Importing flatfiles to a SQL server with a varying number of columns

Ever been as frustrated as I when importing flatfiles to a SQL Server and the format suddenly changes in production?

The mostly used integration tools (like SSIS) are very dependent on the correct, consistent and same metadata when working with flatfiles.

I’ve come up with a solution that I would like to share with you.

When implemented, the process of importing flatfiles with changing metadata is handled in a structured, and most important, flawless way. Even if the columns change order or existing columns are missing.


When importing flatfiles to SQL server almost every standard integration tool (including TSQL bulkload) requires fixed metadata from the files in order to work with them.

This is quite understandable, as the process of data transportation from the source to the destination needs to know where to map every column from the source to the defined destination.

Let me make an example:

A source flatfile table like below needs to be imported to a SQL server database.

This file could be imported to a SQL Server database (in this example named FlatFileImport) with below script:

create table dbo.personlist (
	[name] varchar(20),
	[gender] varchar(10),
	[age] int,
	[city] varchar(20),
	[country] varchar(20)

BULK INSERT dbo.personlist
FROM 'c:\source\personlist.csv'
	FIELDTERMINATOR = ';',  --CSV field delimiter
	ROWTERMINATOR = '\n',   --Use to shift the control to next row

select * from dbo.personlist;

The result:

If the column ‘Country’ would be removed from the file after the import has been setup, the process of importing the file would either break or be wrong (depending on the tool used to import the file) The metadata of the file has changed.

-- import data from file with missing column (Country)
truncate table dbo.personlist;
BULK INSERT dbo.personlist
FROM 'c:\source\personlistmissingcolumn.csv'
	FIELDTERMINATOR = ';',  --CSV field delimiter
	ROWTERMINATOR = '\n',   --Use to shift the control to next row
select * from dbo.personlist;

With this example, the import seems to go well, but upon browsing the data, you’ll see that only one row is imported and the data is wrong.

The same would happen if the columns ‘Gender’ and ‘Age’ where to switch places. Maybe the import would not break, but the mapping of the columns to the destination would be wrong, as the ‘Age’ column would go to the ‘Gender’ column in the destination and vice versa. This due to the order and datatype of the columns. If the columns had the same datatype and data could fit in the columns, the import would go fine – but the data would still be wrong.

-- import data from file with switched columns (Age and Gender)
truncate table dbo.personlist;
BULK INSERT dbo.personlist
FROM 'c:\source\personlistswitchedcolumns.csv'
	FIELDTERMINATOR = ';',  --CSV field delimiter
	ROWTERMINATOR = '\n',   --Use to shift the control to next row
Importing flatfiles to a sql server

When importing the same file, but this time with an extra column (Married) – the result would also be wrong:

-- import data from file with new extra column (Married)
truncate table dbo.personlist;
BULK INSERT dbo.personlist
FROM 'c:\source\personlistextracolumn.csv'
	FIELDTERMINATOR = ';',  --CSV field delimiter
	ROWTERMINATOR = '\n',   --Use to shift the control to next row
select * from dbo.personlist; 

The result:

Above examples are made with pure TSQL code. If it was to be made with an integration tool like SQL Server Integration Services, the errors would be different and the SSIS package would throw more errors and not be able to execute the data transfer.

The cure

When using the above BULK INSERT functionality from TSQL the import process often goes well, but the data is wrong with the source file is changed.

There is another way to import flatfiles. This is using the OPENROWSET functionality from TSQL.

In section E of the example scripts from MSDN, it is described how to use a format file. A format file is a simple XML file that contains information of the source files structure – including columns, datatypes, row terminator and collation.

Generation of the initial format file for a curtain source is rather easy when setting up the import.

But what if the generation of the format file could be done automatically and the import process would be more streamlined and manageable – even if the structure of the source file changes?

From my GitHub project you can download a home brewed .NET console application that solves just that.

If you are unsure of the .EXE files content and origin, you can download the code and build your own version of the GenerateFormatFile.exe application.
Another note is that I’m not hard core .Net developer, so someone might have another way of doing this. You are very welcome to contribute to the GitHub project in that case.

The application demands inputs as below:

Example usage:

generateformatfile.exe -p c:\source\ -f personlist.csv -o personlistformatfile.xml -d ;

Above script generates a format file in the directory c:\source\ and names it personlistFormatFile.xml.

