Advanced SQL Queries, Examples of Queries in SQL List of TOP-50 Items in 2020 - ByteScout
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TOP-60 Most Important SQL Queries in 2020

SQL is incredibly powerful, and like every well-made development tool, it has a few commands which it’s vital for a good developer to know. Here is a list of SQL queries that are really important for coding & optimization. Each of the queries in our SQL tutorial is consequential to almost every system that interacts with an SQL database.

1. Retrieving Tables 31. Database Management
2. Selecting Columns from a Table 32. Adding Tables to Our New DB
3. Outputting Data – Constraint 33. Modifying and Deleting Tables
4. Outputting Data – ‘Order By’ 34. Successful Indexing
5. Outputting Data – ‘Group By’ 35. Improving Performance
6. Data Manipulation – COUNT 36. Conditional Subquery Results
7. Data Manipulation Using SUM 37. Copying Selections
8. Data Manipulation Using AVG 38. Catching NULL Results
9. SQL Query for Listing all Views 39. HAVING can be Relieving!
10. Creating a View 40. Tie things up with Strings!
11. Retrieving a View 41. Use COALESCE
12. Updating a View 42. Use Convert
13. Dropping a View 43. DENSE_RANK()Analytical
14. Display User Tables 44. Query_partition_clause
15. Display Primary Keys 45. Last five records from the table
16. Displaying Unique Keys 46. LAG
17. Displaying Foreign Keys 47. LEAD
18. Displaying Triggers 48. PERCENT_RANK
19. Displaying Internal Tables 49. MIN
20. Displaying a List of Procedures 50. MAX
21. Swapping the Values 51. Top- N queries
22. Returning a Column of Values 52. CORR Analytic Query
23. SELECT TOP Clause 53. NTILE Analytic Query
24. Searching for SQL Tables 54. VARIANCE, VAR_POP, and VAR_SAMP Query
25. Between Monday and Tuesday 55. STDDEV, STDDEV_POP and STDDEV_SAMP Queries
26. Find Intersection of 2 Tables 56. Pattern Matching
27. UNION 57. FIRST_VALUE
28. Friendly Column Labels 58. LAST_VALUE
29. Always and Everywhere! 59. Prediction
30. Developer-Friendly SQL 60. CLUSTER_SET

1.   SQL Query for Retrieving Tables

This query can be run to retrieve the list of tables present in a database where the database is “My_Schema”.

With the SELECT command, users can define the columns that they want to get in the query output. This command is also useful to get which column users want to see as the output table. The output data is saved in a result table. This output table is also termed as the result-set.

SELECT * FROM My_Schema.Tables;

2.   Query for Selecting Columns from a Table

This is perhaps the most widely used SQL queries examples. In the example below, we are extracting the “Student_ID” column or attribute from the table “STUDENT”.

SELECT Student_ID FROM STUDENT;

If you want to display all the attributes from a particular table, this is the right query to use:

SELECT * FROM STUDENT;

3.   Query for Outputting Data Using a Constraint

This SQL query retrieves the specified attributes from the table on the constraint Employee ID =0000

SELECT EMP_ID, NAME FROM EMPLOYEE_TBL WHERE EMP_ID = '0000';

4. Query for Outputting Sorted Data Using ‘Order By’

This query orders the results with respect to the attribute which is referenced to using “Order By” – so for example, if that attribute is an integer data type, then the result would either be sorted in ascending or descending order; likewise if the data type is a String then the result would be ordered in alphabetical order.

SELECT EMP_ID, LAST_NAME FROM EMPLOYEE
WHERE CITY = 'Seattle' ORDER BY EMP_ID;

The ordering of the result can also be set manually, using “asc ” for ascending and “desc” for descending.

Ascending (ASC) is the default condition for the ORDER BY clause. In other words, if users don’t specify ASC or DESC after the column name, then the result will be ordered in ascending order only.

SELECT EMP_ID, LAST_NAME FROM EMPLOYEE_TBL
WHERE CITY = 'INDIANAPOLIS' ORDER BY EMP_ID asc;

5.   SQL Query for Outputting Sorted Data Using ‘Group By’

The ‘Group By’ property groups the resulting data according to the specified attribute.

The SQL query below will select Name, Age columns from Patients table, then will filter them by Age value to include records where Age is more than 40 and then will group records with similar Age value and then finally will output them sorted by Name.

