Important Update
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 the SQL tutorial is consequential to almost every system that interacts with an SQL database.
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 SELECT statement is applied to pick data from a table. The data retrieved is put in a result table, named the result set. The output data is saved in a result table. This output table is also termed the result set.
SELECT * FROM My_Schema.Tables;
These are perhaps the most useful SQL query examples. In the example below, we are extracting the “Student_ID” column or attribute from the table “STUDENT”. The select statement is used to select data from the database.
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;
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';
This query orders the results with respect to the attribute which is referenced 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. The order by clause is used to sort the data from the table. The order by clause should always be used in the last of the SQL query.
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;
The ‘Group By’ property groups the resulting data according to the specified attribute.
The SQL query below will select Name, Age columns from the 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. The basic rule is that the group by clause should always follow a where clause in a Select statement and must precede the Order by clause.
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 the 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 it out!
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.
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 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;
SUM calculates the total of the attribute that is given to it as an argument. SUM is an aggregate function and it calculates the sum of all the distinct values. and the sum of all the duplicate values.
SELECT SUM(Salary)FROM Employee WHERE Emp_Age < 30;
Simple – an average of a given attribute. Average is also an aggregate function in SQL. The AVG() function computes the average of non-NULL values in a column. It ignores the null values.
SELECT AVG(Price)FROM Products;
This SQL query lists all the views available in the schema.
SELECT * FROM My_Schema.views;
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;
The standard syntax of selecting attributes from a table is applicable to views as well.
SELECT * FROM Failing_Students;
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. The view is also called a virtual table. In other words, a view is just a mirrored copy of a table whose data is the result of a stored 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;
This query will drop or delete a view named ‘V1’. The important thing to remember here is that the DROP VIEW is disallowed if there are any views dependent on the view you are about to drop.
DROP VIEW V1;
A user-defined table is a representation of defined information in a table, and it 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. User tables explain the relational tables of the current user.
SELECT * FROM Sys.objects WHERE Type='u'
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 primary key can be defined as a single column or the combination of two columns in a table. It is responsible for all the relationships between the tables.
The following SQL query lists all the fields in a table’s primary key.
SELECT * from Sys.Objects WHERE Type='PK'
A Unique Key allows a column to ensure that all of its values are different. A unique key also recognizes a different tuple uniquely in relation to or table. A table can have more than one unique key. Unique key constraints can take only one NULL value for the column.
SELECT * FROM Sys.Objects WHERE Type='uq'
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.
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'
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'
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'
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 that 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
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
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;
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%'
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’
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 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 advanced SQL commands.
We can combine the results of two SQL query 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 a very powerful new table generation potential.
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
Wouldn’t it be great if there were a set of conditions you could depend on every time? The complex 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)
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)
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
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.
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
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
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.
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.
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.
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 an 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
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;
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!!
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 the first non-null value from the specified argument record.
SELECT COALESCE(NULL,NULL,'ByteScout',NULL,'Byte')
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 a convert function can be used to achieve this. For example,
SELECT CONVERT(int, 27.64)
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 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
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
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. The rownum is temporary in SQL.
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.
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.
SELECT dtno,
eno,
emname,
job,
salary,
LAG(sal, 1, 0) OVER (PARTITION BY dtno ORDER BY salary) AS salary_prev
FROM employee;
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
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
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 is indicated 1. For example, the SQL query example gives the following output.
SELECT
prdid, SUM(amount),
PERCENT_RANK() OVER (ORDER BY SUM(amount) DESC) AS percent_rank
FROM sales
GROUP BY prdid
ORDER BY prdid;
PRDID SUM(AMOUNT) PERCENT_RANK
----------- ----------- ------------
1 22623.5 0
2 223927.08 1
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
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
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.
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) ]
SELECT empid,
name,
dno,
salary,
job,
CORR(SYSDATE - joiningdate, salary) OVER () AS my_corr_val
FROM employee;
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)
SELECT empid,
name,
dno,
salary,
NTILE(6) OVER (ORDER BY salary) AS container_no
FROM employee;
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
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.
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.
MEASURES STRT.tstamp AS initial_tstamp,
LAST(UP.tstamp) AS first_tstamp,
LAST(DOWN.tstamp) AS finished_tstamp
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)
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.
FIRST_VALUE
{ (expr) [NULLS ]
| (expr [NULLS ])
}
OVER (analytic clause)
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.