The content of the format file is as follows:

The console application can also be called from TSQL like this:

-- generate format file
declare @cmdshell varchar(8000);
set @cmdshell = 'c:\source\generateformatfile.exe -p c:\source\ -f personlist.csv -o personlistformatfile.xml -d ;'
exec xp_cmdshell @cmdshell;

If by any chance the xp_cmdshell feature is not enabled on your local machine – then please refer to this post from Microsoft: Enable xp_cmdshell

Using the format file

After generation of the format file, it can be used in TSQL script with OPENROWSET.

Example script for importing the ‘personlist.csv’

-- import file using format file
select *  
into dbo.personlist_bulk
from  openrowset(
	bulk 'c:\source\personlist.csv',  
	) as t;
select * from dbo.personlist_bulk;

This loads the data from the source file to a new table called ‘personlist_bulk’.

From here the load from ‘personlist_bulk’ to ‘personlist’ is straight forward:

-- load data from personlist_bulk to personlist
truncate table dbo.personlist;
insert into dbo.personlist (name, gender, age, city, country)
select * from dbo.personlist_bulk;
select * from dbo.personlist;
drop table dbo.personlist_bulk;

Load data even if source changes

Above approach works if the source is the same every time it loads. But with a dynamic approach to the load from the bulk table to the destination table it can be assured that it works even if the source table is changed in both width (number of columns) and column order.

For some the script might seem cryptic – but it is only a matter of generating a list of column names from the source table that corresponds with the column names in the destination table.

-- import file with different structure
-- generate format file
if exists(select OBJECT_ID('personlist_bulk')) drop table dbo.personlist_bulk
declare @cmdshell varchar(8000);
set @cmdshell = 'c:\source\generateformatfile.exe -p c:\source\ -f personlistmissingcolumn.csv -o personlistmissingcolumnformatfile.xml -d ;'
exec xp_cmdshell @cmdshell;
-- import file using format file
select *  
into dbo.personlist_bulk
from  openrowset(
	bulk 'c:\source\personlistmissingcolumn.csv',  
	) as t;
-- dynamic load data from bulk to destination
declare @fieldlist varchar(8000);
declare @sql nvarchar(4000);
select @fieldlist = 
					',' + QUOTENAME(r.column_name)
						from (
							select column_name from INFORMATION_SCHEMA.COLUMNS where TABLE_NAME = 'personlist'
							) r
							join (
								select column_name from INFORMATION_SCHEMA.COLUMNS where TABLE_NAME = 'personlist_bulk'
								) b
								on b.COLUMN_NAME = r.COLUMN_NAME
						for xml path('')),1,1,'');
print (@fieldlist);
set @sql = 'truncate table dbo.personlist;' + CHAR(10);
set @sql = @sql + 'insert into dbo.personlist (' + @fieldlist + ')' + CHAR(10);
set @sql = @sql + 'select ' + @fieldlist + ' from dbo.personlist_bulk;';
print (@sql)
exec sp_executesql @sql

The result is a TSQL statement what looks like this:

truncate table dbo.personlist;
insert into dbo.personlist ([age],[city],[gender],[name])
select [age],[city],[gender],[name] from dbo.personlist_bulk;

The exact same thing would be able to be used with the other source files in this demo. The result is that the destination table is correct and loaded with the right data every time – and only with the data that corresponds with the source. No errors will be thrown.

From here there are some remarks to be taken into account:

  1. As no errors are thrown, the source files could be empty and the data updated could be blank in the destination table. This is to be handled by processed outside this demo.

Further work

As this demo and post shows it is possible to handle dynamic changing flat source files. Changing columns, column order and other changes, can be handled in an easy way with a few lines of code.

Going from here, a suggestion could be to set up processes that compared the two tables (bulk and destination) and throws an error if X amount of the columns are not present in the bulk table or X amount of columns are new.

It is also possible to auto generate missing columns in the destination table based on columns from the bulk table.

Only your imagination sets the boundaries here.

Summary – importing flatfiles to a SQL server

With this blogpost I hope to have given you inspiration to build your own import structure of flatfiles in those cases where the structure might change.

As seen above the approach needs some .Net skills – but when it is done and the console application has been build, it is a matter of reusing the same application around the different integration solutions in your environment.

