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.

Isolation is one of the properties of SQL Transaction. Isolating / separating transactions from each other to maintain Data Integrity in Database is called Isolation.Before going to implementation part of isolation, we will understand why isolation is required in database ?

Why Is Isolation Level Required ?
While developing large enterprise/public networking kind of applications where a huge number of users access same Database, same Table and at the same Time, Data concurrency situation may occur. We will discuss this situation into 4 parts:

• Loss of Data
• Dirty Read
• Phantom Read
• Inconsistency Analysis

Loss of Data
Let's take an example - Suppose, there are 2 users accessing the same table, at the same moment, to update the same row. Each transaction is unaware of the other transaction. User A updates the row and then User B updates the same row. What happened here is the last transaction made my User B overwrites the updated record of User A and User A lost his/her data in the table.

Dirty Read
This is otherwise known as Uncommitted Dependency. Let's take another example - Suppose, User A and User B are accessing a table row at the same time. User A wants to read and User B wants to update the row. In the friction of time difference, transactions are executed. So, when User B not yet updated the row (during the update process), User A reads that row and got the old record which may not be correct for his/her operation. This situation is known as Dirty Read.

Phantom Read
This is also known as Phantom Problem. Let's again take another example - Suppose User A is granted to insert a row but the same time User B inserted that row. Now, when User A tries to insert, he/she can't. Then, he/she will get angry and say- "Hey you committed that this is available for me to insert, but you cheated on me and granted someone else to do so !" . You may/might get this problem while the reservation of Train/Movie ticket.

Inconsistency Analysis
This is also known as Non-Repeatable Problem. Let's take the same example of User A and User B. Suppose, User A executes a transaction having three queries - a stored procedure or transaction or individual query with a batch. 1st query is to read a table row, the 2nd query is to update that, and the 3rd query is to read that again. By doing this, User A wants to generate the report. As we know User B is not a gentleman he always spoils the intention of User A, he accessed the table row in between the two Read queries of User A and did some operation like Delete ! Now, User A has already modified the data and when he wants to read it again, he is surprised ! He got inconsistency in data.

In the above-mentioned points, we understood that - as User A is a weak person, User B always plays with User A and forcefully does it's job by dominating him. But as a Database Administrator or Developer, we need to help the User A. Now, we have a weapon called "Isolation Level" by using which we can cooperate User A to maintain its integrity.

Isolation Levels
SQL Server provides 5 Isolation levels to implement with SQL Transaction to maintain data concurrency in the database.

Isolation level is nothing but locking the row while performing some task, so that other transaction can not access or will wait for the current transaction to finish its job.

Let's write a transaction without Isolation level.
BEGIN TRANSACTION MyTransaction 
BEGIN TRY 
UPDATE Account SET Debit=100 WHERE Name='John Cena' 
UPDATE ContactInformation SET Mobile='1234567890' WHERE Name='The Rock' 
COMMIT TRANSACTION MyTransaction 
PRINT 'TRANSACTION SUCCESS' 
END TRY 
BEGIN CATCH 
ROLLBACK TRANSACTION MyTransaction 
PRINT 'TRANSACTION FAILED' 
END CATCH  

In the above code snippet, we have not implemented Isolation Level. Therefore, there are chances of data concurrency. Now, we will go through all the five Isolation levels and find the solution for our situation.

Read Uncommitted
When this level is set, the transaction can read uncommitted data resulting in the Dirty Read problem. With this isolation level, we allow a transaction to read the data which is being updated by other transaction and not yet committed. Suppose User A is trying to read a row which is being updated by User B. Here, we are allowing User A to read the un-updated/uncommitted data i.e old data.

Example
SET TRANSACTION ISOLATION LEVEL  
READ UNCOMMITTED 
BEGIN TRANSACTION MyTransaction 
BEGIN TRY 
UPDATE Account SET Debit=100 WHERE Name='John Cena' 
UPDATE ContactInformation SET Mobile='1234567890' WHERE Name='The Rock' 
COMMIT TRANSACTION MyTransaction 
PRINT 'TRANSACTION SUCCESS' 
END TRY 
BEGIN CATCH 
ROLLBACK TRANSACTION MyTransaction 
PRINT 'TRANSACTION FAILED' 
END CATCH 
Read Committed

This prevents Dirty Read. When this level is set, the transaction can not read the data that is being modified by the current transaction. This will force user to wait for the current transaction to finish up its job. Suppose User A is trying to read a row which is being updated by User B. Here, we are asking User A to wait for the User B to finish its update task, and giving the updated/correct data to User A. But the problem with this level is - it can't resolve Phantom Read or Inconsistency Analysis i.e it asks User A to wait for Read but not for update or insert.

