Functions in Oracle can be used to manipulate data values and are categorized as single-row/scalar functions and group/aggregate functions. Single-row functions operate on each row and return one value per row, while group functions operate on sets of values to return one result. The GROUP BY clause is used to group or categorize data and can be used with aggregate functions to return summary results for each group.
Using and Creating SQL Functions with Ammar Hassan Brohi.
String Functions
Numeric Functions
String / Number Conversion Functions
Group Functions
Date and Time Functions
Date Conversion Functions
Using and Creating SQL Functions with Ammar Hassan Brohi.
String Functions
Numeric Functions
String / Number Conversion Functions
Group Functions
Date and Time Functions
Date Conversion Functions
This presentation gives a clear and concise description of joins in sql and several types of sql joins.
These slides also contains the pictorial representation as well as syntax for each type of joins.
Aggregate functions are functions that take a collection of values as input and return a single value.The ISO standard defines five (5) aggregate functions namely :-
1) COUNT
2) SUM
3) AVG
4) MIN
5) MAX
1.COUNT Function
The COUNT function returns the total number of values in the specified field. It works on both numeric and non-numeric data types. All aggregate functions by default exclude nulls values before working on the data.
MIN function
The MIN function returns the smallest value in the specified table field.
2.MAX function
Just as the name suggests, the MAX function is the opposite of the MIN function. It returns the largest value from the specified table field.
3.SUM function
Suppose we want a report that gives total amount of payments made so far. We can use the MySQL SUM function which returns the sum of all the values in the specified column. SUM works on numeric fields only. Null values are excluded from the result returned.
4.AVG function
MySQL AVG function returns the average of the values in a specified column. Just like the SUM function, it works only on numeric data types.
5.MIN function
The MIN function returns the smallest value in the specified table field.
This presentation gives a clear and concise description of joins in sql and several types of sql joins.
These slides also contains the pictorial representation as well as syntax for each type of joins.
Aggregate functions are functions that take a collection of values as input and return a single value.The ISO standard defines five (5) aggregate functions namely :-
1) COUNT
2) SUM
3) AVG
4) MIN
5) MAX
1.COUNT Function
The COUNT function returns the total number of values in the specified field. It works on both numeric and non-numeric data types. All aggregate functions by default exclude nulls values before working on the data.
MIN function
The MIN function returns the smallest value in the specified table field.
2.MAX function
Just as the name suggests, the MAX function is the opposite of the MIN function. It returns the largest value from the specified table field.
3.SUM function
Suppose we want a report that gives total amount of payments made so far. We can use the MySQL SUM function which returns the sum of all the values in the specified column. SUM works on numeric fields only. Null values are excluded from the result returned.
4.AVG function
MySQL AVG function returns the average of the values in a specified column. Just like the SUM function, it works only on numeric data types.
5.MIN function
The MIN function returns the smallest value in the specified table field.
Oracle Analytical Function Include First Value, Last Value, Lead, Lag, Nth Value with Unbounded and Difference between Rank and Dense Rank . Contain Rollup, Cube and Grouping and Different type of Window Function and Analytical Window frame
Introduction Oracle Database 11g Release 2 for developersLucas Jellema
"This presentations provides an overview of the most striking new functionality in Oracle Database 11g Release 2, as seen through the eyes of (database) developers. The presentation introduces new analytical functionality, the successor to the connect by clause for hierarchical queries, parallel statement execution, new packages and especially: Edition Based Redefinition. This presentation was performed (with live demos) during the AMIS Query on 29th September 2009.
Are you an Oracle developer or a DBA?
Do you know the difference between aggregate and analytic functions?
Without complex sub-queries or self-joins, do you know how to:
Calculate running/cumulative totals and moving/centered averages?
List products with revenues above or below their peers or product groups?
Compute the ratio of one category’s sales to the total sales?
Select the Top-N or Top N % of the customers/products?
Classify advertisers into quartiles/n-tiles based on the revenue potential?
Compare period-over-period (year-over-year, month-over-month) growth and rank advancement?
Convert rows into columns (pivot), columns into rows (unpivot) or aggregate strings?
Perform what-if analysis and hypothetical ranking?
Analytic functions are more performant because tables need to be scanned only once. They make you more productive because there is no need to write procedural code. No wonder Tom Kyte, a well-respected Oracle guru, says analytic functions are the best thing to happen after the sliced bread.
