SlideShare uma empresa Scribd logo
1 de 19
Baixar para ler offline
Dynamic Websites
PHP with Oracle DB   By Belal Arfa
Section 2
In this section will discuss
1- Examples on last section
2- Quiz
3- Like Operator
4- Top Stmt
5- Union VS Union ALL
6- IN Operator
7- Aggregation Functions
8- Group By
9- Having
10- Difference Between Having and Where
Quiz (1)
• Relations:
Movie(title, year, length, inColor, studioName, producerC#)
StarsIn(movieTitle, movieYear, starName)
MovieStar(name, address, gender, birthdate)
MovieExec(name, address, cert#, netWorth)
Studio(name, address, presC#)
• Queries:
a) Find the address of MGM studios.
b) Find Sandra Bullock’s birthdate.
c) Find all the stars that appear either in a movie made in 1980 or a movie
with “Love” in the title.
d) Find all executives worth at least $10,000,000.
e) Find all the stars who either are male or live in Miami ( have Miami as a
part of their address).
Quiz (1) Answer
a) SELECT address FROM studio
    WHERE name = ‘MGM’;
b) SELECT birthdate FROM moviestar
    WHERE name = ‘Sandra Bullock’;
c) SELECT starName FROM StarsIn
    WHERE movieYear = 1980 OR movieTitle LIKE ‘%Love%’;
d) SELECT name FROM MovieExec
    WHERE netWorth >= 10,000,000;
e) SELECT name FROM MovieStar
    WHERE gender = ‘M’ OR address LIKE ‘% Miami %’;
LIKE Operator
• The LIKE operator is used to search for a specified pattern in a column..
• SQL LIKE Syntax:
– SELECT column_name(s) FROM table_name
     WHERE column_name LIKE pattern
Persons Table
          P_ID          LastName         FirstName        Address             City

            1            Hansen             Ola         Timoteivn 10      Sandnes

            2           Svendson           Tove          Borgvn 23        Sandnes

            3            Pettersen          Kari          Storgt 20       Stavanger
Ex. Want to select persons living in city ending with "s" from "Persons" table. (%
can be used to define wildcards (missing letters in pattern))
Sol: SELECT * FROM Persons WHERE City LIKE ‘%s';
SELECT TOP Stmt
 TOP clause is used to specify the number of records to return.
 Can be useful on large tables with ‘000s of records as Returning a
large
number of records can impact on performance.
 SQL TOP Syntax:
SELECT column_name(s)
FROM table_name
WHERE ROWNUM <= number
Ex : Want to select only the two first records in the table above
Sol: SELECT * FROM Persons Where RowNum<2
Union VS Union ALL

The UNION operator returns only distinct rows
that appear in either result,
while the UNION ALL operator returns all rows.

The UNION ALL operator does not eliminate
duplicate selected rows.
IN Operator Nested Queries
– SELECT Model FROM Product
WHERE ManufacturerID IN
(SELECT ManufacturerID FROM Manufacturer
WHERE Manufacturer = 'Dell')
• The nested query above will select all models from the “Product” table
manufactured by Dell:
Aggregation Functions
Perform calculation on a set of values and return single value.
Syntax
Select Function(column) fromtable


                 Name             Salary

                Mohamed           4000

                 Hassan           3000

                 Doaa             6000

                 Ahmed            4000
Aggregation Functions

  Requirements                          Select Stmt                  Result

 Average of salary   Select AVG(salary) from employees;              4250

 Number of Rows      Select COUNT(*) from employees;                   4

 Distinct Salaries   Select COUNT(Distinct Salary) from employees;     3


   Highest Salary    Select MAX(salary) from employees;              6000
   Lowest Salary     Select MIN(salary) from employees;              3000
   Total Salaries    Select SUM(salary) from employees;              17000
Group By Clause


The GROUP BY statement is used in conjunction with
the aggregate functions to group the result-set by one
or more columns.
Group By Clause

Syntax
SELECT column_name, aggregate_function(column_name)
FROM table_name
WHERE column_name operator value
GROUP BY column_name
Example
Orders Table

         O_Id          Order_Date         Order_Price Customers
           1             2013/11/12            1000             Hansen
           2             2013/03/12            1600                 Nilsen
           3             2013/05/02            700              Hansen
           4             2013/07/09            300              Hansen
           5             2013/08/01            2000             Jensen
           6             2013/04/03            100                  Nilsen

