Aga Non-Government Institute for Computer Science

Transcription

1 Aga Non-Government Institute for Computer Science Teacher SHERZAD ABDULLAH

2 History of Database Systems s and early 1960s: Data processing using magnetic tapes for storage Tapes provided only sequential access Punched cards for input - Late 1960s and 1970s: Hard disks allowed direct access to data Network and hierarchical data models in widespread use Ted Codd defines the relational data model s: Research relational prototypes evolve into commercial systems SQL becomes industrial standard Parallel and distributed database systems Object-oriented database systems s: Large decision support and data-mining applications Large multi-terabyte data warehouses Emergence of Web commerce Early 2000s: XML and XQuery standards Automated database administration Later 2000s: Giant data storage systems Google BigTable, Yahoo PNuts, Amazon,.. 2

3 Data Models Relational Databases Database Design Storage Manager Query Processing Transaction Manager The Need for Databases Database Management System (DBMS) - DBMS contains information about a particular enterprise Collection of interrelated data Set of programs to access the data An environment that is both convenient and efficient to use - Database Applications: Banking: transactions Airlines: reservations, schedules Universities: registration, grades 3

4 Sales: customers, products, purchases Online retailers: order tracking, customized recommendations Manufacturing: production, inventory, orders, supply chain Human resources: employee records, salaries, tax deductions - Databases can be very large. - Databases touch all aspects of our lives University Database Example - Application program examples Add new students, instructors, and courses Register students for courses, and generate class rosters Assign grades to students, compute grade point averages (GPA) and generate transcripts In the early days, database applications were built directly on top of file systems Disadvantage of using file systems to store data Data redundancy and inconsistency - Multiple file formats, duplication of information in different files Difficulty in accessing data - Need to write a new program to carry out each new task Data isolation 4

5 - Multiple files and formats Integrity problems - Integrity constraints (e.g., account balance > 0) become buried in program code rather than being stated explicitly - Hard to add new constraints or change existing ones Database systems offer solutions to all the above problems Levels of Abstraction Physical level: describes how a record (e.g., instructor) is stored. Logical level: describes data stored in database, and the relationships among the data. type instructor = record end; ID : string; name : string; dept_name : string; salary : integer; View level: application programs hide details of data types. Views can also hide information (such as an employee s salary) for security purposes. 5

6 View of Data An architecture for a database system Instances and Schemas Similar to types and variables in programming languages Logical Schema the overall logical structure of the database Example: The database consists of information about a set of customers and accounts in a bank and the relationship between them - Analogous to type information of a variable in a program Physical schema the overall physical structure of the database instance the actual content of the database at a particular point in time Analogous to the value of a variable Physical Data Independence the ability to modify the physical schema without changing the logical schema - Applications depend on the logical schema - In general, the interfaces between the various levels and components should be well defined so that changes in some parts do not seriously influence others. 6

7 Data Models A collection of tools for describing o Data o Data relationships o Data semantics o Data constraints 7

8 Relational model Entity-Relationship data model (mainly for database design) Object-based data models (Object-oriented and Object-relational) Semistructured data model (XML) Other older models: o Network model o Hierarchical model Data Definition Language (DDL) o Specification notation for defining the database schema o Example: create table shara ( ID char(5), name varchar(20), dept_name varchar(20), salary numeric(8,2)) o DDL compiler generates a set of table templates stored in a data dictionary o Data dictionary contains metadata (i.e., data about data) o Database schema o Integrity constraints o Primary key (ID uniquely identifies instructors) o Authorization o Who can access what Data Manipulation Language (DML) o Language for accessing and manipulating the data organized by the appropriate data model o DML also known as query language 8

9 o Two classes of languages o Pure used for proving properties about computational power and for optimization o Commercial used in commercial systems SQL is the most widely used commercial Database Design The process of designing the general structure of the database: o Logical Design Deciding on the database schema. Database design requires that we find a good collection of relation schemas. o Business decision What attributes should we record in the database? o Computer Science decision What relation schemas should we have and how should the attributes be distributed among the various relation schemas? o Physical Design Deciding on the physical layout of the database 9

10 Object Relational Data Models Extend the relational data model by including object orientation and constructs to deal with added data types. Allow attributes of tuples to have complex types, including non-atomic values such as nested relations. Preserve relational foundations, in particular the declarative access to data, while extending modeling power. Provide upward compatibility with existing relational languages. Database Engine 1. Storage manager 2. Query processing 3. Transaction manager Query Processing 1.Parsing and translation 2. Optimization 3. Evaluation 10

11 Database Architecture The architecture of database systems is greatly influenced by the underlying computer system on which the database is running: Centralized Client-server Parallel (multi-processor) Distributed Entity and Attribute Entity is an entity is a single person, place, or thing about which data can be stored. That is in other words for example when one wants to store information about STUDENT then it has details like student no, student name, address for communication, phone number, class of the student Attribute is the characteristic property of an existing entity. The attribute has two parts in it namely the attribute instance and attribute type. Attribute type is the column name or field name that is in the above student entity student name is the attribute type and the actual value stored in it is the attribute We begin to add attributes to the entities, the diagram will look like this; 11

12 SQL Server 2012 Developer This is identical to the Enterprise edition, except that it is only licensed for development and test systems. Express The Express version of SQL Server is a great entry-level product. It is perfect for learning and building small data-driven applications. Choosing Hardware for SQL Server Processor : Processor type: Intel Pentium IV or AMD Athlon Processor Speed: 2.0 GHz or faster Memory 4 GB or more Hard disk space Database Engine, data files, and replication: 811 MB SSIS: 591 MB Client components: 1823 MB Before Installation SQL Server Instances SQL Server 2012 supports multiple Database Engine instances on the same computer. Typically, the initial install of SQL Server is the default instance, which assumes the name of the computer on which SQL Server is being installed. Any additional installed instances are referred to as named instances. Microsoft SQL Server is one the most popular Relational Database Management System (RDBMS) used in Microsoft universe. It can be used for data storage as well as for data retrieval for applications which can be either on desktop or Web/Browse. 12

13 DBMS : DBMS A database management system is the software system that allows users to define, create and maintain a database and provides controlled access to the data. A Database Management System (DBMS) is basically a collection of programs that enables users to store, modify, and extract information from a database as per the requirements. DBMS is an intermediate layer between programs and the data. Advantages of Database Management System (DBMS) 1. Minimize Data Redundancy In File Processing System, duplicate data is created in many places because all the programs have their own files. 2. Sharing of Data In DBMS, Data can be shared in between authorized user of database. All the users have their own right to access the database up to a level. 3. Data Consistency DBMS controls data redundancy which in turn controls data consistency. Data consistency means if you want to update data in any files then all the files should not be updated again. As in DBMS, data is stored in a single database so data becomes more consistent in comparison to file processing system 13

