Wednesday, 12 June 2019
SQL Server - GROUP BY
Summarizing and Aggregating Data
Being able to roll up and summarize data is important in many applications, particularly in financial and accounting. Though being able to aggregate and summarize data is also important in healthcare, telecommunication, marketing, and just about any other business area where one deals with numbers. The popularity of data warehousing and business intelligence in the marketplace is testimony to this.
Knowing how to write queries that can aggregate and summarize data is important if you are writing reports or engaged in many BI (Business Intelligence) types of activities.
On this page I will show you how to use GROUP BY to roll up data. We will be using the following aggregate functions.
AVG
SUM
COUNT
MIN
MAX
GROUP BY on a Single Column
In this example we are going to roll-up and get the total sales for 2007 broken down by category. When we are rolling up data there are two important things we require up in our SELECT. They are:
The column(s) we want to group the data on
The data columns we want to aggregate - Using SUM
It is also important to remember that your grouping column(s) must also be declared down in the GROUP BY clause. The GROUP BY clause comes after the WHERE clause.
It is also important to understand that any other non grouping columns up in the SELECT must have and aggregate function applied to them or you will recieve a syntax error.
USE AdventureWorksDW2008R2
GO
SELECT
DimProductCategory.EnglishProductCategoryName AS "Category"
,SUM(FactInternetSales.SalesAmount) AS "Total Sales"
,'$' + Convert(varchar,Convert(money,SUM(FactInternetSales.SalesAmount)),1)
AS "Formatted Total Sales"
FROM DimProduct
INNER JOIN DimProductSubcategory ON
DimProduct.ProductSubcategoryKey = DimProductSubcategory.ProductSubcategoryKey
INNER JOIN DimProductCategory ON
DimProductSubcategory.ProductCategoryKey = DimProductCategory.ProductCategoryKey
INNER JOIN FactInternetSales ON
DimProduct.ProductKey = FactInternetSales.ProductKey
WHERE OrderDateKey >= 20070101 AND OrderDateKey < 20080000
GROUP BY DimProductCategory.EnglishProductCategoryName
ORDER BY DimProductCategory.EnglishProductCategoryName
groupby1
Please note that I added a column so you caould see how to format our sales amount as it would be typically displayed. However, this is something you typically are not going to have to do as all report writers will allows you do do this type of formatting in the report itself. Also, if you are passing the results to a host language program and use this technique you are sending it the value in a format that a) has been rounded and as such are losing precision, b) you are sending it as a character string which likely is not the desired format!
The SUM aggregate function is the most commonly used of the aggregate functions.
Multiple Aggregate Functions up in the Select - Adding AVG
We can add multiple aggregate functions up in the SELECT. For example
SELECT
DimProductCategory.EnglishProductCategoryName AS "Category"
,SUM(FactInternetSales.SalesAmount) AS "Total Sales"
,SUM(FactInternetSales.OrderQuantity) AS "Tot# Items Sold"
,AVG(FactInternetSales.SalesAmount) AS "AVG Sale"
avgsales
You will note that I have added another summary column (OrderQuantity) to get the total number of items sold. I have also added the AVG aggregate function to the SalesAmount column. If you get out your calculator and multiply AVG(SalesAmount) by OrderQuantity you would expect to get the value found in SUM(SalesAmount). You will get close but not the exact amount due to rounding errors.
COUNT
You should be familiar with using COUNT(*) to determine the number of rows in a table. The sole purpose of the count function is to return the number of rows based upon your selection and/or group by and/or having critieria. Let's look at a few examples to understand this a little better.
If we extend the last example
DimProductCategory.EnglishProductCategoryName AS "Category"
,SUM(FactInternetSales.SalesAmount) AS "Total Sales"
,SUM(FactInternetSales.OrderQuantity) AS "Tot# Items Sold"
,COUNT(FactInternetSales.OrderQuantity) AS "Tot# Rows1"
,COUNT(FactInternetSales.SalesAmount) AS "Tot# Rows2"
,COUNT(*) AS "Tot# Rows3"
,AVG(FactInternetSales.SalesAmount) AS "AVG Sale"
count
If you notice the count is the same no matter which column we apply the function to. This is because count returns the number of rows in context to the selection criteria and grouping.