Example
SET TRANSACTION ISOLATION LEVEL  
READ COMMITTED 
BEGIN TRANSACTION MyTransaction 
BEGIN TRY 
UPDATE Account SET Debit=100 WHERE Name='John Cena' 
UPDATE ContactInformation SET Mobile='1234567890' WHERE Name='The Rock' 
COMMIT TRANSACTION MyTransaction 
PRINT 'TRANSACTION SUCCESS' 
END TRY 
BEGIN CATCH 
ROLLBACK TRANSACTION MyTransaction 
PRINT 'TRANSACTION FAILED' 
END CATCH 
Repeatable Read

This level does every work that Read Committed does. but it has one additional benefit. User A will wait for the transaction being executed by User B to execute it's Update query as well, like Read Query. But Insert query doesn't wait, this also creates Phantom Read problem.

Example
SET TRANSACTION ISOLATION LEVEL  
REPEATABLE READ
Snapshot

This level takes a snapshot of current data. Every transaction works on its own copy of data. When User A tries to update or insert or read anything, we ask him to re-verify the table row once again from the starting time of its execution, so that he can work on fresh data. with this level. We are not giving full faith to User A that he is going to work on fresh data but giving high-level changes of data integrity.

Example
SET TRANSACTION ISOLATION LEVEL  
SNAPSHOT 
Serializable

This is the maximum level of Isolation level provided by SQL Server transaction. We can prevent Phantom Read problem by implementing this level of isolation. It asks User A to wait for the current transaction for any kind of operation he wants to perform.

Example
SET TRANSACTION ISOLATION LEVEL  
SERIALIZABLE

Isolation level also has a problem called "Dead Lock"- "Both the transactions lock the object and waits for each other to finish up the job". Dead Lock is very dangerous because it decreases the concurrency and availability of database and the database object. We will discuss Dead-Lock in the later part of this topic.

Hope, I covered the necessary points that can help many of us to implement Isolation level in the project with the identification of real situations. Believe me friends, it is a good practice to implement Transaction with "Isolation Level" in every type of projects.

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In this post, I will show you how to using bigint with FORMATMESSAGE. SQL Server 2016 added the FORMATMESSAGE function.  According to Books On Line, FORMATMESSAGE constructs a message from an existing message in sys.messages or from a provided string. The functionality of FORMATMESSAGE resembles that of the RAISERROR statement. However, RAISERROR prints the message immediately, while FORMATMESSAGE returns the formatted message for further processing.

So let's take a look at this new function, run the following
SELECT FORMATMESSAGE('Signed int %i, %i', 50, -50)
SELECT FORMATMESSAGE('Unsigned int %u, %u', 50, -50);

Here is the output if you run that

--------------------------------------------
Signed int 50, -50
Unsigned int 50, 4294967246

Here is what the type specifications that you can use are

Type specification         Represents
d or i                          Signed integer
o                                Unsigned octal
s                                String
u                               Unsigned integer
x or X                         Unsigned hexadecimal

We used i to denote a signed integer, we also used u to denote a unsigned integer

Let's look at another example, this time we are using a variable. The variable will be an integer and we  are using i as the type specification

DECLARE @Val int = 1
SELECT FORMATMESSAGE('The value you supplied %i is incorrect!', @Val);

Here is the output
---------------------------------------
The value you supplied 1 is incorrect!

That worked without a problem. Now let's use a variable of the bigint data type, we are using the same type specification as before

DECLARE @Val bigint = 1
SELECT FORMATMESSAGE('The value you supplied %i is incorrect!', @Val);

Here is the output
---------------------------------------------------------------------------
Error: 50000, Severity: -1, State: 1. (Params:).

The error is printed in terse mode because there was error during formatting.
Tracing, ETW, notifications etc are skipped.

As you can see that did not work, so what can we do?
One thing we can do is converting the value to a varchar and then use s as the type specification
DECLARE @Val bigint = 1
SELECT FORMATMESSAGE('The value you supplied %s is incorrect!',
   CONVERT(VARCHAR(100),@Val));

You will again get this as output
---------------------------------------

So converting to varchar worked, but what if we want to use a bigint data type without converting to a varchar?