Happy coding 🙂

External links:




GitHub link:

Undelete object from database


Have you ever tried to delete an object from the database by mistake or other error? You can undelete object – sometimes.

Then you should read on in this short post.

I recently came across a good co-worker of mine who lost one of the views on the developer database. He called me for help.

Fortunately the database was in FULL RECOVERY mode – so I could extract the object from the database log and send the script to him for his further work that day. I think I saved him a whole day of work…

The undelete object script

Here is the script I used:

	convert(varchar(max),substring([RowLog Contents 0], 33, LEN([RowLog Contents 0]))) as [Script]
where 1=1
	and [Operation]='LOP_DELETE_ROWS' 
	and [Context]='LCX_MARK_AS_GHOST'
and [AllocUnitName]='sys.sysobjvalues.clst'

Ready, SET, go – how does SQL server handle recursive CTE’s

This blogpost will cover some of the basics in recursive CTE’s and explain the approach done by the SQL Server engine.

First of all, a quick recap on what a recursive query is.

Recursive queries are useful when building hierarchies, traverse datasets and generate arbitrary rowsets etc. The recursive part (simply) means joining a rowset with itself an arbitrary number of times.

A recursive query is defined by an anchor set (the base rowset of the recursion) and a recursive part (the operation that should be done over the previous rowset).

The basics in recursive CTE

A recursive query helps in a lot of scenarios. For instance, where a dataset is built as a parent-child relationship and the requirement is to “unfold” this dataset and show the hierarchy in a ragged format.

A recursive CTE has a defined syntax – and can be written in general terms like this – and don’t run way because of the general syntax – a lot of examples (in real code) will come:

select result_from_previous.*
 from result_from_previous
 union all
 select result_from_current.*
 from set_operation(result_from_previous, mytable) as result_from_current

Or rewritten in another way:

select result_from_previous.*
 from result_from_previous
 union all
 select result_from_current.*
 from result_from_previous.*
 join mytable
 on condition(result_from_previous)

Another way to write the query (using cross apply):

select result_from_current.*
from result_from_previous
cross apply (
select result_from_previous.*
union all
select *
from mytable
where condition(result_from_previous.*)
) as result_from_current

The last one – with the cross apply – is row based and a lot slower than the other two. It iterates over every row from the previous result and computes the scalar condition (which returns true or false). The same row then gets compared to each row in mytable and the current row of result_from_previous. When these conditions are real – the query can be rewritten as a join. Why you should not use the cross apply for recursive queries.

The reverse – from join to cross apply – is not always true. To know this, we need to look at the algebra of distributivity.

Distributivity algebra

Most of us have already learned that below mathematics is true:

X x (Y + Z) = (X x Y) + (X x Z)

But below is not always true:

X ^ (Y x Z) = (X ^ Z) x (X ^ Y)

Or said with words, distributivity means that the order of operations is not important. The multiplication can be done after the addition and the addition can be done after the multiplication. The result will be the same no matter what.

This arithmetic can be used to generate the relational algebra – it’s pretty straight forward:

set_operation(A union all B, C) = set_operation(A, C) union all set_operation(B, C)

The condition above is true as with the first condition in the arithmetic.

So the union all over the operations is the same as the operations over the union all. This also implies that you cannot use operators like top, distinct, outer join (more exceptions here). The distribution is not the same between top over union all and union all over top. Microsoft has done a lot of good thinking in the recursive approach to reach one ultimate goal – forbid operators that do not distribute over union all.

With this information and knowledge our baseline for building a recursive CTE is now in place.

The first recursive query

Based on the intro and the above algebra we can now begin to build our first recursive CTE.

Consider a sample rowset (sampletree):

id parentId name
1 NULL Ditlev
2 NULL Claus
3 1 Jane
4 2 John
5 3 Brian

From above we can see that Brian refers to Jane who refers to Ditlev. And John refers to Claus. This is fairly easy to read from this rowset – but what if the hierarchy is more complex and unreadable?

A sample requirement could be to “unfold” the hierarchy in a ragged hierarchy so it is directly readable.

The anchor

We start with the anchor set (Ditlev and Claus). In this dataset the anchor is defined by parentId is null.

This gives us an anchor-query like below:

recursive CTE 1

Now on to the next part.

The recursive

 After the anchor part, we are ready to build the recursive part of the query.

