Stuff is a function in SQL Server used to perform special operations on a string value.
The below operations can be performed,

• Remove string part from string expression.
• Insert/Append string at specified index.

Syntax
select STUFF(string_value, start_index, no_of_chars_to_replace, replace_string); 

Remove String Part
select STUFF('hai_hello',0,2,''); 

Important Note
Start Index begins from 1 in STUFF Function.

Proper Index
select STUFF('hai_hello',1,2,''); 

Insert String Content
You can insert a string content by specifying the index location and set number of characters to replace to zero. Note that the third parameter value should be zero.
DECLARE @testString varchar(3) = 'abc'; 
select STUFF('hai_hello', 1, 0, @testString); 

Replace String Content
You cannot replace string by specifying the old characters here.
But you can replace the string by specifying start location and number of characters to replace.

Note
The third parameter value should be the length to replace.

Example 1
DECLARE @testString varchar(3) = 'abc'; 
select STUFF('hai_hello', 1, DATALENGTH (@testString), @testString); 

Example 2
select STUFF('hai_hello',1,2,'abc');

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In this post, we will learn about how to replace newline characters from SQL fields. In this post, we have to use replace function for replace string and also use char function to remove newline characters and replace them. Here I will give you the syntax of replace function, how to use char function, and the meaning of 10 in char function.
Syntax
Description of parameter value,

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In this article, I will give one of the solutions to overcome the "The given key was not present in the dictionary" exception in MySQL. Developers may face this error in many situations, but I faced this error in query execution after migrating the MSSQL Database to MySQL.

Recently, I have migrated a database from MSSQL to MySQL. After migration, I tried to run the simple select query with joins which already worked in my application with MSSQL connection string. When I ran the same query with MySQL Connection string, I got the “The given key was not present in the dictionary” Exception. But if I run the same query in MySQL Workbench, it's working fine.

So, here is my sample query which I tried to execute through MySqlDataAdapter.
Select * from usersdetails; 
nMySqlDataAdapter = new MySqlDataAdapter(xszQuery, zSqlConn); 

When this query passes MySqlDataAdapter, I'm getting the exception “The given key was not present in the dictionary”. But the same query is working fine in MySQL Workbench.

It's the same process if I pass the query like,
Select usersID,usersname from usersdetails; 
nMySqlDataAdapter = new MySqlDataAdapter(xszQuery, zSqlConn); //Working fine without any exception. 

I have searched for many solutions on Google, but none worked for me. Some solutions said that error in connection string needs to include the charset=utf8 parameter with the connection string. But that didn't work for me.

Then I found something:
Select usersID,usersname from usersdetails; // this query working fine, no error 
Select usersID,usersname,usersAddress from usersdetails; // this query returns exception 
Select usersID,userGUID from usersdetails; // this query working fine, no error 
Select usersID,userGUID,userDepartment from usersdetails;  // this query returns exception 

So, the problem is something with the columns which I trying to select, then I found what makes each column differ from the other.

What I found is “Collation” value of each column is different based on column datatype, I don’t know in what basis these values are assigned during migration.

What is Collation in MySQL?
A collation is a set of rules that defines how to compare and sort character strings. Each collation in MySQL belongs to a single character set. Every character set has at least one collation, and most have two or more collations.

A collation orders characters based on weights. Each character in a character set maps to a weight. Characters with equal weights compare as equal, and characters with unequal weights compare according to the relative magnitude of their weights.

To know more about “Collation” click here.

I followed two different methods to overcome this exception.

Method 1
By selecting “Table default” value in “Collation” option for the all columns in a table we can overcome this exception.

Method 2
During migration from MSSQL to MySQL, on manual editing step, we can see the tables, column names, and respective datatypes. In the following image on step 3, by default MySQL had taken “CHARACTER SET ‘utf8mb4’ ” as charset value for some columns. We can edit this section, just select and delete “CHARACTER SET ‘utf8mb4’ ” and apply changes. Now all columns with “Collation” value become “Table default” after migration.

