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Author: Kevin Feasel

Confirming Checkpoints

Arun Sirpal shows how to log when checkpointing runs:

Via Configuration manager I enabled trace flags 3502 and 3605 – both needed to get the checkpoint information and write it to the error log.

I then shutdown the machine, on start-up I looked into the error log.


Notice the ‘s’ in front of the spid<number>? Well that means the checkpoint was done via the automatic process; if you do a manual checkpoint it won’t see this letter.

I did not know that the “s” indicated that this was an automated process.

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Generating Absurd Numbers Of Columns

Brent Ozar wants to generate SmallInt.Max columns:

Alright, so we’ve learned that I can’t return more than 65,535 columns, AND I can only use 4,096 elements in my SELECT. I can think of several workarounds there – 65,535 / 4096 = about 16, which means I could create a few tables or CTEs and do SELECT *’s from them, thereby returning all 65,535 columns with less than 4,096 things in my SELECT. But for now, we’ll just start with 4,096 things in my SELECT:

If you think you need 65K columns returned, I refer you to Swart’s Ten Percent Rule.

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Auditing Within Power BI

Adam Saxton has a video on how to use Power BI Auditing:

In this video, I look at the Power BI Auditing feature that was made available a few weeks ago. I show how to turn it on and how to search. This can be helpful with understanding who is doing what within your organization.

You can read more about Power BI Auditing by checking out the official docs.

Auditing Power BI in your organization

Adding the ability to audit data access is important enough within regulated environments that this was probably a deal-killer until a few weeks ago.

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Migrating Data To SQL Server Using Data Factory

Ginger Grant moves data from Azure Blob Storage into Azure SQL Database using Data Factory:

There are instances where data resides in Azure Blob Storage and the data is needed in a SQL database. For example, if one ran a Machine Learning experiment in Data Factory, the results would be stored in Azure Blob storage, and for analysis purposes, it may make a lot more sense to move the data to SQL database. Moving data around in Data Factory, means writing JSON. In this example we will be using an Azure SQL DB, but it is not essential that the data be stored in Azure. An on-premises SQL Server could also be used, as long as a gateway was added for the connection, the other steps would be the same. There are five different Data Factory elements required to move data from an Azure blob to a database: a pipeline for the data, a data set containing the definition for the blob, a linked service for the blob, a data set containing a definition for the SQL Data, and a linked service to connect to the SQL database.

There’s a lot of JSON ahead.

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Calculating DTU

John Sterrett gives us a measure for calculating DTUs in Azure SQL Database:

The whole query is below. Right now, let’s just focus on the secret sauce. The secret sauce is how DTU percentage gets calculated.  In a nutshell, the maximum of CPU, Data IO, Log Write Percent determine your DTU percentage.  What does this mean to you? Your max consumer limits you. So, you can be using 1% of your IO but still be slowed down because CPU could be your max consumer resource.

That’s a rather interesting finding.  I think the next step (which may be so context-dependent that it’s not possible to generalize) might be to figure out what various workloads do to the metrics and if there’s a way to predict with some reasonable accuracy the expected DTU load given an anticipated change in workload, rather than seeing the value spike and reacting to it later.

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Releasing An Azure Page/Blob Lease

Denny Cherry has VB code to release an Azure page or blob storage lease:

Sometimes when firing up VMs or moving VMs from the page or blob store you’ll get an error that there is still a lease on the file.  To solve this you need to release the lease. But waiting won’t do the trick, as the leases don’t have an expiration date.

I found some VB.NET code online that with some tweaking (with the help of Eli Weinstock-Herman and Christiaan Baes) I was able to get to release the lease.

Click through for the code.

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Copying On-Prem Databases To Azure SQL Database

Kenneth Fisher walks us through migrating a database to Azure SQL Database:

It turns out it’s pretty easy (even if it takes some time). So where to start? Well the first thing we need is a place to put our database. An Azure SQL Database Server. If you don’t already have one creating a new one is fairly easy.

First start at Log in and follow these steps

This is the longer, manual process.  It’s good to walk through it this way at least once before writing a Powershell script, just to see what the script is doing.

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Aaron Bertrand compares SQL Server Management Studio now that there’s better support for high-resolution monitors:

At first I used Gianluca’s solution (“SSMS in High-DPI Displays: How to Stop the Madness“), but it wasn’t perfect – fonts in some places were really blurry, and some dialogs were totally unusable. He has several examples in his post if you’re curious. But I have several too.

This is the previous version of SSMS (13.0.15600.2), out of the box, which now foregoes any type of DPI scaling at all, using the old-fashioned jaggy type we’ve been suffering for decades (except check out the smooth text on the About dialog title bar!).

Cf Gianluca Sartori.  There’s still some work to do, but more and more of us are moving to high-resolution and 4K monitors; 1080p isn’t cutting it anymore.

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Power BI Dataset Refresh Errors

Chris Webb has a workaround for dataset refresh errors when using Power BI and writing M code:

This is just a variation on the widely-used M pattern for using functions to iterate over and combine data from multiple data sources; Matt Masson has a good blog describing this pattern here. In this case I’m doing the following:

  • Defining a table using #table() with three rows containing three search terms.
  • Defining a function that calls the metadata API. It takes one parameter, a search term, and returns a value indicating whether the search was successful or not from the JSON document returned. What the API actually returns isn’t relevant here, though, just the fact that I’m calling it. Note the highlighted lines in the code above that show how I’m constructing the URL passed to Web.Contents() by simply concatenating the base URL with the string passed in via the custom function’s Term parameter.
  • Adding a custom column to the table returned by the first step, and calling the function defined in the second step using the search term given in each row.

This query refreshes with no problems in Power BI Desktop. However, when you publish a report that uses this code to and try to refresh the dataset, you’ll see that refresh fails and returns a rather unhelpful error message:

Data source error Unable to refresh the model (id=1264553) because it references an unsupported data source.

The nature of the problem makes sense, and Chris provides one method of getting around this error.

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Data Access And Streaming

Kartik Paramasivam discusses data access problems and solutions within a streaming architecture:

Using a remote store: This is the traditional model for building applications. Here, when an application needs to process an event, it makes a remote call to a separate SQL or No-SQL database. In this model, write operations are always remote calls, but reads can be performed on a local cache in certain scenarios. There are a large number of applications at LinkedIn that fall into this category.

Another pattern is to use a remote cache (e.g., Couchbase) that is fronting a remote database (e.g., Oracle). If the remote cache is used primarily for reading adjunct data, then applications use an Oracle change capture stream (using Databus) to populate the remote cache.

This is a must-read if you’re looking at implementing a streaming architecture and need to do any kind of data enrichment.

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