14 4. Data Integrity Data integrity means unification of so many files into a single file. In DBMS data is stored in different tables. 5. Search Capability Users of database may require to fetch data from the database. There are numerous queries users may ask about the data. Search speed of the database must be fast to produce quick results. 6. Security Data security means protecting your precious data from unauthorized access. Data in database should be kept secure and safe to unauthorized modifications 7. Privacy Privacy means up to what extent a user can access the data 9. Backup and Recovery Data loss is a very big problem for all the organizations. In traditional file processing system, a user needs to backup the database 8. Simplicity DBMS is very simple for its users who use it. All the operations like insert, delete, create and update are very easy to implement. 14

15 Table Basics A relational database system (DBMS) contains one or more objects called tables. The data or information for the database are stored in these tables Tables are uniquely identified by their names and are comprised of columns and rows. Columns contain the column name, data type, and any other attributes for the column Rows contain the records or data for the columns. The table and column names must start with a letter and can be followed by letters, numbers, or underscores - not to exceed a total of 30 characters in length Do not use any SQL reserved keywords as names for tables or column names (such as "select", "create", "insert", etc) Data types specify what the type of data can be for that particular column. If a column called "Last_Name", is to be used to hold names, then that particular column should have a "varchar" (variable-length character) data type. Here are the most common Data types: char(size) varchar(size) number(size) date Fixed-length character string. Size is specified in parenthesis. Max 255 bytes. Variable-length character string. Max size is specified in parenthesis. Number value with a max number of column digits specified in parenthesis. Date value number(size,d) Number value with a maximum number of digits of "size" total, with a maximum number of "d" digits to the right of the decimal. 15

16 What are constraints? When tables are created, it is common for one or more columns to have constraints associated with them. A constraint is basically a rule associated with a column that the data entered into that column must follow. For example, a "unique" constraint specifies that no two records can have the same value in a particular column. They must all be unique. The other two most popular constraints are "not null" which specifies that a column can't be left blank, and "primary key". A "primary key" constraint defines a unique identification of each record (or row) in a table. Constraints can be entered in this SQL interpreter, 1- Open SQL Server and Login 2- Create new data base by Right click on Database 16

17 3- Creating Tables and Define Fields and Data type 4- Data Entry to Table by ( Right Click on Table and Click Edit top 200 Rows ) 17

18 5- Adding data and creating new view -- By clicking Add command, the Execute SQL command in the tool bar will be active. 18

19 History of SQL n IBM Sequel language developed as part of System R project at the IBM San Jose Research Laboratory n Renamed Structured Query Language (SQL) n ANSI and ISO standard SQL: l SQL-86 l SQL-89 l SQL-92 What is SQL? l SQL:1999 (language name became Y2K compliant!) l SQL:2003 SQL Stands for Structured Query Language. SQL lets you access and manipulate databases SQL is used to communicate with a database. According to ANSI (American National Standards Institute),it is the standard language for relational database management systems. SQL statements are used to perform tasks such as update data on a database, or retrieve data from a database. All SQL statements should end with a ";". Table (Student) Student_ID Name Gender year SHYAR Male ALAND Male AHMED Male ZHYAR Female ZHALA Female BANAZ Female SHANAZ Female

20 Table (Course) ID Student_ID Stage Department CourseName Network SQL Server Network CCNA Network Web Network Server Programming C# Programming PHP Programming Visual Basic Programming Oracle Create Table Construct Example: Example: create table instructor ( ID char(5), name varchar(20), dept_name varchar(20), salary numeric(8,2)) create table instructor ( ID char(5), name varchar(20) not null, dept_name varchar(20), salary numeric(8,2), primary key (ID), foreign key (dept_name) references department); create table student ( ID varchar(5), name varchar(20) not null, dept_name varchar(20), tot_cred numeric(3,0), primary key (ID), foreign key (dept_name) references department); 20

21 create table takes ( ID varchar(5), course_id varchar(8), sec_id varchar(8), semester varchar(6), year numeric(4,0), grade varchar(2), primary key (ID, course_id, sec_id, semester, year), foreign key (ID) references student, foreign key (course_id, sec_id, semester, year) references section); Note: sec_id can be dropped from primary key above, to ensure a student cannot be registered for two sections of the same course in the same semester create table course ( course_id varchar(8), title varchar(50), dept_name varchar(20), credits numeric(2,0), primary key (course_id), foreign key (dept_name) references department); Updates to tables Insert insert into instructor values ( 10211, Smith, Biology, 66000); Delete Remove all tuples from the student relation delete from student Drop Table drop table r Alter 21

22 alter table r add A D alter table r drop A where A is the name of the attribute to be added to relation r and D is the domain of A. All exiting tuples in the relation are assigned null as the value for the new attribute. where A is the name of an attribute of relation r Dropping of attributes not supported by many databases. Basic Query Structure A typical SQL query has the form: select A 1, A 2,..., A n from r 1, r 2,..., r m where P A i represents an attribute R i represents a relation P is a predicate. The result of an SQL query is a relation. Inserting into a Table The insert statement is used to insert or add a row of data into the table. insert into "tablename" (first_column,...last_column) values (first_value,...last_value); Example: insert into employee (first, last, age, address, city, state) values ('Luke', 'Duke', 45, '2130 Boars Nest', 'Hazard Co', 'Georgia'); 22

23 Note: All strings should be enclosed between singlequotes: 'string Table (Employee) Employe_ID Firstname Lastname Gender Job tile Salary Age 10 SHARA SHERZAD Female Programmer ZHAKAW AZAD Male IT Officer AHMED MOHAMMED Male Teacher ZHYAR AHMED Female Teacher ZHALA KAZM Female Manager MEDIA NAWZAD Female Teacher SHANAZ OMED Female Programmer After they're inserted into the table, enter select statements to: 1. Select all columns for everyone in your employee table. 2. Select all columns for everyone with a salary over Select first and last names for everyone that's under 30 years old. 4. Select first name, last name, and salary for anyone with "Programmer" in their title. 5. Select all columns for everyone whose last name contains "ME". 6. Select the first name for everyone whose first name equals "ZH". 7. Select all columns for everyone over 35 years old. 8. Select all columns for everyone whose last name ends in "ZAD". Selecting Data The select statement is used to query the database and retrieve selected data that match the criteria that you specify. Here is the format of a simple select statement: select "column1" [,"column2",etc] from "tablename" [where "condition"]; [] = optional Conditional selections used in the where clause: = Equal > Greater than < Less than >= Greater than or equal 23

24 <= Less than or equal <> Not equal to LIKE *See note below The LIKE pattern matching operator can also be used in the conditional selection of the where clause example: select first, last, city from empinfo where first LIKE 'Er%'; This SQL statement will match any first names that start with 'Er'. Strings must be in single quotes. Or you can specify, select first, last from empinfo where last LIKE '%s'; This statement will match any last names that end in a 's'. select * from empinfo where first = 'Eric'; This will only select rows where the first name equals 'Eric' exactly. select first, last, city from empinfo; select last, city, age from empinfo where age > 30; select first, last, city, state from empinfo where first LIKE 'J%'; select * from empinfo; select first, last, from empinfo where last LIKE '%s'; select first, last, age from empinfo where last LIKE '%illia%'; select * from empinfo where first = 'Eric'; Selecting Data Answers 1. Display everyone's first name and their age for everyone that's in table. 24

