1- Single Table Select * From Students. Select * from Students Limit 100 Select * From Students Where Name_Last Like (%Smit%) Limit 100 Select * From Students Where Name_Last Like (%Smit%) Order by Name_First Desc Limit 100 Select SUM(Stu_ID) From Students

2- Multiple Table Select * From Students join Major Where Students.Major = Major.Major Select * From Students join Major Where Students.Major = Major.Major and Students.Major = CSI and Students.Name_Last = Jones Select * From Students join Major on Students.Major = Major.Major join Enrolled on Students.Stu_ID = Enrolled.Stu_ID Select * From Students join Major on Students.Major = Major.Major join Enrolled on Students.Stu_ID = Enrolled.Stu_ID join Section on Section.ID = Enrolled.Section_ID Where Section.Semester = Spring and Year = 2018 Order By Students.Name_Last Asc Select Course.Course_Desc, Count(Stu_ID) Students, AVG(Grade) as GPA from Student join Enrollment on Student.Stu_ID = Enrollment.Stu_ID join Section on Section.Section_ID = Enrollment.Section_ID join Course on Course.Dept = Section.Dept and Course.Course_Num = Section.Course_Num Where Course.Course_Desc like %intro% and Section.Professor_ID= X

3- Select * from Student join Faculty on Student.Name_Last = Faculty.Name_Last

Submission: For Each of the three top level categories above (1,2,3) answer the following Questions:

What queries were the fastest to run? What did these queries have in common?

Based on what is conveyed in the execution plan, which queries used an index?

Based on what is conveyed in the execution plan, hich queries did not use an index?

For Question 3 only perform the following evaluations

Performance of the query with the index that existed (primary keys only)

Performance of the query with a multiple column index on ID and Name_Last for both the Student and Faculty Table

Performance of the query with an index only on Name_Last in the Student and Faculty tables