In this instance the return value of count is the SAME as the SUM(OrderQuantity). This is merely a coincidence as the their is no quantity greater than 1 in the recordset! It is always important to understand the data in the database or one can jump to incorrect conclusions when working with SQL.
orderqty
So we can drive this all home lets look at an example that has a smaller dataset. Below is our full dataset.
simpletable
Now lets look at a query equivalent to the one above.
simpletablegroupby
You can clearly see that the SUM() function is adding up the values in the num1 column, whereas the COUNT() function is returning a count of the number of rows.
MIN/MAX
The MIN and MAX functions will simply return the smallest and the largest value for that column.
DimProductCategory.EnglishProductCategoryName AS "Category"
,SUM(FactInternetSales.SalesAmount) AS "Total Sales"
,MIN(FactInternetSales.SalesAmount) AS "Lowest Sale Amount"
,MAX(FactInternetSales.SalesAmount) AS "Highest Sale Amount"
,AVG(FactInternetSales.SalesAmount) AS "AVG Sales"
min_max
Multiple GROUP BY Levels
The ability to provide an atomic recordset to BI software has made the task of rolling up and summarizing data easier... at least for the person doing this type of work. Tools like PowerPivot can take a recordset from either a dimensional model of typical OLTP database and allow on to quickly slice and dice it into many different pivot views without having to write these type of queries.
None the less, it is still good to know how to write SQL queries than rollup and aggregate data. In the following example we will rollup and get total sales by product.
USE AdventureWorksDW2008R2
GO
SELECT
DimProductCategory.EnglishProductCategoryName AS "Category",
DimProductSubcategory.EnglishProductSubcategoryName AS "Subcategory",
DimProduct.EnglishProductName AS "Product",
SUM(FactInternetSales.OrderQuantity) AS "Tot Qty",
SUM(FactInternetSales.SalesAmount) AS "Tot Sales"
FROM DimProduct
INNER JOIN DimProductSubcategory ON
DimProduct.ProductSubcategoryKey = DimProductSubcategory.ProductSubcategoryKey
INNER JOIN DimProductCategory ON
DimProductSubcategory.ProductCategoryKey = DimProductCategory.ProductCategoryKey
INNER JOIN FactInternetSales ON
DimProduct.ProductKey = FactInternetSales.ProductKey
WHERE OrderDateKey >= 20070101 AND OrderDateKey < 20080000
GROUP BY DimProductCategory.EnglishProductCategoryName,
DimProductSubcategory.EnglishProductSubcategoryName,
DimProduct.EnglishProductName
ORDER BY DimProductCategory.EnglishProductCategoryName ASC,
DimProductSubcategory.EnglishProductSubcategoryName ASC,
DimProduct.EnglishProductName ASC
totsalesbycat
We can define multiple group by levels. It is the last group by level that are data will be summarized against. It is possible to build a view against this query and them we would be able to take the existing recordset and roll it up to also report total sales by Subcategory and then by Category.
The more atomic out resultant recordset the great the ability we have to drill into it.
Being able to roll up and summarize data is important in many applications, particularly in financial and accounting. Though being able to aggregate and summarize data is also important in healthcare, telecommunication, marketing, and just about any other business area where one deals with numbers. The popularity of data warehousing and business intelligence in the marketplace is testimony to this.
Knowing how to write queries that can aggregate and summarize data is important if you are writing reports or engaged in many BI (Business Intelligence) types of activities.
On this page I will show you how to use GROUP BY to roll up data. We will be using the following aggregate functions.
AVG
SUM
COUNT
MIN
MAX
GROUP BY on a Single Column
In this example we are going to roll-up and get the total sales for 2007 broken down by category. When we are rolling up data there are two important things we require up in our SELECT. They are:
The column(s) we want to group the data on
The data columns we want to aggregate - Using SUM
It is also important to remember that your grouping column(s) must also be declared down in the GROUP BY clause. The GROUP BY clause comes after the WHERE clause.
It is also important to understand that any other non grouping columns up in the SELECT must have and aggregate function applied to them or you will recieve a syntax error.
