Sql server T-sql basics ppt-3

2
Introduction To MS SQL
Server

Systems Development Life Cycle
3
Project Identification
and Selection
Project Initiation
and Planning
Analysis
Physical Design
Implementation
Maintenance
Logical Design
Enterprise modeling
Conceptual data modeling
Logical database design
Physical database design and
definition
Database implementation
Database maintenance
Database Development ProcessDatabase Development Process

Part Four: Implementation
• Chapter 7 – Introduction to SQL
• Chapter 8 – Advanced SQL
• Chapter 9 – Client/Server Environment
• Chapter 10 – Internet
• Chapter 11 – Data Warehousing
4

Overview
• Define a database using SQL data definition language
• Work with Views
• Write single table queries
• Establish referential integrity
5

SQL Overview
• Structured Query Language
• The standard for relational database management systems (RDBMS)
• SQL-92 and SQL-99 Standards – Purpose:
– Specify syntax/semantics for data definition and manipulation
– Define data structures
– Enable portability
– Specify minimal (level 1) and complete (level 2) standards
– Allow for later growth/enhancement to standard
6

SQL Environment
• Catalog
– A set of schemas that constitute the description of a database
• Schema
– The structure that contains descriptions of objects created by a user (base tables,
views, constraints)
• Data Definition Language (DDL)
– Commands that define a database, including creating, altering, and dropping
tables and establishing constraints
• Data Manipulation Language (DML)
– Commands that maintain and query a database
• Data Control Language (DCL)
– Commands that control a database, including administering privileges and
committing data
8

SQL Data types (from Oracle 9i)
• String types
– CHAR(n) – fixed-length character data, n characters long Maximum
length = 2000 bytes
– VARCHAR2(n) – variable length character data, maximum 4000 bytes
– LONG – variable-length character data, up to 4GB. Maximum 1 per table
• Numeric types
– NUMBER(p,q) – general purpose numeric data type
– INTEGER(p) – signed integer, p digits wide
– FLOAT(p) – floating point in scientific notation with p binary digits
precision
• Date/time type
– DATE – fixed-length date/time in dd-mm-yy form
9

SQL Database Definition
• Data Definition Language (DDL)
• Major CREATE statements:
– CREATE SCHEMA – defines a portion of the database
owned by a particular user
– CREATE TABLE – defines a table and its columns
– CREATE VIEW – defines a logical table from one or
more views
• Other CREATE statements: CHARACTER SET, COLLATION,
TRANSLATION, ASSERTION, DOMAIN
11

The following slides create tables for this enterprise
data model
12

14
Non-nullable specification
Identifying primary key
Primary keys can
never have NULL
values
Create PRODUCT table

15
Non-nullable specifications
Primary key
Some primary keys are composite – composed of multiple attributes

16
Default value
Domain constraint
Controlling the values in attributes

17
Identifying foreign keys and establishing relationships
Primary key of
parent table
Foreign key of
dependent table

Data Integrity Controls
• Referential integrity – constraint that ensures that foreign
key values of a table must match primary key values of a
related table in 1:M relationships
• Restricting:
– Deletes of primary records
– Updates of primary records
– Inserts of dependent records
18

Using and Defining Views
• Views provide users controlled access to tables
• Base Table – table containing the raw data
• Dynamic View
– A “virtual table” created dynamically upon request by a user
– No data actually stored; instead data from base table made available to user
– Based on SQL SELECT statement on base tables or other views
• Materialized View
– Copy or replication of data
– Data actually stored
– Must be refreshed periodically to match the corresponding base tables
20

Sample CREATE VIEW
CREATE VIEW EXPENSIVE_STUFF_V AS
SELECT PRODUCT_ID, PRODUCT_NAME, UNIT_PRICE
FROM PRODUCT_T
WHERE UNIT_PRICE >300
WITH CHECK_OPTION;
21
View has a name
View is based on a SELECT statement
CHECK_OPTION works only for updateable views and prevents
updates that would create rows not included in the view

Advantages of Views
• Simplify query commands
• Assist with data security (but don't rely on views for security, there are
more important security measures)
• Enhance programming productivity
• Contain most current base table data
• Use little storage space
• Provide customized view for user
• Establish physical data independence
22

Disadvantages of Views
• Use processing time each time view is referenced
• May or may not be directly updateable
23

Create Four Views
CREATE VIEW CUSTOMER_V AS SELECT * FROM CUSTOMER_T;
CREATE VIEW ORDER_V AS SELECT * FROM ORDER_T;
CREATE VIEW ORDER_LINE_V AS SELECT * FROM ORDER_LINE_T;
CREATE VIEW PRODUCT_V AS SELECT * FROM PRODUCT_T;
‘*’ is the wildcard
24

