Unfortunately when you have a sorted result set in SSIS using T-SQL this sorting is not automatically picked up in the metadata. This subsequently causes additional issues with components like the Merge Join that expect the result set to be sorted and the metadata to pick this up. Because of this fact there are two additional steps that must be performed in the OLE DB Source.
- Add your sorted select statement to your OLE DB source.
- Right click and go to Show Advanced Editor.
- Click the last tab on top, “Input and Output Properties”.
- Click OLE DB Source Output
- Change the IsSorted property to True, the default is false.
- Now expand the tree view for “OLE DB Source Output” and expand “Output Columns”
- Find the column that you sorted in your result set and assign it a SortKeyPosition of 1.
Thanks for looking.
Why does the Cache Transform in SSIS have a file cache option? When would I use this, especially when compared with using the default option which is to store the results in memory? One downside of the default option of storing the results in memory is that the cache is cleared immediately once the package is completed and that memory cache cannot be shared among other packages.
However, the File connection option will allow the results to be reused among multiple packages. But hold up, that’s not quite as good as you might think, each package still must read from the file and then subsequently load that file into memory!
So why are we discussing this if it’s essentially doing the same thing as the default option? Good point, and well received, did you read the title?
Imagine now that you have a rather large table that you need to access many times during your nightly ETL, this is very common in data warehousing situations. You can now lessen the effect of a poor performing network by using the file cache option in the cache transform. You can bring the data across the network only once, load it into a file, and in the future you can reference that file on your local machine and avoid going across the network. As you can imagine this can be quite advantageous!
Set up is quite simple as well, see below:
- Bring the cache transform into your data flow and connect it to your source.
- Open the cache transform for editing.
- Select NEW for cache connection manager.
- Select “Use file Cache” and then browse to the location where the file will be stored.
- Next click the Column tab at the top and specify at least one index column.
- Click OK, map the columns, Click OK to close out the transform.
Now that the initial set up is complete I will demonstrate how to reference this file:
- In the next data flow I set up an OLE DB source and connection that output to a Lookup transform.
- Inside the lookup transform select “Cache Connection Manager” on the general screen.
- On the connection screen select your cache connection manager from the drop down list.
- And that’s it!, set up the rest of your lookup transform like you normally would.
Thanks for looking!
I have wanted to blog about this for quite some time. The Export Column is one of those SSIS components that you never use but need to know about just in case. This is also a very easy component to configure.
- The source query requires the photo column and the path of where you want to store the photo on your file system.
- Next bring the Export Column transform into your data flow and connect it to your source.
- Finally, open the export column and set the properties for the Extract Column(Photo) and File Path Column.
Execute the package and you’re done! This transform saved me some time ago when I needed to extract images for a report. Thanks for looking!
There are three generally accepted methods to perform the tricky range lookup in SSIS. A great blog post that outlines all three of these methods and how they perform can be found here. Per the post the best performing solution is unfortunately the one that is arguably the most difficult to set up. Fortunately for us we can get all the performance of scripting without all the work!
We need to perform a lookup to the segment table based on the Weekday and Time that the transaction took place. For example, assume we had a transaction take place on Tuesday at 15:30:00. This transaction would need to be mapped to the SegmentKey of 3. Screenshot of segment table below:
Now that we have identified our business requirement, let’s look at how we can use the Advanced Lookup Transform in Task Factory to solve this tricky problem.
- For this example we will need two data flow tasks.
- The first data flow task will load our segment table into the TF ADV Lookup Cache Transform.
- Inside the first data flow task I bring in an OLEDB Source and select my Segment table.
- Next I bring in the TF Advanced Lookup Cache transform and connect the two transforms. (Screenshot 1)
- Open the TF Cache transform and select “Create New Lookup Cache Transform” (Screenshot 2)
- Click the Input Parameter tab at the top. We are going to add two parameters, one for day and one for the time of the actual transaction. In the bottom dialog box we are going to define our conditions for when a lookup should occur. For this example we have three criteria.
- First the day of the transaction must equal the day in the Segment table.
- Second the begin time must be less than or equal to the time of the transaction.
- Finally the end time must be greater than or equal to the time of the transaction.
- Once the criteria have been entered click ok twice to close out of the cache connection manager.