25 select first, age from empinfo; 2. Display the first name, last name, and city for everyone that's not from Payson. select first, last, city from empinfo where city <> 'Payson'; 3. Display all columns for everyone that is over 40 years old. select * from empinfo where age > 40; 4. Display the first and last names for everyone whose last name ends in an "ay". select first, last from empinfo where last LIKE '%ay'; 5. Display all columns for everyone whose first name equals "Mary". select * from empinfo where first = 'Mary'; 6. Display all columns for everyone whose first name contains "Mary". select * from empinfo where first LIKE '%Mary%'; Creating Tables The create table statement is used to create a new table. Here is the format of a simple create tablestatement: create table "tablename" ("column1" "data type", "column2" "data type", "column3" "data type"); Format of create table if you were to use optional constraints: create table "tablename" ("column1" "data type" [constraint], "column2" "data type" [constraint], "column3" "data type" [constraint]); [ ] = optional 25

26 Example: create table employee (first varchar(15), last varchar(20), age number(3), address varchar(30), city varchar(20), state varchar(20)); Updating Records The update statement is used to update or change records that match a specified criteria. This is accomplished by carefully constructing a where clause. update "tablename" set "columnname" = "newvalue" [,"nextcolumn" = "newvalue2"...] where "columnname" OPERATOR "value" [and or "column" OPERATOR "value"]; [] = optional [The above example was line wrapped for better viewing on this Web page.] Examples: update phone_book set area_code = 623 where prefix = 979; update phone_book set last_name = 'Smith', prefix=555, suffix=9292 where last_name = 'Jones'; update employee set age = age+1 where first_name='mary' and last_name='williams'; 26

27 Update statement exercises After each update, issue a select statement to verify your changes. 1. Jonie Weber just got married to Bob Williams. She has requested that her last name be updated to Weber-Williams. 2. Dirk Smith's birthday is today, add 1 to his age. 3. All secretaries are now called "Administrative Assistant". Update all titles accordingly. 4. Everyone that's making under are to receive a 3500 a year raise. 5. Everyone that's making over are to receive a 4500 a year raise. 6. All "Programmer II" titles are now promoted to "Programmer III". 7. All "Programmer" titles are now promoted to "Programmer II". Deleting Records The delete statement is used to delete records or rows from the table. delete from "tablename" where "columnname" OPERATOR "value" [and or "column" OPERATOR "value"]; [ ] = optional [The above example was line wrapped for better viewing on this Web page.] Examples: delete from employee where lastname = 'May'; delete from employee where firstname = 'Mike' or firstname = 'Eric'; Drop a Table The drop table command is used to delete a table and all rows in the table. To delete an entire table including all of its rows, issue the drop table command followed by the tablename. drop table is different from deleting all of the records in the table. Deleting all of the records in the table leaves the table including column and constraint information. Dropping the table removes the table definition as well as all of its rows. 27

28 drop table "tablename" Example: drop table myemployees_ts0211; Look at the "Customers" table: Example SELECT * FROM Customers; Every table is broken up into smaller entities called fields. The fields in the Customers table consist of CustomerID, CustomerName, ContactName, Address, City, PostalCode and Country. A field is a column in a table that is designed to maintain specific information about every record in the table. SELECT CustomerName, City FROM Customers; SELECT * FROM Customers; CustomerName Alfreds Futterkiste Ana Trujillo Emparedados y helados Antonio Moreno Taquería Around the Horn City Berlin México D.F. México D.F. London SQL SELECT DISTINCT Statement SELECT DISTINCT Country FROM Customers; Country Germany Mexico UK Sweden SELECT * FROM Customers WHERE Country='Mexico'; 28

29 Custome rid CustomerName ContactName Address City PostalCode Countr y 2 Ana Trujillo Ana Trujillo Avda. de la México Mexico Emparedados y helados Constitución 2222 D.F. 3 Antonio Moreno Antonio Mataderos 2312 México Mexico Taquería Moreno D.F. 13 Centro comercial Francisco Sierras de Granada México Mexico Moctezuma Chang 9993 D.F. 58 Pericles Comidas Guillermo Calle Dr. Jorge Cash México Mexico clásicas Fernández 321 D.F. 80 Tortuga Restaurante Miguel Angel Paolino Avda. Azteca 123 México D.F Mexico Table Custome rid CustomerName ContactNa me Address City PostalCo de Count ry 1 Alfreds Futterkiste Maria Anders Obere Str. 57 Berlin Germa ny 2 Ana Trujillo Emparedados y helados Ana Trujillo Avda. de la Constitució n 2222 Méxic o D.F Mexico 3 Antonio Moreno Taquería Antonio Moreno Mataderos 2312 Méxic o D.F Mexico 4 Around the Horn Thomas Hardy 120 Hanover Sq. Londo n WA1 1DP UK 5 Berglunds snabbköp Christina Berglund Berguvsväg en 8 Luleå S Swede n SQL AND, OR and NOT Operators SELECT * FROM Customers WHERE Country='Germany' AND City='Berlin'; 29

30 Result: CustomerID CustomerName ContactName Address City PostalCode Country 1 Alfreds Futterkiste Maria Anders Obere Str. 57 Berlin Germany SELECT * FROM Customers WHERE City='Berlin' OR City='München'; Result: CustomerID CustomerName ContactName Address City PostalCode Country 1 Alfreds Futterkiste Maria Anders Obere Berlin Germany Str Frankenversand Peter Franken Berliner Platz 43 München Germany SELECT * FROM Customers ORDER BY Country; Result: CustomerI CustomerName ContactName Address City PostalCode Country D 12 Cactus Comidas Patricio Cerrito 333 Buenos Aires 1010 Argentina para llevar Simpson 20 Ernst Handel Roland Mendel Kirchgasse 6 Graz 8010 Austria 76 Suprêmes Pascale Cartrain Boulevard Charleroi B-6000 Belgium délices Tirou, Mère Paillarde Jean Fresnière 43 rue St. Montréal H1J 1C3 Canada Laurent 73 Simons bistro Jytte Petersen Vinbæltet 34 København 1734 Denmark INSERT INTO Example INSERT INTO Customers (CustomerName, ContactName, Address, City, PostalCode, Country) VALUES ('Cardinal', 'Tom B. Erichsen', 'Skagen 21', 'Stavanger', '4006', 'Norway'); How to Test for NULL Values? Demo Database Assume we have the following "Persons" table: 30

31 ID LastName FirstName Address City 1 Doe John 542 W. 27th Street New York 2 Bloggs Joe London 3 Roe Jane New York 4 Smith John 110 Bishopsgate London SELECT LastName, FirstName, Address FROM Persons WHERE Address IS NULL; IS NOT NULL Operator SELECT LastName, FirstName, Address FROM Persons WHERE Address IS NOT NULL; UPDATE Table UPDATE Customers SET ContactName = 'Alfred Schmidt', City= 'Frankfurt' WHERE CustomerID = UPDATE Multiple Records UPDATE Customers SET ContactName='Juan' WHERE Country='Mexico'; SQL DELETE Example DELETE FROM Customers WHERE CustomerName='Alfreds Futterkiste'; The SQL SELECT TOP Clause SELECT TOP 3 * FROM Customers; SELECT * FROM Customers LIMIT 3; 31

