} \right) = \begin{array}{ c | c}

 T.A & T.B \\
 \hline \hline
 1 & 2 \\ 
 3 & 4 \\

\end{array}$


Examples

find all hospitals within 5 ms of a school


SELECT DISTINCT h.name 
FROM Hospital h, School s
WHERE distance(h.location, s.location) < 5

$\pi_\text{name} ( \rho_{\text{h}}(\text{Hospital}) \Join_{\text{h.location = s.location}} \rho_{\text{s}}(\text{School}) )$


Outer Joins

...


Semi Joins

$R \ltimes S = \pi_{R.*}(R \Join S)$

Extended RA

  • Adds additional operations to Transitional RA
  • Allows Bag semantics for operations

Sets vs Bags

  • Set of tuples: no duplicates allowed
  • Bag of tuples: there can be duplicates
  • In theory set semantics is usually assumed
  • But in implementation - bag semantics

Duplicates

  • Practically we don't care about duplicates
  • We remove them only when required (duplicate elimination: $d$)

RA has two semantics:

  • set semantics = traditional RA
  • bag semantics = extended RA

All set-based operations are straightforwardly extended to bags


Bag-Based Intersection, Difference, Union

Intersection

  • If the same tuple occurs twice in one relation,
  • It must also occur twice in the second relation
  • Then the result will also contain 2 tuples

Same idea with Difference and Union


Grouping

$\gamma_{\text{grouping_attribute}, \ \text{func}(A) \ \to \ \text{name}}(R)$

  • unary relation that takes $R$ as input
  • first parameter ($\text{grouping_attribute}$) is attribute on which $R$ will be grouped
  • function $\text{func}$ is applied to each group, and the result is written to attribute $\text{name}$
  • NB: all other (non-mentioned) attributes are not output to the result!

Example

  • $\gamma_{A, \ \text{min}(B) \ \to \ D} \left(

\begin{array}{ c | c | c }

 A & B & C \\
 \hline \hline
 1 & 2 & a \\
 1 & 3 & b \\
 2 & 3 & c \\
 2 & 4 & a \\
 2 & 5 & d \\

\end{array} \right) = \begin{array}{ c | c | c }

 A & D \\
 \hline \hline
 1 & 2 \\
 2 & 3 \\

\end{array} $


Projection


In Extended RA we also can allow renaming in projection

  • $\pi_{A, C \to D} \left(

\begin{array}{ c | c | c | c}

 A & B & C & D \\
 \hline \hline
 1 & 2 & 2 & 6 \\
 1 & 2 & 2 & 7 \\
 1 & 2 & 2 & 9 \\
 3 & 4 & 3 & 6 \\
 3 & 4 & 3 & 7 \\
 3 & 4 & 3 & 9 \\

\end{array} \right) = \begin{array}{ c | c}

 A & D \\
 \hline \hline
 1 & 2 \\
 1 & 2 \\
 1 & 2 \\
 3 & 3 \\
 3 & 3 \\
 3 & 3 \\

\end{array} $ (bag semantics)

  • $\pi_{A, C \to D} \left(

\begin{array}{ c | c | c | c}

 A & B & C & D \\
 \hline \hline
 1 & 2 & 2 & 6 \\
 1 & 2 & 2 & 7 \\
 1 & 2 & 2 & 9 \\
 3 & 4 & 3 & 6 \\
 3 & 4 & 3 & 7 \\
 3 & 4 & 3 & 9 \\

\end{array} \right) = \begin{array}{ c | c}

 A & D \\
 \hline \hline
 1 & 2 \\
 3 & 3 \\

\end{array} $ (set semantics)

  • $C$ is renamed to $D$

Translating SQL to RA

Main Article: Translating SQL to Relational Algebra


Sources

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