The recursive part is actually the same query with small differences. The main select is the same as the anchor part. We need to make a self join in the select statement for the recursive part.

Before we dive more into the total statement – I’ll show the statement below. Then I’ll run through the details.

recursive CTE 2

Back to the self-reference. Notice the two red underlines in the code. The top one indicates the CTE’s name and the second line indicates the self-reference. This is joined directly in the recursive part in order to do the arithmetic logic in the statement. The join is done between the recursive results parentId and the id in the anchor result. This gives us the possibility to get the name column from the anchor statement.

Notice that I’ve also put in another blank field in the anchor statement and added the parentName field in the recursive statement. This gives us the “human readable” output where I can find the hierarchy directly by reading from left to right.

To get data from the above CTE I just have to make a select statement from this:

recursive CTE 3

And the results:

recursive CTE 4

I can now directly read that Jane refers to Ditlev and Brian refers to Jane.

But how is this done when the SQL engine executes the query – the next part tries to explain that.

The SQL engines handling

Given the full CTE statement above I’ll try to explain what the SQL engine does to handle this.

The documented semantics is as follows:

  1. Split the CTE into anchor and recursive parts
  2. Run the anchor member creating the first base result set (T0)
  3. Run the recursive member with Ti as an input and Ti+1 as an output
  4. Repeat step 3 until an empty result set is returned
  5. Return the result set. This is a union all set of T0 to Tn

So let me try to rewrite the above query to match this sequence.

The anchor statement we already know:

recursive CTE 5

First recursive query:

recursive CTE 6

Second recursive query:

recursive CTE 7

The n recursive query:

The union all statement:

This gives us the exactly same result as we saw before with the rewrite:

Notice that the statement that I’ve put in above named Tn is actually empty. This to give the example of the empty statement that makes the SQL engine stop its execution in the recursive CTE.

This is how I would describe the SQL engines handling of a recursive CTE.

Based on this very simple example, I guess you already can think of ways to use this in your projects and daily tasks.

But what about the performance and execution plan?


The execution plan for the original recursive CTE looks like this:

The top part of this execution plan is the anchor statement and the bottom part is the recursive statement.

Notice that I haven’t made any indexes in the table, so we are reading on heaps here.

But what if the data is more complex in structure and depth. Let’s try to base the answer on an example:

From the attached sql code you’ll find a script to generate +20.000 rows in a new table called complextree. This data is from a live solution and contains medical procedure names in a hierarchy. The data is used to show the relationships in medical procedures done by the Danish hospital system. It is both deep and complex in structure. (Sorry for the Danish letters in the data…).

When we run a recursive CTE on this data – we get the exactly same execution plan:

This is also what I would expect as the amount of data when read from heaps very seldom impact on the generated execution plan.

The query runs on my PC for 25 seconds.

Now let me put an index in the table and let’s see the performance and execution plan.

The index is only put on the parentDwId as, according to our knowledge from this article is the recursive parts join column.

The query now runs 1 second to completion and generates this execution plan:

The top line is still the anchor and the bottom part is the recursive part. Notice now the SQL engine uses the non-clustered index to perform the execution and the performance gain is noticeable.


I hope that you’ve now become more familiar with the recursive CTE statement and are willing to try it on your own projects and tasks.

The basics is somewhat straight forward – but beware that the query can become complex and hard to debug as the demand for data and output becomes stronger. But don’t be scared. As I always say – “Don’t do a complex query all at once, start small and build it up as you go along”.

Happy coding.

External links:

The with operator in T-SQL:

Recursive CTE’s from MSDN:

Wikipedia on distributivity:

Use of hierarchyid in SQL Server

I attended a TDWI conference in May 2016 in Chicago. Here I got a hint about the datatype hierarchyid in SQL Server which could optimize and eliminate the good old parent/child hierarchy.

Until then I (and several other in the class) haven’t heard about the hierarchyid datatype in SQL Server. So I had to find out and learn this.

Here’s a blogpost covering some of the aspects of the datatype hierarchyid – including:

  • Introduction
  • How to use it
  • How to optimize data in the table
  • How to work with data in the hierarchy-structure
  • Goodies


The datatype hierarchyid was introduced in the SQL Server as from version 2008. It is a variable length system datatype. The datatype can be used to represent a given element’s position in a hierarchy – e.g. an employee’s position within an organization.