32 SELECT * FROM Customers WHERE ROWNUM <= 3; SQL TOP PERCENT Example SELECT TOP 50 PERCENT * FROM Customers; ADD a WHERE CLAUSE SELECT TOP 3 * FROM Customers WHERE Country='Germany'; SELECT * FROM Customers WHERE Country='Germany' LIMIT 3; SELECT * FROM Customers WHERE Country='Germany' AND ROWNUM <= 3; Demo Database Below is a selection from the "Products" table in the Northwind sample database: ProductID ProductName SupplierID CategoryID Unit Price 1 Chais boxes x 20 bags 18 2 Chang oz bottles 19 3 Aniseed Syrup ml bottles 10 4 Chef Anton's Cajun Seasoning oz jars 22 5 Chef Anton's Gumbo Mix boxes SQL MIN() and MAX() Functions SELECT MIN(Price) AS SmallestPrice FROM Products; SmallestPrice 2.5 SELECT MAX(Price) AS LargestPrice FROM Products; LargestPrice 32

33 263.5 SQL COUNT(), AVG() and SUM() Functions COUNT() Example SELECT COUNT(ProductID) FROM Products; 5 AVG() Example SELECT AVG(Price) FROM Products; Demo Database Below is a selection from the "OrderDetails" table in the Northwind sample database: OrderDetailID OrderID ProductID Quantity SUM() Example SELECT SUM(Quantity) FROM OrderDetails; SQL LIKE Operator Here are some examples showing different LIKE operators with '%' and '_' wildcards: LIKE Operator Description WHERE CustomerName LIKE 'a%' Finds any values that starts with "a" 33

34 WHERE CustomerName LIKE '%a' Finds any values that ends with "a" WHERE CustomerName LIKE '%or%' Finds any values that have "or" in any position WHERE CustomerName LIKE '_r%' Finds any values that have "r" in the second position WHERE CustomerName LIKE 'a_%_%' Finds any values that starts with "a" and are at least 3 characters in length WHERE ContactName LIKE 'a%o' Finds any values that starts with "a" and ends with "o" Demo Database Below is a selection from the "Customers" table in the Northwind sample database: CustomerID CustomerName ContactName Address City PostalCode Country 1 Alfreds Futterkiste Maria Anders Obere Str. 57 Berlin Germany 2 Ana Trujillo Emparedados y helados Ana Trujillo Avda. de la Constitución 2222 México D.F Mexico 3 Antonio Moreno Taquería Antonio Moreno Mataderos 2312 México D.F Mexico 4 Around the Horn Thomas Hardy 120 Hanover Sq. London WA1 1DP UK 34

35 5 Berglunds snabbköp Christina Berglund Berguvsvägen 8 Luleå S Sweden SQL LIKE Examples SELECT * FROM Customers WHERE CustomerName LIKE 'a%'; Result: CustomerID CustomerName ContactName Address City PostalCode Country 1 Alfreds Maria Anders Obere Str. 57 Berlin Germany Futterkiste 2 Ana Trujillo Ana Trujillo Avda. de la México Mexico Emparedados y helados Constitución 2222 D.F. 3 Antonio Moreno Antonio Mataderos 2312 México Mexico Taquería Moreno 4 Around the Horn Thomas Hardy D.F. 120 Hanover Sq. London WA1 1DP UK SELECT * FROM Customers WHERE CustomerName LIKE '%a'; Result: CustomerID CustomerName ContactName Address City PostalCode Country 3 Antonio Moreno Antonio Mataderos México Mexico Taquería Moreno 2312 D.F. 13 Centro Francisco Sierras de México Mexico comercial Moctezuma Chang Granada 9993 D.F. 30 Godos Cocina José Pedro C/ Romero, Sevilla Spain Típica Freyre 61 Que Delícia Bernardo Batista 62 Queen Cozinha Lúcia Carvalho 88 Wellington Paula Parente Importadora 90 Wilman Kala Matti Karttunen 33 Rua da Panificadora, 12 Alameda dos Canàrios, 891 Rua do Mercado, 12 Keskuskatu 45 Rio de Janeiro Brazil São Brazil Paulo Resende Brazil Helsinki Finland 35

36 SELECT * FROM Customers WHERE CustomerName LIKE '%or%'; Result: CustomerID CustomerName ContactName Address City PostalCode Country 3 Antonio Moreno Antonio Mataderos 2312 México Mexico Taquería Moreno D.F. 4 Around the Horn Thomas Hardy 120 Hanover Sq. London WA1 1DP UK 36 Hungry Coyote Yoshi Latimer City Center Plaza Elgin USA Import Store 516 Main St. 40 La corne Daniel Tonini 67, avenue de Versailles France d'abondance l'europe 43 Lazy K Kountry John Steel 12 Orchestra Walla USA Store Terrace Walla 52 Morgenstern Alexander Heerstr. 22 Leipzig Germany Gesundkost Feuer 53 North/South Simon Crowther South House 300 Queensbridge London SW7 1RZ UK SELECT * FROM Customers WHERE CustomerName LIKE '_r%'; Result: CustomerID CustomerName ContactName Address City PostalCode Country 4 Around the Horn Thomas Hardy 120 Hanover London WA1 1DP UK Sq. 17 Drachenblut Sven Ottlieb Walserweg 21 Aachen Germany Delikatessend 20 Ernst Handel Roland Mendel Kirchgasse 6 Graz 8010 Austria 25 Frankenversand Peter Franken Berliner Platz München Germany France restauration Carine Schmitt 54, rue Royale Nantes France 27 Franchi S.p.A. Paolo Accorti Via Monte Torino Italy Bianco Great Lakes Food Howard Snyder 2732 Baker Eugene USA Market Blvd. 33 GROSELLA- Manuel Pereira 5ª Ave. Los Caracas 1081 Venezuela Restaurante Palos Grandes 60 Princesa Isabel Isabel de Estrada da Lisboa 1756 Portugal Vinhoss Castro saúde n Tradição Anabela Av. Inês de São Brazil Hipermercados Domingues Castro, 414 Paulo 82 Trail's Head Gourmet Provisioners Helvetius Nagy 722 DaVinci Blvd. Kirkland USA SELECT * FROM Customers WHERE CustomerName LIKE 'a_%_%'; 36