SELECT Name, Age FROM Patients WHERE Age > 40
GROUP BY Name, Age ORDER BY Name;

Another sample of use of Group By: this expression will select records with a price lesser than 70 from Orders table, will group records with a similar price, will sort the output by price and will also add the column COUNT(price) that will display how many records with similar price were found:

SELECT COUNT(price), price FROM orders 
WHERE price < 70 GROUP BY price ORDER BY price

Note: you should use the very same set of columns for both SELECT and GROUP BY commands, otherwise you will get an error. Many thanks to Sachidannad for pointing out!

SQL Queries for Data Manipulation Using Math Functions

There are a lot of built-in math functions like COUNT and AVG which provide basic functionalities of counting the number of results and averaging them respectively.

6. Data Manipulation Using COUNT

This query displays the total number of customers by counting each customer ID. In addition, it groups the results according to the country of each customer. In count, if users define DISTINCT, then they cal also define the query_partition_clause. This clause is a part of the analytic clause, and  the other clauses such as order_by_clause and windowing_clause are not permitted.

Syntax: SELECT COUNT(colname) FROM table name;

SELECT COUNT(CustomerID), Country FROM Customers GROUP BY Country;

7. Data Manipulation Using SUM

SUM calculates the total of the attribute that is given to it as an argument.

SELECT SUM(Salary)FROM Employee WHERE Emp_Age < 30;

8. Data Manipulation Using AVG

Simple – an average of a given attribute.

SELECT AVG(Price)FROM Products;

9.   SQL Query for Listing all Views

This SQL query lists all the views available in the schema.

SELECT * FROM My_Schema.views;

10. Query for Creating a View

A view is a tailored table that is formed as a result of a query. It has tables and rows just like any other table. It’s usually a good idea to run queries in SQL as independent views because this allows them to be retrieved later to view the query results, rather than computing the same command every time for a particular set of results.

CREATE VIEW Failing_Students AS
SELECT S_NAME, Student_ID
FROM STUDENT
WHERE GPA > 40;

11. Query for Retrieving a View

The standard syntax of selecting attributes from a table is applicable to views as well.

SELECT * FROM Failing_Students;

12. Query for Updating a View

This query updates the view named ‘Product List’ – and if this view doesn’t exist, then the Product List view gets created as specified in this query.

A view is a legitimate copy of a different table or sequence of tables. A view obtains its information or data from the tables from previously created tables known as base tables. Base tables are real tables. All procedures implemented on a view really modify the base table. Users can use views just like the real or base tables. In view, users can apply various DDL, DML commands such as update, insert into, and delete.

CREATE OR REPLACE VIEW [ Product List] AS
SELECT ProductID, ProductName, Category
FROM Products
WHERE Discontinued = No;

13. Query for Dropping a View

This query will drop or delete a view named ‘V1’.

DROP VIEW V1;

14. Query to Display User Tables

A user-defined table is a representation of defined information in a table, and they can be used as arguments for procedures or user-defined functions. Because they’re so useful, it’s useful to keep track of them using the following query.

SELECT * FROM Sys.objects WHERE Type='u'

15. Query to Display Primary Keys

A primary key uniquely identifies all values within a table. A primary key imposes a NOT NULL restriction and a unique constraint in one declaration. In other words, it prevents various rows from having similar values or sequences of columns. It doesn’t allow null values.

The following SQL query lists all the fields in a table’s primary key.

SELECT * from Sys.Objects WHERE Type='PK'

16. Query for Displaying Unique Keys

A Unique Key allows a column to ensure that all of its values are different.

SELECT * FROM Sys.Objects WHERE Type='uq'

17. Displaying Foreign Keys

Foreign keys link one table to another – they are attributes in one table which refer to the primary key of another table.

SELECT * FROM Sys.Objects WHERE Type='f'

Primary, Unique, and Foreign are part of the constraints in SQL. Constraints are essential to the scalability, compliance, and sincerity of the data. Constraints implement particular rules, assuring the data adheres to the conditions outlined. For example, these are the laws imposed on the columns of the database tables. These are applied to restrict the kind of data in the table. This assures the efficiency and authenticity of the database.

18. Displaying Triggers

A Trigger is sort of an ‘event listener’ – i.e, it’s a pre-specified set of instructions that execute when a certain event occurs. The list of defined triggers can be viewed using the following query.

SELECT * FROM Sys.Objects WHERE Type='tr'

19. Displaying Internal Tables

Internal tables are formed as a by-product of a user-action and are usually not accessible. The data in internal tables cannot be manipulated; however, the metadata of the internal tables can be viewed using the following query.