USE AdventureWorksDW2008R2
GO
SELECT
DimProductCategory.EnglishProductCategoryName AS "Category"
,SUM(FactInternetSales.SalesAmount) AS "Total Sales"
,'$' + Convert(varchar,Convert(money,SUM(FactInternetSales.SalesAmount)),1)
AS "Formatted Total Sales"
FROM DimProduct
INNER JOIN DimProductSubcategory ON
DimProduct.ProductSubcategoryKey = DimProductSubcategory.ProductSubcategoryKey
INNER JOIN DimProductCategory ON
DimProductSubcategory.ProductCategoryKey = DimProductCategory.ProductCategoryKey
INNER JOIN FactInternetSales ON
DimProduct.ProductKey = FactInternetSales.ProductKey
WHERE OrderDateKey >= 20070101 AND OrderDateKey < 20080000
GROUP BY DimProductCategory.EnglishProductCategoryName
ORDER BY DimProductCategory.EnglishProductCategoryName
groupby1
Please note that I added a column so you caould see how to format our sales amount as it would be typically displayed. However, this is something you typically are not going to have to do as all report writers will allows you do do this type of formatting in the report itself. Also, if you are passing the results to a host language program and use this technique you are sending it the value in a format that a) has been rounded and as such are losing precision, b) you are sending it as a character string which likely is not the desired format!
The SUM aggregate function is the most commonly used of the aggregate functions.
Multiple Aggregate Functions up in the Select - Adding AVG
We can add multiple aggregate functions up in the SELECT. For example
SELECT
DimProductCategory.EnglishProductCategoryName AS "Category"
,SUM(FactInternetSales.SalesAmount) AS "Total Sales"
,SUM(FactInternetSales.OrderQuantity) AS "Tot# Items Sold"
,AVG(FactInternetSales.SalesAmount) AS "AVG Sale"
avgsales
You will note that I have added another summary column (OrderQuantity) to get the total number of items sold. I have also added the AVG aggregate function to the SalesAmount column. If you get out your calculator and multiply AVG(SalesAmount) by OrderQuantity you would expect to get the value found in SUM(SalesAmount). You will get close but not the exact amount due to rounding errors.
COUNT
You should be familiar with using COUNT(*) to determine the number of rows in a table. The sole purpose of the count function is to return the number of rows based upon your selection and/or group by and/or having critieria. Let's look at a few examples to understand this a little better.
If we extend the last example
DimProductCategory.EnglishProductCategoryName AS "Category"
,SUM(FactInternetSales.SalesAmount) AS "Total Sales"
,SUM(FactInternetSales.OrderQuantity) AS "Tot# Items Sold"
,COUNT(FactInternetSales.OrderQuantity) AS "Tot# Rows1"
,COUNT(FactInternetSales.SalesAmount) AS "Tot# Rows2"
,COUNT(*) AS "Tot# Rows3"
,AVG(FactInternetSales.SalesAmount) AS "AVG Sale"
count
If you notice the count is the same no matter which column we apply the function to. This is because count returns the number of rows in context to the selection criteria and grouping.
In this instance the return value of count is the SAME as the SUM(OrderQuantity). This is merely a coincidence as the their is no quantity greater than 1 in the recordset! It is always important to understand the data in the database or one can jump to incorrect conclusions when working with SQL.
orderqty
So we can drive this all home lets look at an example that has a smaller dataset. Below is our full dataset.
simpletable
Now lets look at a query equivalent to the one above.
simpletablegroupby
You can clearly see that the SUM() function is adding up the values in the num1 column, whereas the COUNT() function is returning a count of the number of rows.
MIN/MAX
The MIN and MAX functions will simply return the smallest and the largest value for that column.
DimProductCategory.EnglishProductCategoryName AS "Category"
,SUM(FactInternetSales.SalesAmount) AS "Total Sales"
,MIN(FactInternetSales.SalesAmount) AS "Lowest Sale Amount"
,MAX(FactInternetSales.SalesAmount) AS "Highest Sale Amount"
,AVG(FactInternetSales.SalesAmount) AS "AVG Sales"
min_max
Multiple GROUP BY Levels
The ability to provide an atomic recordset to BI software has made the task of rolling up and summarizing data easier... at least for the person doing this type of work. Tools like PowerPivot can take a recordset from either a dimensional model of typical OLTP database and allow on to quickly slice and dice it into many different pivot views without having to write these type of queries.
