2. Η παρουσίαση αυτή θα καταγραφεί ώστε να είναι διαθέσιμη
για όσους θέλουν να την ξαναδούν, ή δεν είχαν την
δυνατότητα να την παρακολουθήσουν σε πραγματικό χρόνο.
Εάν κάποιος από τους παραβρισκόμενους σε αυτή έχει το
οποιοδήποτε πρόβλημα ή αντίρρηση να είναι μέρος της
καταγραφή αυτής, παρακαλείται να αποχωρήσει άμεσα.
Σε διαφορετική περίπτωση η παραμονή σε αυτή εκλαμβάνεται
ως αποδοχή της καταγραφής.
Η παρουσίαση αυτή διατίθεται δωρεάν,
και θα αρχίσει σε 1 λεπτό…
3. Αυτή την στιγμή ο παρουσιαστής μιλάει και σας ζητάει να
βεβαιώσετε ότι τον ακούτε.
Εάν αυτό δεν είναι δυνατόν παρακαλείστε να αλλάξετε το
χρώμα της κάρτας σας στο αντίστοιχο χρώμα ώστε να
τον ενημερώσετε.
Αυτό μπορεί να γίνει πατώντας την αντίστοιχη επιλογή
που βρίσκεται στο πάνω δεξί μέρος του περιβάλλοντος
του live meeting.
Σας ευχαριστούμε για την συνεργασία.
5. SP_WHO
Sotiris Fivi Antonios
Karras Panopoulou Chatzipavlis
SQL Server Jr. DBA SQL Server Jr. DBA SQL Server Evangelist
- Microsoft Student Partner - Microsoft Student Partner - MVP on SQL Server
- N.T.U.A Student - N.T.U.A Student - MCSE on Data Platform
- MCSA on SQL Server 2012
- Microsoft Trainer (MCT)
- MCT Regional Leader
8. PRESENTATION TOPICS - 1
• Introduction to Microsoft SQL Server 2012
• SQL Server Management Studio
• Introduction to T-SQL Querying
• SELECT Statements / Queries
• Querying Multiple Tables
• Sorting and Filtering Data
• Grouping and Aggregating Data
9. PRESENTATION TOPICS - 2
• Using Sub-queries
• Using Views, Table Value Functions and
Table Expressions
• Using Set Operators
• Using Stored Procedures
• Using Window Functions
• What’s New in SQL Server 2012
14. SQL SERVER VERSIONS
Operating
Version Release Year Code Name Company
System
1.0 1989 OS2 Aston Tate, Microsoft, Sybase
1.1 1991 OS2 Aston Tate, Microsoft, Sybase
4.2 1992 Windows Microsoft
4.21 1994 SQLNT Windows Microsoft
6.0 1995 SQL95 Windows Microsoft
6.5 1996 Hydra Windows Microsoft
7.0 1998 Sphinx Windows Microsoft
2000 2000 Shiloh Windows Microsoft
2005 2005 Yukon Windows Microsoft
2008 2008 Katmai Windows Microsoft
2008 R2 2010 Kilimanjaro Windows Microsoft
2012 2012 Denali Windows Microsoft
15. SQL SERVER 2012 EDITIONS
Main Editions Other Editions
Enterprise Parallel Data Warehouse
Standard Web
Business Intelligence Developer
Express
Express LocalDB
17. SQL SERVER SYSTEM DATABASES
master
The system configuration database.
msdb
The configuration database for the SQL Server Agent service and other system services.
model
Τhe template for new user databases.
tempdb
Used by the database engine to store temporary data such as work tables. Dropped and recreated
each time SQL Server restarts
resource
Α hidden system configuration database that provides system objects to other databases.
distribution
The distribution database stores metadata and history data for all types of replication, and
transactions for transactional replication.
20. SQL SERVER MANAGEMENT STUDIO
(SSMS)
“SQL Server Management Studio is an integrated
environment for accessing, configuring, managing,
administering, and developing all components of
SQL Server. SQL Server Management Studio
combines a broad group of graphical tools with a
number of rich script editors to provide access to
SQL Server to developers and administrators of all
skill levels. ”
23. INTRODUCING TRANSACT-SQL (T-SQL)
Is the language in which you will write your
queries for SQL Server.
