SQL GROUP BY Example

The GROUP BY clause causes data in a table (or any data source) to be divided into groups based on the expressions (or conditions) given in the GROUP BY clause. Seems confusing? Probably.. Let's see what it means,

Say you have Column1, Column 2..etc on your table.

Now you say GROUP BY Column1 , what this does is it divides your table's data based on Column1. So all data which has  Column1='Apple' will be in one group, then all data which has  Column1='Orange' will be in another group.

Again if you had GROUP BY Column1, Column2 then your data will be divided into groups of Column1 + Column2 combinations . If Column2 had values of Green and Red in different rows then one group would be based on Apple+Red another group Apple+Green. So you get the point.

So in a nutshell,

GROUP BY creates groups for output rows, according to unique combination of values specified in the GROUP BY clause

But why would you need to divide them into groups? So we can operate on those groups.

I'll use AdventureWorks database as an example.

Download it here,
https://msftdbprodsamples.codeplex.com/releases/view/93587

SELECT SalesPersonID, YEAR(OrderDate) AS OrderYear,

COUNT(CustomerID) AS All_Customers

FROM Sales.SalesOrderHeader

GROUP BY SalesPersonID, YEAR(OrderDate);

The above query will return all unique combinations of SalesPersonID  + YEAR(OrderDate) and display all customers each of those combinations.

In other words this means you are seeing all customers for a particular Sales Person in a given year.

Point to note:

SELECT DISTINCT SalesPersonID ,YEAR(OrderDate) AS OrderYear

FROM Sales.SalesOrderHeader

and

SELECT  SalesPersonID ,YEAR(OrderDate) AS OrderYear

FROM Sales.SalesOrderHeader

GROUP BY SalesPersonID, YEAR(OrderDate);

Will give you the same results.

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SQL JOIN Example

Joins can be quite perplexing initially hopefully this simple example will help. 

Joins are used to combine data from multiple tables (usually two tables)

There are three types of joins,

1. INNER

2. OUTER - FULL, LEFT, RIGHT

3. CROSS

For all examples I'll be using the below given tables and use the Name field as the condition.

                                                                         Table1

Id1	Name1
1	Padmika
2	Dissanayake
3	Sukitha
4	Pahan

                                                                        Table2

Id2	Name2
1	Fernando
2	Malaka
3	Padmika
4	Sukitha

1. INNER JOIN

Returns only the set of records that match the Name field in both Table 1 and Table2

SELECT * FROM Table1

INNER JOIN Table2

ON Table1.Name1 = Table2.Name2

Result,

Id1	Name1	Id2	Name2
1	Padmika	3	Padmika
3	Sukitha	4	Sukitha

* This gets executed in the following manner, first get the first record from Table 1 get its name field compare it to Table 2 first record name field if it’s a match return it, now compare with Table2 second record name field…  etc.

You will see that only the records that have a common name is returned

This is similar to the intersection of two sets (Table1 and Table2) based on Name.  

2. OUTER JOIN

2.1 FULL OUTER JOIN

Returns all records in Table 1 and Table 2, with matching records from both sides where available. If there is no match, the missing side will contain null.

SELECT * FROM Table1

FULL OUTER JOIN Table2

ON Table1.Name1 = Table2.Name2

Result,

Id1	Name1	Id2	Name2
1	Padmika	3	Padmika
2	Dissanayake	null	Null
3	Sukitha	4	Sukitha
4	Pahan	null	Null
null	Null	1	Fernando
null	Null	2	Malaka

This is similar to the union of two sets (Table1 and Table2) based on Name.

2.2 LEFT OUTER JOIN

 Returns the complete set of records from Table 1 (Left table), with the matching records for Name field (where available) in Table 2. If there is no match, the right side will contain null.

SELECT * FROM Table1

LEFT OUTER JOIN Table2

ON Table1.Name1 = Table2.Name2

Results,

Id1	Name1	Id2	Name2
1	Padmika	3	Padmika
2	Dissanayake	null	null
3	Sukitha	4	Sukitha
4	Pahan	null	null

Special case of the above result would be to select records that do not have any matching records from the right table (Table2)

To do this just add a ‘WHERE Table2.Id2 IS NULL’ clause to the above query.

Ex: You have a Customer table and an Order table (Has a CustomerID column) and want to find out which customers have not placed any orders  

2.3 RIGHT OUTER JOIN

Similar to the above but all the right table (Table2) records will return rather than the left table ones.

SELECT * FROM Table1

RIGHT OUTER JOIN Table2

ON Table1.Name1 = Table2.Name2

Results,

Id1	Name1	Id2	Name2
null	null	1	Fernando
null	null	2	Malaka
1	Padmika	3	Padmika
3	Sukitha	4	Sukitha

Note : When inspected carefully you will see that in this example,

OUTER JOIN result = LEFT JOIN result + RIGHT JOIN result – INNER JOIN result

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SQL COUNT Example

COUNT returns the number of rows in a select statement (Also note that it doesn't consider null values),

Note : I'll be using the AdventureWorks2008R2 sample database which can be downloaded from here,

http://msftdbprodsamples.codeplex.com/releases/view/59211


Example 1,

/* By using the * we return the number of records in the Sales.Customer table */

select

count(*)

from Sales.Customer;

Example 2,

/* By using StoreID inside the COUNT() function we return the number of store id's in the Sales.Customer table */

select

count(StoreID)

from Sales.Customer;

Example 3,
  /* By using "DISTINCT StoreID" inside the COUNT() function we return the unique number of store id's in the Sales.Customer table */

select

count(distinct StoreID)

from Sales.Customer;

Note: From a performance point of view it is better to use 1 instead of * inside the COUNT function.

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DDL vs DML in SQL Server

DDL
Data Definition Language (DDL) statements are used to define the database structure or schema. Examples:

• CREATE - to create objects in the database
• ALTER - alters the structure of the database
• DROP - delete objects from the database

DML
Data Manipulation Language (DML) statements are used for managing data within schema objects. Examples:

• SELECT - retrieve data
• INSERT - add data
• UPDATE - modify data
• DELETE - remove data

Note : This is not limited to SQL Server

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SQL Server Data Types

Here are the SQL server data types for quick reference,

1. Exact numerics – (bigint, bit, decimal, int, money, numeric, smallint)
2. Approximate numerics (float, real)
3. Date and time (date, datetime2, datetime, datetimeoffset, time)
4. Character strings (char, varchar, text)
5. Unicode character strings (nchar, ntext, nvarchar)
6. Binary strings (binary, varbinary, image)
7. Other data types (cursor, timestamp, uniqueidentifier, table)
8. Large valued data types (varchar(max), nvarchar(max))
9. Large object data types (text, ntext, image, xml)

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