By using these two ways we can overcome this exception. In this article, I have given one of the ways to overcome “The given key was not present in the dictionary” exception. If anybody knows any other way to overcome this exception, please mention in the comment  box. I hope this article is very useful.

One of the most important routes to high performance in SQL server database is an index. It is a database object which is used to speed up the querying process by providing quick access to rows in the database tables. By using Indexes we can save time and can improve the performance of database queries and applications. An Index contains keys built from one or more columns in the table mapped to the storage location of the specified data. When we create an index on any column, SQL server internally maintains a separate table called index table, so that whenever a user tries to retrieve the data from the existing table,  depending on the index Table SQL server goes directly to the table and retrieves the required data very quickly.

In the Table we can use a maximum of 250 Indexes. The Index Type refers to the way the index is stored internally by SQL server. So a Table can contain two types of indexes:

Clustered Indexes
The only time the data rows in a table are stored in sorted (ascending order only) order structure is when the table contains a clustered index. When a table has a clustered index, then it is called a clustered table. If a table has no clustered index, its data rows are stored in an unordered structure. A table can have only 1 clustered Index on it, which will be created when a primary key constraint is used in a Table.

Non-Clustered Indexes
Non-clustered Indexes will not have any arrangement order (unordered structure) of the data in the table. In a table, we can create 249 non clustered Indexes.If we don't mention clustered indexes in a table then a default is stored as non-clustered Indexes.

The command "UNION" is the perfect way to join two tables with the same data context. Whether they have or do not have the same fields, you need to classify the data.
Look at this selected query.
Select t.* From ( 
(Select 1 as typePerson, tenName as namePerson, tenSalary as payMoney From Teachers Where tenAge>30) 
UNION 
(Select 0 as typePerson, stdName as namePerson, 0 as payMoney From Students Where stdYearFinish=2016) 
) t 
Where t.namePerson like ‘Maria%’ 
Order By t.tenName , t.typePerson;*

It creates a temporary alias “t”;
It classifies each data row “typePerson”, 1 (true) for teachers and 0 (false) to Students;
It filters the age of the teachers;
It filters the end year on the school to the year 2016;
After the UNION, it filters by the field name Person that begins with Maria.

Observations
All fields must be at the same position and the same data type, you can make all kinds of selects, joins, where etc. The "UNION ALL" command is better than UNION if you want to select all rows. If they are the same**, this is not the case. In this sample, you can make it a View.

CONCLUSION
You must be careful of the position of the fields and the type, and you can use cast too.

This selection is just an example.
The SQL UNION ALL operator is used to combine the result sets of 2 or more SELECT statements. It does not remove duplicate rows between the various SELECT statements (all rows are returned).

If you’re a developer, irrespective of the platform, you  have to work with databases. Creating SQL statements for tables is quite often a monotonous job and it gets hectic especially when dealing with gigantic tables that have hundreds of columns. Writing SQL statements manually every time becomes a tiresome process. Before explaining the script, I want to share the reason to write this script and how it is helping my peers. We have code standard on the database side. Below points are standards.

• Need to maintain a separate stored procedure to every table
• Don’t use * in the query instead specify the column
• Use the correct data type and size of a column
• Every parameter should be nullable in a stored procedure.

I am developing an application which is related to machines using .NET and SQL Server. The database design consists of some master tables and transactional tables. All the transactional table has more than 30 columns.

To meet my code standards, I need to mention all columns with correct data type and size in stored procedure parameters like below,
CREATEproc [dbo].[USP_PCNitemCreation] ( @Id int, @machineName varchar(50)=NULL, @furnacename varchar(50)=NULL, @minValue int=NULL, @maxValue int=NULL, @createdDate datetime=nullvarchar(100)=NULL ) 

All the queries should specify the column instead of using the start(*).
select machineName,furnacename from trn_furnace where Id=@Id 

It consumes more time and is a boring task. So, I plan to write the script to is cut down on the time it takes and boring repeated work. We cannot automate the logic, but we can automate the repeated task.

