Question

You have been hired as database administrator for Athabasca University and the computing services director asked you to tune the following database that is too slow for query processing. The database has two relations: Professor(sin, prof_name, office_no, age, gender, specialty, dept_did) Department(did, dept_name, budget, nbr_programs, chair_sin) After examining the application you found that the following queries are the five most common queries in the workload for this university application and that all are roughly equivalent in frequency and importance: o List the names, ages, and offices of professors of a user-specified gender (male or female) who have a user-specified research specialty (e.g., recursive query processing). Assume that the university has a diverse set of faculty members, making it very uncommon for more than a few professors to have the same research specialty. o List all the department information for departments with professors in a user-specified age range. o List the department id, department name, and chairperson name for departments with a user-specified number of majors. o List the lowest budget for a department in the university. o List all the information about professors who are department chairpersons. These queries occur much more frequently than updates, so you should build whatever indexes you need to speed up the queries. However because updates do occur, you should not build any unnecessary indexes that would slow down the updates. Given this information, design a physical schema for the university database that will perform well for the expected workload. In particular, decide which attributes should be indexed and whether each index should be clustered or unclustered. Assume that both B+ trees and hashed indexes are supported by the DBMS and that both single- and multiple-attribute index search keys are permitted.

a. Specify your physical design by identifying the attributes you recommend indexing on, indicating whether each index should be clustered or unclustered and whether it should be a B+ tree or a hashed index.

b. Assume that this workload is to be tuned with an automatic index tuning wizard. Outline the main steps in the algorithm and the set of candidate configurations considered.

Answer 1

Professor(sin, prof_name, office_no, age, gender, specialty, dept_did)

Department(did, dept_name, budget, nbr_programs, chair_sin)

A.

• Create an unclustered hash index on (specialty, gender) on Professor relation to enable people to find professors of user specified gender and specialty.
• Create a dense clustered B+ tree on (age, dept_did) on Professor relation along with an unclustered hash index on did in department relation. This will allow people to find professors in a user specified department within a user specified age range.
• Unclustered hash index on nbr_programs in Department Relation to find the department id, department name, and chairperson name for departments with a user-specified number of majors.
• Clustered B+ tree index on budget in Department relation to find the lowest budget for a department in the university.
• Dense unclustered B+ tree index on chair_sin for Department relation along with a dense unclustered hash index on sin for the Professor relation to find all the information about professors who are department chairpersons.

B.

• Dense clustered B+ tree index on (dept_did, specialty) for the Professor Relation to scan to count the different specialties in each department.
• Unclustered B+ tree index on nbr_programs for Department realtion to find department with fewest majors.
• Unclustered hash index on (sin,age) for Professor relation along with Unclustered B+ tree index on chair_sin for Department Relation in order to find the oldest and youngest professor who is a department's chairperson.