SELECT * FROM Sys.Objects WHERE Type='it'

20. Displaying a List of Procedures

A stored procedure is a group of advanced SQL queries that logically form a single unit and perform a particular task. Thus, using the following query you can keep track of them:

SELECT * FROM Sys.Objects WHERE Type='p'

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.. and TWENTY More Advanced SQL Queries for our Users!

21. Swapping the Values of Two Columns in a table

In this and subsequent examples, we will use a common company database including several tables that are easily visualized. Our practice DB will include a Customer table and an Order table. The Customers table will contain some obvious columns including ID, Name, Address, zip, and email, for example, where we assume for now that the primary key field for indexing is the Customer_ID field.

With this in mind, we can easily imagine an Orders table which likewise contains the indexed customer ID field, along with details of each order placed by the customer. This table will include the order Number, Quantity, Date, Item, and Price. In our first one of SQL examples, imagine a situation where the zip and phone fields were transposed and all the phone numbers were erroneously entered into the zip code field. We can easily fix this problem with the following SQL statement:

UPDATE Customers SET Zip=Phone, Phone=Zip

22. Returning a Column of Unique Values

Now, suppose that our data entry operator added the same Customers to the Customers table more than once by mistake. As you know, proper indexing requires that the key field contains only unique values. To fix the problem, we will use SELECT DISTINCT to create an indexable list of unique customers:

SELECT DISTINCT ID FROM Customers

23. Making a Top 25 with the SELECT TOP Clause

Next, imagine that our Customers table has grown to include thousands of records, but we just want to show a sample of 25 of these records to demonstrate the column headings and The SELECT TOP clause allows us to specify the number of records to return, like a Top-25 list. In this example we will return the top 25 from our Customers table:

SELECT TOP 25 FROM Customers WHERE Customer_ID<>NULL;

24. Searching for SQL Tables with Wildcards

Wildcard characters or operators like “%” make it easy to find particular strings in a large table of thousands of records. Suppose we want to find all of our customers who have names beginning with “Herb” including Herberts, and Herbertson. The % wildcard symbol can be used to achieve such a result. The following SQL query will return all rows from the Customer table where the Customer_name field begins with “Herb”:

SELECT * From Customers WHERE Name LIKE 'Herb%'

25. Between Monday and Tuesday

Today is Wednesday, and we arrive at work and discover that our new data entry clerk in training has entered all new orders incorrectly on Monday and Tuesday. We wish to teach our new trainee to find and correct all erroneous records. What’s the easiest way to get all the records from the Orders table entered on Monday and Tuesday? The Between clause makes the task a breeze:

SELECT ID FROM Orders WHERE 
Date BETWEEN ‘01/12/2018’ AND ‘01/13/2018’

26. Finding the Intersection of Two Tables

Undoubtedly the whole reason that a relational database exists in the first place is to find matching records in two tables! The JOIN statement accomplishes this core objective of SQL and makes the task easy. Here we are going to fetch a list of all records which have matches in the Customers and Orders tables:

SELECT ID FROM Customers INNER 
JOIN Orders ON Customers.ID = Orders.ID

The point of INNER JOIN, in this case, is to select records in the Customers table which have a matching customer ID values in the Orders table and return only those records. Of course, there are many types of JOIN, such as FULL, SELF, and LEFT, but for now, let’s keep things interesting and move on to more diverse types of SQL queries.

27. Doubling the Power with UNION

We can combine the results of two SQL queries examples into one naturally with the UNION keyword. Suppose we want to create a new table by combining the Customer_name and phone from Customers with a list of that customer’s recent orders so that we can look for patterns and perhaps suggest future purchases. Here is a quick way to accomplish the task:

SELECT phone FROM Customers 
UNION SELECT item FROM Orders

The UNION keyword makes it possible to combine JOINS and other criteria to achieve very powerful new table generation potential.

28. Making Column Labels More Friendly

Aliasing column labels give us the convenience of renaming a column label to something more readable. There is a tradeoff when naming columns to make them succinct results in reduced readability in subsequent daily use. In our Orders table, the item column contains the description of purchased products. Let’s see how to alias the item column to temporarily rename it for greater user-friendliness:

SELECT Item AS item_description FROM Orders

29. Always and Everywhere!

Wouldn’t it be great if there were a set of conditions you could depend on every time? The SQL queries using ANY and ALL can make this ideal a reality! Let’s look at how the ALL keyword is used to include records only when a set of conditions is true for ALL records. In the following example, we will return records from the Orders table where the idea is to get a list of high volume orders for a given item, in this case for customers who ordered more than 50 of the product:

SELECT Item FROM Orders 
WHERE id = ALL 
(SELECT ID FROM Orders
WHERE quantity > 50)

30. Writing Developer Friendly SQL

An often overlooked but very important element of SQL scripting is adding comments to a script of queries to explain what it’s doing for the benefit of future developers who may need to revise and update your SQL queries.

