Based on the output from the explain command, determine the steps you would take (building indexes on certain columns, modifying the query, both, etc.) to get the same output, but improve overall performance.Execute three steps that you think would make for improvement in the performance of this query. Report their run time in the table below.In 250-500 words evaluate the impact of the steps you took, what their potential benefits and setbacks may be, and what you would advise as the next steps to improve the performance of this query.Select * From Student join enrollment on student.ID = enrollment.Student_ID join section on section.ID = enrollment.section_ID join department on major = department.name join faculty on faculty.id = section.faculty_ID join address on address.id = student.address_ID join Course on section.course_Number = course.num and section.dept_id = course.dept_IDWhere--we want to make sure we have name information for students if we want to reach out to themStudent.Name_Last Not Like ('')-- the theater department has asked to be out of this study and Student.Major <> 'Theater' --no students who have failed as we're looking for passing grades and Grade > '1.33' --we want to make sure we only have instructors, and the theater department is not part of this study and Faculty.job in (Select job From Faculty Where Job not in ('Administrative','General Services','Human Resources') and Dept <> 'THT') and Section_ID >=1 --summer courses don't always reflect accurately given their tight schedule and rapid fire delivery of materials and Semester <> 'Summer' --we don't want bias of an adivosr giving better grades and Student.Advisor_ID <> Section.Faculty_ID -- we don't want bias if a student is possibly a faculty members child and Student.Address_ID <> Faculty.Address_ID Order by Student.Name_Last, Grade desc, Faculty.Name_Last, Major The image displays an SQL query execution plan from a database named "AShahbaIn." The SQL script involves a query to select data from multiple joined tables: Student, Enrollment, Section, Department, Faculty, Address, and Course. Key details from the script and execution plan are as follows:### SQL Query ScriptThe query aims to:- Retrieve student information while excluding certain departments and students based on specific criteria.- Maintain a focus on students with passing grades and exclude the theater department.- Ensure the inclusion of only faculty members.#### Main Conditions:- Student names must not be blank.- Student majors different from 'Theater.'- Students with grades higher than '1.33.'### Missing Index RecommendationsThe query optimizer suggests creating non-clustered indexes on columns to improve performance:1. **Enrollment Table:** Indexed on `grade` and `section_id`. Include the `student_id` column.2. **Enrollment Table:** Indexed on `section_id` and `grade`. Include the `student_id` column.3. **Address Table:** Indexed on `id`. Include `street`, `city`, `country`, and `postal_code`.### Execution Plan GraphThe execution plan consists of several operations, each represented by nodes connected with arrows indicating data flow:1. **Nested Loops and Table Scans:** Initiates with table scans on `faculty` and `address` tables, using nested loops (semi-joins and inner joins) to match rows.2. **Sort Operations:** Sorting operations are conducted on the `address` and intermediate result sets to facilitate merging.3. **Intermediate Joins:** Merge joins and computations combine the information from `faculty`, `address`, and `students`.4. **Hash Match and Sort:** A hash match join combines sections and enrollment data, further processed by sort operations.5. **Final Output:** Results are finally fetched through a clustered index scan on the `student` table.Overall, the query plan suggests significant resource usage on sorting and joining operations, with potential improvements through recommended indexing. ### SQL Query Analysis and Execution PlanThis SQL query is executed in a database named 'AShahahbain' and aims to extract student data based on specific conditions. Here’s a breakdown of the query and the execution plan.#### SQL Query Breakdown1. **Joins:** - The query performs multiple JOIN operations to integrate data from several tables: - `Student` is joined with `Enrollment`. - `Section` is joined on `Enrollment`. - Additional joins are performed with `Department`, `Faculty`, `Address`, and `Course` based on relevant associations.2. **Where Conditions:** - Filters students to ensure the `Name_Last` field is not empty. - Excludes students from the Theater department. - Stores only those with passing grades above 1.33. - Ensures all included faculty are instructors and not part of the Theater department.#### Missing IndexesThree suggestions for creating non-clustered indexes are highlighted to optimize query performance:- **Index 1:** On the `Enrollment` table for `([grade],[section_id])` including `[student_id]`.- **Index 2:** On the `Enrollment` table for `([section_id],[grade])` including `[student_id]`.- **Index 3:** On the `Address` table for `([country],[postal_code])` including `[id],[street],[city]`.#### Execution Plan Explanation1. **SELECT Operator:** - The query begins with a `SELECT` operation to retrieve the specified data.2. **Nested Loops & Joins:** - The primary operations involve nested loops performing inner and semi joins. This helps integrate data from multiple tables based on join keys, optimizing data retrieval paths.3. **Table Scans:** - Multiple table scan operations indicate sequential searches: - `Enrollment`, `Section`, `Department`, and `Course` contribute to a complete dataset. - Scans of `Faculty` and `Address` also occur for ancillary data retrieval.4. **Sort Operations:** - Sorting is used to order the data, with minimal impact on performance (0.1% cost).5. **Weights & Statistics:** - Percentages next to operations (e.g., `0.01%`, `3.04%`, `10.45%`) show the relative cost of these operations in terms of resource usage and processing time

The question asks for steps to improve the performance of a given SQL query, with a focus on…

Answered: In 250-500 words evaluate the impact of…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Concept explainers

Question

Based on the output from the explain command, determine the steps you would take (building indexes on certain columns, modifying the query, both, etc.) to get the same output, but improve overall performance.
Execute three steps that you think would make for improvement in the performance of this query. Report their run time in the table below.