Structure Query Language (SQL)
Developed by IBM in 1970
Adopted as ANSI and ISO standards
Widely used in industry
SQL is declarative not procedural
24. CATEGORIES OF T-SQL STATEMENTS
Data Manipulation Language (DML)
Statement for querying and modify data
SELECT, INSERT, UPDATE, DELETE
Data Definition Language (DDL)
Statements for Objects definitions
CREATE, ALTER, DROP
Data Control Language (DCL)
Statements for security permissions
GRANT, DENY, REVOKE
25. PREDICATES AND OPERATORS
Predicates
IN, BETWEEN, LIKE Operators Precedence
1 ( ) Parentheses
Comparison Operators 2 *, /, %
=, >, <, >=, <=, <>, !=, !>, !< 3 +, -
Logical Operators
4 =, <, >, >=, <=, !=, !>, !<
5 NOT
AND, OR, NOT 6 AND
Arithmetic Operators 7
8
BETWEEN, IN, LIKE, OR
=
+, -, *, /, %
Concatenation
+
26. FUNCTIONS CATEGORIES
• Rowset Functions • Mathematical Functions
• Aggregate Functions • Metadata Functions
• Ranking Functions
• Security Functions
• Configuration
Functions • String Functions
• Conversion Functions • System Functions
• Cursor Functions
• System Statistical
• Date and Time Data Functions
Types and Functions
• Logical Functions • Text and Image
Functions
27. VARIABLES
• Local variables in T-SQL temporarily store a value of specific data
type
• Name begins with single @ sign
• @@ reserved for system functions
• Assigned a data type
• Must be declared and used within the same batch
• You can declare and initialize in the same statement (from SQL
Server 2008 and later)
DECLARE @variablename datatype [=init value]
DECLARE @myVar INT = 20;
28. EXPRESSIONS
• Combination of identifiers, values and
operators evaluated to obtain a single result
• Can be used in many situations like
• SELECT clause
• WHERE clause
• SET clause
SELECT YEAR(SalesOrderDate) + 1 …
SELECT Quantity * UnitPrice …
33. COMMENTS
Block comments
/*
SELECT …
other code…
*/
Inline comments
-- inline comment
DECLARE @myVar INT = 20; -- initial value
34. BATCHES AND BATCH SEPARATOR
• Batches are sets of commands sent to SQL Server as a unit
• Batches determine variable scope and name resolution
• To separate statements into batches SQL Server uses the
GO keyword
• GO is not a SQL Server command!
• We can define our batch separator
• GO n time feature added in SQL Server 2005 and later.
DECLARE @CustomerID NCHAR(5) = ‘ANTON’;
SELECT CustomerID, CompanyName FROM Orders
WHERE CustomerID = @CustomerID;
GO
CREATE TABLE T(c1 INT);
GO
CREATE VIEW V AS SELECT * FROM T;
GO
35. SQL SERVER DATA TYPES
• SQL Server defines a set of system data types
for
• storing data in columns,
• holding values temporarily in variables,
• for operating on data in expressions,
• for passing parameters in stored procedures and functions
• Data Types specify the
• Type
• Length
• Precision
• Scale
36. SQL SERVER DATA TYPES CATEGORIES
• Exact numeric
• Approximate numeric
• Date & Time
• Character string
• Unicode character strings
• Binary string
• Other data types
37. EXACT NUMERIC DATA TYPES
Data type Range Storage (bytes)
tinyint 0 to 255 1
smallint -32,768 to 32,768 2
int 2^31 (-2,147,483,648) to 4
2^31-1 (2,147,483,647)
bigint -2^63 - 2^63-1 8
(+/- 9 quintillion)
bit 1, 0 or NULL 1
decimal - 10^38 +1 through 10^38 – 1 when 5-17
maximum precision is used
numeric - 10^38 +1 through 10^38 – 1 when 5-17
maximum precision is used
money -922,337,203,685,477.5808 to 8
922,337,203,685,477.5807
smallmoney - 214,748.3648 to 214,748.3647 4
38. APPROXIMATE NUMERIC DATA TYPES
Data Type Range Storage (bytes)
float(n) - 1.79E+308 to -2.23E-308, 0 and 2.23E- Depends on value of n, 4
308 to 1.79E+308 or 8 bytes
real - 3.40E + 38 to -1.18E - 38, 0 and 1.18E -
4
38 to 3.40E + 38
• float(24) is the ISO synonym for float
• In float(n), n is the number of bytes used to store the mantissa of the float
number in scientific notation
• Values of float are truncated when converted
to integer types
39. DATE & TIME DATA TYPES
• Older versions of SQL Server supported only
DATETIME and SMALLDATETIME
• DATE, TIME, DATETIME2, and
DATETIMEOFFSET introduced in SQL Server
2008
• SQL Server 2012 adds new functionality for
working with date and time data types
40. DATE & TIME DATA TYPES
Storage Recommended Entry
Data Type Date Range Accuracy
(bytes) Format
DATETIME 8 January 1, 1753 to 3-1/3 'YYMMDD
December 31, 9999 milliseconds hh:mm:ss:nnn'
SMALLDATETIME 4 January 1, 1900 to 1 minute 'YYMMDD
June 6, 2079 hh:mm:ss:nnn'
DATETIME2 6 to 8 January 1, 0001 to 100 'YYMMDD