Then I write the below script which really cuts down on all of our above pain points.

Auto Query Generator Stored Procedure for MSSQL Server,
CREATEproc [dbo].[USP_QuerycreationSupport] ( @table_Name varchar(100)=NULL ) AS  
BEGINDECLARE @InserCols   NVARCHAR(max)DECLARE @Inserparam  NVARCHAR(max)DECLARE @Insertquery NVARCHAR(max)DECLARE @Selectquery NVARCHAR(max)DECLARE @Update      NVARCHAR(max)DECLARE @DeleteQuery NVARCHAR(max) 
  -- sp paramSELECT '@'+c.NAME+Space(1)+Casecast(t.Nameasnvarchar(40))WHEN'nvarchar'THEN  
  t.NAME    +'('+cast(c.max_length asnvarchar(30))+')'  
WHEN'varchar'THEN  
  t.NAME+'('+cast(c.max_length asnvarchar(30))+')'  
WHEN'char'THEN  
  t.NAME+'('+cast(c.max_length asnvarchar(30))+')'  
WHEN'decimal'THEN  
  t.NAME        +'(18,2)'  
  ELSE t.nameend+'=null,'AS colss FROM sys.columns c innerjoin sys.types t ON c.user_type_id = t.user_type_id leftouterjoin sys.index_columns ic ON ic.object_id= c.object_idand ic.column_id = c.column_id leftouterjoin sys.indexes i ON ic.object_id= i.object_idand ic.index_id = i.index_id WHERE c.object_id=object_id(@table_Name)SELECT'Insert query'SET @InserCols=(selectdistinct  
  (  
         select sc.NAME+','  
         FROM   sys.tables st innerjoinsys.columns sc  
         ON st.object_id= sc.object_id  
         WHERE  st.NAME= @table_Name forxmlpath(''),  
                type).value('.','NVARCHAR(MAX)'))  
  -- Return the result of the functionSELECT @InserCols=LEFT(@InserCols,Len(@InserCols)-1)  
  --select @InserColsSET @Inserparam=(selectdistinct  
  (  
         select'@'+sc.NAME+','  
         FROM   sys.tables st innerjoinsys.columns sc  
         ON st.object_id= sc.object_id  
         WHERE  st.NAME= @table_Name forxmlpath(''),  
                type).value('.','NVARCHAR(MAX)'))  
  -- Return the result of the functionSELECT @Inserparam=LEFT(@Inserparam,Len(@Inserparam)-1)  
  --select @InserparamSET @Insertquery='insert into '+@table_Name+'('+@InserCols+')'+'values'+'('+@Inserparam+')'SELECT @InsertquerySELECT'Update Query'SET @Update=(selectdistinct  
  (  
         select sc.NAME+'=@'+sc.NAME+','  
         FROM   sys.tables st innerjoinsys.columns sc  
         ON st.object_id= sc.object_id  
         WHERE  st.NAME= @table_Name forxmlpath(''),  
                type).value('.','NVARCHAR(MAX)'))  
  -- Return the result of the functionSELECT @Update=LEFT(@Update,Len(@Update)-1)  
  --select @UpdateSET @Update='UPdate '+@table_Name+' set '+@UpdateSELECT @Update  
  -- For select QuerySELECT'Select Query'SET @Selectquery='select '+@InserCols +' from '+ @table_NameSELECT @Selectquery 
  -- For Delete QuerySELECT'Delete Query'SET @DeleteQuery='delete from '+ @table_NameSELECT @DeleteQuery 
end 

How to use this script,
Step 1 - Create the stored procedure using the above code or attached code.
Step 2 - Execute the stored procedure and pass your table name as a parameter.
Exec USP_QuerycreationSupport@table_Name='mstCustomer' 

Should not pass the database object in the table name
Exec USP_QuerycreationSupport@table_Name='[dbo].[mstCustomer]' 

Once you execute the Stored Procedure as mentioned above, you get all the SQL statements as shown here. You could easily use the generated SQL statements elsewhere. You get all basic SQL statements like Select, Insert, Update & Delete.