37 Result: CustomerID CustomerName ContactName Address City PostalCode Country 1 Alfreds Futterkiste Maria Anders Obere Str. 57 Berlin Germany 2 Ana Trujillo Ana Trujillo Avda. de la México Mexico Emparedados y helados Constitución 2222 D.F. 3 Antonio Moreno Antonio Mataderos 2312 México Mexico Taquería Moreno D.F. 4 Around the Horn Thomas Hardy 120 Hanover Sq. London WA1 1DP UK SELECT * FROM Customers WHERE ContactName LIKE 'a%o'; Result: CustomerID CustomerName ContactName Address City PostalCode Country 2 Ana Trujillo Ana Trujillo Avda. de la México Mexico Emparedados y helados Constitución 2222 D.F. 3 Antonio Moreno Antonio Mataderos 2312 México Mexico Taquería Moreno 69 Romero y tomillo Alejandra Camino D.F. Gran Vía, 1 Madrid Spain SELECT * FROM Customers WHERE CustomerName NOT LIKE 'a%'; Result: CustomerID CustomerName ContactName Address City PostalCode Country 5 Berglunds snabbköp Christina Berglund Berguvsvägen 8 Luleå S Sweden 6 Blauer See Delikatessen Hanna Moos Forsterstr. 57 Mannheim Germany 7 Blondel père et fils Frédérique Citeaux 24, place Kléber Strasbourg France SQL Wildcard Characters Here are some examples showing different LIKE operators with '%' and '_' wildcards: 37

38 LIKE Operator Description WHERE CustomerName LIKE 'a%' Finds any values that starts with "a" WHERE CustomerName LIKE '%a' Finds any values that ends with "a" WHERE CustomerName LIKE '%or%' Finds any values that have "or" in any position WHERE CustomerName LIKE '_r%' Finds any values that have "r" in the second position WHERE CustomerName LIKE 'a_%_%' Finds any values that starts with "a" and are at least 3 characters in length WHERE ContactName LIKE 'a%o' Finds any values that starts with "a" and ends with "o" Demo Database Below is a selection from the "Customers" table in the Northwind sample database: CustomerID CustomerName ContactName Address City PostalCode Country 1 Alfreds Futterkiste Maria Anders Obere Str. 57 Berlin Germany 2 Ana Trujillo Emparedados y helados Ana Trujillo Avda. de la Constitución 2222 México D.F Mexico 3 Antonio Moreno Taquería Antonio Moreno Mataderos 2312 México D.F Mexico 38

39 4 Around the Horn Thomas Hardy 120 Hanover Sq. London WA1 1DP UK 5 Berglunds snabbköp Christina Berglund Berguvsvägen 8 Luleå S Sweden SELECT * FROM Customers WHERE City LIKE 'ber%'; Result: Number of Records: 3 CustomerID CustomerName ContactName Address City PostalCode Country 1 Alfreds Futterkiste Maria Anders Obere Str. Berlin Germany Chop-suey Yang Wang Hauptstr. Bern 3012 Switzerland Chinese Magazzini Alimentari Riuniti Giovanni Rovelli Via Ludovico il Moro 22 Bergamo Italy SELECT * FROM Customers WHERE City LIKE '%es%'; Result: Number of Records: 9 CustomerID CustomerName ContactName Address City PostalCode Country 12 Cactus Comidas Patricio Cerrito 333 Buenos 1010 Argentina para llevar Simpson Aires 18 Du monde entier Janine 67, rue des Nantes France Labrune Cinquante Otages 26 France Carine Schmitt 54, rue Royale Nantes France restauration 38 Island Trading Helen Bennett Garden House Cowes PO31 7PJ UK Crowther Way 40 La corne Daniel Tonini 67, avenue de Versailles France d'abondance l'europe 50 Maison Dewey Catherine Rue Joseph- Bruxelles B-1180 Belgium 54 Océano Atlántico Ltda. Dewey Yvonne Moncada Bens 532 Ing. Gustavo Moncada 8585 Buenos Aires 1010 Argentina 39

40 64 Rancho grande Sergio Gutiérrez 88 Wellington Paula Parente Importadora Piso 20-A Av. del Libertador 900 Rua do Mercado, 12 Buenos 1010 Argentina Aires Resende Brazil SELECT * FROM Customers WHERE City LIKE '_erlin'; Result: Number of Records: 1 CustomerID CustomerName ContactName Address City PostalCode Country 1 Alfreds Futterkiste Maria Anders Obere Str. 57 Berlin Germany SELECT * FROM Customers WHERE City LIKE 'L_n_on'; Result: Number of Records: 6 CustomerID CustomerName ContactName Address City PostalCode Country 4 Around the Horn Thomas Hardy 120 London WA1 1DP UK Hanover Sq. 11 B's Beverages Victoria Fauntleroy London EC2 5NT UK Ashworth Circus 16 Consolidated Holdings Elizabeth Brown Berkeley Gardens 12 Brewery London WX1 6LT UK SELECT * FROM Customers WHERE City LIKE '[bsp]%'; Customer CustomerNa ID me 1 Alfreds Futterkiste 7 Blondel père et fils 12 Cactus Comidas para llevar ContactNa me Maria Anders Frédérique Citeaux Patricio Simpson Addre ss Obere Str , place Kléber Cerrito 333 City PostalCo de Countr y Berlin German y Strasbour France g Buenos Aires 1010 Argenti na 40

41 SELECT * FROM Customers WHERE City LIKE '[a-c]%'; CustomerI CustomerNa D me 1 Alfreds Futterkiste 12 Cactus Comidas para llevar 14 Chop-suey Chinese ContactNa me Maria Anders Patricio Simpson Yang Wang Addres s Obere Str. 57 Cerrito 333 Hauptst r. 29 City PostalCo Country de Berlin Germany Bueno s Aires 1010 Argentina Bern 3012 Switzerlan d SQL IN Operator Below is a selection from the "Customers" table in the Northwind sample database: CustomerID CustomerName ContactName Address City PostalCode Country 1 Alfreds Futterkiste Maria Anders Obere Str. 57 Berlin Germany 2 Ana Trujillo Emparedados y helados Ana Trujillo Avda. de la Constitución 2222 México D.F Mexico 3 Antonio Moreno Taquería Antonio Moreno Mataderos 2312 México D.F Mexico SELECT * FROM Customers WHERE Country IN ('Germany', 'France', 'UK'); Result: Number of Records: 29 CustomerID CustomerName ContactName Address City PostalCode Country 1 Alfreds Futterkiste Maria Anders Obere Str. Berlin Germany 57 4 Around the Horn Thomas Hardy 120 Hanover Sq. London WA1 1DP UK 6 Blauer See Delikatessen Hanna Moos Forsterstr. 57 Mannheim Germany 41

42 SELECT * FROM Customers WHERE Country NOT IN ('Germany', 'France', 'UK'); CustomerID CustomerName ContactName Address City PostalCode Country 2 Ana Trujillo Ana Trujillo Avda. de la México Mexico Emparedados y helados Constitución 2222 D.F. 3 Antonio Moreno Antonio Mataderos 2312 México Mexico Taquería Moreno 5 Berglunds snabbköp Christina Berglund D.F. Berguvsvägen 8 Luleå S Sweden SELECT * FROM Customers WHERE Country IN (SELECT Country FROM Suppliers); SQL BETWEEN Operator Demo Database Below is a selection from the "Products" table in the Northwind sample database: ProductID ProductName SupplierID CategoryID Unit Price 1 Chais boxes x 20 bags 18 2 Chang oz bottles 19 BETWEEN Example SELECT * FROM Products WHERE Price BETWEEN 10 AND 20; Result: ProductID ProductName SupplierID CategoryID Unit Price 1 Chais boxes x 20 bags 18 2 Chang oz bottles 19 3 Aniseed Syrup ml bottles 10 42