December 31, 9999 nanoseconds hh:mm:ss.nnnn
nn'
DATE 3 January 1, 0001 to 1 day 'YYYY-MM-DD'
December 31, 9999
TIME 3 to 5 100 'hh:mm:ss:nnn
nanoseconds nnnn'
DATETIMEOFFSET 8 to 10 January 1, 0001 to 100 'YY-MM-DD
December 31, 9999 nanoseconds hh:mm:ss:nnnn
nnn [+|-
]hh:mm'
41. CHARACTER STRING DATA TYPES
• One byte per character
Data Type Range Storage
CHAR(n) 1-8000 characters n bytes, padded Fixed Length
VARCHAR(n) 1-8000 characters n+2 bytes Variable Length
VARCHAR(MAX) 1-2^31-1 characters Actual length + 2 Variable Length
• TEXT deprecated
• Use VARCHAR(MAX) instead
42. UNICODE CHARACTER STRING DATA TYPES
• Two bytes per character
Data Type Range Storage
NCHAR(n) 1-8000 characters 2*n bytes, padded Fixed Length
NVARCHAR(n) 1-8000 characters (2*n)+2 bytes Variable Length
NVARCHAR(MAX) 1-2^31-1 characters Actual length + 2 Variable Length
• NTEXT deprecated
• Use NVARCHAR(MAX) instead
43. BINARY STRING
Data Type Range Storage (bytes)
binary(n) 1-8000 bytes n bytes
varbinary(n) 1-8000 bytes n bytes + 2
varbinary(MAX) 1-2.1 billion (approx) bytes actual length + 2
• IMAGE deprecated
• Use VARBINARY(MAX) instead
44. OTHER DATA TYPES
Data Type Range Storage Remarks
(bytes)
rowversion Auto-generated 8 Successor type to
timestamp
uniqueidentifier Auto-generated 16 Globally unique identifier
(GUID)
xml 0-2 GB 0-2 GB Stores XML in native
hierarchical structure
cursor N/A N/A Not a storage data type
hierarchyid N/A Depends on Represents position in a
content hierarchy
sql_variant 0-8000 bytes Depends on Can store data of various
content data types
table N/A N/A Not a storage data type,
used for query and
programmatic operations
45. DATA TYPES PRESENTENCE
• Data type precedence determines which data
type will be chosen when expressions of different
types are combined
• Data type with the lower precedence is converted
to the data type with the higher precedence
• Important for understanding implicit conversions
• Conversion to type of lower precedence must be made explicitly (with CAST
function)
• Example (low to high):
• CHAR -> VARCHAR -> NVARCHAR -> TINYINT -> INT -> DECIMAL -> TIME ->
DATE -> DATETIME2 -> XML
46. DATA TYPES CONVERSION
• Data type conversion scenarios
• When data is moved, compared, or combined with other data
• During variable assignment
• Implicit conversion
• When comparing data of one type to another
• Transparent to user
WHERE <column of smallint type> = <value of int type>
• Explicit conversion
• Uses CAST or CONVERT functions
CAST(unitprice AS int)
• Not all conversions allowed by SQL Server
48. SELECT STATEMENT – MAIN CLAUSES
• [ WITH <common_table_expression>]
• SELECT select_list [ INTO new_table ]
• [ FROM table_source ] [ WHERE search_condition ]
• [ GROUP BY group_by_expression ]
• [ HAVING search_condition ]
• [ ORDER BY order_expression [ ASC | DESC ] ]
49. SELECT STATEMENT – LOGICAL PROCESSING
• FROM
• ON
• JOIN
• WHERE
• GROUP BY
• WITH CUBE or WITH ROLLUP
• HAVING
• SELECT
• DISTINCT
• ORDER BY
• TOP
51. SELECT DISTINCT
• Specifies that only unique rows can appear in the
result set
• Removes duplicates based on column list results,
not source table
• Provides uniqueness across set of selected
columns
• Removes rows already operated on by WHERE,
HAVING, and GROUP BY clauses
• Some queries may improve performance by
filtering out duplicates prior to execution of
SELECT clause
52. COLUMN ALIASES
• Column aliases using AS
SELECT orderid, unitprice, qty AS quantity
FROM OrderDetails;
• Column aliases using =
SELECT orderid, unitprice, quantity = qty
FROM OrderDetails;
• Accidental column aliases
SELECT orderid, unitprice quantity
FROM OrderDetails;
53. TABLE ALIASES
• Create table aliases in the FROM clause
• Table aliases with AS
SELECT custid, orderdate
FROM dbo.Orders AS SO;
• Table aliases without AS
SELECT custid, orderdate
FROM Sales.Orders SO;
• Using table aliases in the SELECT clause
SELECT SO.custid, SO.orderdate
FROM Sales.Orders AS SO;
54. SELECT STATEMENT – CASE EXPRESSION
Simple Case expression
CASE input_expression
WHEN when_expression THEN result_expression [ ...n ]
[ ELSE else_result_expression ]
END
Searched CASE expression
CASE
WHEN boolean_expression THEN result_expression [ ...n ]
[ ELSE else_result_expression ]
END
57. JOIN CLAUSE – INTRODUCTION
• By using joins, you can retrieve data from two or more tables
based on logical relationships between the tables.