None the less, it is still good to know how to write SQL queries than rollup and aggregate data. In the following example we will rollup and get total sales by product.
USE AdventureWorksDW2008R2
GO
SELECT
DimProductCategory.EnglishProductCategoryName AS "Category",
DimProductSubcategory.EnglishProductSubcategoryName AS "Subcategory",
DimProduct.EnglishProductName AS "Product",
SUM(FactInternetSales.OrderQuantity) AS "Tot Qty",
SUM(FactInternetSales.SalesAmount) AS "Tot Sales"
FROM DimProduct
INNER JOIN DimProductSubcategory ON
DimProduct.ProductSubcategoryKey = DimProductSubcategory.ProductSubcategoryKey
INNER JOIN DimProductCategory ON
DimProductSubcategory.ProductCategoryKey = DimProductCategory.ProductCategoryKey
INNER JOIN FactInternetSales ON
DimProduct.ProductKey = FactInternetSales.ProductKey
WHERE OrderDateKey >= 20070101 AND OrderDateKey < 20080000
GROUP BY DimProductCategory.EnglishProductCategoryName,
DimProductSubcategory.EnglishProductSubcategoryName,
DimProduct.EnglishProductName
ORDER BY DimProductCategory.EnglishProductCategoryName ASC,
DimProductSubcategory.EnglishProductSubcategoryName ASC,
DimProduct.EnglishProductName ASC
totsalesbycat
We can define multiple group by levels. It is the last group by level that are data will be summarized against. It is possible to build a view against this query and them we would be able to take the existing recordset and roll it up to also report total sales by Subcategory and then by Category.
The more atomic out resultant recordset the great the ability we have to drill into it.
SQL Server - Formatting Date/Time
Formatting date time in SQL Server is done using the convert function. It requires knowing the length of the output string and the format code number.
SQL Server Date/Time Formatting - US
Format
| Output |
Statement
|
| MM/DD/YY | 03/04/13 | CONVERT(VARCHAR(8), GETDATE(), 1) |
| MM/DD/YYYY | 03/04/2013 | CONVERT(VARCHAR(10), GETDATE(), 101) |
| MM-DD-YY | 03-04-13 | CONVERT(VARCHAR(8), GETDATE(), 10) |
| MM-DD-YYYY | 03-04-2013 | CONVERT(VARCHAR(10), GETDATE(), 110) |
SELECT TOP 1
-- MM/DD/YY
CONVERT(VARCHAR(8), GETDATE(), 1) AS "1"
-- MM/DD/YYYY
,CONVERT(VARCHAR(10), GETDATE(), 101) AS "101"
-- MM-DD-YY
,CONVERT(VARCHAR(8), GETDATE(), 10) AS "10"
-- MM-DD-YYYY
,CONVERT(VARCHAR(10), GETDATE(), 110) AS "110"
FROM AdventureWorks2008R2.Sales.Store
SQL Server Date/Time Formatting - European
Format
| Output |
Statement
|
| DD/MM/YY | 23/05/13 | CONVERT(VARCHAR(8), GETDATE(), 3) |
| DD/MM/YYYY | 23/05/2013 | CONVERT(VARCHAR(10), GETDATE(), 103) |
| DD.MM.YY | 23.05.13 | CONVERT(VARCHAR(8), GETDATE(), 4) |
| DD.MM.YYYY | 23.05.2013 | CONVERT(VARCHAR(10), GETDATE(), 104) |
| DD-MM-YY | 23-05-13 | CONVERT(VARCHAR(8), GETDATE(), 5) |
| DD-MM-YYYY | 23-05-2013 | CONVERT(VARCHAR(8), GETDATE(), 105) |
| 04 Mar 2013 17:27:09:113 | CONVERT(VARCHAR(24), GETDATE(), 113) |
SELECT TOP 1
CONVERT(VARCHAR(24), GETDATE(), 113) AS "113"