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 to 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;
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
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.
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 a group table that distributes 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.
.. and TEN More Advanced SQL Queries for our Users!
A common table expression (CTE) is a defined short result set that endures within the range of a particular statement and that can be called later within that statement, perhaps on many occasions. The following query is describing the CTE:
WITH all_emp AS ( SELECT empId, BossId, FirstName, LastName FROM Emp WHERE BossId is NULL UNION ALL SELECT e.empId, e.BossId, e.FirstName, e.LastName FROM Emp e INNER JOIN all_emp r ON e.BossId = r.Id ) SELECT * FROM all_emp
This function is utilized to deliver an optional value n1 if the inserted value n2 is NaN (not a number), and gives n2 if n2 is not a number. This function is used only for type BINARY_FLOAT. The following query is displaying its use:
SELECT bin_float, NANVL(bin_float,0) FROM my_demo_table;
This function is used to obtain the bucket number. In this, it gives the value of the expression that would come under after being assessed. The following query is displaying its use:
SELECT emp_id, first_name,last_name,dept_id,mgr_id, WIDTH_BUCKET(department_id,20,40,10) "Exists in Dept" FROM emp WHERE mgr_id < 300 ORDER BY "Exists in Dept";
This function is used to deliver the hyperbolic cosine of a number. It accepts all numeric or non-numeric data types as an argument. The following query is displaying its use:
SELECT COSH(0) "COSH of 0" FROM DUAL;
The SOUNDEX function delivers a character string comprising the description of char. It allows users to match words that are spelled antagonistically, but sound similar in English. It does not support CLOB. The following query is displaying its use:
SELECT last_name, first_name
FROM hr.emp
WHERE SOUNDEX(last_name)
= SOUNDEX('SCOTTY');
The TZ_OFFSET gives the time zone offset identical to the case based on the date the statement is given. The following query is displaying its use:
SELECT TZ_OFFSET('US/Eastern') FROM DUAL;
CARDINALITY is utilized to obtain the number of components in a nested table. It is supported in different versions. The following query is displaying its use:
SELECT product_id, CARDINALITY(ad_mydocs_get) FROM my_media_table;
DUMP is one of the important string/char functions. It is utilized to get a VARCHAR2 value. The value delivered defines the data type code. The following query is displaying its use:
SELECT DUMP('pqr', 1033)
FROM DUAL;
PATH is applied simply with the UNDER_PATH and EQUALS_PATH requirements. It gives the corresponding path that points to the resource defined in the main state. The following query is displaying its use:
SELECT ANY_PATH FROM RESOURCE_VIEW WHERE EQUALS_PATH(res, '/sys/schemas/OE/www.pqr.com')=3;
UNISTR accepts an expression that determines character data and delivers it in the general character set. It gives support to the Unicode string literals by allowing users to define the Unicode value. The following query is displaying its use:
SELECT UNISTR('pqr\00e4\00f3\00f9') FROM DUAL;
These are some of the commonly used database queries in SQL.
We will be looking at some SELECT examples of a query in a database, as this is the most common command.
SELECT * FROM employeeTable WHERE emp_no = ‘12’;
This query filters out results that do not match a specified search.
SELECT * FROM sys.objects WHERE object_id=object_id(‘<table name>’);
This database query will list all the columns of the table whose name matches the argument.
The following are some database queries examples that deal with creating tables, in a bit more advanced fashion.
CREATE TABLE table_name ( PRIMARY KEY(column_name) );
CREATE INDEX idx_name ON table_name (column_name);
CREATE VIEW view1 AS SELECT MAX(column1), MAX(column2) FROM table1;
Some more common database queries you will need are:
1) The Maximum Value for a Column.
SELECT MAX(column_name) FROM table_name
2) The Minimum Value for a Column.
SELECT MIN(column_name) FROM table_name
3) The Count of Rows in a Table.
SELECT COUNT(*) FROM table_name;
DBMS stands for DataBase Management System. Following are some DBMS queries with examples; these are very important for developers and database admins alike.
1. The List of Values for a Column.
SELECT column_name, column_commalist FROM table_name;
2. The Difference between two Column Values.
SELECT column_name(+) FROM table_name WHERE column_name(+) != value;
III. The List of Unique Values for a Column.
SELECT DISTINCT column_name FROM table_name;
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