This is where we start the decline phase in our story. Our temporary procedures existed as a bridge from the old procedures which took
ClientIDand new procedures which will take
ProfileID. With our final versions of procedures, we replace
@ProfileIDin the input parameters and update any conditional logic within filters to use
The only remaining use of
ClientIDin these procedures is as an output in select statements, as we still need that for the old code; by this time, all of those references are
ProfileID AS ClientID. Otherwise, these new procedures are built around
We still have the original procedures that the application code uses and they reference our now-finalized procedures. These procedures are now transition procedures—we need them right now but as we move application code over to the new procedures, we can deprecate these. But I’m getting ahead of myself a little bit.
Click through for the full story as well as a bunch of pictures which completely understate the vastness of work done.
I’m going to keep this intro short and sweet. I’d like to say it’s because I know these roundups are for acknowledging and thanking the people who have contributed, and this is the case! Mainly though it’s because I wrote this bit last and I’ve already written… checks … 2,795 words!
So thank you one and all for participating. I’ve never thought the posts would raise feelings of happiness, sadness, thoughtfulness, and appreciative-ness that this one has.
Read on for the full list and Shane’s thoughts.
To clarify, the extension in ADS is like XEvent Profiler in Management Studio (which also is built using Extended Events). The name “SQL Server Profiler” is confusing, as this is not the same tool (UI) that’s been available since SQL Server 7.0.
To install the extension, click on it, and then select Install. Once it’s installed you can select Reload and it will move into the top half of the window under Enabled. Notice that when you select the extension, information about how to use it also appears.
Erin has a lot of useful information here, so check it out.
The first thing you’ll notice is what Oracle refers to as an INSTANCE is different to what SQL Server calls one.
Oracle’s instance is most closely related to what SQL Server calls their database, (although it includes the files that Oracle includes in their description, too) and the Oracle home is *relatively* SQL Server’s version of an instance.
Read on for a deeper comparison from someone who has spent quite a bit of time working with both platforms.
When first learning SQL Server, I had the brilliant idea of defining every column on my table as VARCHAR(8000). After all, the VARCHAR datatype only consumes as much storage space as the string itself (plus two bytes), so even if I defined the max length of a column as 8000, I wouldn’t be wasting storage space if my data was smaller than that.
My development life would be easier too since I wouldn’t have to think about the kind of data I was storing in my columns; I could define everything as VARCHAR(8000) once and never have to go back to make any changes. Brilliant!
While I was correct about not wasting storage space, it turns out the idea of making every column VARCHAR(8000) is a terrible idea.
Click through to learn why.
Let me tell you about one of my least favorite things I like to see in PolyBase:
Error: Could not find or load main class org.apache.hadoop.mapreduce.v2.app.MRAppMaster
This error is not limited to PolyBase but is instead an issue when trying to run MapReduce jobs in Hadoop. There are several potential causes, so let’s cover each of them as they relate to PolyBase and hopefully one of these solves your issue.
Click through for four potential solutions to what ails you.
Do you want to identify the correct Service Tier and Compute Size ( was once known as performance level) for your Azure SQL Database? How would you go about it? Would you use the DTU (Database Transaction Unit) calculator? What about the new pricing model vCore? How would you translate you current on-premises workload to the cloud?
It can be a form of trial and error especially if you are new to this but I really do recommend trying out the PowerShell script that you can access once you have installed DMA – Database Migration Assistant.
Read on to see how to run this tool and potentially save some money.
The next step is to write the code to capture the counter values and insert the data it the temporary table created above. Because we need to capture the values over a period of time, the WAITFOR DELAY is used. In this case the delay is 10 seconds, although you can change this to suit your needs. Of course, don’t forget to increment the counter variable. You will need to determine what counters you would like to capture. Notice in the WHERE clause, we are looking for an instance_name of ” or ‘_total’. This will allow the code to only capture one row for each counter. The number 10 is the number of times we want to capture the counter values. If you want to capture the data more frequently, simpley modify the number of seconds in the WAITFOR DELAY. Here is link to my post in this topic, WAITFOR.
Dynamic pivoting in SQL is unnecessarily difficult, especially compared to languages like R.
File management may not be at the top of my list of priorities during data integration projects. I assume that once I learn enough about sourcing data systems and target destination platform, I’m ready to design and build a data integration solution between two or more connecting points. Then, a historical file management process becomes a necessity or a need to log and remove some of the incorrectly loaded data files. Basically, a step in my data integration process to remove (or clean) such files would be helpful.
Click through to see how to do this.
Recently, I had to use Azure Data Studio to access a application intent read only secondary replica. I had to use Azure Data Studio because I was using a Mac. I usually use SSMS on my Windows machines. If you want to connect with the “applicationintent=readonly” property via SQL Server Management Studio, you do so by typing it out in the “Additional Connection Parameters” as shown in the screenshot below:
Since I am fairly new to Azure Data Studio I was fumbling my way around to find the equivalent setting. And I finally found it…
Read on to see how you can set this in Azure Data Studio.