-- UK / France
-- DD/MM/YY
,CONVERT(VARCHAR(8), GETDATE(), 3) AS "3"
-- DD/MM/YYYY
,CONVERT(VARCHAR(10), GETDATE(), 103) AS "103"
-- German
-- DD.MM.YY
,CONVERT(VARCHAR(8), GETDATE(), 4) AS "4"
-- DD.MM.YYYY
,CONVERT(VARCHAR(10), GETDATE(), 104) AS "104"
-- Italian
-- DD-MM-YY
,CONVERT(VARCHAR(8), GETDATE(), 5) AS "5"
-- DD-MM-YYYY
,CONVERT(VARCHAR(10), GETDATE(), 105) AS "105"
FROM AdventureWorks2008R2.Sales.Store
SQL Server - Format Time
Format
| Output |
Statement
|
| HH:MM:SS | 17:30:45 | CONVERT(VARCHAR(10), GETDATE(), 108) |
SELECT TOP 1
-- HH:MM:SS
CONVERT(VARCHAR(10), GETDATE(), 108) AS "108"
FROM AdventureWorks2008R2.Sales.Store
SQL Server - Date/Time
Format
| Output |
Statement
|
| Mon DD YYYY HH:MM[AM|PM] | Mar 4 2013 6:00PM | CONVERT(VARCHAR(20), GETDATE(), 100) |
| Mon DD YYYY HH:MM:SS:MMM[AM|PM] | Mar 4 2013 6:00:32:330PM | CONVERT(VARCHAR(26), GETDATE(), 109) |
| YYYY-MM-DD HH:MM:DD | 2013-03-04 18:00:32 | CONVERT(VARCHAR(19), GETDATE(), 120) |
| YYYY-MM-DD HH:MM:DD:MMM | 2013-03-04 18:00:32.330 | CONVERT(VARCHAR(23), GETDATE(), 121) |
| YYYY-MM-DDTHH:MM:DD:MMM | 2013-03-04T18:00:32.330 | CONVERT(VARCHAR(23), GETDATE(), 126) |
SELECT TOP 1
-- Mon DD YYYY HH:MM[AM|PM]
CONVERT(VARCHAR(20), GETDATE(), 100) AS "100"
-- Mon DD YYYY HH:MM:SS:MMM[AM|PM]
,CONVERT(VARCHAR(26), GETDATE(), 109) AS "109"
-- YYYY-MM-DD HH:MM:DD
,CONVERT(VARCHAR(19), GETDATE(), 120) AS "120"
-- YYYY-MM-DD HH:MM:DD:MMM
,CONVERT(VARCHAR(23), GETDATE(), 121) AS "121"
-- YYYY-MM-DDTHH:MM:DD:MMM
,CONVERT(VARCHAR(23), GETDATE(), 126) AS "126"
FROM AdventureWorks2008R2.Sales.Store
SQL Server Date/Time Formatting - Other
Format
| Output |
Statement
|
| DD Mon YY | 03 Mar 13 | CONVERT(VARCHAR(9), GETDATE(), 6) |
| DD Mon YYYY | 03 Mar 2013 | CONVERT(VARCHAR(11), GETDATE(), 106) |
| Mon DD, YY | Mar 04, 13 | CONVERT(VARCHAR(10), GETDATE(), 7) |
| Mon DD, YYYY | Mar 04, 2013 | CONVERT(VARCHAR(12), GETDATE(), 107) |
| YY.MM.DD | 13.03.04 | CONVERT(VARCHAR(8), GETDATE(), 2) |
| YYYY.MM.DD | 2013.03.04 | CONVERT(VARCHAR(10), GETDATE(), 102) |
| YY/MM/DD | 13/03/04 | CONVERT(VARCHAR(8), GETDATE(), 11) |
| YYYY/MM/DD | 2013/03/04 | CONVERT(VARCHAR(10), GETDATE(), 111) |
| YYMMDD | 130304 | CONVERT(VARCHAR(6), GETDATE(), 12) |
| YYYYMMDD | 20130304 | CONVERT(VARCHAR(8), GETDATE(), 112) |
SELECT TOP 1
-- DD Mon YY
CONVERT(VARCHAR(9), GETDATE(), 6) AS "6"
-- DD Mon YYYY
,CONVERT(VARCHAR(11), GETDATE(), 106) AS "106"
-- Mon DD, YY
,CONVERT(VARCHAR(10), GETDATE(), 7) AS "7"
-- Mon DD, YYYY
,CONVERT(VARCHAR(12), GETDATE(), 107) AS "107"
-- YY.MM.DD
,CONVERT(VARCHAR(8), GETDATE(), 2) AS "2"
-- YYYY.MM.DD
,CONVERT(VARCHAR(10), GETDATE(), 102) AS "102"
-- YY/MM/DD
,CONVERT(VARCHAR(8), GETDATE(), 11) AS "11"
-- YYYY/MM/DD
,CONVERT(VARCHAR(10), GETDATE(), 111) AS "111"
-- YYMMDD
,CONVERT(VARCHAR(6), GETDATE(), 12) AS "12"
-- YYYYMMDD
,CONVERT(VARCHAR(8), GETDATE(), 112) AS "112"