43 SELECT * FROM Products WHERE Price BETWEEN 10 AND 20; Result: ProductID ProductName SupplierID CategoryID Unit Price 1 Chais boxes x 20 bags 18 2 Chang oz bottles 19 3 Aniseed Syrup ml bottles 10 SQL Aliases SQL aliases are used to give a table, or a column in a table, a temporary name. Aliases are often used to make column names more readable. An alias only exists for the duration of the query. Alias Column Syntax SELECT column_name AS alias_name FROM table_name; Alias for Columns Examples SELECT CustomerID as ID, CustomerName AS Customer FROM Customers; SELECT CustomerName, Address + ', ' + PostalCode + ' ' + City + ', ' + Country AS Address FROM Customers; CustomerName Alfreds Futterkiste Ana Trujillo Emparedados y helados Antonio Moreno Taquería Around the Horn Address Obere Str. 57, Berlin, Germany Avda. de la Constitución 2222, México D.F., Mexico Mataderos 2312, México D.F., Mexico 120 Hanover Sq., WA1 1DP London, UK 43

44 SQL JOIN A JOIN clause is used to combine rows from two or more tables, based on a related column between them. Let's look at a selection from the "Orders" table: OrderID CustomerID OrderDate Then, look at a selection from the "Customers" table: CustomerID CustomerName ContactName Country 1 Alfreds Futterkiste Maria Anders Germany 2 Ana Trujillo Emparedados y helados Ana Trujillo Mexico 3 Antonio Moreno Taquería Antonio Moreno Mexico Notice that the "CustomerID" column in the "Orders" table refers to the "CustomerID" in the "Customers" table. The relationship between the two tables above is the "CustomerID" column. Then, we can create the following SQL statement (that contains an INNER JOIN), that selects records that have matching values in both tables: Example SELECT Orders.OrderID, Customers.CustomerName, Orders.OrderDate FROM Orders 44

45 INNER JOIN Customers ON Orders.CustomerID=Customers.CustomerI D; Result: OrderID CustomerName OrderDate Wilman Kala Tradição Hipermercados Hanari Carnes Victuailles en stock Suprêmes délices Hanari Carnes Chop-suey Chinese Richter Supermarkt Wellington Importadora Different Types of SQL JOINs Here are the different types of the JOINs in SQL: (INNER) JOIN: Returns records that have matching values in both tables LEFT (OUTER) JOIN: Return all records from the left table, and the matched records from the right table RIGHT (OUTER) JOIN: Return all records from the right table, and the matched records from the left table FULL (OUTER) JOIN: Return all records when there is a match in either left or right table SQL UNION Operator The UNION operator is used to combine the result-set of two or more SELECT statements. Each SELECT statement within UNION must have the same number of columns The columns must also have similar data types The columns in each SELECT statement must also be in the same order 45

46 emo Database In this tutorial we will use the well-known Northwind sample database. Below is a selection from the "Customers" table: CustomerID CustomerName ContactName Address City PostalCode Country 1 Alfreds Futterkiste Maria Anders Obere Str. 57 Berlin Germany 2 Ana Trujillo Emparedados y helados Ana Trujillo Avda. de la Constitución 2222 México D.F Mexico 3 Antonio Moreno Taquería Antonio Moreno Mataderos 2312 México D.F Mexico And a selection from the "Suppliers" table: SupplierID SupplierName ContactName Address City PostalCode Country 1 Exotic Liquid Charlotte Cooper 49 Gilbert St. London EC1 4SD UK 2 New Orleans Cajun Delights Shelley Burke P.O. Box New Orleans USA 3 Grandma Kelly's Homestead Regina Murphy 707 Oxford Rd. Ann Arbor USA SQL UNION Example SELECT City FROM Customers UNION SELECT City FROM Suppliers ORDER BY City; 46

47 Result: Number of Records: 94 City Aachen Albuquerque Anchorage SELECT City, Country FROM Customers WHERE Country='Germany' UNION SELECT City, Country FROM Suppliers WHERE Country='Germany' ORDER BY City; Result: City Aachen Berlin Brandenburg SQL GROUP BY Statement Country Germany Germany Germany Demo Database Below is a selection from the "Customers" table in the Northwind sample database: CustomerID CustomerName ContactName Address City PostalCode Country 1 Alfreds Futterkiste Maria Anders Obere Str. 57 Berlin Germany 2 Ana Trujillo Emparedados y Ana Trujillo Avda. de la Constitución México D.F Mexico 47

48 helados 2222 SQL GROUP BY Examples SELECT COUNT(CustomerID), Country FROM Customers GROUP BY Country; Result: COUNT(CustomerID) Country 3 Argentina 2 Austria 2 Belgium SELECT COUNT(CustomerID), Country FROM Customers GROUP BY Country ORDER BY COUNT(CustomerID) DESC; Result: COUNT(CustomerID) Country 13 USA 11 France SQL HAVING Clause Demo Database Below is a selection from the "Customers" table in the Northwind sample database: CustomerID CustomerName ContactName Address City PostalCode Country 1 Alfreds Futterkiste Maria Anders Obere Str. 57 Berlin Germany 2 Ana Trujillo Emparedados y helados Ana Trujillo Avda. de la Constitución 2222 México D.F Mexico 48

49 3 Antonio Moreno Taquería Antonio Moreno Mataderos 2312 México D.F Mexico 4 Around the Horn Thomas Hardy 120 Hanover Sq. London WA1 1DP UK 5 Berglunds snabbköp Christina Berglund Berguvsvägen 8 Luleå S Sweden SELECT COUNT(CustomerID), Country FROM Customers GROUP BY Country HAVING COUNT(CustomerID) > 5; Result: COUNT(CustomerID) Country 9 Brazil 11 France 11 Germany 7 UK 13 USA SELECT COUNT(CustomerID), Country FROM Customers GROUP BY Country HAVING COUNT(CustomerID) > 5 ORDER BY COUNT(CustomerID) DESC; Result: COUNT(CustomerID) Country 13 USA 11 France 11 Germany 9 Brazil 7 UK 49

50 SQL INSERT INTO SELECT Examples INSERT INTO Customers (CustomerName, City, Country) SELECT SupplierName, City, Country FROM Suppliers; INSERT INTO Customers (CustomerName, ContactName, Address, City, PostalCode, Country) SELECT SupplierName, ContactName, Address, City, PostalCode, Country FROM Suppliers; SQL CREATE DATABASE Statement CREATE DATABASE databasename; SQL DROP DATABASE Statement DROP DATABASE databasename; DROP DATABASE testdb; SQL CREATE TABLE Statement CREATE TABLE Persons ( PersonID int, LastName varchar(255), FirstName varchar(255), Address varchar(255), City varchar(255) ); SQL DROP TABLE Statement DROP TABLE table_name; DROP TABLE Shippers; 50