Now we want to find the total sum (total order) of each customer.
Example
Sol
SELECT Customer,SUM(Order_Price) FROM Orders
GROUP BY Customer;

            Customer                   Sum(Order_Price)

 Hansen                        2000

 Nilsen                        1700

 Jinsen                        2000

PS: When using the same select stmt without Group By clause
it will fire an error.
Example
Ex: Sells (bar, beer, price)
Find average price for each peer.
Sol: Select Beer, AVG(Price)
       From Sells Group By Beer;

Ex: Get the name of the dept and its No. of Emp that
       make over 25,000$ per year.
Sol: Select department, COUNT(*) as 'Number of Employees'
       From Employees
       Where salary > 25000
       Group by department
Having Clause
The HAVING clause was added to SQL because the WHERE keyword
could not be used with aggregate functions.
• Also, HAVING is used in conjunction with the SELECT clause to
specify a search condition for a group. The HAVING clause behaves
like the WHERE clause, but is applicable to groups.
Syntax:
SELECT column_name, aggregate_function(column_name)
FROM table_name
WHERE column_name operator value
GROUP BY column_name
HAVING aggregate_function(column_name) operator value
Example
Ex: want to find if any of the customers have a total order of less than 2000.
Sol: SELECT Customer,SUM(OrderPrice)
    FROM Orders S
    GROUP BY Customer
    HAVING SUM(OrderPrice)<2000

Ex: want to find if the customers “Hansen” or “Jensen” have a total
order of more than 1500.
Sol: SELECT Customer,SUM(OrderPrice) FROM Orders
    WHERE Customer='Hansen' OR Customer='Jensen'
    GROUP BY Customer
    HAVING SUM(OrderPrice)>1500
Example
SELECT COUNT(*)
FROM EMPLOYEES GROUP BY ID
HAVING SALARY > 15000;
this query is illegal, because the column SALARY is not a grouping column, it
does not appear within an aggregate, and it is not within a subquery;

SELECT COUNT(*)
FROM EMPLOYEES GROUP BY ID
HAVING SUM(SALARY) > 15000;


Aggregates in the HAVING clause do not need to appear in the SELECT list
Diff. btw. Having & Where
1. HAVING specifies a search condition for a group or an aggregate
function used in SELECT statement. The WHERE clause specifies the
criteria which individual records must meet to be selected by a query. It
can be used without the GROUP BY clause. The HAVING clause
cannot be used without the GROUP BY clause.

2. The WHERE clause selects rows before grouping. The HAVING
clause selects rows after grouping.

3. The WHERE clause cannot contain aggregate functions. The
HAVING clause can contain aggregate functions.

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Lab3 aggregating data
Lab3   aggregating dataLab3   aggregating data
Lab3 aggregating data
 
Restricting and sorting data
Restricting and sorting dataRestricting and sorting data
Restricting and sorting data
 
Les03
Les03Les03
Les03
 
06.02 sql alias
06.02 sql alias06.02 sql alias
06.02 sql alias
 
Sql wksht-1
Sql wksht-1Sql wksht-1
Sql wksht-1
 
Les02
Les02Les02
Les02
 
Sql (Introduction to Structured Query language)
Sql (Introduction to Structured Query language)Sql (Introduction to Structured Query language)
Sql (Introduction to Structured Query language)
 
Sql queires
Sql queiresSql queires
Sql queires
 
Structured query language constraints
Structured query language constraintsStructured query language constraints
Structured query language constraints
 
Les08
Les08Les08
Les08
 
Module03
Module03Module03
Module03
 
Basic Sql Handouts
Basic Sql HandoutsBasic Sql Handouts
Basic Sql Handouts
 
Structured query language functions
Structured query language functionsStructured query language functions
Structured query language functions
 
Commands
CommandsCommands
Commands
 
Les04
Les04Les04
Les04
 
e computer notes - Manipulating data
e computer notes - Manipulating datae computer notes - Manipulating data
e computer notes - Manipulating data
 