FROM AdventureWorks2008R2.Sales.Store
SQL Server General Questions - Interview / Test Questions Answers
1. What is RDBMS?
Relational Data Base Management Systems (RDBMS) are database
management systems that maintain data records and indices in tables.
Relationships may be created and maintained across and among the data and
tables. In a relational database, relationships between data items are
expressed by means of tables. Interdependencies among these tables are
expressed by data values rather than by pointers. This allows a high degree of
data independence. An RDBMS has the capability to recombine the data items from
different files, providing powerful tools for data usage.
2. What are the properties of the
Relational tables?
Relational tables have six properties:
1. Values are atomic.
2. Column values are of
the same kind.
3. Each row is unique.
4. The sequence of columns
is insignificant.
5. The sequence of rows
is insignificant.
6. Each column must have
a unique name.
3. What is Normalization?
Database normalization is a data design and organization
process applied to data structures based on rules that help building relational
databases. In relational database design, the process of organizing data to
minimize redundancy is called normalization. Normalization usually involves
dividing a database into two or more tables and defining relationships between
the tables. The objective is to isolate data so that additions, deletions, and
modifications of a field can be made in just one table and then propagated
through the rest of the database via the defined relationships.
4. What is De-normalization?
De-normalization is the process of attempting to optimize
the performance of a database by adding redundant data. It is sometimes
necessary because current DBMSs implement the relational model poorly. A true
relational DBMS would allow for a fully normalized database at the logical level,
while providing physical storage of data that is tuned for high performance.
De-normalization is a technique to move from higher to lower normal forms of
database modeling in order to speed up database access.
5. What are different normalization
forms?
1. 1NF: Eliminate
Repeating Groups Make a separate table for each set of
related attributes, and give each table a primary key. Each field contains at
most one value from its attribute domain.
2. 2NF: Eliminate
Redundant Data If an attribute depends on only part of
a multi-valued key, remove it to a separate table.
3. 3NF: Eliminate
Columns Not Dependent On Key If attributes do not contribute to a
description of the key, remove them to a separate table. All attributes must be
directly dependent on the primary key.
4. BCNF: Boyce-Codd
Normal Form If there are non-trivial dependencies
between candidate key attributes, separate them out into distinct tables.
5. 4NF: Isolate
Independent Multiple Relationships No table may contain
two or more 1:n or n:m relationships that are not directly related.
6. 5NF: Isolate
Semantically Related Multiple Relationships There may be
practical constrains on information that justify separating logically related
many-to-many relationships.
7. ONF: Optimal Normal
Form A model limited to only simple (elemental)
facts, as expressed in Object Role Model notation.
8. DKNF: Domain-Key
Normal Form A model free from all modification
anomalies is said to be in DKNF.
Remember,
these normalization guidelines are cumulative. For a database to be in 3NF, it
must first fulfill all the criteria of a 2NF and 1NF database.
6. What is Stored Procedure?
A stored procedure is a named group of SQL statements that
have been previously created and stored in the server database. Stored
procedures accept input parameters so that a single procedure can be used over
the network by several clients using different input data. And when the
procedure is modified, all clients automatically get the new version. Stored
procedures reduce network traffic and improve performance. Stored procedures
can be used to help ensure the integrity of the database.
e.g. sp_helpdb, sp_renamedb, sp_depends etc.
e.g. sp_helpdb, sp_renamedb, sp_depends etc.