• Three types of Joins
1. Inner Joins, which use a comparison operator to match rows from two tables
based on the values in common columns from each table
2. Outer Joins, in which all the rows of the specified table(s) are returned, not
just the ones in which the joined columns match
3. Cross Joins, which produce the Cartesian product of the tables involved in the
join (if there is no WHERE clause present)
4. Self Joins which you need to compare and retrieve data stored in the same
table
• Main Join Clause
SELECT select_list
FROM first_table
join_type second_table [ON (join_condition)]
[WHERE (search_condition)]
58. T-SQL JOIN SYNTAX CHOICES
• ANSI SQL-92
• Tables joined by JOIN operator in FROM Clause
• Preferred syntax
SELECT ...
FROM Table1 JOIN Table2
ON <on_predicate>
• ANSI SQL-89
• Tables joined by commas in FROM Clause
• Not recommended: accidental Cartesian products!
SELECT ...
FROM Table1, Table2
WHERE <where_predicate>
61. SORTING/FILTERING – SORTING
• You can sort data retrieved by a SELECT
statement using the ORDER BY clause
• ORDER BY clause syntax:
ORDER BY order_by_expression
[ COLLATE collation_name ]
[ ASC | DESC ]
[ ,...n ]
[ <offset_fetch> ]
62. SORTING/FILTERING – COLLATION
• Specifies that the ORDER BY operation
should be performed according to the
collation specified in collation_name
• Using collations to sort data, depending
on encoding or case sensitive/accent
sensitive options
63. SORTING/FILTERING – TOP/OFFSET
<offset_fetch> ::=
{
OFFSET { integer_constant | offset_row_count_expression } { ROW |
ROWS }
[
FETCH { FIRST | NEXT } {integer_constant |
fetch_row_count_expression } { ROW | ROWS } ONLY
]
}
• Specifies the number of rows to return after the OFFSET
clause has been processed. The value can be an integer
constant or expression that is greater than or equal to one.
• Both OFFSET and FETCH can use variables as input and thus
are able to be optimized for specified values of those values
using the OPTIMIZE FOR hint
64. SORTING/FILTERING – WORKING WITH NULL VALUES
• NULL has three logical interpretations
• Unknown value
• Unavailable value
• Not supported value
• SET ANSI_NULLS { ON | OFF }
• Setting ANSI_NULLS ON, causes the SELECT
statements using WHERE column_name = NULL
to return zero rows even if there are null values
in column_name ( thus operators =, <> follow the ISO
standard)
• Option in future versions ANSI_NULLS will by default
be set to ON
67. AGGREGATE FUNCTIONS
• Aggregate functions perform a calculation
on a set of values and return a single value.
• Deterministic in nature. They return the
same value any time that they are called by
using a specific set of input values.
List of Aggregate Functions
AVG MIN
CHECKSUM_AVG SUM
COUNT STDEV
COUNT_BIG STDEVP
GROUPING VAR
GROUPING_ID VARP
MAX
68. GROUP BY CLAUSE
• Groups sets of rows, using the values of one or
more columns or expressions.
• Each group is represented by one and only one
row.