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In the release of SQL Server 2016 CTP 2 one of the features that was introduced is JSON clause. So, the first question that comes into everyone’s mind is What is JSON? JSON stands for JavaScript Object Notation. JSON is a lightweight format which is used for storing and interchanging the data. JSON uses standard JavaScript functions to convert the JSON data into native JavaScript objects. The main purpose of using FOR JSON is used to create new JSON objects. We can format the query results using FOR JSON clause in these ways,
With AUTO mode
With PATH mode
With ROOT option
Output with INCLUDE_NULL_VALUES option

In this blog, we will discuss the query formatting using FOR JSON clause with AUTO mode option.

Syntax for FOR JSON clause with AUTO option is like this:
FOR JSON AUTO

When AUTO option is used, the format of JSON is determined automatically on the basis of the number of columns present in the SELECT statement list. A FROM clause is necessary inquery with FOR JSON AUTO option.

When you join tables, columns present in the first table are used as properties of the root object in JSON array while columns present in the second table will be automatically formatted as a nested object within the root object.

Let’s execute the below query and see the JSON output.
SELECT sp.BusinessEntityID, 
   sp.TerritoryID, 
   st.CountryRegionCode, 
   st.[Group] TerrritoryGroup 
   FROM sales.salesperson sp 
   JOIN Sales.SalesTerritory st ON sp.TerritoryID = st.TerritoryID 
   WHERE sp.TerritoryID = 10 
FOR JSON AUTO 

After executing the above query, we get the output in this format.
[{ 
    "BusinessEntityID": 289, 
    "TerritoryID": 10, 
    "st": [{ 
        "CountryRegionCode": "GB", 
        "TerrritoryGroup": "Europe" 
    }] 
}]

Brackets [ ] represents JSON array in output.

Here, in the output, we can see that table Sales.SalesTerritory is automatically formatted as a nested object under parent object.

So we have generated a formatted query output using JSON clause. I will continue with other ways of formatted output using JSON clause in my next blogs.

I was working on a scenario in which I needed to assign a unique value to a column. I was trying to update the column using window ranking function ROW_NUMBER() but I got an error that ‘Windowed function can only appear in SELECT or ORDER BY clause’:

Then I did some workaround and used the Windows function indirectly using a CTE (Common Table Expression) for this. I will show you step by step how I accomplished this using CTE.

Let’s first create a table with some test data,
    CREATE TABLE Test 
    ( 
        ID INT, 
        Value VARCHAR(10) NOT NULL 
    ) 
    GO 
     
    INSERT INTO Test (Value) VALUES('Sunday'),('Monday'),('Tuesday'),('Wednesday'),('Thursday'),('Friday'),('Saturday') 
    GO 

As we can see that in column ID NULL values get inserted as we didn’t specify any values for this column during INSERT statement. So, when I tried this UPDATE statement using ROW_NUMBER() with SET I got the following error,
    UPDATE Test 
    SET ID = ROW_NUMBER() OVER(ORDER BY Value) 
    GO 

Then I used CTE to update the ID values in table test in this manner,
    ;WITH CTE AS 
    ( 
        SELECT Value, ROW_NUMBER() OVER(ORDER BY Value) as RN 
        FROM Test 
    ) 
     
    UPDATE T 
    SET ID = RN 
    FROM CTE C JOIN Test T ON T.value = C.Value 

When I ran this SQL code I didn’t get any error and ID column values to get updated with unique values.

In this article, I continue to review the exciting features available in SQL Server 2016. One such feature is the long awaited T-SQL support for JSON formatted data. In this article we take a look at how JSON support will impact data warehouse solutions.

Background

Since the advent of EXtensible Markup Language (XML) many modern web applications have focused on providing data that is both human-readable and machine-readable. From a relational database perspective, SQL Server kept up with these modern web applications by providing support for XML data in a form of an XML data type and several functions that could be used to parse, query and manipulate XML formatted data.