The datatype is extremely compact. The storage is dependent in the average fanout (fanout = the number of children in all nodes). For smaller fanouts (0-7) the typical storage is about 6 x Log A * n bits. Where A is the average fanout and n in the total number of nodes in the tree. Given above formula an organization with 100,000 employees and a fanout of 6 levels will take around 38 bits – rounded to 5 bytes of total storage for the hierarchy structure.

Though the limitation of the datatype is 892 bytes there is a lot of room for extremely complex and deep structures.

When representing the values to and from the hierarchyid datatype the syntax is:

[level id 1]/[level id 2]/..[level id n]



The data between the ‘/ can be of decimal types e.g. 0.1, 2.3 etc.

Given two specific levels in the hierarchy a and b given that a < b means that b comes after a in a depth first order of comparison traversing the tree structure. Any search and comparison on the tree is done this way by the SQL engine.

The datatype directly supports deletions and inserts through the GetDescendant method (see later for full list of methods using this feature). This method enables generation of siblings to the right of any given node and to the left of any given node. Even between two siblings. NOTE: when inserting a new node between two siblings will produce values that are slightly less compact.

Hierarchyid in SQL Server how to use it

Given an example of data – see compete sql script at the end of this post to generate the example used in this post.

hierarchyid in SQL Server 1

The Num field is a simple ascending counter for each level member in the hierarchy.

There are some basic methods to be used in order to build the hierarchy using the hierarchy datatype.

GetRoot method

The GetRoot method gives the hierarchyid of the rootnode in the hierarchy. Represented by the EmployeeId 1 in above example.

The code and result could look like this:

hierarchyid in SQL Server 2

The value ‘0x’ from the OrgPath field is the representation of the string ‘/’ giving the root of the hierarchy. This can be seen using a simple cast to varchar statement:

hierarchyid in SQL Server 3

Building the new structure with the hierarchyid dataype using a recursive SQL statement:

hierarchyid in SQL Server 4

Notice the building of the path after the union all. This complies to the above mentioned syntax for building the hierarchy structure to convert to a hierarchyid datatype.

If I was to build the path for the EmployeeId 10 (Name = ‘Mads’) in above example it would look like this: ‘/2/2/’. A select statement converting the hierarchyid field OrgPath for the same record, reveals the same thing:

hierarchyid in SQL Server 5

Notice the use of the ToString method here. Another build in method to use for the hierarchyid in SQL Server.

GetLevel method

The GetLevel method returns the current nodes level with an index of 0 from the top:

hierarchyid in SQL Server 6

GetDescendant method

This method returns a new hierarchyid based on the two parameters child1 and child2.

The use of these parameters is described in the BOL HERE.

Below is showed some short examples on the usage.

Getting a new hierarchyid when a new employee referring to top manager is hired:

hierarchyid in SQL Server 7

Getting a new hierarchyid when a new hire is referring to Jane on the hierarchy:

hierarchyid in SQL Server 8

Dynamic insert new records in the hierarchy table – this can easily be converted into a stored procedure:

hierarchyid in SQL Server 9

Notice the new GetAncestor method which takes one variable (the number of steps up the hierarchy) and returns that levels Hierarchyid. In this case just 1 step up the hierarchy.

More methods

There are several more methods to use when working on a hierarchy table – as found on BOL:

GetDescendant – returns a new child node of a given parent. Takes to parameters.

GetLevel – returns the given level for a node (0 index)

GetRoot – returns a root member

ToString – converts a hierarchyid datatype to readable string

IsDescendantOf – returns boolean telling if a given node is a descendant of given parent

Parse – converts a string to a hierarchyid

Read – is used implicit in the ToString method. Cannot be called by the T-SQL statement

GetParentedValue – returns node from new root in case of moving a given node

Write – returns a binary representation of the hierarchyid. Cannot be called by the T-SQL statement.


As in many other scenarios of the SQL Server the usual approach to indexing and optimization can be used.

To help on the usual and most used queries I would make below two indexes on the example table:

hierarchyid in SQL Server 10

But with this like with any other indexing strategy – base it on the given scenario and usage.


So why use this feature and all the coding work that comes with it?

Well – from my perspective – it has just become very easy to quickly get all elements either up or down from a given node in the hierarchy.