• Usually aggregates are used to provide the
grouping criterion
• Can filter the groups created using the HAVING
keyword
• A GROUP BY clause can include GROUPING
SETS, CUBE AND ROLLUP keywords
69. DISTINCT & NULL WITH AGGREGATE FUNCTIONS
• Use DISTINCT with aggregate functions to summarize only unique
values
• DISTINCT aggregates eliminate duplicate values, not rows (unlike
SELECT DISTINCT)
• Most aggregate functions ignore NULL
• COUNT(<column>) ignores NULL
• COUNT(*) counts all rows
• NULL may produce incorrect results (such as use of AVG)
• Use ISNULL or COALESCE to replace NULLs before aggregating
70. GROUPING SETS
• GROUPING SETS subclause builds on T-SQL
GROUP BY clause
• Allows multiple groupings to be defined in
same query
SELECT <column list with aggregate(s)>
FROM <source>
GROUP BY
GROUPING SETS(
(<column_name>),--one or more columns
(<column_name>),--one or more columns
() -- empty parentheses if aggregating all rows
);
71. GROUPING_ID
• Multiple grouping sets present a problem in
identifying the source of each row in the result set
• NULLs could come from the source data or could be
a placeholder in the grouping set
• The GROUPING_ID function provides a method to
mark a row with a 1 or 0 to identify which grouping
set the row is a member of
SELECT GROUPING_ID(Category)AS grpCat,
GROUPING_ID(CustomerID) AS grpCust,
Category, CustomerID, SUM(Qty) AS TotalQty
FROM CategorySales
GROUP BY CUBE(Category,CustomerID)
ORDER BY Category, CustomerID;
73. CUBE/ROLLUP
• CUBE provides shortcut for defining grouping sets given a list of
columns
• All possible combinations of grouping sets created
SELECT <column list with aggregate(s)>
FROM <source>
GROUP BY CUBE (<column_name>, <column_name>, ...);
• ROLLUP provides shortcut for defining grouping sets, creates
combinations assuming input columns form a hierarchy
SELECT <column list with aggregate(s)>
FROM <source>
GROUP BY CUBE (<column_name>, <column_name>, ...);
74. PIVOT
• Pivoting data is rotating data from a rows-based orientation
to a columns-based orientation
• Distinct values from a single column are projected across as
headings for other columns - may include aggregation
75. PIVOT SYNTAX
• Pivoting includes three phases:
1. Grouping determines which element gets a row in the
result set
2. Spreading provides the distinct values to be pivoted across
3. Aggregation performs an aggregation function (such as
SUM)
Grouping
SELECT Category, [2006],[2007],[2008]
FROM ( SELECT Category, Qty, Orderyear
FROM Sales.CategoryQtyYear) AS D
PIVOT(SUM(QTY) FOR orderyear
IN([2006],[2007],[2008]) Spreading
) AS pvt;
Aggregation
76. UNPIVOT
• Unpivoting data is rotating data from a columns-based orientation to a
rows-based orientation
• Spreads or splits values from one source row into one or more target rows
• Each source row becomes one or
more rows in result set based on
number of columns being pivoted
77. UNPIVOT SYNTAX
• Unpivoting includes three elements:
• Source columns to be unpivoted
• Name to be assigned to new values column
• Name to be assigned to names columns
SELECT category, qty, orderyear
FROM CategorySales
UNPIVOT(qty FOR orderyear
IN([2006],[2007],[2008])) AS unpvt;
80. SUBQUERIES
• Nesting queries within queries
• Outer query : result set returned to caller
• Inner query : result set returned to outer query
• Types of subqueries based on return value:
• Single –valued (scalar)
• Multivalued
• Table-valued
81. DEPENDENCY ON OUTER QUERY
• Self-Contained Subqueries
• Subquery not dependent on outer query
• Can be evaluated by executing the subquery once and substituting the
result into the WHERE clause of the outer query
• Easier to debug
• Correlated Subqueries
• The subquery depends on the outer query for its values
• The subquery is executed repeatedly, once for each row that might be
selected by the outer query
• Known as repeating subquery
82. USE
• Anywhere an expression is allowed
• Not in an ORDER BY clause
• Subqueries within subqueries
• Commonly used with (NOT) IN and EXISTS
83. EXISTS PREDICATE
• Tests existence of rows.
• Returns TRUE or FALSE
• Usually * consists the SELECT list of the subquery,
no reason to list column names
• Enough to determine whether the subquery returns
at least one row, so engine doesn’t need to process
all rows
86. VIEWS
A view is a virtual table whose contents (rows and
columns) are defined by a query.
• The rows and columns of data come from tables
referenced in the query defining the view and are
produced dynamically when the view is referenced.
• Updatable view: modifying an underlying base table
through the view
• Indexed view: The view definition has been
computed and the resulting data stored just like a
table.
87. USE
• To focus, simplify, and customize the perception
each user has of the database.