51 SQL ALTER TABLE Statement ALTER TABLE table_name MODIFY COLUMN column_name datatype; SQL Create Constraints Constraints can be specified when the table is created with the CREATE TABLE statement, or after the table is created with the ALTER TABLE statement. CREATE TABLE table_name ( column1 datatype constraint, column2 datatype constraint, column3 datatype constraint,... ); SQL NOT NULL Constraint By default, a column can hold NULL values. The NOT NULL constraint enforces a column to NOT accept NULL values. This enforces a field to always contain a value, which means that you cannot insert a new record, or update a record without adding a value to this field. The following SQL ensures that the "ID", "LastName", and "FirstName" columns will NOT accept NULL values CREATE TABLE Persons ( ID int NOT NULL, LastName varchar(255) NOT NULL, FirstName varchar(255) NOT NULL, Age int ); 51

52 SQL UNIQUE Constraint The UNIQUE constraint ensures that all values in a column are different. Both the UNIQUE and PRIMARY KEY constraints provide a guarantee for uniqueness for a column or set of columns. A PRIMARY KEY constraint automatically has a UNIQUE constraint. However, you can have many UNIQUE constraints per table, but only one PRIMARY KEY constraint per table. SQL UNIQUE Constraint on CREATE TABLE The following SQL creates a UNIQUE constraint on the "ID" column when the "Persons" table is created: SQL Server / Oracle / MS Access: CREATE TABLE Persons ( ID int NOT NULL UNIQUE, LastName varchar(255) NOT NULL, FirstName varchar(255), Age int ); SQL PRIMARY KEY on CREATE TABLE SQL Server / Oracle / MS Access: CREATE TABLE Persons ( ID int NOT NULL PRIMARY KEY, LastName varchar(255) NOT NULL, FirstName varchar(255), Age int ); SQL FOREIGN KEY Constraint A FOREIGN KEY is a key used to link two tables together. A FOREIGN KEY is a field (or collection of fields) in one table that refers to the PRIMARY KEY in another table. 52

53 The table containing the foreign key is called the child table, and the table containing the candidate key is called the referenced or parent table. Look at the following two tables: "Persons" table: PersonID LastName FirstName Age 1 Hansen Ola 30 2 Svendson Tove 23 3 Pettersen Kari 20 "Orders" table: OrderID OrderNumber PersonID Notice that the "PersonID" column in the "Orders" table points to the "PersonID" column in the "Persons" table. The "PersonID" column in the "Persons" table is the PRIMARY KEY in the "Persons" table. The "PersonID" column in the "Orders" table is a FOREIGN KEY in the "Orders" table. The FOREIGN KEY constraint is used to prevent actions that would destroy links between tables. 53

54 The FOREIGN KEY constraint also prevents invalid data from being inserted into the foreign key column, because it has to be one of the values contained in the table it points to. SQL Server / Oracle / MS Access: CREATE TABLE Orders ( OrderID int NOT NULL PRIMARY KEY, OrderNumber int NOT NULL, PersonID int FOREIGN KEY REFERENCES Persons(PersonID) ); SQL CHECK on CREATE TABLE SQL Server / Oracle / MS Access: CREATE TABLE Persons ( ID int NOT NULL, LastName varchar(255) NOT NULL, FirstName varchar(255), Age int CHECK (Age>=18) ); SQL DEFAULT on CREATE TABLE The following SQL sets a DEFAULT value for the "City" column when the "Persons" table is created: My SQL / SQL Server / Oracle / MS Access: CREATE TABLE Persons ( ID int NOT NULL, LastName varchar(255) NOT NULL, FirstName varchar(255), Age int, City varchar(255) DEFAULT 'Sandnes' ); SQL AUTO INCREMENT Field 54

55 CREATE TABLE Persons ( ID int NOT NULL AUTO_INCREMENT, LastName varchar(255) NOT NULL, FirstName varchar(255), Age int, PRIMARY KEY (ID) ); SQL Working With Dates SQL Date Data Types MySQL comes with the following data types for storing a date or a date/time value in the database: DATE - format YYYY-MM-DD DATETIME - format: YYYY-MM-DD HH:MI:SS TIMESTAMP - format: YYYY-MM-DD HH:MI:SS YEAR - format YYYY or YY SQL Server comes with the following data types for storing a date or a date/time value in the database: DATE - format YYYY-MM-DD DATETIME - format: YYYY-MM-DD HH:MI:SS SMALLDATETIME - format: YYYY-MM-DD HH:MI:SS TIMESTAMP - format: a unique number Assume we have the following "Orders" table: OrderId ProductName OrderDate 1 Geitost Camembert Pierrot Mozzarella di Giovanni

56 4 Mascarpone Fabioli Now we want to select the records with an OrderDate of " " from the table above. We use the following SELECT statement: SELECT * FROM Orders WHERE OrderDate=' ' The result-set will look like this: OrderId ProductName OrderDate 1 Geitost Mozzarella di Giovanni Now, assume that the "Orders" table looks like this (notice the time component in the "OrderDate" column): OrderId ProductName OrderDate 1 Geitost :23:44 2 Camembert Pierrot :45:21 3 Mozzarella di Giovanni :12:01 4 Mascarpone Fabioli :56:59 If we use the same SELECT statement as above: SELECT * FROM Orders WHERE OrderDate=' ' we will get no result! This is because the query is looking only for dates with no time portion. 56

57 Tip: To keep your queries simple and easy to maintain, do not allow time components in your dates! SQL Arithmetic Operators Operator Description + Add - Subtract * Multiply / Divide % Modulo Text data types: Data type Description CHAR(size) Holds a fixed length string (can contain letters, numbers, and special characters). The fixed size is specified in parenthesis. Can store up to 255 characters VARCHAR(size) Holds a variable length string (can contain letters, numbers, and special characters). The maximum size is specified in parenthesis. Can store up to 255 characters. Note:If you put a greater value than 255 it will be converted to a TEXT type TINYTEXT Holds a string with a maximum length of 255 characters TEXT Holds a string with a maximum length of 65,535 characters BLOB For BLOBs (Binary Large OBjects). Holds up to 65,535 bytes of data MEDIUMTEXT Holds a string with a maximum length of 16,777,215 characters 57

58 MEDIUMBLOB For BLOBs (Binary Large OBjects). Holds up to 16,777,215 bytes of data LONGTEXT Holds a string with a maximum length of 4,294,967,295 characters LONGBLOB For BLOBs (Binary Large OBjects). Holds up to 4,294,967,295 bytes of data ENUM(x,y,z,etc.) Let you enter a list of possible values. You can list up to values in an ENUM list. If a value is inserted that is not in the list, a blank value will be inserted. Note: The values are sorted in the order you enter them. SET You enter the possible values in this format: ENUM('X','Y','Z') Similar to ENUM except that SET may contain up to 64 list items and can store more than one choice Number data types: Data type Description TINYINT(size) -128 to 127 normal. 0 to 255 UNSIGNED*. The maximum number of digits may be specified in parenthesis SMALLINT(size) to normal. 0 to UNSIGNED*. The maximum number of digits may be specified in parenthesis MEDIUMINT(size) to normal. 0 to UNSIGNED*. The maximum number of digits may be specified in parenthesis INT(size) to normal. 0 to UNSIGNED*. The maximum number of digits may be specified in parenthesis BIGINT(size) to normal. 0 to UNSIGNED*. The maximum number of digits may be specified in parenthesis FLOAT(size,d) A small number with a floating decimal point. The maximum number of digits may be specified in the size parameter. The maximum number of 58