7. What is Trigger?
A trigger is a SQL procedure that initiates an action when
an event (INSERT, DELETE or UPDATE) occurs. Triggers are stored in and managed
by the DBMS. Triggers are used to maintain the referential integrity of data by
changing the data in a systematic fashion. A trigger cannot be called or
executed; DBMS automatically fires the trigger as a result of a data
modification to the associated table. Triggers can be viewed as similar to
stored procedures in that both consist of procedural logic that is stored at
the database level. Stored procedures, however, are not event-drive and are not
attached to a specific table as triggers are. Stored procedures are explicitly
executed by invoking a CALL to the procedure while triggers are implicitly
executed. In addition, triggers can also execute stored procedures.
8. What is Nested Trigger?
A trigger can also contain INSERT, UPDATE and DELETE logic
within itself, so when the trigger is fired because of data modification it can
also cause another data modification, thereby firing another trigger. A trigger
that contains data modification logic within itself is called a nested trigger.
9. What is View?
A simple view can be thought of as a subset of a table. It
can be used for retrieving data, as well as updating or deleting rows. Rows
updated or deleted in the view are updated or deleted in the table the view was
created with. It should also be noted that as data in the original table
changes, so does data in the view, as views are the way to look at part of the
original table. The results of using a view are not permanently stored in the
database. The data accessed through a view is actually constructed using
standard T-SQL select command and can come from one to many different base
tables or even other views.
10. What is Index?
An index is a physical structure containing pointers to the
data. Indices are created in an existing table to locate rows more quickly and
efficiently. It is possible to create an index on one or more columns of a
table, and each index is given a name. The users cannot see the indexes; they
are just used to speed up queries. Effective indexes are one of the best ways
to improve performance in a database application. A table scan happens when
there is no index available to help a query. In a table scan SQL Server
examines every row in the table to satisfy the query results. Table scans are
sometimes unavoidable, but on large tables, scans have a terrific impact on
performance.
11. What is a Linked Server?
Linked Servers is a concept in SQL Server by which we can
add other SQL Server to a Group and query both the SQL Server dbs using T-SQL
Statements. With a linked server, you can create very clean, easy to follow,
SQL statements that allow remote data to be retrieved, joined and combined with
local data. Stored Procedure sp_addlinkedserver, sp_addlinkedsrvlogin will be
used add new Linked Server.
12. What is Cursor?
Cursor is a database object used by applications to
manipulate data in a set on a row-by- row basis, instead of the typical SQL
commands that operate on all the rows in the set at one time.
In order to work with a cursor we need to perform some steps in the following order:
In order to work with a cursor we need to perform some steps in the following order:
1. Declare cursor
2. Open cursor
3. Fetch row from the
cursor
4. Process fetched row
5. Close cursor
6. Deallocate cursor
13. What is Collation?
Collation refers to a set of rules that determine how data
is sorted and compared. Character data is sorted using rules that define the
correct character sequence, with options for specifying case sensitivity,
accent marks, kana character types and character width.
14. What is Difference between Function
and Stored Procedure?
UDF can be used in the SQL statements anywhere in the
WHERE/HAVING/SELECT section where as Stored procedures cannot be. UDFs that
return tables can be treated as another rowset. This can be used in JOINs with
other tables. Inline UDF's can be thought of as views that take parameters and
can be used in JOINs and other Rowset operations.
15. What is sub-query? Explain
properties of sub-query?
Sub-queries are often referred to as sub-selects, as they
allow a SELECT statement to be executed arbitrarily within the body of another
SQL statement. A sub-query is executed by enclosing it in a set of parentheses.
Sub-queries are generally used to return a single row as an atomic value,
though they may be used to compare values against multiple rows with the IN
keyword.
A subquery is a SELECT statement that is nested within
another T-SQL statement. A subquery SELECT statement if executed independently
of the T-SQL statement, in which it is nested, will return a resultset. Meaning
a subquery SELECT statement can standalone and is not depended on the statement
in which it is nested. A subquery SELECT statement can return any number of
values, and can be found in, the column list of a SELECT statement, a FROM,
GROUP BY, HAVING, and/or ORDER BY clauses of a T-SQL statement. A Subquery can
also be used as a parameter to a function call. Basically a subquery can be
used anywhere an expression can be used.