A SQL script is a collection of SQL elements and commands accumulated as a file in SQL Scripts. This script file can include many SQL commands or PL/SQL codes. One can utilize SQL Scripts to build, edit, design, execute, and delete files.

The single line and the /* .. */ multi-line delimiters empower us to add useful comments to scripts, but this is also used in another valuable way. Sometimes a section of code may not be in use, but we don’t want to delete it, because we anticipate using it again. Here we can simply add the comment delimiter to deactivate it momentarily:

/* This query below is commented so it won't execute*/
/*
SELECT item FROM Orders 
WHERE date ALL = (SELECT Order_ID FROM Orders
WHERE quantity > 50)
*/

/* the SQL query below the will be executed 
ignoring the text after "--"
*/

SELECT item -- single comment  
FROM Orders -- another single comment
WHERE id 
ALL = (SELECT ID FROM Orders
WHERE quantity > 25)

31.  SQL queries for Database Management

So far we have explored SQL query examples for querying tables and combining records from multiple queries. Now it’s time to take a step upward and look at the database on a structural level. Let’s start with the easiest SQL statement of all which creates a new database. Here, we are going to create the DB as a container for our Customers and Orders tables used in the previous ten examples above:

CREATE DATABASE AllSales

32. Adding Tables to Our New DB

Next, we will actually add the Customers table which we’ve been using in previous examples, and then add some of the column labels which we are already familiar with:

CREATE TABLE Customers (
ID varchar(80),
Name varchar(80),
Phone varchar(20),
....
);

Although most databases are created using a UI such as Access or OpenOffice, it is important to know how to create and delete databases and tables programmatically via code with SQL statements. This is especially so when installing a new web app and the UI asks new users to enter names for DBs to be added during installation.

33. Modifying and Deleting Tables with SQL

The ALTER statement is used to modify or change the meaning of a table. In the case of the relational tables with columns, ALTER statement is used to update the table to the new or modified rules or definition. Alter belongs to the DDL category of Commands. Data definition language can be described as a pattern for commands through which data structures are represented.

Imagine that you decide to send a birthday card to your customers to show your appreciation for their business, and so you want to add a birthday field to the Customers table. In these SQL examples, you see how easy it is to modify existing tables with the ALTER statement:

ALTER TABLE Customers ADD Birthday varchar(80)

If a table becomes corrupted with bad data you can quickly delete it like this:

DROP TABLE table_name

34. The Key to Successful Indexing

An index is a schema element that includes a record for each content that arrives in the indexed column of the database table or cluster and gives a high-speed path to rows. There are many types of indexes such as Bitmap indexes, Partitioned indexes, Function-based indexes, and Domain indexes.

Accurate indexing requires that the Primary Key column contains only unique values for this purpose. This guarantees that JOIN statements will maintain integrity and produce valid matches. Let’s create our Customers table again and establish the ID column as the Primary Key:

CREATE TABLE Customers (
ID int NOT NULL,
Name varchar(80) NOT NULL,
PRIMARY KEY (ID)
);

We can extend the functionality of the Primary Key so that it automatically increments from a base. Change the ID entry above to add the AUTO_INCREMENT keyword as in the following statement:

ID int NOT NULL AUTO_INCREMENT

35. Advanced Concepts For Improving Performance

Whenever practical, is always better to write the column name list into a SELECT statement rather than using the * delimiter as a wildcard to select all columns. SQL Server has to do a search and replace operation to find all the columns in your table and write them into the statement for you (every time the SELECT is executed). For example:

SELECT * FROM Customers

Would actually execute much faster on our database as:

SELECT Name, Birthday, Phone, 
Address, Zip FROM Customers

Performance pitfalls can be avoided in many ways. For example, avoid the time sinkhole of forcing SQL Server to check the system/master database every time by using only a stored procedure name, and never prefix it with SP_. Also setting NOCOUNT ON reduces the time required for SQL Server to count rows affected by INSERT, DELETE, and other commands. Using INNER JOIN with a condition is much faster than using WHERE clauses with conditions. We advise developers to learn SQL server queries to an advanced level for this purpose. For production purposes, these tips may be crucial to adequate performance. Notice that our tutorial examples tend to favor the INNER JOIN.