In the first half, I will cover the basics of the various analytic functions:
Ranking: RANK, DENSE_RANK, ROW_NUMBER, NTILE, CUME_DIST, PERCENTILE_RANK
Windowing: SUM, AVG, MAX, MIN, FIRST_VALUE, LAST_VALUE
Reporting: RATIO_TO_REPORT
Others: FIRST/LAST, LEAD/LAG, hypothetical ranking,
In the second half, I will show how powerful these functions are with a few examples.
If there is time, I will cover enhanced aggregation (ROLLUP, CUBE, GROUPING SET extensions to GROUP BY clause)
This class would be useful for both developers and DBAs alike, especially for those working in Analytic, Business Intelligence, and Datawarehouse environments.
Are you already an expert in analytic functions? Then come and help me refine the content.
For more info, read
http://download.oracle.com/docs/cd/E11882_01/server.112/e16579/analysis.htm
http://download.oracle.com/docs/cd/E11882_01/server.112/e16579/aggreg.htm
rollup, cross-tabulation across different dimensions using ROLLUP, CUBE and GROUPING SETS extension to GROUP BY clause
, most active time-periods (i.e. days when the most number of tickets are open in BZ, hours with the most take-off and landings, months with the highest sales, 5-minute periods with the maximum number of calls made, etc)
data densification?
their rank last year, this year, rank growth, running/cumulative total (Year-To-Date/Month-To-Date summation), moving averages, Year-Over-Year comparison, sales projection, average/min/max time between one sale and the next sale, products with above and below average sales.
overall average, sum, departmental average, sum, ranking, job wise ranking in one SQL.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
2. Introduction to Oracle
Functions
Functions make the result of the query
easier and are used to manipulate the data
values. Functions can accept any number of
constant values or variables. These
variables or constant are called as
arguments. SQL functions can perform
different types of operations such as
modify individual data items, convert
column data types, format dates and
numbers etc.
3. Categories of Functions
Oracle functions are categorized into two categories:
• Single Row/Scalar Functions
• Group/Aggregate Functions
Functions, which operate on single rows and return one
value per row, are called as Scalar functions or Single
Row Functions.
On the other hand functions, which operate on set of
values to give one result, are called as Group Functions
or Aggregate Functions.
4. Single-Row Functions (Scalar functions)
These functions act on each row of the table and
return a single value for each row selected. These
functions may accept one or more arguments and can
return a value of different data type than that it
has accepted.
5. Classification of Single Row Functions
Single Row Functions can be classified into the following
categories:
(i) Character
(ii) Number
(iii) Date
(iv) Conversion
(v) General
8. • length(x)
It returns the length of the string x.
Example:
SQL> Select LENGTH ('Ipso Facto') ergo FROM dual;
9. • ltrim(string[,char(s)])
It removes all the blank spaces from the left side of the
string if no char is specified. If we give a char, then it
removes the leading occurrences of that character from the
string.
10.
11.
12.
13.
14.
15. • Translate(char,find,new)
This function is used to find a char and replace it with new
character. All occurrences of find are replaced by the
character in new.
16.
17.
18.
19. • floor(x)
Where x is a number. This function returns the largest integer that is less than or equal to n. FLOOR round
down to a whole number.
20. • round(x[,y])
It rounds off x to the decimal precision of y. If y is negative, rounds to the precision of y places to the left of the
decimal point.
21. • sqrt(x)
This function returns the square root of the given number x. If the given number x is negative or NULL then the result is
NULL.
Example:
SQL>select sqrt(36) as square_root from dual;
27. SQL> SELECT TO_CHAR(SYSDATE,'HH') HOUR,
TO_CHAR(SYSDATE,'MI') MIN,TO_CHAR(SYSDATE,'SS')
SEC FROM DUAL;
The output is:
HO MI SE
-- -- --
03 01 16
SQL> SELECT TO_DATE('15-MAR-1999','DD-MON-YYYY')
FROM DUAL;
SQL>SELECT TO_NUMBER('49583') FROM DUAL;
31. Aggregate Functions (Group
Functions)
These functions are used to produce summarized
results. They are applied on set of rows to give you
single value as a result. A group function allows you to
perform a data operation on several values in a column
of data as though the column was one collective group
of data. These functions are called group functions
also, because they are often used in a special clause of
select statements called a group by clause.
32. COUNT (x)
This function returns the number of rows or non-null values
for column x. When we use * in place of x, it returns the
total number of rows in the table.
Syntax:
count([distinct|all]column name)
Example:
1. Count the number of employees in the emp table.
SQL>Select count(empno) from emp;
The Output is:
COUNT(EMPNO)
------------
16
33. List the number of different names in the emp table.
SQL>Select count (distinct ename) from emp;
The output is:
COUNT(DISTINCTENAME)
--------------------
16
List the number of departments in the employee table .