Get all descendants from a specific node

If I would like to get all elements below Jane in the hierarchy I just have to run this command:

hierarchyid in SQL Server 11

Think of the work you would have to do if this was a non hierarchy structured table using only parent/child and recursice sql if the structure was very complex and deep.

I know what I would choose.


As seen above the datatype hierarchyid can be used to give order to the structure of a hierarchy in a way that is both efficient and fairly easy maintained.

If one should optimize the structure even further, then the EmployeeId and the ManagerId could be dropped as the EmployeeId is now as distinct as the OrgPath and can be replaced by this. The ManagerId is only used to build the structure – but this is now also given by the OrgPath.

Happy coding…

External references:

Hierarchyid from MSDN

Using hierarchyid from TechNet

Update SCD Type 2 dimension in one single transaction using only T-SQL

Merge logo new

Recently I got a request inside my organization to make sure that a SCD Type 2 dimension would keep track of the changes due to requrementes from the business.

This needed to be done in a single transaction in pure T-SQL code.

So – what to do and how to do it. Here’s one way.

The sourcetable looks like this:

SCD type 2 dimension

The request was to keep track of changes in the ManagerId according to CaseId.

I’ve created a SCD2 table like this:

CREATE TABLE [dbo].[CaseProjectManagerHistory](
	[dwid] [bigint] IDENTITY(1,1) NOT NULL,
	[CaseId] [int] NULL,
	[ManagerId] [int] NULL,
	[dwDateFrom] [date] NULL,
	[dwDateTo] [date] NULL,
	[dwIsCurrent] [bit] NULL,
	[dwChangeDate] [date] NULL

The fields are as follows:
dwid: Identifier for the table
CaseId: The caseid for the rows
ManagerId: The managerid for the row
dwDateFrom: The date from where the row is actual
dwDateTo: The date to where the row is actual
dwIsCurrent: Boolean that tells if the row is the current one or not
dwChangeDate: The date of the change (if the row has changed since the first write)

If you need to catch up on the history types in a dimension – then take a look at Kennie’s blogppost HERE.

First of all I started out with a merge statement that would insert all the new values not in the table and update the ones that needed update.

Something like this:

merge dbo.CaseProjectManagerHistory as target
	using (select CaseId, ManagerId, cast(getdate() as date) as startDate, datefromparts(2199,1,1) as endDate, 1 as [current], cast(getdate() as date) as changeDate from dbo.[Case]) as source
	on target.CaseId = source.CaseId
	when not matched by target
			insert (CaseId, ManagerId, dwDateFrom, dwDateTo, dwIsCurrent, dwChangeDate)
			values (source.CaseId, source.ManagerId, source.startDate, source.endDate, source.[current], source.changeDate)
	when matched 
		and target.dwIsCurrent = 1
		and exists (select source.CaseId, source.ManagerId
					select target.CaseId, target.ManagerId)
		and target.dwChangeDate <= source.ChangeDate
		and source.changeDate < target.dwDateTo
			update set dwIsCurrent = 0, target.dwChangeDate = source.changeDate, target.dwDateTo = dateadd(d,-1,source.startDate)

Those of you who haven’t tried and worked with a merge-statement – you can get the 101 from BOL here.

But this merge statement only inserts new rows and updates existing rows. The rows that are updated still needs to be in the table in order to fully apply to the SCD 2 rules.

This can be done by using the cluse ‘output’ from the merge-statement and then use the output rows to insert into the same table.

It will look like this:

insert into dbo.CaseProjectManagerHistory_demo (CaseId, ManagerId, dwDateFrom, dwDateTo, dwIsCurrent, dwChangeDate)
select CaseId, ManagerId, startDate, endDate, [current], changeDate 
from (
	merge dbo.CaseProjectManagerHistory_demo as target
	using (
			,cast(getdate() as date) as startDate
			,datefromparts(2199,1,1) as endDate
			,1 as [current]
			,cast(getdate() as date) as changeDate 
		where 1=1
			and caseid in (2005,2013,2015,2016,2019,2021,2023,2025,2027,2028)
			) as source
	on target.CaseId = source.CaseId
	when not matched by target -- indsæt nye rækker
			insert (CaseId, ManagerId, dwDateFrom, dwDateTo, dwIsCurrent, dwChangeDate)
			values (source.CaseId, source.ManagerId, source.startDate, source.endDate, source.[current], source.changeDate)
	when matched -- opdater eksisterende rækker
		and target.dwIsCurrent = 1
		and exists (select source.CaseId, source.ManagerId --filtrer kun på rækker der ikke allerede eksisterer i target
					select target.CaseId, target.ManagerId)
		and target.dwChangeDate <= source.ChangeDate
		and source.changeDate < target.dwDateTo
		update set dwIsCurrent = 0, target.dwChangeDate = source.changeDate, target.dwDateTo = dateadd(d,-1,source.startDate) 
		output $action ActionOut, source.CaseId, source.ManagerId, source.startDate, source.endDate, source.changeDate, source.[current]) as mergeOutput
where mergeOutput.ActionOut = 'UPDATE';