36. Conditional Subquery Results

The SQL operator EXISTS tests for the existence of records in a subquery and returns a value TRUE if a subquery returns one or more records. Have a look at this query with a subquery condition:

SELECT Name FROM Customers WHERE EXISTS 
(SELECT Item FROM Orders 
WHERE Customers.ID = Orders.ID AND Price < 50)

In this example above, the SELECT returns a value of TRUE when a customer has orders valued at less than $50.

37. Copying Selections from Table to Table

There are a hundred and one uses for this SQL tool. Suppose you want to archive your yearly Orders table into a larger archive table. This next example shows how to do it.

INSERT INTO Yearly_Orders 
SELECT * FROM Orders 
WHERE Date<=1/1/2018

This example will add any records from the year 2018 to the archive.

38. Catching NULL Results

The NULL is the terminology applied to describe an absent value. Null does not mean zero. A NULL value in a column of a table is a condition in a domain that seems to be empty. A column with a NULL value is a domain with absent value. It is essential to recognize that a NULL value is distinct from a zero.

In cases where NULL values are allowed in a field, calculations on those values will produce NULL results as well. This can be avoided by the use of the IFNULL operator. In this next example, a value of zero is returned rather than a value of NULL when the calculation encounters a field with a NULL value:

SELECT Item, Price * 
(QtyInStock + IFNULL(QtyOnOrder, 0)) 
FROM Orders

39. HAVING can be Relieving!

The problem was that the SQL WHERE clause could not operate on aggregate functions. The problem was solved by using the HAVING clause. As an example, this next query fetches a list of customers by the region where there is at least one customer per region:

SELECT COUNT(ID), Region
FROM Customers
GROUP BY Region
HAVING COUNT(ID) > 0;

40. Tie things up with Strings!

Let’s have a look at processing the contents of field data using functions. Substring is probably the most valuable of all built-in functions. It gives you some of the power of Regex, but it’s not so complicated as Regex. Suppose you want to find the substring left of the dots in a web address. Here’s how to do it with an SQL Select query:

SELECT SUBSTRING_INDEX("www.bytescout.com", ".", 2);

This line will return everything to the left of the second occurrence of “. ” and so, in this case, it will return

<a href="https://bytescout.com">www.bytescout.com</a>

Check this video to learn about every SQL query:

 

.. and 20 more useful SQL Queries examples!!

41. Use COALESCE to return the first non-null expression

The SQL Coalesce is used to manage the NULL values of the database. In this method, the NULL values are substituted with the user-defined value. The SQL Coalesce function assesses the parameters in series and always delivers first non-null value from the specified argument record.

Syntax

SELECT COALESCE(NULL,NULL,'ByteScout',NULL,'Byte')

Output

ByteScout

42. Use Convert to transform any value into a particular datatype

This is used to convert a value into a defined datatype. For example, if you want to convert a particular value into int datatype then convert function can be used to achieve this. For example,

Syntax

SELECT CONVERT(int, 27.64)

Output

27

43. DENSE_RANK()Analytical query

It is an analytic query that computes the rank of a row in an arranged collection of rows. An output rank is a number starting from 1. DENSE_RANK is one of the most important analytic SQL queries. It returns rank preferences as sequential numbers. It does not jump rank in event of relations. For example, the following query will give the sequential ranks to the employee.

SELECT eno,
dno,
salary,
DENSE_RANK() OVER (PARTITION BY dno ORDER BY salary) AS ranking
FROM employee;

ENO  DNO SALARY RANKING
---------- ---------- ---------- ----------
7933  10 1500   1
7788  10 2650   2
7831  10 6000   3
7362  20 900    1
7870  20 1200   2
7564  20 2575   3
7784  20 4000   4
7903  20 4000   4
7901  30 550    1
7655  30 1450   2
7522  30 1450   2
7844  30 1700   3
7493  30 1500   4
7698  30 2850   5

44. Query_partition_clause

The query_partition_clause breaks the output set into distributions, or collections, of data. The development of the analytic query is limited to the confines forced by these partitions, related to the process a GROUP BY clause modifies the performance of an aggregate function. If the query_partition_clause is eliminated, the entire output collection is interpreted as a separate partition.

The following query applies an OVER clause, so the average displayed is based on all the records of the output set.