Sql powerpoint
Sql powerpointSql powerpoint
Sql powerpoint
 
SQL Course - QA
SQL Course - QASQL Course - QA
SQL Course - QA
 
Sql task
Sql taskSql task
Sql task
 
Les07
Les07Les07
Les07
 

Semelhante a Dynamic websites lec2

SQL-AGG-FUN.pdfiiiijuyyttfffgyyuyyyyyhhh
SQL-AGG-FUN.pdfiiiijuyyttfffgyyuyyyyyhhhSQL-AGG-FUN.pdfiiiijuyyttfffgyyuyyyyyhhh
SQL-AGG-FUN.pdfiiiijuyyttfffgyyuyyyyyhhhNaveeN547338
 
Module 3.1.pptx
Module 3.1.pptxModule 3.1.pptx
Module 3.1.pptxANSHVAJPAI
 
Unit 3-Select Options and Aggregate Functions in SQL (1).pptx
Unit 3-Select Options and Aggregate Functions in SQL (1).pptxUnit 3-Select Options and Aggregate Functions in SQL (1).pptx
Unit 3-Select Options and Aggregate Functions in SQL (1).pptxHAMEEDHUSSAINBU21CSE
 
Database Systems - SQL - DDL Statements (Chapter 3/3)
Database Systems - SQL - DDL Statements (Chapter 3/3)Database Systems - SQL - DDL Statements (Chapter 3/3)
Database Systems - SQL - DDL Statements (Chapter 3/3)Vidyasagar Mundroy
 
ADV Powepoint 3 Lec.pptx
ADV Powepoint 3 Lec.pptxADV Powepoint 3 Lec.pptx
ADV Powepoint 3 Lec.pptxArjayBalberan1
 
Structured query language(sql)
Structured query language(sql)Structured query language(sql)
Structured query language(sql)Huda Alameen
 
Beginers guide for oracle sql
Beginers guide for oracle sqlBeginers guide for oracle sql
Beginers guide for oracle sqlN.Jagadish Kumar
 
Complex Queries using MYSQL00123211.pptx
Complex Queries using MYSQL00123211.pptxComplex Queries using MYSQL00123211.pptx
Complex Queries using MYSQL00123211.pptxmetriohanzel
 
Group by clause mod
Group by clause modGroup by clause mod
Group by clause modNitesh Singh
 
Data Manipulation Language.pptx
Data Manipulation Language.pptxData Manipulation Language.pptx
Data Manipulation Language.pptxEllenGracePorras
 
ADV Powepoint 4 Lec.pptx
ADV Powepoint 4 Lec.pptxADV Powepoint 4 Lec.pptx
ADV Powepoint 4 Lec.pptxArjayBalberan1
 
12. Basic SQL Queries (2).pptx
12. Basic SQL Queries  (2).pptx12. Basic SQL Queries  (2).pptx
12. Basic SQL Queries (2).pptxSabrinaShanta2
 

Semelhante a Dynamic websites lec2 (20)

75864 sql
75864 sql75864 sql
75864 sql
 
Sql query [select, sub] 4
Sql query [select, sub] 4Sql query [select, sub] 4
Sql query [select, sub] 4
 
SQL-AGG-FUN.pdfiiiijuyyttfffgyyuyyyyyhhh
SQL-AGG-FUN.pdfiiiijuyyttfffgyyuyyyyyhhhSQL-AGG-FUN.pdfiiiijuyyttfffgyyuyyyyyhhh
SQL-AGG-FUN.pdfiiiijuyyttfffgyyuyyyyyhhh
 
SQL.pptx
SQL.pptxSQL.pptx
SQL.pptx
 
Clauses
ClausesClauses
Clauses
 
Module 3.1.pptx
Module 3.1.pptxModule 3.1.pptx
Module 3.1.pptx
 
SQL
SQLSQL
SQL
 
Unit 3-Select Options and Aggregate Functions in SQL (1).pptx
Unit 3-Select Options and Aggregate Functions in SQL (1).pptxUnit 3-Select Options and Aggregate Functions in SQL (1).pptx
Unit 3-Select Options and Aggregate Functions in SQL (1).pptx
 
Database Systems - SQL - DDL Statements (Chapter 3/3)
Database Systems - SQL - DDL Statements (Chapter 3/3)Database Systems - SQL - DDL Statements (Chapter 3/3)
Database Systems - SQL - DDL Statements (Chapter 3/3)
 