SQL>Select count( distinct deptno) from emp;
The output is:
COUNT(DISTINCTDEPTNO)
---------------------
3
34. SUM(x)
This function returns the sum of values for the
column x. This function is applied on columns having numeric
datatype and it returns the numeric value.
syntax : sum([distinct|all]column name)
Example:
List the total salary paid to the employees in the emp table.
SQL>select sum(sal) from emp ;
The output is:
SUM(SAL)
---------
29025
35. AVG(x)
This function returns the average of values for
the column x. This function is applied on columns having
numeric datatype and it returns the numeric value. It ignores
the null values in the column x.
syntax : avg([distinct|all]column name)
Example:
List the average salary and the number of employees in the
emp table .
SQL>select avg(sal) ,count(sal) from emp ;
36. MIN(x)
This function returns the minimum of values for the column
x for all the rows .this function can be applied on any
datatype .
syntax : min([distinct|all]column name)
Example:
List the minimum salary in the emp table .
SQL>select min(sal) from emp ;
The output is:
MIN(SAL)
--------
800
37. MAX(x)
This function returns the maximum of values for
the column x for all the rows .this function can be applied on
any datatype.
syntax : max([distinct|all]column name)
Example:
List the maximum salary and commission in the emp table .
SQL>select max(sal) ,max(comm) from emp ;
38. Note : The avg() and sum() functions will always be applied
on numeric datatype while min() and max() functions can be
applied on any datatype.
Example
SQL>select avg(sal),sum(sal),min(ename),max(ename) from
emp ;
39. Exercise :
•list the names of the employees earning minimum salary .
•list the names of the employees earning second highest
salary .
•list the details of the employees who earn salary greater
than the average salary . also count their number .
•count the number of employees whose salary is equal to
the highest salary .
•list the number of employees ,their average salary
,minimum salary and maximum salary in the employee
table.
40. Grouping Data with GROUP
BY
GROUP BY clause is used to group or categorize the data. In other
words it divide rows in a table into smaller groups. We can then
use the group functions to return summary information for each
group.
If no GROUP BY clause is specified, then the default grouping is
the entire result set. When the query executes and the data is
fetched, it is grouped based on the GROUP BY clause and the
group function is applied.
41. Syntax:
SELECT column,group_function(column) FROM table
[WHERE condition]
[GROUP BY group_by_expression]
[ORDER BY column];
Here, group_by_expression specifies columns whose values
determine the basis for grouping rows.
42. For example, If we have to find the total salary of each department
manually, first we group the records on the basis of department
number and then we apply the sum function on salary of each group
to obtain the required result. Similarly in SQL we apply the GROUP
BY clause on deptno and then calculate the total salary for each group
by Sum(sal) function as shown below:
SQL>SELECT deptno, Sum(sal) FROM emp GROUP BY
deptno;
The output is:
DEPTNO SUM(SAL)
10 2916.6667
20 2175
30 1566.6667
43.
44. Here is how this SELECT statement, containing a GROUP BY clause,
is evaluated:
• The SELECT clause specifies the columns to be retrieved i.e
Department number column in the EMP table, the sum of all the
salaries in the group you specified in the GROUP By clause
• The FROM clause specifies the tables that the database must
access i.e EMP table.
• The WHERE clause specifies the rows to be retrieved. Since
there is no WHRE clause, by default all rows are retrieved.
45. The GROUP BY clause specifies how the rows should be grouped.
Department number groups the rows, so the AVG function that is
being applied to the salary column will calculate the average salary
for each department.
• List the average salary of each job in the emp table.
SQL>SELECT JOB,AVG(SAL) FROM EMP GROUP BY JOB;
•
List the maximum salary for each dept.
SQL>SELECT DEPTNO,MAX(SAL) FROM EMP GROUP BY
DEPTNO;
46. Grouping by more than one
column
Sometimes there is a need to see results for groups within groups.
For example if we have to find the total salary being paid to each
job title, within each department. Then there is a need to having
grouping on department number and within each department
number grouping on the basis of job or in other words there is a
need for grouping within a group.
Thus, the EMP table is grouped first by department number, and
within that grouping, it is grouped by job title. For example, the two
clerks in department 20 are grouped together and a single result
(total salary) is produced for all clerks people within that group.
49. By above example it is clear that we can return summary results for
groups and subgroups by listing more than one GROUP BY column.
We can determine the default sort order of the results by the order of
the columns in the GROUP BY clause.