Another way is to use I64d as the type specification
DECLARE @Val bigint = 1<br />
SELECT FORMATMESSAGE('The value you supplied %I64d is incorrect!', @Val);

You will get this
---------------------------------------

The value you supplied 1 is incorrect!
So there you have it, if you want to use bigint with FORMATMESSGAE use I64d as the type specification, or convert to varchar and use s as the type specification.

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This article is about Auto Increment ID Column using nvarchar datatype in SQL Server 2016 . SQL server provides identity property to get auto-increment record values where the datatype used is “int”. This article explains a way to use nvarchar datatype column to get auto-increment record values.
e.g. S00001, E00001, INV00001. (Here there are prefix like S, E, INV used to denote the record ids.)

This article provides a solution to use alphanumeric record ids incremented automatically.

Solution
Create an Employee table as below,

Here “ID” column is for storing record id as similar to Serial No of any entry made. “EmployeeNo” column is used to store the alphanumeric auto increment id. “Name” stores the name of the Employee. Create a Windows Form with a TextBox and a Button control

The above figure shows the use of a textbox to enter name in the ”employee” table where the id is auto-incremented.
Code
    Import the NameSpace

Create 2 methods OpenConnection() and CloseConnection() to handle the SQL Server Connection object.
        private void OpenConnection(SqlConnection con) { 
            con.ConnectionString = "Data Source=.; Initial Catalog=demodb; Integrated Security=True"; 
            if (con.State == ConnectionState.Closed) { 
                con.Open(); 
            } 
        } 
        private void CloseConnection(SqlConnection con) { 
            con.Close(); 
        } 

On the Insert button click the id is auto-incremented and the relevant data is inserted in the employee table. (id, employeeno and name)
        SqlConnection con = new SqlConnection(); 
        OpenConnection(con); 
        string id = AutoIncrementID(); 
        int idLimit = 7; 
        string eno = "E" + ZeroAppend("0000000" + id, idLimit); 
        string query = "insert into employee values ('" + id + "', '" + eno + "', '" + txtName.Text + "')"; 
        SqlCommand cmd = new SqlCommand(query, con); 
        int i = cmd.ExecuteNonQuery(); 
        if (i > 0) { 
            MessageBox.Show("Data Added", "Add", MessageBoxButtons.OK, MessageBoxIcon.Information); 
        } 
        CloseConnection(con); 

Here a method AutoIncrementID() is used to do the task of auto-incrementing based on the last entry inserted in the table.
        private string AutoIncrementID() { 
            SqlConnection con = new SqlConnection(); 
            OpenConnection(con); 
            SqlCommand cmd = new SqlCommand("SELECT ISNULL(MAX(ID),0) + 1 from employee", con); 
            SqlDataReader dr = cmd.ExecuteReader(); 
            string id = null; 
            if (dr.Read()) { 
                id = dr[0].ToString(); 
            } 
            CloseConnection(con); 
            return id; 
        } 
        //The function ZeroAppend is used to append “0” after the prefix to the code. 
        public static string ZeroAppend(string data, int idLimit) { 
            return data.Substring(data.Length - idLimit); 
        } 

Output,

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This is something interesting; i.e., how to enforce the compiler to use the inner join instead of the outer join. Let's take an example. We have two tables State_Tab and City_Tab. An SQL JOIN clause is used to combine rows from two or more tables, based on a common field between them. The most common type of join is: SQL INNER JOIN (simple join). An SQL INNER JOIN returns all rows from multiple tables where the join condition is met.

State_Tab

City_Tab

Now, we write two queries, using outer join, and compare the execution plan of both the queries.

Query 1

Query 2

We can see that both the queries use the left outer join but the second query implements the inner join, instead of outer join, because for "On condition", we define the 1=1, which will be always true, so the query optimizer converts the left outer join to cross join due to the "Where condition". This cross join will convert into the inner join.

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With over a thousand votes on the Microsoft Connect site, JSON support is the most requested feature for SQL Server 2016. This month, Microsoft announced that the upcoming release of SQL Server 2016 will fulfill that request. Sort of.

Microsoft will certainly be touting this feature as an additional reason to upgrade when the time comes. Natively supporting JSON helps bridge the gap between the desirable aspects of a NoSQL database and a relational database like SQL Server. The trouble is that with this current implementation, you're not really gaining anything over what's currently available. It's nothing like the native XML data type that became available starting with SQL Server 2005.