59 digits to the right of the decimal point is specified in the d parameter DOUBLE(size,d) A large number with a floating decimal point. The maximum number of digits may be specified in the size parameter. The maximum number of digits to the right of the decimal point is specified in the d parameter DECIMAL(size,d) A DOUBLE stored as a string, allowing for a fixed decimal point. The maximum number of digits may be specified in the size parameter. The maximum number of digits to the right of the decimal point is specified in the d parameter *The integer types have an extra option called UNSIGNED. Normally, the integer goes from an negative to positive value. Adding the UNSIGNED attribute will move that range up so it starts at zero instead of a negative number. Date data types: Data type Description DATE() DATETIME() TIMESTAMP() TIME() YEAR() A date. Format: YYYY-MM-DD Note: The supported range is from ' ' to ' ' *A date and time combination. Format: YYYY-MM-DD HH:MI:SS Note: The supported range is from ' :00:00' to ' :59:59' *A timestamp. TIMESTAMP values are stored as the number of seconds since the Unix epoch (' :00:00' UTC). Format: YYYY-MM-DD HH:MI:SS Note: The supported range is from ' :00:01' UTC to ' :14:07' UTC A time. Format: HH:MI:SS Note: The supported range is from '-838:59:59' to '838:59:59' A year in two-digit or four-digit format. Note: Values allowed in four-digit format: 1901 to Values allowed in two-digit format: 70 to 69, representing years from 1970 to 2069 *Even if DATETIME and TIMESTAMP return the same format, they work very differently. In an INSERT or UPDATE query, the TIMESTAMP automatically set itself to the current date and time. TIMESTAMP also accepts various formats, like 59

60 YYYYMMDDHHMISS, YYMMDDHHMISS, YYYYMMDD, or YYMMDD. SQL Server Data Types String data types: Data type Description Max size Storage char(n) Fixed width character string 8,000 characters Defined width varchar(n) Variable width character string 8,000 characters 2 bytes + number of chars varchar(max) Variable width character string 1,073,741,824 characters 2 bytes + number of chars text Variable width character string 2GB of text data 4 bytes + number of chars nchar Fixed width Unicode string 4,000 characters Defined width x 2 nvarchar Variable width Unicode string 4,000 characters nvarchar(max) Variable width Unicode string 536,870,912 characters 60

61 ntext Variable width Unicode string 2GB of text data binary(n) Fixed width binary string 8,000 bytes varbinary Variable width binary string 8,000 bytes varbinary(max) Variable width binary string 2GB image Variable width binary string 2GB Number data types: Data type Description Storage bit Integer that can be 0, 1, or NULL tinyint Allows whole numbers from 0 to byte smallint Allows whole numbers between -32,768 and 32,767 2 bytes int Allows whole numbers between -2,147,483,648 and 2,147,483,647 4 bytes bigint Allows whole numbers between - 9,223,372,036,854,775,808 and 9,223,372,036,854,775,807 8 bytes 61

62 decimal(p,s) numeric(p,s) Fixed precision and scale numbers. Allows numbers from -10^38 +1 to 10^38 1. The p parameter indicates the maximum total number of digits that can be stored (both to the left and to the right of the decimal point). p must be a value from 1 to 38. Default is 18. The s parameter indicates the maximum number of digits stored to the right of the decimal point. s must be a value from 0 to p. Default value is 0 Fixed precision and scale numbers. Allows numbers from -10^38 +1 to 10^38 1. The p parameter indicates the maximum total number of digits that can be stored (both to the left and to the right of the decimal point). p must be a value from 1 to 38. Default is bytes 5-17 bytes The s parameter indicates the maximum number of digits stored to the right of the decimal point. s must be a value from 0 to p. Default value is 0 smallmoney Monetary data from -214, to 214, money Monetary data from -922,337,203,685, to 922,337,203,685, float(n) Floating precision number data from -1.79E to 1.79E The n parameter indicates whether the field should hold 4 or 8 bytes. float(24) holds a 4-byte field and float(53) holds an 8-byte field. Default value of n is 53. real Floating precision number data from -3.40E + 38 to 3.40E + 38 Date data types: 4 bytes 8 bytes 4 or 8 bytes 4 bytes Data type Description Storage 62

63 datetime From January 1, 1753 to December 31, 9999 with 8 bytes datetime2 From January 1, 0001 to December 31, 9999 with 6-8 bytes smalldatetime From January 1, 1900 to June 6, 2079 with an 4 bytes date Store a date only. From January 1, 0001 to 3 bytes time Store a time only to an accuracy of bytes datetimeoffset The same as datetime2 with the addition of a 8-10 bytes timestamp Stores a unique number that gets updated every Other data types: Data type sql_variant uniqueidentifier xml cursor table Description Stores up to 8,000 bytes of data of various data types, except text, ntext, and timestamp Stores a globally unique identifier (GUID) Stores XML formatted data. Maximum 2GB Stores a reference to a cursor used for database operations Stores a result-set for later processing Teacher SHERZAD ABDULLAH

64 SQL LIKE Operator with '%' and '_' In SQL Server 2012 LIKE Operator Description 1- WHERE CustomerName LIKE 'a%' Finds any values that starts with "a" 2- WHERE CustomerName LIKE '%a' Finds any values that ends with "a" 3- WHERE CustomerName LIKE '%or%' Finds any values that have "or" in any position 4- WHERE CustomerName LIKE '_r%' Finds any values that have "r" in the second position 5- WHERE CustomerName LIKE 'a_%_%' Finds any values that starts with "a" and are at least 3 characters in length 6- WHERE ContactName LIKE 'a%o' Finds any values that starts with "a" and ends with "o" 1- SELECT * FROM Customer WHERE CustomerName LIKE 'a%'; 2- SELECT * FROM Customer WHERE CustomerName LIKE '%a'; 3- SELECT * FROM Customer WHERE CustomerName LIKE '%or%'; 4- SELECT * FROM Customer WHERE CustomerName LIKE '_r%'; 5- SELECT * FROM Customer WHERE CustomerName LIKE 'a_%_%'; 6- SELECT * FROM Customer WHERE ContactName LIKE 'a%o'; 7- SELECT * FROM Customer WHERE CustomerName NOT LIKE 'a%'; 64

65 3 CustomerName LIKE '%or%'; 4 CustomerName LIKE '_r%'; 5 CustomerName LIKE 'a_%_%'; 6 ContactName LIKE 'a%o'; 7- CustomerName NOT LIKE 'a%'; پهيمانگهى )ئاگا( ى ناحكومى بۆ زانستهكانى كۆمپيوتهر 65

66 Sum, AVG, Count, Min, Max SELECT Sum(Price) FROM ProductID; SELECT sum(price)/2 FROM ProductID; SELECT AVG(Price) FROM ProductID; SELECT Count(Price) FROM ProductID; SELECT Max(Price) FROM ProductID; SELECT Min(Price) FROM ProductID; 66