- Now in our second data flow we will pull in our transactional data and perform our lookup. For this example I have written a simple select statement to simulate a transaction.
SELECT 'Tuesday' AS DayOfWeek, '15:30:00' AS TIME
- Now that the source has been set up we need to bring in the TF Advanced Lookup Transform and open it for editing.
- Select our Cache Connection manager from the drop down and then map our input columns to our input parameters. Screenshot below.
- Now execute the package!
Thanks for looking.
Recently I was working on a project where we needed to process upward of 12,000 files per day. As most would expect we used the Foreach Loop container in SSIS to loop through all 12,000 files. The results? A whopping 77 minutes to run. WOW! (Incase you were wondering this is WOW that’s AWEFUL!, not wow that’s great)
I assume the excessive time is here is due to the time it takes to dynamically set the flat file connection manager and close/reopen the connection. I don’t know exactly how long this process takes but hypothetically let’s say it takes 1 second for every 3 iterations of the loop.
Now some simple math. 12,000 files / 3 (files per second) / 60 seconds = 67 minutes.
So how did we get around this problem? Well we tried two different design patterns. The first and the one I will be blogging about here is the “MultiFlatfile” connection manager. The second, more complicated to set up, but extremely efficient can be found here: Anthony Martin.
Let’s performance test the MultiFlatFile connection manager!
For this post I created three packages.
- The first package generates 1000 flat files for testing.
- The second package iterates through all 1000 files. Runtime (22 Minutes)
- The third package uses the multiflatfile connection to bring in all the files instantaneously. (13 Seconds)
The Foreach loop got utterly destroyed in this contest. The multiflatfile connection manager was able to process all 1000 files in only 13 seconds!! Let’s look at how to set this up.
- Right click in the connection manager window and select New Connection > MultiFlatFile > Click Add
- Set up the general screen like your typical Flat File connection, you will need to hardcode an existing file.
- Click ok to close the connection manager editor, verify the connection manager is selected and then open up the properties window.
- In the properties window, scroll down to Expressions > Click the Ellipsis “…” and select “Connection String” from the drop down menu.
- Next click the ellipsis to the right to open up the Expression editor. This part is critical because this is where we set an expression that will determine which files are extracted from the directory. For this simple example my expression was “C:\\Blogs\\1000_Flat_Files\\Test*” (Don’t miss the asterisk here!). The connection manager will now retrieve all files that start with “Test”. Screenshots for set up below.
This is an awesome alternative to the For Each Loop and it’s much easier to set up, however it has some obvious drawbacks with how flexible it is. If you discover this pattern does not perform as well as you hoped or is not flexible enough please review the blog post I linked above by Anthony Martin. It takes a little more set up but offers incredible flexibility and is the best performing solution!
As always, thanks for looking!
Working with SSIS and the BI Stack from Microsoft I have discovered a few tips and tricks that come in quite handy. To my surprise a lot of these are not very well know in the BI World. In this series I am going to try and post one or two SSIS Tips and Tricks on a weekly basis.
As you are well aware the SELECT * syntax in SQL Server should not be used inside of SSIS. This is because the metadata in SSIS is very rigid and if you add or remove columns to the table in your select statement it can affect other transforms in the pipeline that have been defined with very specific metadata properties.
So if you are like many people who use SSIS it’s likely you alt + tab over to SQL Server Management Studios and then view the table and write your select syntax there with all the applicable columns for your query. Then you copy that syntax and alt + tab back over to BIDS / SSDTs and paste.
It’s unfortunate that there isn’t some kind of tool inside SSIS that will assist us with our incredibly difficult predicament. Just kidding, of course there is, if there wasn’t I wouldn’t be blogging about it! Let’s demonstrate.
First – Open up your OLE DB Source component in your data flow and write your Select * from table statement. For this example I used SELECT * FROM SALES.CUSTOMER in the AdventureWorks2012 database.
Next click “Build Query…” found on the right side of the OLE DB Source Editor.
Click ok and you are done! Thanks for looking and check back for more time saving tips and tricks.
If you have spent anytime with SSIS you have heard that Asynchronous components also known as “Blocking” components are bad for performance and should be avoided where possible. You have also heard some analogy or another that explained how these components “block” the data in the data flow and slow down performance. But I’m here to tell you that it gets much worse.