The mergestatement ‘output’ action is used to insert the same rows to the history table once more. The only change is the ‘end date’.

Happy coding!

Note: I did a short presentation with this at my workplace a few weeks ago, and here Kennie (l, b, t) told me that there is a bug in the merge statement that needs to be taken into account. Read more of that here.

Enlarge AdventureWorks2012


Just recently I had to have a big datawarehouse solution to test some performance optimization using BIML.
I could use the AdventureWorks2012 database, but I needed the clean datawarehouse tables in order to have minimum data maintennance when testing the BIML scripts.

I could not find it, and figures out it was faster to make my own.

So heavily inspired by this post from Jonathan Kehayias (blog), I’ve made a script that can be used to enlarge the dbo.FactInternetSales table.

The script creates a new table called dbo.FactInternetSalesEnlarged and copies data from dbo.FactInternetSales into it with a randomizer. Exploding the data to a 100 times bigger table – est. 6 mio rows.

Get the script here:


Happy coding 🙂

SSIS expressions I often use


If either you are doing your SSIS expressions by hand or using the BIML framework, you’ve came across the expressions and the expression-builder.

This is a helper list, with my most often used, and wich are allways forgotten when I need them, of my commonly used SSIS expressions.


Filename from fully qualified pathstring

  • RIGHT([FilePath],FINDSTRING(REVERSE([FilePath]),”\\”,1) – 1)

Folderpath from fully qualified pathstring

  • SUBSTRING([FilePath], 1, LEN([FilePath]) – FINDSTRING(REVERSE([FilePath] ), “\\” ,1 ) + 1)

Foldername from fully qualified pathstring

  • TOKEN[FilePath],”\\”,TOKENCOUNT([FilePath],”\\”) – 1)

This is only for SSIS2012 and onwards. The TOKEN and TOKENCOUNT expressions are not in prior versions of SQL Server

For prior versions of SQL Server:

  • SUBSTRING([FilePath],LEN([FilePath]) – FINDSTRING(REVERSE([FilePath]),”\\”,2) + 2,(LEN([FilePath]) – FINDSTRING(REVERSE([FilePath]),”\\”,1)) – (LEN([FilePath]) – FINDSTRING(REVERSE([FilePath]),”\\”,2)) – 1)

Replace empty strings

With SQL Server 2012 the new REPLACENULL function was implemented, making it alot easier to replace empty values.

  • REPLACENULL([ColumnName], [replace value])

For earlier versions of SQL Server

  • LEN([ColumnName]) == 0 ? [replace value] : [ColumnName]
  • ISNULL([ColumnName]) ? [replace value] : [ColumnName]

Date and time

Date from datetime

If you want to remove the time element in a datetime object, you should cast it to DT_DBDATE. But because that datatype is very inconvenient to use, you should cast it back to the original datatype. That will set the time to 0:00.

  • (DT_DATE)(DT_DBDATE)@[User::datetimeVariable]
  • (DT_DATE)(DT_DBDATE)[datetimeColumn]

Time from datetime

If you want to remove the date element in a datetime object, you should cast it to DT_DBTIME. And optional cast it to a string.

  • (DT_STR,8,1252)(DT_DBTIME)@[User::datetimeVariable]
  • (DT_STR,8,1252)(DT_DBTIME)[datetimeColumn]

First day of the current month

If you want to get the first day of the current month, you take the current datetime and deduct the current day number (minus 1). Optional you can remove the time part:

  • DATEADD(“d”, -DAY(GETDATE()) + 1, GETDATE())

Last day of the current month

If you want to get the last day of the current month, you add 1 month and deduct the current day number. Optional you can remove the time part:


And if you realy want the last second of the current month 30-06-2011 23:59:59


Weeknumber of the month

1-june-2012 is weeknumber 23 in the year, but weeknumber 1 of the month june 2012.