• As a security mechanism by allowing users to
access data through the view, without granting the
users permissions to directly access the underlying
base tables.
• To provide a backward compatible interface to
emulate a table whose schema has changed.
88. OPTIONS
• CHECK OPTION
Prevent data modifications through the view that conflict
with the views filter
• ENCRYPTION
Encrypt text of the CREATE VIEW statement in
sys.syscomments
• SCHEMABINDING
Prevent modification of underlying views that would affect
the view definition
90. INLINE TABLE-VALUED FUNCTIONS
• Table-valued functions (TVFs) are named table
expressions with definitions stored in a database
• TVFs return a virtual table to the calling query
• SQL Server provides two types of TVFs
• Inline, based on a single SELECT statement
• Multi-statement, which creates and loads a table variable
• Unlike views, TVFs support input parameters
• Inline TVFs may be thought of as parameterized
views
91. WRITING INLINE TVF
• Table-valued functions are created by administrators and
developers
• Create and name function and optional parameters with
CREATE FUNCTION
• Declare return type as TABLE
• Define inline SELECT statement following RETURN
CREATE FUNCTION Sales.fn_LineTotal (@orderid INT)
RETURNS TABLE
AS
RETURN
SELECT orderid,
CAST((qty * unitprice * (1 - discount)) AS
DECIMAL(8, 2)) AS line_total
FROM OrderDetails
WHERE orderid = @orderid ;
93. DERIVED TABLES
• Derived tables are named query expressions created within an
outer SELECT statement
• Not stored in database – represents a virtual relational table
• When processed, unpacked into query against underlying
referenced objects
• Allow you to write more modular queries
SELECT <column_list>
FROM (
<derived_table_definition>
) AS <derived_table_alias>;
• Scope of a derived table is the query in which it is defined
95. COMMON TABLE EXPRESSIONS
• CTEs are named table expressions defined in
a query
• CTEs are similar to derived tables in scope
and naming requirements
• Unlike derived tables, CTEs support multiple
definitions, multiple references, and recursion
WITH <CTE_name>
AS (
<CTE_definition>
)
<outer query referencing CTE>;
96. WRITING CTE
• Define the table expression in WITH clause
• Assign column aliases (inline or external)
• Pass arguments if desired
• Reference the CTE in the outer query
WITH CTE_year AS
(
SELECT YEAR(orderdate) AS orderyear, customerid
FROM Orders
)
SELECT orderyear,
COUNT(DISTINCT customerid) AS cust_count
FROM CTE_year
GROUP BY orderyear;
99. SET OPERATORS
Set operators combine results from two or more queries into
a single result set.
• The number and the order of the columns must be the same in all
queries.
• The data types must be compatible
• ORDER BY clause applied to result of operator
• Names of columns in result are specified by the first query
• NULLS are equal Input Query 1
set operator
Input Query 2
[ORDER BY … ]
100. SET OPERATORS
• UNION: returns all rows that belong to at least one
of the input queries.
• By default result set has no duplicates. To get result set with
duplicates specify ALL option.
• INTERSECT: returns any distinct values that are
returned by both the left query and the right one.
• EXCEPT: returns any distinct values from the left
query that are not also found on the right query.
101. APPLY
• Invoke a table-valued function for each row
returned by an outer table expression of a query.
• CROSS APPLY returns only rows from the outer
table that produce a result set from the table-
valued function.
• OUTER APPLY returns both rows that produce a
result set, and rows that do not, with NULL values in
the columns produced by the table-valued
function.
104. STORED PROCEDURES
A stored procedure in SQL Server is a group of one
or more Transact-SQL statements which can :
• Accept input parameters and return multiple values in the
form of output parameters to the calling program.
• Contain programming statements that perform operations in
the database. These include calling other procedures.
• Return a status value to a calling program to indicate success
or failure (and the reason for failure)
108. SQL WINDOWING
Window functions are functions applied to sets of rows defined by
a clause called OVER
They are used mainly for analytical purposes allowing you to
calculate running totals, calculate moving averages, identify gaps
and islands in your data, and perform many other computations
These functions are based on the concept of windowing in
standard SQL, which is both an ISO and ANSI standard
The idea behind this concept is to allow you to apply various
calculations to a set, or window, of rows and return a single value
Window functions can help to solve a wide variety of querying
tasks by helping you express set calculations more easily,
intuitively, and efficiently than ever before
110. OVER CLAUSE
OVER defines a window, or set, of rows to be used
by a window function, including any ordering
With a specified window partition clause, the OVER
clause restricts the set of rows to those with the
same values in the partitioning elements
By itself, OVER() is unrestricted and includes all
rows
Multiple OVER clauses can be used in a single
query, each with its own partitioning and ordering,
if needed
111. WINDOW PARTITION CLAUSE
The partitioning element allows you to restrict
the window to only those rows that have the
same values in the partitioning attributes as
the current row.