ADV Powepoint 3 Lec.pptx
ADV Powepoint 3 Lec.pptxADV Powepoint 3 Lec.pptx
ADV Powepoint 3 Lec.pptx
 
Structured query language(sql)
Structured query language(sql)Structured query language(sql)
Structured query language(sql)
 
Introduction to-sql
Introduction to-sqlIntroduction to-sql
Introduction to-sql
 
Beginers guide for oracle sql
Beginers guide for oracle sqlBeginers guide for oracle sql
Beginers guide for oracle sql
 
Complex Queries using MYSQL00123211.pptx
Complex Queries using MYSQL00123211.pptxComplex Queries using MYSQL00123211.pptx
Complex Queries using MYSQL00123211.pptx
 
Sql
SqlSql
Sql
 
Group by clause mod
Group by clause modGroup by clause mod
Group by clause mod
 
Data Manipulation Language.pptx
Data Manipulation Language.pptxData Manipulation Language.pptx
Data Manipulation Language.pptx
 
ADV Powepoint 4 Lec.pptx
ADV Powepoint 4 Lec.pptxADV Powepoint 4 Lec.pptx
ADV Powepoint 4 Lec.pptx
 
Chapter08
Chapter08Chapter08
Chapter08
 
12. Basic SQL Queries (2).pptx
12. Basic SQL Queries  (2).pptx12. Basic SQL Queries  (2).pptx
12. Basic SQL Queries (2).pptx
 

Último

CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century educationjfdjdjcjdnsjd
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsJoaquim Jorge
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Igalia
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...Martijn de Jong
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Scriptwesley chun
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEarley Information Science
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking MenDelhi Call girls
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProduct Anonymous
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024The Digital Insurer
 
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfTech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfhans926745
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsMaria Levchenko
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoffsammart93
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 

Último (20)

CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
 
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfTech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdf
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 