The SELECT clause specifies the column to be retrieved:
• Department number in the EMP table
• Job title in the EMP table
• The sum of all the salaries in the group that you specified in the
GROUP BY clause
• The FROM clause specifies the tables that the database must
access the EMP table
• The GROUP BY clause specifies how we must group the rows
First, department number groups the rows. Second, within the
department number groups, the rows are grouped by job title. So, the
SUM function is being applied to the salary column for all job titles
within each department number group.
50. Illegal Queries Using Group
Functions
Whenever you use a mixture of individual items (DEPTNO) and
group functions (COUNT) in the same select statement, you must
include a Group By clause that specifies the individual items (in
this case, DEPTNO). If the GROUP By clause is missing, then the
error message “not a single-group group function” appears and an
asterisk (*) points to the offending column. You can correct the
error by adding the GROUP BY clause.
51. For example, following is the illegal query:
SQL> SELECT deptno, COUNT(ename) FROM emp;
The Output will be:
Column missing in the Group By clause
Select deptno,count(ename)
*
ERROR at line 1:
ORA-00937: not a single-group group function In above select
statement individual items DEPTNO and group function COUNT
appears in the same SELECT statement without GROUP BY clause
which results error, it can be corrected by adding the GROUP BY
clause as shown below:
SQL> SELECT deptno,COUNT(ename) FROM emp Group By deptno;
52. DEPTNO COUNT(ENAME)
10 3
20 5
30 6
Note:
Any column or expression in the SELECT list that is not an
aggregate function must be in the GROUP By clause.
53. Restricting Group Results
As we use the WHERE clause to restrict the rows that we select,
we can use the HAVING clause to restrict groups.
For example: To find the maximum salary of each department,
but show only the departments that have a maximum salary of
more than Rs.2900, we need to do the following.
• Find the maximum salary for each department by grouping
by department number.
• Restrict the groups to those departments with a maximum
salary greater the Rs.2900.
54. Syntax:
SELECT column, group_function
FROM table
{WHERE condition]
[GROUP BY group_by_expression]
[HAVING group_condition]
[ORDER BY Column];
55. Here we use the HAVING clause to specify which groups are to be
displayed. Therefore, we further restrict the groups on the basis of
aggregate information.
In the syntax:
HAVING clause restricts the groups of rows returned to those
groups for which the specified condition is TRUE
The Oracle Server performs the following steps when you use the
HAVING clause:
56. • Rows are grouped
• The group function is applied to the group.
• The groups that match the criteria in the HAVING clause are
displayed.
The HAVING clause can precede the GROUP By clause, but it is
recommended that you place the GROUP By clause first because it is
more logical. Groups are formed and group functions are calculated
before the HAVING clause is applied to the groups in the SELECT
list.
57. For example: To find the maximum salary of each department, but
show only the departments that have a maximum salary of more than
Rs.2900
SQL> SELECT deptno,max(sal) FROM emp
GROUP BY deptno Having max(sal)>2900;
The output is:
DEPTNO MAX(SAL)
10 5000
20 3000
58. Use of WHERE clause with GROUP BY clause
List the total salary, maximum and minimum salary and the average
salary of employees job wise, for department number 20 and
display only those rows having average salary greater than 1000
SQL>SELECT job, SUM(sal), avg(sal), max(sal), min(sal) from
emp
WHERE deptno=20
GROUP by job
HAVING AVG(sal)>1000;
59. The output is:
JOB SUM(SAL) AVG(SAL) MAX(SAL) MIN(SAL)
ANALYST 6000 3000 3000 3000
MANAGER 2975 2975 2975 2975
60. SQL> SELECT job, SUM(sal) PAYROLL
FROM emp
WHERE job NOT LIKE 'SALE%'
GROUP BY job
HAVING SUM(sal)>5000
ORDER BY SUM(sal);
The output is:
JOB PAYROLL
ANALYST 6000
MANAGER 8275
The above query displays the job title and total monthly salary for
each job title with a total payroll exceeding Rs.5000. The example
excludes salespeople and sorts the list by the total monthly salary.
61. Display total no of suppliers supplying red part
Display total qty supplied by by each supplier
Display total Qty supplied for each part excluding P3
Only display those where supplied qty is greater than100
Display info in descending order of Qty
Select Pno, Sum(QTY) from P,SP WHERE PNO<>’P3’
GROUP BY PNO HAVING SUM(QTY)>1000 ORDER BY
PNO;
62. Display total Qty supplied for each part excluding part having red
color, Only display those where supplied qty is greater than100
Select Pno, Sum(QTY) from P,SP WHERE PNO NOT
IN(SELECT PNO FROM P WHERE COLOR=‘RED’) GROUP
BY PNO HAVING SUM(QTY)>1000 ORDER BY PNO;