In 250-500 words evaluate the impact of the steps you took, what their potential benefits and setbacks may be, and what you would advise as the next steps to improve the performance of this query.

Select *

From Student join enrollment on student.ID = enrollment.Student_ID

join section on section.ID = enrollment.section_ID

join department on major = department.name

join faculty on faculty.id = section.faculty_ID

join address on address.id = student.address_ID

join Course on section.course_Number = course.num and section.dept_id = course.dept_ID

Where

--we want to make sure we have name information for students if we want to reach out to them

Student.Name_Last Not Like ('')

-- the theater department has asked to be out of this study

and Student.Major <> 'Theater'

--no students who have failed as we're looking for passing grades

and Grade > '1.33'

--we want to make sure we only have instructors, and the theater department is not part of this study

and Faculty.job in

(Select job

From Faculty

Where Job not in ('Administrative','General Services','Human Resources')

and Dept <> 'THT')

and Section_ID >=1

--summer courses don't always reflect accurately given their tight schedule and rapid fire delivery of materials

and Semester <> 'Summer'

--we don't want bias of an adivosr giving better grades

and Student.Advisor_ID <> Section.Faculty_ID

-- we don't want bias if a student is possibly a faculty members child

and Student.Address_ID <> Faculty.Address_ID

Order by Student.Name_Last, Grade desc, Faculty.Name_Last, Major

The image displays an SQL query execution plan from a database named "AShahbaIn." The SQL script involves a query to select data from multiple joined tables: Student, Enrollment, Section, Department, Faculty, Address, and Course. Key details from the script and execution plan are as follows:

### SQL Query Script
The query aims to:
- Retrieve student information while excluding certain departments and students based on specific criteria.
- Maintain a focus on students with passing grades and exclude the theater department.
- Ensure the inclusion of only faculty members.

#### Main Conditions:
- Student names must not be blank.
- Student majors different from 'Theater.'
- Students with grades higher than '1.33.'

### Missing Index Recommendations
The query optimizer suggests creating non-clustered indexes on columns to improve performance:
1. **Enrollment Table:** Indexed on `grade` and `section_id`. Include the `student_id` column.
2. **Enrollment Table:** Indexed on `section_id` and `grade`. Include the `student_id` column.
3. **Address Table:** Indexed on `id`. Include `street`, `city`, `country`, and `postal_code`.

### Execution Plan Graph
The execution plan consists of several operations, each represented by nodes connected with arrows indicating data flow:

1. **Nested Loops and Table Scans:** Initiates with table scans on `faculty` and `address` tables, using nested loops (semi-joins and inner joins) to match rows.
2. **Sort Operations:** Sorting operations are conducted on the `address` and intermediate result sets to facilitate merging.
3. **Intermediate Joins:** Merge joins and computations combine the information from `faculty`, `address`, and `students`.
4. **Hash Match and Sort:** A hash match join combines sections and enrollment data, further processed by sort operations.
5. **Final Output:** Results are finally fetched through a clustered index scan on the `student` table.

Overall, the query plan suggests significant resource usage on sorting and joining operations, with potential improvements through recommended indexing.

### SQL Query Analysis and Execution Plan

This SQL query is executed in a database named 'AShahahbain' and aims to extract student data based on specific conditions. Here’s a breakdown of the query and the execution plan.

#### SQL Query Breakdown

1. **Joins:**
- The query performs multiple JOIN operations to integrate data from several tables:
- `Student` is joined with `Enrollment`.
- `Section` is joined on `Enrollment`.
- Additional joins are performed with `Department`, `Faculty`, `Address`, and `Course` based on relevant associations.

2. **Where Conditions:**
- Filters students to ensure the `Name_Last` field is not empty.
- Excludes students from the Theater department.
- Stores only those with passing grades above 1.33.
- Ensures all included faculty are instructors and not part of the Theater department.

#### Missing Indexes

Three suggestions for creating non-clustered indexes are highlighted to optimize query performance:
- **Index 1:** On the `Enrollment` table for `([grade],[section_id])` including `[student_id]`.
- **Index 2:** On the `Enrollment` table for `([section_id],[grade])` including `[student_id]`.
- **Index 3:** On the `Address` table for `([country],[postal_code])` including `[id],[street],[city]`.

#### Execution Plan Explanation

1. **SELECT Operator:**
- The query begins with a `SELECT` operation to retrieve the specified data.

2. **Nested Loops & Joins:**
- The primary operations involve nested loops performing inner and semi joins. This helps integrate data from multiple tables based on join keys, optimizing data retrieval paths.

3. **Table Scans:**
- Multiple table scan operations indicate sequential searches:
- `Enrollment`, `Section`, `Department`, and `Course` contribute to a complete dataset.
- Scans of `Faculty` and `Address` also occur for ancillary data retrieval.

4. **Sort Operations:**
- Sorting is used to order the data, with minimal impact on performance (0.1% cost).

5. **Weights & Statistics:**
- Percentages next to operations (e.g., `0.01%`, `3.04%`, `10.45%`) show the relative cost of these operations in terms of resource usage and processing time

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