Dynamic websites lec2

  • 1. Dynamic Websites PHP with Oracle DB By Belal Arfa
  • 2. Section 2 In this section will discuss 1- Examples on last section 2- Quiz 3- Like Operator 4- Top Stmt 5- Union VS Union ALL 6- IN Operator 7- Aggregation Functions 8- Group By 9- Having 10- Difference Between Having and Where
  • 3. Quiz (1) • Relations: Movie(title, year, length, inColor, studioName, producerC#) StarsIn(movieTitle, movieYear, starName) MovieStar(name, address, gender, birthdate) MovieExec(name, address, cert#, netWorth) Studio(name, address, presC#) • Queries: a) Find the address of MGM studios. b) Find Sandra Bullock’s birthdate. c) Find all the stars that appear either in a movie made in 1980 or a movie with “Love” in the title. d) Find all executives worth at least $10,000,000. e) Find all the stars who either are male or live in Miami ( have Miami as a part of their address).
  • 4. Quiz (1) Answer a) SELECT address FROM studio WHERE name = ‘MGM’; b) SELECT birthdate FROM moviestar WHERE name = ‘Sandra Bullock’; c) SELECT starName FROM StarsIn WHERE movieYear = 1980 OR movieTitle LIKE ‘%Love%’; d) SELECT name FROM MovieExec WHERE netWorth >= 10,000,000; e) SELECT name FROM MovieStar WHERE gender = ‘M’ OR address LIKE ‘% Miami %’;
  • 5. LIKE Operator • The LIKE operator is used to search for a specified pattern in a column.. • SQL LIKE Syntax: – SELECT column_name(s) FROM table_name WHERE column_name LIKE pattern Persons Table P_ID LastName FirstName Address City 1 Hansen Ola Timoteivn 10 Sandnes 2 Svendson Tove Borgvn 23 Sandnes 3 Pettersen Kari Storgt 20 Stavanger Ex. Want to select persons living in city ending with "s" from "Persons" table. (% can be used to define wildcards (missing letters in pattern)) Sol: SELECT * FROM Persons WHERE City LIKE ‘%s';
  • 6. SELECT TOP Stmt TOP clause is used to specify the number of records to return. Can be useful on large tables with ‘000s of records as Returning a large number of records can impact on performance. SQL TOP Syntax: SELECT column_name(s) FROM table_name WHERE ROWNUM <= number Ex : Want to select only the two first records in the table above Sol: SELECT * FROM Persons Where RowNum<2
  • 7. Union VS Union ALL The UNION operator returns only distinct rows that appear in either result, while the UNION ALL operator returns all rows. The UNION ALL operator does not eliminate duplicate selected rows.
  • 8. IN Operator Nested Queries – SELECT Model FROM Product WHERE ManufacturerID IN (SELECT ManufacturerID FROM Manufacturer WHERE Manufacturer = 'Dell') • The nested query above will select all models from the “Product” table manufactured by Dell:
  • 9. Aggregation Functions Perform calculation on a set of values and return single value. Syntax Select Function(column) fromtable Name Salary Mohamed 4000 Hassan 3000 Doaa 6000 Ahmed 4000
  • 10. Aggregation Functions Requirements Select Stmt Result Average of salary Select AVG(salary) from employees; 4250 Number of Rows Select COUNT(*) from employees; 4 Distinct Salaries Select COUNT(Distinct Salary) from employees; 3 Highest Salary Select MAX(salary) from employees; 6000 Lowest Salary Select MIN(salary) from employees; 3000 Total Salaries Select SUM(salary) from employees; 17000
  • 11. Group By Clause The GROUP BY statement is used in conjunction with the aggregate functions to group the result-set by one or more columns.
  • 12. Group By Clause Syntax SELECT column_name, aggregate_function(column_name) FROM table_name WHERE column_name operator value GROUP BY column_name
  • 13. Example Orders Table O_Id Order_Date Order_Price Customers 1 2013/11/12 1000 Hansen 2 2013/03/12 1600 Nilsen 3 2013/05/02 700 Hansen 4 2013/07/09 300 Hansen 5 2013/08/01 2000 Jensen 6 2013/04/03 100 Nilsen Now we want to find the total sum (total order) of each customer.
  • 14. Example Sol SELECT Customer,SUM(Order_Price) FROM Orders GROUP BY Customer; Customer Sum(Order_Price) Hansen 2000 Nilsen 1700 Jinsen 2000 PS: When using the same select stmt without Group By clause it will fire an error.
  • 15. Example Ex: Sells (bar, beer, price) Find average price for each peer. Sol: Select Beer, AVG(Price) From Sells Group By Beer; Ex: Get the name of the dept and its No. of Emp that make over 25,000$ per year. Sol: Select department, COUNT(*) as 'Number of Employees' From Employees Where salary > 25000 Group by department
  • 16. Having Clause The HAVING clause was added to SQL because the WHERE keyword could not be used with aggregate functions. • Also, HAVING is used in conjunction with the SELECT clause to specify a search condition for a group. The HAVING clause behaves like the WHERE clause, but is applicable to groups. Syntax: SELECT column_name, aggregate_function(column_name) FROM table_name WHERE column_name operator value GROUP BY column_name HAVING aggregate_function(column_name) operator value
  • 17. Example Ex: want to find if any of the customers have a total order of less than 2000. Sol: SELECT Customer,SUM(OrderPrice) FROM Orders S GROUP BY Customer HAVING SUM(OrderPrice)<2000 Ex: want to find if the customers “Hansen” or “Jensen” have a total order of more than 1500. Sol: SELECT Customer,SUM(OrderPrice) FROM Orders WHERE Customer='Hansen' OR Customer='Jensen' GROUP BY Customer HAVING SUM(OrderPrice)>1500
  • 18. Example SELECT COUNT(*) FROM EMPLOYEES GROUP BY ID HAVING SALARY > 15000; this query is illegal, because the column SALARY is not a grouping column, it does not appear within an aggregate, and it is not within a subquery; SELECT COUNT(*) FROM EMPLOYEES GROUP BY ID HAVING SUM(SALARY) > 15000; Aggregates in the HAVING clause do not need to appear in the SELECT list
  • 19. Diff. btw. Having & Where 1. HAVING specifies a search condition for a group or an aggregate function used in SELECT statement. The WHERE clause specifies the criteria which individual records must meet to be selected by a query. It can be used without the GROUP BY clause. The HAVING clause cannot be used without the GROUP BY clause. 2. The WHERE clause selects rows before grouping. The HAVING clause selects rows after grouping. 3. The WHERE clause cannot contain aggregate functions. The HAVING clause can contain aggregate functions.