  • (DATEPART(“ww”,[YourDate]) – DATEPART(“ww”,DATEADD(“d”, -DAY([YourDate]) + 1, [YourDate]))) + 1

Datetime as concatenated string

  • (DT_STR, 4, 1252)DATEPART(“yyyy”, @[System::StartTime]) +
    RIGHT(“0” + (DT_STR, 2, 1252)DATEPART(“mm”, @[System::StartTime]), 2) +
    RIGHT(“0” + (DT_STR, 2, 1252)DATEPART(“dd”, @[System::StartTime]), 2) +
    RIGHT(“0” + (DT_STR, 2, 1252)DATEPART(“hh”, @[System::StartTime]), 2) +
    RIGHT(“0” + (DT_STR, 2, 1252)DATEPART(“mi”, @[System::StartTime]), 2) +
    RIGHT(“0” + (DT_STR, 2, 1252)DATEPART(“ss”, @[System::StartTime]), 2)

Multidimensional ROLAP partitions and updatable columnstore indexes – the new black?


I came across a colleague of mine, who asked me if the new updatable columnstore index and ROLAP partitions in a Multidimensional cube is the new trend of fast and no-latency Business Intelligence.

Well – here is my 25 cents.

I’ll start with the updatable columnstore indexes.

With SQL Server 2014 Microsoft introduces updatable columnstore indexes. Which in short terms defines that the columnstore no longer has to be dropped/disabled when loading data to the table. When the new data is loaded to the table, it is therefore first loaded to a temporary deltastore where background processes splits it into rowgroups, does indexing and compression.

This progress can be observed through the DMV sys.column_store_row_groups. Books online documents that every rowgroup can have 3 statuses: OPEN, CLOSED and COMPRESSED.

OPEN: A rowgroup in read/write state that is accepting new records. An OPEN rowgroup is still in rowstore format and has not been compressed to columnstore format yet.

CLOSED: A rowgroup which is filled and therefore locked for read/write. This rowgroup still needs compression

COMPRESSED: A rowgroup that has been locked and compressed and a part of the CS index.

The first two states are not yet part of the CS Index – but resides as internal objects in the database. They are not to be found in sys.objects or sys.partitions. They are there… They can be found trough the sys.system_internals_partitions view.

The latter one above, sys.partitions, only shows row groups that are in COMPRESED state and can therefore give wrong answers if it’s used to for example calculate space used.

Therefore, when a query is executed against a table that contains a CS index that has rowgroups in OPEN or CLOSED state we will not get full benefit of the CS index, as the engine then needs to read from the rowgroups and not the CS index. Even worse is it when data already resides in the CS index and new data is loaded to the table. Then the engine needs to look at both the CS index and the rowgroups. There is no guarantee that the CS index contains all the data that is required for the query, so even if it is so, then a scan of the rowgroups are still executed.

One could wish that the background process of getting the rowgroups from state OPEN to COMPRESSED are blazing fast. But it is not. It is slow slow slow. A quick test on a virtual machine (4 cores 6 GB ram) shows that the background process of getting the rowgroups to COMPRESSED state is averaging with approx. 180.000 rows pr. second.

Next up – the ROLAP partitions in Multidimensional

I will not go through the explanation of building a ROLAP partitions as they are found in very good blogs around the BI universe.

But I’ll take a look at the way the SSAS engine gets the data from a ROLAP partition. Based in the dimensional relationships inside the SSAS cube the engine generates a t-sql statement to get the data.

This statement is not pretty and is not optimized in any way. It cannot be altered or user defined in any way. I have seen these queries in Xevents – and they do not perform very good overall.


If the data is fully loaded every night in the layer just before the cube, and this data is more than a couple of million rows, then the combination is not a good option. It will take way to long time for the background process to get the CS index up and running, resulting in very poor performance at the end users.

If the updated data is small (a couple of 100.000 rows) and is only loaded at nighttime, then there can be arguments to use the combination. But then again, why not just load the data to a normal table and process the corresponding partition.

So – it depends. Partly on the solution build, the environment and the architecture.

Happy coding.