SELECT eno, dno, salary,
AVG(salary) OVER () AS avg_sal
FROM employee;

EO  DNO SALARY AVG_SAL
---------- ---------- ---------- ----------
7364 20 900 2173.21428
7494 30 1700 2173.21428
7522 30 1350 2173.21428
7567 20 3075 2173.21428
7652 30 1350 2173.21428
7699 30 2950 2173.21428
7783 10 2550 2173.21428
7789 20 3100 2173.21428
7838 10 5100 2173.21428
7845 30 1600 2173.21428
7877 20 1200 2173.21428
7901 30 1050 2173.21428
7903 20 3100 2173.21428
7935 10 1400 2173.21428

45. Finding the last five records from the table

Now, if you want to fetch the last eight records from the table then it is always difficult to get such data if your table contains huge information. For example, you want to get the last 8 records from the employee table then you can use rownum and a union clause. 

For example,

Select * from Employee A where rownum <=8
union
select * from (Select * from Employee A order by rowid desc) where rownum <=8;

The above SQL query will give you the last eight records from the employee table where rownum is a pseudo column. It indexes the data in an output set.

46. LAG

The LAG is applied to get data from a prior row. This is an analytical function. For example, the following query gives the salary from the prior row to compute the difference between the salary of the current row and that of the prior row. In this query, the ORDER BY of the LAG function is applied. The default is 1 if you do not define offset. The arbitrary default condition is given if the offset moves past the range of the window. The default is null if you do not define default.

Syntax

SELECT dtno,
       eno,
       emname,
       job,
       salary,
       LAG(sal, 1, 0) OVER (PARTITION BY dtno ORDER BY salary) AS salary_prev
FROM   employee;

Output

DTNO ENO ENAME JOB SAL SAL_PREV
---------- ---------- ---------- --------- ---------- ----------
10 7931 STEVE CLERK 1300 0
10 7783 JOHN MANAGER 2450 1300
10 7834 KING PRESIDENT 5000 2450
20 7364 ROBIN CLERK 800 0
20 7876 BRIAN CLERK 1100 800
20 7567 SHANE MANAGER 2975 1100
20 7784 SCOTT ANALYST 3000 2975
20 7908 KANE ANALYST 3000 3000
30 7900 JAMES CLERK 950 0
30 7651 CONNER SALESMAN 1250 950
30 7522 MATTHEW SALESMAN 1250 1250
30 7843 VIVIAN SALESMAN 1500 1250
30 7494 ALLEN SALESMAN 1600 1500
30 7695 GLEN MANAGER 2850 1600

47. LEAD

The LEAD is also an analytical query that is applied to get data from rows extra down the output set. The following query gives the salary from the next row to compute the deviation between the salary of the prevailing row and the subsequent row. The default is 1 if you do not define offset. The arbitrary default condition is given if the offset moves past the range of the window. The default is null if you do not define default.

SELECT eno,
       empname,
       job,
       salary,
       LEAD(salary, 1, 0) OVER (ORDER BY salary) AS salary_next,
       LEAD(salary, 1, 0) OVER (ORDER BY salary) - salary AS salary_diff
FROM   employee;

ENO EMPNAME JOB SALARY SALARY_NEXT SALARY_DIFF
---------- ---------- --------- ---------- ---------- ----------
7369 STEVE CLERK 800 950 150
7900 JEFF CLERK 950 1100 150
7876 ADAMS CLERK 1100 1250 150
7521 JOHN SALESMAN 1250 1250 0
7654 MARK SALESMAN 1250 1300 50
7934 TANTO CLERK 1300 1500 200
7844 MATT SALESMAN 1500 1600 100
7499 ALEX SALESMAN 1600 2450 850
7782 BOON MANAGER 2450 2850 400
7698 BLAKE MANAGER 2850 2975 125
7566 JONES MANAGER 2975 3000 25
7788 SCOTT ANALYST 3000 3000 0
7902 FORD ANALYST 3000 5000 2000
7839 KING PRESIDENT 5000 0 -5000

48. PERCENT_RANK

The PERCENT_RANK analytic query. The ORDER BY clause is necessary for this query. Excluding a partitioning clause from the OVER clause determines the entire output set is interpreted as a separate partition. The first row of the standardized set is indicated 0 and the last row of the set are indicated 1. For example, the SQL query example gives the following output.

Syntax

SELECT
     prdid, SUM(amount),
     PERCENT_RANK() OVER (ORDER BY SUM(amount) DESC) AS percent_rank
     FROM sales
     GROUP BY prdid
     ORDER BY prdid;

Output

PRDID        SUM(AMOUNT)  PERCENT_RANK
----------- ----------- ------------
          1    22623.5            0
          2   223927.08           1

49. MIN

Utilizing a blank OVER clause converts the MIN into an analytic function. This is also an analytical query. In this, the entire result set is interpreted as a single partition. It gives you the minimum salary for all employees and their original data. For example, the following query is displaying the use of MIN in the Select query.