The data flow sends data down in buffers. Each buffer in the data flow will first utilize memory if available, the memory that is captured for that buffer of records will be released once the data flow is done with that buffer. For example once those records in that buffer have been loaded to the destination the buffer and the corresponding memory is released. This memory can now be used for the next buffer of memory coming down the pipeline in the data flow task. This is where the true evil with asynchronous components come in.
What happens if the buffer is not released because it got caught up in the data flow at some blocking transform? In a worse case scenario your machine/server becomes starved for memory and then SSIS is forced use disk instead of memory. Too many buffers spooled to disk can exponentially increase your package run time. Let me demonstrate.
For this example I have created two packages. Each package uses a sorting transform, however in different locations.
Package One. This package takes an unknown amount of time to run, I cut it off after approximately 68 minutes. The first screenshot shows the package in it’s current state, the second and third screenshot give some insight into why this is taking so long to run.
The following screenshot is obtained from SSMS after I run the package from integration services and then run the following query against the SSISDB.
- “SELECT * FROM [catalog].[dm_execution_performance_counters] (NULL) “
Screenshot 3: (Here we see the execution performance from the SSISDB)
Unfortunately there are times when you just have no choice and must use asynchronous components in SSIS. One option to consider, can the blocking transform be placed in a more strategic location to minimize the negative impact to performance? For example. In package one the sorting transform was placed before the lookup, the lookup is only forwarding non matching records to the destination, so why do the matching records need to be sorted? If matching records don’t need to be sorted then we can move the blocking transform to after the lookup and keep everything in memory.
Package Two: (This package completes in 1 minute and 20 seconds)
Thanks for looking!
I know I got your attention with that title. If you speak to any experienced BI Developer you will always be informed that you should perform all of your aggregations in T-SQL where possible in the source and not using the SSIS components. To this point I agree, well most of the time anyway. There are however times when using SSIS will out perform T-SQL if all the stars align. Let me explain.
Recently I was using a CTE and the row_number function in T-SQL to bring back only distinct customers from a list of transactions. This result set was then used to insert inferred members into the customer dimension, if that customer did not yet exist of course. Once the result set returned the list of distinct customers we performed a lookup against the customer dimension and we loaded the non matching customers into the customer dimension.
Now let me note, the T-SQL query honestly wasn’t that bad, it was only taking around 3 minutes to run. The bigger problem here was that the query was using temp db and this was causing locking issues with a more advanced design pattern (Partition switch) that the SQL Server Engine could not resolve.
So, we turned to good ole SSIS to help us out.
- First, bring all records from the source exactly as they were, all 30 million transactions.
- Next, perform the lookup to the customer dimension and redirect non matching rows to aggregate transform.
- Finally, use the aggregate transform to group the new customers and then load the distinct values into the customer dimension.
This works because we filter out all the existing customers before doing the aggregation in SSIS and therefore only small percentage of the records require aggregation. The data flow task to load inferred members runs in approximately 80 seconds, where as the original T-SQL query alone was taking over 3 minutes.
Thanks for looking!
There are quite a few different scenarios for when cache transforms can be used to improve usability and performance inside SSIS. In this post I am going to quickly outline a scenario where cache connection managers can greatly improve performance when loading a fact table.
During the pre-execution phase of package execution SSIS “caches” the lookups inside your package into memory, unfortunately if you are using OLE DB connections in your Data Flow Task this caching happens in a serialized format (one after the other). Typically this isn’t a big deal because you may only have one or two large dimension lookups and all others are very small in size and very fast. But what if this isn’t the case? What if you have 5, 6, 7, or more dimensions all with millions of records that you must cache before the package begins execution? Here is how we can get around this little issue.
- Inside the data flow tasks we will set each source to point to our cache transforms. Each one of the Cache Transforms will require a connection manager to store the data.
- Below is a screenshot of the connection managers created by the Cache transforms:
- Now that the cache connection managers have been set up, you will need to point each of your lookup transforms to the “Cache Connection manager” instead of the default of OLE DB connection manager.
- Now to the last step. Now click on the connection tab and point to the cache connection manager that stores the information for your lookup:
- All other configuration steps are the same as normal. Thanks for looking, and hope this helped.