The announcement starts off with a big caveat: they will be providing native JSON support, but not a native JSON type. In fact, storage of JSON data will happen the same way it happens today, in a NVARCHAR typed column. They list 3 hollow reasons for this:

• Backward compatibility
• Cross feature compatibility
• Non Microsoft controlled JSON parsers on the client (C#)

Backward compatibility is weak, if you're already storing JSON data you wouldn't have a hard time moving into the JSON type. Cross feature compatibility means that they're not interested in implementing JSON in other SQL Server components, so instead everything that already works with NVARCHAR (aka everything) will still work (aka nothing's changed). The client side JSON parser point is an odd one and it leads into my next gripe of this feature implementation.

On the client side, such as in a C# application, it's already common to serialize data back and forth in JSON using a JSON parsing library. The most common, though maybe not the fastest, is the Newtonsoft JSON.Net library which comes packaged with the default templates in ASP.NET projects etc. The fact that there are other options out there doesn't really matter in my opinion. Sure some behave differently, but in the end, a JSON object has a specific syntax and any proper parser should be able to serialize and deserialize the output of any other parser.

What's baffling are the features that are being supported in this native JSON support.

You can now use FOR JSON to export the results of a query as JSON. This means that you can make a normal T-SQL query and ask for the result back as a JSON formatted result.  OK, but I could have just serialized the result on the client side in literally one line of code. They use the example of returning the results directly from an OData request through a web service.

You can transform a JSON object to a relational table with OPENJSON. This means that within a T-SQL query, you can provide a JSON object as part of the query and each item in the JSON will be returned as a table row which can be used to query or insert records into a relational table. They use the example of loading a JSON document into rows of a database, but again, that can easily be done on the client site by deserializing the JSON Array first.

Finally they are providing some built in functions for processing JSON data in the database. ISJSON will check if a NVARCHAR column has JSON data and JSON_VALUE which provides some scalar selection ability similar to the dot notation of JavaScript, e.g. 

$.property1.property2.array1[5].property3.array2[15].property4

That's basically the least they could do with this implementation. And as for indexing of JSON data, you're left with some basic support on the level of full-text indexing of any other NVARCHAR column.

So in the end, this "native JSON support" is basically nothing more that a few convenience function on top of a normal NVARCHAR column. It takes a familiar eye about 2 minutes to uncover the sad truth about this feature and the response has shown that Microsoft isn't fooling anybody with this JSON implementation. That's not to say there aren't great features coming in SQL Server 2016 (stretch database seems pretty cool), but JSON support isn't one of them.

ORMs like Entity Framework have always been shrouded in a bit of controversy from SQL purists who aren't in control of the types of queries that these tools are building behind the scenes. While this is a perfectly valid stance to take, these same tools can often assist in getting things done much faster than tinkering with an endless chain of JOINs, sub-queries, etc.

But what if you want to know exactly what is going on behind the scenes? What SQL is being generated by Entity Framework? And is it efficient enough to suit your needs, or do you need to handle writing some yourself?

This post will cover a quick tip that will allow you to see the SQL behind the scenes and judge for yourself using Entity Framework Core.

How Did This Work Prior to Entity Framework Core?

Previously, you could use Reflection to create an ObjectQuery object and then call the ToTraceString()method to actually store the query results as seen below:

// Build a query using Entity Framework
var query = _context.Widgets.Where(w => w.IsReal && w.Id == 42); 
// Get the generated SQL
var sql = ((System.Data.Objects.ObjectQuery)query).ToTraceString(); 

And that's really it. The result of the ToTraceString() call will return a string variable containing the entire SQL query being executed.

Options for Entity Framework Core

The previous approach no longer works within the Entity Framework Core (EF7) world, so we have to resort to one of three options, which may vary depending on your needs:

• Using Built-in or Custom Logging. Logging the executing query using your logger of choice or the built-in Logger in .NET Core as mentioned in this tutorial.
• Using a Profiler. Using an SQL Profiler like MiniProfiler to monitor the executing query.
• Using Crazy Reflection Code. You can implement some custom reflection code similar to the older approach to perform the same basic concept.

Since both of the first two options are fairly well documented, we will be focusing on the crazy reflection approach.

Getting Behind the Scenes in Entity Framework Core

Using the following snippets of code, which rely on Reflection to resolve information about the compiler, parser, database, and fields being targeted, we can use those things to reconstruct what is actually going on behind the scenes.