SELECT eno,
       empname,
       dtno,
       salary,
       MIN(salary) OVER (PARTITION BY dtno) AS min_result
FROM   employee;

     ENO   EMPNAME          DTNO     SALARY MIN_RESULT
---------- ---------- ---------- ---------- ---------------
      7782 CLARK              10       2450            1300
      7839 KING               10       5000            1300
      7934 MILLER             10       1300            1300
      7566 JONES              20       2975             800
      7902 FORD               20       3000             800
      7876 ADAMS              20       1100             800
      7369 SMITH              20        800             800
      7788 SCOTT              20       3000             800
      7521 WARD               30       1250             950
      7844 TURNER             30       1500             950
      7499 ALLEN              30       1600             950
      7900 JAMES              30        950             950
      7698 BLAKE              30       2850             950
      7654 MARTIN             30       1250             950

50. MAX

Using a blank row OVER clause converts the MAX into an analytic function. The lack of a partitioning clause indicates the entire output set is interpreted as a separate partition. This gives the maximum salary for all employees and their original data. For example, the following query displays the use of MAX in the select query.

SELECT eno,
       empname,
       dtno,
       salary,
       MAX(salary) OVER () AS max_result
FROM   employee;

     ENO   EMPNAME          DTNO       SALARY    MAX_RESULT
---------- ---------- ---------- ---------- ----------
      7369 SMITH              20        800       3000
      7499 ALLEN              30       1600       3000
      7521 WARD               30       1250       3000
      7566 JONES              20       2975       3000
      7654 MARTIN             30       1250       3000
      7698 BLAKE              30       2850       3000
      7782 CLARK              10       2450       3000
      7788 SCOTT              20       3000       3000
      7839 KING               10       5000       3000
      7844 TURNER             30       1500       3000
      7876 ADAMS              20       1100       3000
      7900 JAMES              30        950       3000
      7902 FORD               20       3000       3000
      7934 MILLER             10       1300       3000

51. Top- N queries

Top-N queries give a process for restricting the number of rows delivered from organized assemblages of data. They are remarkably beneficial when users want to give the top or bottom number of rows from a table.

For example, the following query gives the 20 rows with 10 different values:

SELECT price
FROM   sales_order
ORDER BY price;

PRICE
----------
100
100
200
200
300
300
400
400
500
500
600

PRICE
----------
600
700
700
800
800
900
900
1000
1000

20 rows selected.

52. CORR Analytic Query

The CORR analytic function is utilized to determine the coefficient of correlation. This query is also used to calculate the Pearson correlation coefficient. The function calculates the following on rows in the table with no null values. This query always returns the values between +1 and -1, which describe the following:

Syntax: CORR(exp1, exp2) [ OVER (analytic_clause) ]

Example

SELECT empid,
       name,
       dno,
       salary,
       job,
       CORR(SYSDATE - joiningdate, salary) OVER () AS my_corr_val
FROM   employee;

53. NTILE Analytic Query

The NTILE enables users to split a sequence set into a detailed number of relatively similar groups, or containers, rows sanctioning. If the number of rows in the collection is less than the number of containers defined, the number of containers will be decreased. The basic syntax is as displayed below:

NTILE(exp) OVER ([ partition_clause ] order_by)

Example

SELECT empid,
       name,
       dno,
       salary,
       NTILE(6) OVER (ORDER BY salary) AS container_no
FROM   employee;

54. VARIANCE, VAR_POP, and VAR_SAMP Query

The VARIANCE, VAR_POP, and VAR_SAMP are aggregate functions. These are utilized to determine the variance, group variance, and sample variance of a collection of data individually. As aggregate queries or functions, they decrease the number of rows, therefore the expression “aggregate”. If the data isn’t arranged we change the total rows in the Employee table to a separate row with the aggregated values. For example, the following query is displaying the use of these functions:

SELECT VARIANCE(salary) AS var_salary,
       VAR_POP(salary) AS pop_salary,
       VAR_SAMP(salary) AS samp_salary
FROM   employee;

VAR_SALARY   POP_SALARY   SAMP_SALARY
------------ ----------- ------------
1479414.97  1588574.81   1388717.27

55. STDDEV, STDDEV_POP and STDDEV_SAMP Queries

The STDDEV, STDDEV_POP, and STDDEV_SAMP aggregate queries or functions are applied to determine the standard deviation, population standard deviation, and cumulative sample standard deviation individually. As aggregate queries, they decrease the number of rows, therefore the expression “aggregate”. If the data isn’t arranged we convert all the rows in the EMPLOYEE table to a separate row. For example, the following query is displaying the use of all these functions.

