This presentation is the property of its rightful owner.
1 / 16

# Relational Algebra 2 Extended-Relational Algebra PowerPoint PPT Presentation

Adam Nafke CS157A. Relational Algebra 2 Extended-Relational Algebra. Generalized Projection -Review. Extends projection operation by allowing arithmetic functions. Standard projection – ΠstudentName, grade(classList)

Relational Algebra 2 Extended-Relational Algebra

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

CS157A

### Generalized Projection -Review

• Extends projection operation by allowing arithmetic functions.

• Standard projection – ΠstudentName, grade(classList)

• Generalized projection -ΠstudentName, quizAverage + testAvg(classList)

• Will return a list of names with the sum of the two values.

### Generalized Projection - continued

• ΠstudentName, quizAverage + testAvg(classList) will return a attribute without a name.

• To name the attribute we use “AS” to cast it to a new attribute for the relation:ΠstudentName, quizAverage + testAvg as testScore(classList)

### Aggreate Functions - Review

• Aggreate functions are functions on relations which return a single value. However, many values can be retrieved from specific groups within relations.

• e.g. G sum(salary)(professors) would return the total salary of all professors on the relation “professors”.

### Aggreate functions -continued

• However, we may want to find the total salaries by department. The querydepartment-name G sum(salary) (professors)

• would give us just that.

### Aggreate functions -continued

• One way to look at the left-hand subscript in any aggreate function is as a for loop. For example:

• department-name G sum(salary)(professors)

• Is just

• for each (department-name){

• sum all salaries}

Combining aggreate functions with generalized projection we have:

department-name G sum(salary) as Total Salary, max(salary) as HighestPaidProfessor(professors)

Would perform a “for-each” on the department list and list the sum of the salaries and the amount of the highest paid professor.

### Aggreate functions -continued

It is important to note that if you are trying to find a specific entry in a relation via a aggregate function, do not list a unique name on the left-hand subscript of G.

professor-name G max(salary)(professors)

Will return the same relation as you started with (provided no two professors are name

the same). Find the specific name via a normal query.

### Modifications to the Database

• Now I will discuss how to add, remove, or change information in the Database.

• We use the assignment operation ( <-) to make modifications to the database.

### Deletion

• Expressed by r <- r - X (where r is a relation, and X is a query)

• Examples:To remove all of professor Davis's records:professor <- professor – Oprofessor_name = “Davis”(professor)

• Any query which returns a tuple or set of tuples can be used.

### Insertion

• To insert data into a relation, either a tuple, or a set of tuples must be defined.

• The format of expressing insertion is:

• r <- r U E (r is a relation and E is a expression).

Let's assume there are two relations: Vehicle and Owner.

Vehicle has attributes {make, license plate #, color} and Owner maps license plates to owners {license plate #, name}. We add a value to the relations as follows:

Vehicle <- Vehicle U {(Corvette, 12345, blue)}

Owner <- Owner U {(12345, “John Smith”)}

### Updating

• Updating is used to change a value in a tuple without changing all values in the tuple. The form is:r <- π F1, F2, ...., Fn (r)

• Where each Fi is an expression, involving only constants and the attributes of r, that gives the new value for the attribute.

### Updating - Example

• Suppose we wanted to halve the tuition for all students in relation (student). We would update this relation as follows:

• student <- п name, id, age, tuition * .5 (student)

• What if we wanted to do different updates for different tuples?

### Updating -continued

• An update must cover all tuples in a given relation. So if updating only some tuples is desired, the following format must be used:

• r <- пF1, F2, ... (OP(r)) U (r- OP(r))

• What this says, is that in a update you must union whatever you select with whatever is left in that relation.

### Updating - example

• Lets say you wanted to double the tuition of all students above the age of 30.

• Пname, age, tution * 2 (O age > 30(students)) selects all students over 30 and doubles the value of tution.

• Пname, age, tution (O age < 30(students)) will select all students under 30.

• Students <- Пname, age, tution * 2 (O age > 30(students)) U Пname, age, tution (O age < 30(students)) Will update all values.