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 are a few of the most important ones – each of these queries is consequential to almost every system that interacts with an SQL database.
Check this video to learn about every SQL query:
This query can be run to retrieve the list of tables present in a database where the database is “My_Schema”.
SELECT * FROM My_Schema.Tables;
This is perhaps the most widely used of 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;
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 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.
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 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 price lesser than 70 from Orders table, will group records with similar price, will sort output by price and will also add the column COUNT(price) that will display how many records with similar price where 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!
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.
SELECT COUNT(CustomerID), Country FROM Customers GROUP BY Country;
SUM calculates the total of the attribute that is given to it as an argument.
SELECT SUM(Salary)FROM Employee WHERE Emp_Age < 30;
Simple – an average of a given attribute.
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.
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’.
DROP VIEW V1;
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'
A primary key uniquely identifies all values within a table. 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.
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'
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 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'
.. and TWENTY More Advanced SQL Queries for our Users!
In this and subsequent examples, we will use a common company database including several tables which are easily visualized. Our practice DB will include a Customers 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
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 contain 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 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 queries.
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.
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 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 queries.
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 commands 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.
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
Accurate indexing requires that the Primary Key column contain 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.
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 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 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