SELECT STDDEV(salary) AS stddev_salary,
       STDDEV_POP(salary) AS pop_salary,
       STDDEV_SAMP(salary) AS samp_salary
FROM   employee;

STDDEV_SALARY POP_SALARY SAMP_SALARY
---------- -------------- ---------------
1193.50     1159.588      1193.603

If there is more than one account after dropping nulls, the STDDEV function gives the result of the STDDEV_SAMP. Using an empty OVER clause converts the STDDEV query result into an analytic query. The absence of a partitioning indicates the entire output set is interpreted as a particular partition, so we accept the standard deviation of the salary and the primary data.

56. Pattern Matching

The pattern matching syntax adds various alternatives. Data must be treated precisely and in a proper form. The PARTITION BY and ORDER BY conditions of all SQL analytic queries is applied to split the data into accumulations and within each group. If no partitions are specified, it is considered the entire sequence set is one huge partition.

For example,

The MEASURES clause specifies the column result that will be provided for each match.

Syntax

MEASURES  STRT.tstamp AS initial_tstamp,
          LAST(UP.tstamp) AS first_tstamp,
          LAST(DOWN.tstamp) AS finished_tstamp

Example

DEFINE
  UP AS UP.products_sold > PREV(UP.products_sold),
  FLAT AS FLAT.products_sold = PREV(FLAT.products_sold),
  DOWN AS DOWN.products_sold < PREV(DOWN.products_sold)

57. FIRST_VALUE

The simplest way to get analytic functions is to begin by studying aggregate functions. An aggregate function collects or gathers data from numerous rows into a unique result row. For instance, users might apply the AVG function to get an average of all the salaries in the EMPLOYEE table. Let’s take a look at how First_Value can be used. The primary explanation for the FIRST_VALUE analytic function is displayed below.

Syntax:

FIRST_VALUE 
  { (expr) [NULLS ]
  | (expr [NULLS ])
  }
  OVER (analytic clause)

Example

SELECT eno,
       dno,
       salary,
       FIRST_VALUE(salary) IGNORE NULLS 
         OVER (PARTITION BY dno ORDER BY salary) AS lowest_salary_in_dept
FROM   employee;

The above query will ignore null values.

58. LAST_VALUE

The primary explanation for the LAST_VALUE analytic query or function is displayed below.

Syntax: LAST_VALUE
  { (expr) [ { NULLS ]
  | (expr [ NULLS ])
  OVER (analytic clause)

The LAST_VALUE analytic query is related to the LAST analytic function. The function enables users to get the last output from an organized column. Applying the default windowing the output can be surprising. For example,

SELECT eno,
       dno,
       salary,
       LAST_VALUE(salary) IGNORE NULLS
         OVER (PARTITION BY dno ORDER BY salary) AS highest_salary_in_dept
FROM   employee;

59. Prediction

The design sample foretells the gender and age of clients who are most expected to adopt an agreement card (target = 1). The PREDICTION function takes the price matrix correlated with the design and applies for marital status, and house size as predictors. The syntax of the PREDICTION function can also apply a piece of arbitrary GROUPING information when getting a partitioned model.

SELECT client_gender, COUNT(*) AS ct, ROUND(AVG(age)) AS average_age
   FROM mining_data_shop
   WHERE PREDICTION(sample COST MODEL
      USING client_marital_status, house_size) = 1
   GROUP BY client_gender
   ORDER BY client_gender;
   
CUST_GENDER         CNT    AVG_AGE
------------ ---------- ----------
F                   270         40
M                   585         41

60. CLUSTER_SET

CLUSTER_SET can get the data in one of the couple steps: It can use a mining type object to the information, or it can mine the data by performing an analytic clause that creates and uses one or more moving mining patterns.

This example enumerates the properties that have the biggest influence on cluster distribution for client ID 1000. The query requests the CLUSTER_DETAILS and CLUSTER_SET functions, which use the clustering model my_sample.

Example

SELECT S.cluster_id, prob,
       CLUSTER_DETAILS(my_sample, S.cluster_id, 7 USING T.*) kset
FROM
  (SELECT v.*, CLUSTER_SET(my_sample, USING *) nset
    FROM mining_data
   WHERE client_id = 1000) T,
  TABLE(T.nset) Q
ORDER BY 2 DESC;  

A cluster is group tables that distribute the corresponding data blocks i.e. all the tables are actually put together. For example, EMPLOYEE and DEPARTMENT tables are connected to the DNO column. If you cluster them, it will actually store all rows in the same data blocks.

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