SELECT orderid, custid, val,
SUM(val) OVER() AS sumall,
SUM(val) OVER(PARTITION BY custid) AS sumcust
FROM OrderValues AS O1;
112. WINDOW ORDER CLAUSE
The ordering element defines the ordering for
the calculation, if relevant, within the partition
SELECT custid, orderid, val,
RANK() OVER(ORDER BY val DESC) AS rnk_all,
RANK() OVER(PARTITION BY custid
ORDER BY val DESC) AS rnk_cust
FROM Orders;
113. WINDOW FRAME CLAUSE
Framing is another option that enables you to
further restrict the rows in the window
partition.
SELECT empid, ordermonth, qty,
SUM(qty) OVER(PARTITION BY empid
ORDER BY ordermonth
ROWS BETWEEN UNBOUNDED PRECEDING
AND CURRENT ROW) AS runqty
FROM EmpOrders;
115. WINDOW AGGREGATE FUNCTIONS
• Similar to grouped aggregate functions
• SUM, MIN, MAX, etc.
• Applied to windows defined by OVER clause
• Window aggregate functions support
partitioning, ordering, and framing
SELECT custid, ordermonth, qty,
SUM(qty) OVER(PARTITION BY custid) AS
totalpercust
FROM Sales.CustOrders;
116. WINDOW RANKING FUNCTIONS
• Ranking functions require a window order
clause
• Partitioning is optional
• To display results in sorted order still requires ORDER BY!
Function Description
RANK Returns the rank of each row within the partition of a
result set. May include ties and gaps.
DENSE_RANK Returns the rank of each row within the partition of a
result set. May include ties. Will not include gaps.
ROW_NUMBER Returns a unique sequential row number within
partition based on current order.
NTILE Distributes the rows in an ordered partition into a
specified number of groups. Returns the number of
the group to which the current row belongs.
117. WINDOW DISTRIBUTION FUNCTIONS
• Window distribution functions perform
statistical analysis on data, and require a
window order clause
• Rank distribution performed with
PERCENT_RANK and CUME_DIST
• Inverse distribution performed with
PERCENTILE_CONT and PERCENTILE_DISC
118. WINDOW OFFSET FUNCTIONS
• Window offset functions allow comparisons between
rows in a set without the need for a self-join
• Offset functions operate on an position relative to the
current row, or to the start or end of the window
frame
Function Description
LAG Returns an expression from a previous row that is a defined offset
from the current row. Returns NULL if no row at specified position.
LEAD Returns an expression from a later row that is a defined offset from
the current row. Returns NULL if no row at specified position.
FIRST_VALUE Returns the first value in the current window frame. Requires window
ordering to be meaningful.
LAST_VALUE Returns the last value in the current window frame. Requires window
ordering to be meaningful.
120. What’s New in
SQL Server 2012
Improvements of T-SQL in SQL Server 2012
121. USING SEQUENCES
• Sequence objects added in SQL Server 2012
• Independent objects in database
• More flexible than the IDENTITY property
• Can be used as default value for a column
• Manage with CREATE/ALTER/DROP statements
• Retrieve value with the NEXT VALUE FOR clause
-- Define a sequence
CREATE SEQUENCE dbo.InvoiceSeq AS INT
START WITH 1 INCREMENT BY 1;
-- Retrieve next available value from sequence
SELECT NEXT VALUE FOR dbo.InvoiceSeq;
122. USING MERGE TO MODIFY DATA
• MERGE modifies data based on a condition
• When the source matches the target
• When the source has no match in the target
• When the target has no match in the source
MERGE INTO schema_name.table_name AS TargetTbl
USING (SELECT <select_list>) AS SourceTbl
ON (TargetTbl.col1 = SourceTbl.col1)
WHEN MATCHED THEN
UPDATE SET col2 = SourceTbl.col2
WHEN NOT MATCHED THEN
INSERT (<column_list>)
VALUES (<value_list>);
124. NEW CONVERSION FUNCTIONS
• PARSE
SELECT PARSE('Monday, 13 December 2010' AS datetime2
USING 'en-US') AS Result;
Result : 2010-12-13 00:00:00.0000000
• TRY_CONVERT
SELECT CASE WHEN TRY_CONVERT(float, 'test') IS NULL
THEN 'Cast failed'
ELSE 'Cast succeeded'
END AS Result;
• TRY_PARSE
SELECT TRY_PARSE('AAAAA' AS datetime2
USING 'en-US') AS Result;
Result : NULL
126. DATEFROMPARTS
• Returns a date value with the date portion set
to the specified year, month and day, and the
time portion set to the default.