NOTE: Since this relies on Reflection, it may be subject to breaking in the future, especially with any API changes.

public class IQueryableExtensions 
{
    private static readonly FieldInfo QueryCompilerField =
    typeof(EntityQueryProvider).GetTypeInfo().DeclaredFields.First
    (x => x.Name == "_queryCompiler");

    private static readonly PropertyInfo NodeTypeProviderField =
    QueryCompilerTypeInfo.DeclaredProperties.Single(x => x.Name == "NodeTypeProvider");

    private static readonly MethodInfo CreateQueryParserMethod =
    QueryCompilerTypeInfo.DeclaredMethods.First(x => x.Name == "CreateQueryParser");

    private static readonly FieldInfo DataBaseField =
    QueryCompilerTypeInfo.DeclaredFields.Single(x => x.Name == "_database");

    private static readonly FieldInfo QueryCompilationContextFactoryField =
    typeof(Database).GetTypeInfo().DeclaredFields.Single
    (x => x.Name == "_queryCompilationContextFactory");

    public static string ToSql<TEntity>
    (this IQueryable<TEntity> query) where TEntity : class
    {
        if (!(query is EntityQueryable<TEntity>)
        && !(query is InternalDbSet<TEntity>))
        {
            throw new ArgumentException("Invalid query");
        }

        var queryCompiler = (IQueryCompiler)QueryCompilerField.GetValue(query.Provider);
        var nodeTypeProvider =
        (INodeTypeProvider)NodeTypeProviderField.GetValue(queryCompiler);
        var parser = (IQueryParser)CreateQueryParserMethod.Invoke
        (queryCompiler, new object[] { nodeTypeProvider });
        var queryModel = parser.GetParsedQuery(query.Expression);
        var database = DataBaseField.GetValue(queryCompiler);
        var queryCompilationContextFactory =
        (IQueryCompilationContextFactory)QueryCompilationContextFactoryField.GetValue(database);
        var queryCompilationContext = queryCompilationContextFactory.Create(false);
        var modelVisitor =
        (RelationalQueryModelVisitor)queryCompilationContext.CreateQueryModelVisitor();
        modelVisitor.CreateQueryExecutor<TEntity>(queryModel);
        var sql = modelVisitor.Queries.First().ToString();

        return sql;
    }
}

And as far as actual usage goes, you would simply call the ToSql() method to return your SQL query string:

// Build a query using Entity Framework
var query = _context.Widgets.Where(w => w.IsReal && w.Id == 42); 
// Get the generated SQL
var sql = query.ToSql(); 

Now, I will show you how to Get The Header Information Of A Table in SQL Server. If someone asks the question, "How do I get the header information of the table in SQL Server?" the first solution that arises in our mind will be "use any condition that never matches," like 1=0 or 2=0. However, in this case, we only get the header information of the table, and no result will return.

It is true that using the above solution, we will not get any data and the result will be the header information of our table.

But this is not the best solution because in this case, SQL Server reads all rows and compares the conditions each time. That increases the turnaround time and resource complexity.

For this, SQL Server provides the "SET FMTONLY ON" keyword. "SET FMTONLY ON" returns only metadata to the client. It can be used to test the format of the response without actually running the query. This method returns header information without performing any read operation.

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Have you ever been in a situation where you've got to clear down some data within a database that's chock filled with constraints but don't desire to wipe out your precious relationships, indices and all that other jazz?

I found myself in a similar situation earlier this week, and required a clear-down script that might wipe out all of the data within a whole database, without being bothered by any existing constraints. Here it is.
    USE @YourTable;  
    EXEC sp_MSForEachTable "ALTER TABLE ? NOCHECK CONSTRAINT ALL"  
    EXEC sp_MSForEachTable "DELETE FROM ?"  
    EXEC sp_MSForEachTable "ALTER TABLE ? WITH CHECK CHECK CONSTRAINT ALL"  
    GO  

The script itself takes advantage of an undocumented stored procedure within SQL Server called sp_MSForEachTable that will actually iterate through all of the tables within a given database.

Now that we know we are going to be looping through each of the tables within the specified database, let's see what is going to happen to each of the tables.

ALTER TABLE ? NOCHECK CONSTRAINT ALL
This will disable any constraint checking that is present on the table (so, operations like deleting a primary key or a related object won't trigger any errors).

DELETE FROM ?
This will delete every record within the table.

ALTER TABLE ? WITH CHECK CHECK CONSTRAINT ALL
This re-enables the constraint checking, bringing your table back to its original state, sans data.

It is very important that you properly scope this query to the table that you are targeting to avoid any crazy data loss. While I don't think that you could just leave that out and execute on master, I wouldn't want to even risk testing that out (although feel free to try it out and let me know if it nukes everything).

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