As a result of being supported in SQL Server, data warehouse solutions based off SQL Server were then able to source XML-based OLTP data into a data mart. To illustrate this point, let’s take a look at the XML representation of our fictitious Fruit Sales data shown in figure below.

To process this data in data warehouse, we would first have to convert it into relational format of rows and columns using T-SQL XML built-in functions such as the nodes() function. 

The results of the above script are shown in figure below in a recognisable format for data warehouse.

Soon after XML became a dominant language for data interchange for many modern web applications, JavaScript Object Notation (JSON) was introduced as a lightweight data-interchange format that is more convenient for web applications to process than XML. Likewise most relational database vendors released newer versions of their database systems that included the support for JSON formatted data. Unfortunately, Microsoft SQL Server was not one of those vendors and up until SQL Server 2014, JSON data was not supported. Obviously this lack of support for JSON, created challenges for data warehouse environments that are based off SQL Server.

Although there were workarounds (i.e. using Json.Net) to addressing the lack of JSON support in SQL Server, there was always sense that these workarounds were inadequate, time-wasting, and were forcing data warehouse development teams to pick up a new skill (i.e. learn .Net). Fortunately, the release of SQL Server 2016 has ensured that development teams can throw away their JSON workarounds as JSON is supported in SQL Server 2016.

Parsing JSON Data into Data Warehouse

Similarly to XML support in SQL Server, SQL Server supports of JSON can be classified into two ways:

• Converting Relational dataset into JSON format
• Converting JSON dataset into relational format

However, for the purposes of this discussion we are focusing primarily on the second part – which is converting a JSON formatted data (retrieved from OLTP sources) into a relational format of rows and columns. To illustrate our discussion points we once again make use of the fictitious fruit sales dataset. This time around the fictitious dataset has been converted into a JSON format as shown below.

ISJSON function

As part of supporting JSON formatted data in other relational databases such as MySQL and PostgreSQL 9.2, there is a separate JSON data type that has been introduced by these vendors. Amongst other things, JSON data type conducts validation checks to ensure that values being stored are indeed of valid JSON format.

Unfortunately, SQL Server 2016 (and ORACLE 12c) do not have a special data type for storing JSON data instead a variable character (varchar/nvarchar) data type is used. Therefore, a recommended practice to dealing with JSON data in SQL Server 2016 is to firstly ensure that you are indeed dealing with a valid JSON data. The simplest way to do so is to use the ISJSON function. This is a built-in T-SQL function that returns 1 for a valid JSON dataset and 0 for invalids.

Image below shows us the implementation of ISJSON function whereby we validate our fictitious sample dataset.

OPENJSON function

Now that we have confirmed that we are working with a valid JSON dataset, the next step is to convert the data into a table format. Again, we have a built-in T-SQL function to do this in a form of OPENJSON. OPENJSON works similar to OPENXML in that it takes in an object and convert its data into rows and columns.

Figure below shows a complete T-SQL script for converting JSON object into rows and columns.

Once we execute the above script, we get relational output shown below.

Now that we have our relational dataset, we can process this data into data warehouse.

JSON_VALUE function

Prior to concluding our discussion of JSON in SQL Server 2016, it is worth mentioning that in addition to OPENJSON, you have other functions such as JSON_VALUE that could be used to query JSON data. However this function returns a scalar value which means that unlike the multiple rows and columns returned using OPENJSON, JSON_VALUE returns a single value as shown below.

If you the JSON object that you are querying doesn’t have multiple elements, than you don’t have to specify the row index (i.e. [0]) as shown below.

Conclusion

The long wait is finally over and with the release of SQL Server 2016, JSON is now supported. Similarly to XML, T-SQL support the conversion of JSON object to relational format as well the conversion of relational tables to a JSON object. This support is implemented via built-in T-SQL functions such as OPENJSON and JSON_VALUE. In spite of all the excitement with the support of JSON is SQL Server 2016, we still don’t have a JSON data type. The ISJSON function can then be used to validate JSON text.