16. What are different Types of Join?
1. Cross Join A cross join that
does not have a WHERE clause produces the Cartesian product of the tables
involved in the join. The size of a Cartesian product result set is the number
of rows in the first table multiplied by the number of rows in the second
table. The common example is when company wants to combine each product with a
pricing table to analyze each product at each price.
2. Inner Join A join that displays
only the rows that have a match in both joined tables is known as inner Join.
This is the default type of join in the Query and View Designer.
3. Outer Join A join that includes
rows even if they do not have related rows in the joined table is an Outer
Join. You can create three different outer join to specify the unmatched rows
to be included:
1. Left Outer Join: In Left Outer Join
all rows in the first-named table i.e. "left" table, which appears
leftmost in the JOIN clause are included. Unmatched rows in the right table do
not appear.
2. Right Outer Join: In Right Outer Join
all rows in the second-named table i.e. "right" table, which appears
rightmost in the JOIN clause are included. Unmatched rows in the left table are
not included.
3. Full Outer Join: In Full Outer Join
all rows in all joined tables are included, whether they are matched or not.
4. Self Join This is a particular
case when one table joins to itself, with one or two aliases to avoid
confusion. A self join can be of any type, as long as the joined tables are the
same. A self join is rather unique in that it involves a relationship with only
one table. The common example is when company has a hierarchal reporting
structure whereby one member of staff reports to another. Self Join can be
Outer Join or Inner Join.
17. What are primary keys and foreign
keys?
Primary keys are the unique identifiers for each row. They
must contain unique values and cannot be null. Due to their importance in
relational databases, Primary keys are the most fundamental of all keys and
constraints. A table can have only one Primary key. Foreign keys are both a
method of ensuring data integrity and a manifestation of the relationship
between tables.
18. What is User Defined Functions?
What kind of User-Defined Functions can be created?
User-Defined Functions allow defining its own T-SQL
functions that can accept 0 or more parameters and return a single scalar data
value or a table data type.
Different Kinds of User-Defined Functions created are:
Different Kinds of User-Defined Functions created are:
1. Scalar User-Defined
Function A Scalar user-defined function returns
one of the scalar data types. Text, ntext, image and timestamp data types are
not supported. These are the type of user-defined functions that most
developers are used to in other programming languages. You pass in 0 to many
parameters and you get a return value.
2. Inline Table-Value
User-Defined Function An Inline Table-Value user-defined
function returns a table data type and is an exceptional alternative to a view
as the user-defined function can pass parameters into a T-SQL select command
and in essence provide us with a parameterized, non-updateable view of the
underlying tables.
3. Multi-statement
Table-Value User-Defined Function A Multi-Statement
Table-Value user-defined function returns a table and is also an exceptional
alternative to a view as the function can support multiple T-SQL statements to
build the final result where the view is limited to a single SELECT statement.
Also, the ability to pass parameters into a TSQL select command or a group of
them gives us the capability to in essence create a parameterized,
non-updateable view of the data in the underlying tables. Within the create
function command you must define the table structure that is being returned.
After creating this type of user-defined function, It can be used in the FROM
clause of a T-SQL command unlike the behavior found when using a stored
procedure which can also return record sets.
19. What is Identity?
Identity (or AutoNumber) is a column that automatically generates
numeric values. A start and increment value can be set, but most DBA leave
these at 1. A GUID column also generates numbers; the value of this cannot be
controlled. Identity/GUID columns do not need to be indexed.
20. What is DataWarehousing?
1. Subject-oriented,
meaning that the data in the database is organized so that all the data
elements relating to the same real-world event or object are linked together;
2. Time-variant, meaning
that the changes to the data in the database are tracked and recorded so that
reports can be produced showing changes over time;
3. Non-volatile, meaning
that data in the database is never over-written or deleted, once committed, the
data is static, read-only, but retained for future reporting.
4. Integrated, meaning
that the database contains data from most or all of an organization's
operational applications, and that this data is made consistent.
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