• If the arguments are not valid, then an error
is raised.
• If required arguments are null, then null is
returned.
SELECT DATEFROMPARTS ( 2010, 12, 31 ) AS Result;
Result 2010-12-31
127. DATETIME2FROMPARTS
• Returns a fully initialized datetime2 value.
• If the arguments are not valid, an error is
raised.
• If required arguments are null, then null is
returned.
• If the precision argument is null, then an error
is raised.
SELECT DATETIME2FROMPARTS( 2010, 12, 31, 23, 59, 59, 0, 0 );
Result 2010-12-31 23:59:59.0000000
128. DATETIMEFROMPARTS
• Returns a fully initialized datetime value.
• If the arguments are not valid, then an error
is raised.
• If required arguments are null, then a null is
returned.
SELECT DATETIMEFROMPARTS( 2010, 12, 31, 23, 59, 59, 0 );
Result 2010-12-31 23:59:59.000
129. DATETIMEOFFSETFROMPARTS
• Returns a fully initialized datetimeoffset data type.
• The offset arguments are used to represent the time zone offset.
• If the offset arguments are omitted, then the time zone offset is assumed
to be 00:00.
• If the offset arguments are specified, then both arguments must be
present and both must be positive or negative.
• If minute_offset is specified without hour_offset, an error is raised.
• If other arguments are not valid, then an error is raised.
• If required arguments are null, then a null is returned.
• If the precision argument is null, then an error is raised.
SELECT DATETIMEOFFSETFROMPARTS(2010,12,31,14,23,23,0,12,0,7);
Result 2010-12-07 00:00:00.0000000 +00:00
130. EOMONTH
• Returns the last day of the month that
contains the specified date, with an optional
offset.
DECLARE @date DATETIME = '12/1/2011';
SELECT EOMONTH ( @date ) AS Result;
GO
Result 2011-12-31
131. SMALLDATETIMEFROMPARTS
• Returns a smalldatetime value for the
specified date and time.
SELECT SMALLDATETIMEFROMPARTS ( 2010, 12, 31, 23, 59 );
Result 2011-01-01 00:00:00
132. TIMEFROMPARTS
• Returns a fully initialized time value.
• If the arguments are invalid, then an error is
raised.
• If any of the parameters are null, null is
returned.
• If the precision argument is null, then an error
is raised.
SELECT TIMEFROMPARTS ( 23, 59, 59, 0, 0 ) AS Result;
Result 23:59:59.0000000
134. CHOOSE
• Returns the data type with the highest
precedence from the set of types passed to
the function
• CHOOSE acts like an index into an array,
where the array is composed of the
arguments that follow the index argument.
SELECT CHOOSE (3,'Manager','Director','Developer','Tester');
Result Developer
135. IIF
• Returns the data type with the highest
precedence from the types in true_value and
false_value
• IIF is a shorthand way for writing a CASE
expression.
DECLARE @a int = 45, @b int = 40;
SELECT IIF ( @a > @b, 'TRUE', 'FALSE' ) AS Result;
GO
Result TRUE
137. CONCAT
• Returns a string that is the result of
concatenating two or more string values.
SELECT CONCAT ( 'Happy ', 'Birthday ', 11, '/', '25' );
Result Happy Birthday 11/25
138. FORMAT
• Returns a value formatted with the specified
format and optional culture.
DECLARE @d DATETIME = '10/01/2011';
SELECT FORMAT ( @d, 'd', 'en-US' ) AS 'US English Result'
,FORMAT ( @d, 'd', 'en-gb' ) AS 'GB English Result'
,FORMAT ( @d, 'd', 'de-de' ) AS 'German Result';
US English Result : 10/1/2011
Result GB English Result : 01/10/2011
German Result : 01.01.2011
140. SUMMARY
• Introduction to Microsoft SQL Server 2012
• SQL Server Management Studio
• Introduction to T-SQL Querying
• SELECT Statements / Queries
• Querying Multiple Tables
• Sorting and Filtering Data
• Grouping and Aggregating Data
141. SUMMARY
• Using Sub-queries
• Using Views, Table Value Functions and
Table Expressions
• Using Set Operators
• Using Stored Procedures
• Using Window Functions
• What’s New in SQL Server 2012