Changing and Removing Tables
• ALTER TABLE statement allows you to change column
specifications:
– ALTER TABLE CUSTOMER_T ADD (TYPE VARCHAR(2))
• DROP TABLE statement allows you to remove tables from
your schema:
– DROP TABLE CUSTOMER_T
25

Schema Definition
• Control processing/storage efficiency:
– Choice of indexes
– File organizations for base tables
– File organizations for indexes
– Data clustering
– Statistics maintenance
• Creating indexes
– Speed up random/sequential access to base table data
– Example
• CREATE INDEX NAME_IDX ON CUSTOMER_T(CUSTOMER_NAME)
• This makes an index for the CUSTOMER_NAME field of the
CUSTOMER_T table
26

Insert Statement
• Adds data to a table
• Inserting a record with all fields
– INSERT INTO CUSTOMER_T VALUES (001, ‘Contemporary Casuals’, 1355 S.
Himes Blvd.’, ‘Gainesville’, ‘FL’, 32601);
• Inserting a record with specified fields
– INSERT INTO PRODUCT_T (PRODUCT_ID, PRODUCT_DESCRIPTION,
PRODUCT_FINISH, STANDARD_PRICE, PRODUCT_ON_HAND) VALUES (1, ‘End
Table’, ‘Cherry’, 175, 8);
• Inserting records from another table
– INSERT INTO CA_CUSTOMER_T SELECT * FROM CUSTOMER_T WHERE STATE =
‘CA’;
27

Delete Statement
• Removes rows from a table
• Delete certain rows
– DELETE FROM CUSTOMER_T WHERE STATE = ‘HI’;
• Delete all rows
– DELETE FROM CUSTOMER_T;
32

Update Statement
• Modifies data in existing rows
UPDATE PRODUCT_T SET UNIT_PRICE = 775 WHERE
PRODUCT_ID = 7;
33

SELECT Statement
• Used for queries on single or multiple tables
• Clauses of the SELECT statement:
– SELECT : List the columns (and expressions) that should be returned from the query
– FROM : Indicate the table(s) or view(s) from which data will be obtained
– WHERE : Indicate the conditions under which a row will be included in the result
– GROUP BY : Indicate columns to group the results
– HAVING : Indicate the conditions under which a group will be included
– ORDER BY : Sorts the result according to specified columns
34

35
Figure : SQL statement
processing order

SELECT Example
• Find products with standard price less than $275
SELECT PRODUCT_NAME, STANDARD_PRICE
FROM PRODUCT_V
WHERE STANDARD_PRICE < 275;
36
Product table

SELECT Example using Alias
• Alias is an alternative column or table name
SELECT CUST.CUSTOMER AS NAME,
CUST.CUSTOMER_ADDRESS
FROM CUSTOMER_V CUST
WHERE NAME = ‘Home Furnishings’;
38

SELECT Example
Using a Function
• Using the COUNT aggregate function to find totals
• Aggregate functions: SUM(), MIN(), MAX(), AVG(), COUNT()
SELECT COUNT(*) FROM ORDER_LINE_V
WHERE ORDER_ID = 1004;
39
Order line table

SELECT Example – Boolean Operators
• AND, OR, and NOT Operators for customizing conditions in WHERE clause
SELECT PRODUCT_DESCRIPTION, PRODUCT_FINISH, STANDARD_PRICE
FROM PRODUCT_V
WHERE (PRODUCT_DESCRIPTION LIKE ‘%Desk’
OR PRODUCT_DESCRIPTION LIKE ‘%Table’)
AND UNIT_PRICE > 300;
40
Note: the LIKE operator allows you to compare strings using wildcards. For example,
the % wildcard in ‘%Desk’ indicates that all strings that have any number of
characters preceding the word “Desk” will be allowed

SELECT Example –
Sorting Results with the ORDER BY Clause
• Sort the results first by STATE, and within a state by CUSTOMER_NAME
SELECT CUSTOMER_NAME, CITY, STATE
FROM CUSTOMER_V
WHERE STATE IN (‘FL’, ‘TX’, ‘CA’, ‘HI’)
ORDER BY STATE, CUSTOMER_NAME;
41
Note: the IN operator in this example allows you to include rows whose STATE value
is either FL, TX, CA, or HI. It is more efficient than separate OR conditions

SELECT Example –
Categorizing Results Using the GROUP BY Clause
SELECT STATE, COUNT(STATE)
FROM CUSTOMER_V
GROUP BY STATE;
Note: you can use single-value fields with aggregate functions if they are
included in the GROUP BY clause
42
Customer table

SELECT Example –
Qualifying Results by Categories Using the HAVING
Clause
• For use with GROUP BY
SELECT STATE, COUNT(STATE)
FROM CUSTOMER_V
GROUP BY STATE
HAVING COUNT(STATE) > 1;
Like a WHERE clause, but it operates on groups (categories), not on
individual rows. Here, only those groups with total numbers greater than
1 will be included in final result
43

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