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Month: September 2019

Reading SQL Server Logs from Kubernetes

Published 2019-09-16 by Kevin Feasel

Anthony Nocentino shows us how we can use kubectl logs to read the SQL Server Error Log:

We can use follow flag and that will continuously write the error log to your console, similar to using tail with the -f option. If you remove the follow flag it will write the current log to your console. This can be useful in debugging failed startups or in the case below, monitoring the status of a database restore. When finished you can use CTRL+C to break out and return back to your prompt.

Read on for a brief demonstration.

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T-SQL Tuesday 118 Roundup

Published 2019-09-16 by Kevin Feasel

Kevin Chant played Santa Claus this month:

I hope they had as much fun contributing their posts as I had reading them afterwards. For those who missed the invitation you can read about it here.

For some reason I thought it’d be a good idea to do it on my birthday month when I first given choices about which month to host. However, now I cannot remember why.

Read on for 23 separate blog posts covering quite a few peoples’ desires for new features, bugfixes, or changes in functionality.

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Comparing Power BI Files

Published 2019-09-16 by Kevin Feasel

Imke Feldmann shows off a new Power BI file comparison tool:

What’s not covered?
Nothing. The comparison includes everything from the pbit-files: So beneath your M and DAX code, you’ll see all about your visual definitions (incl. filters set !), row level security and much, much more. Actually, I found some information a bit noisy (like many date fields, telling you when which changes happened). So I filtered them out in Excel. I’d recommend to check it out and play a bit with it to find the most suitable settings for you.

This looks quite useful.

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Modeling Semi-Additive Measures

Published 2019-09-16 by Kevin Feasel

Paul Poco shows a couple techniques for modeling semi-additive measures in Analysis Services and Power BI:

As mentioned earlier, the most commonly encountered approach is Option 2, the snapshot fact table. The main drawback of this approach is that the fact table’s size will grow extremely fast. For example, if you want to calculate the headcount in a company with 10,000 employees on average, and you want 5 years of historical data, you will add 10,000 rows per day to your fact table – that gives you (10,000 * 365 * 5 =) 18,250,000 rows after 5 years.  

If you used the first approach, Option 1, the fact table would be (10,000 * 5 =) 50,000 rows after 5 years, assuming your employees change position or quit the company once a year, on average. 

The snapshot fact table (Option 2) is (18,250,000 / 50,000 =) 365 times bigger. On the bright side, as the data is very repetitive, you might get a very good compression ratio on these tables.  

Check it out. Semi-additive measures are not as common as additive measures, but you’re liable to have a couple of them in your data model.

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Icon Maps in R

Published 2019-09-13 by Kevin Feasel

Laura Ellis shows how you can build maps full of little icons:

That was ok, but we should try to make the images more aesthetically pleasing using the magick package. We make each image transparent with the image_transparent() function. We can also make the resulting image a specific color with image_colorize().

I then saved the images using the image_write() function. I manually re-uploaded them to GH.

This was a great example of where laying icons on a map works.

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Derivative Event Sourcing

Published 2019-09-13 by Kevin Feasel

Anna McDonald explains the concept of derivative event sourcing:

If you happen to be the proud owner of a single order service, then you are all set to begin.

But what if you have more than one order service?

Something that tends to happen at companies that have been around for more than a sprint is the accumulation of technical debt. Sometimes that debt takes the form of duplicate applications. Mergers happen and you adopt other applications that, for reasons beyond your control, cannot be retired or rewritten right away. In other words, sometimes you end up with more than one order service—enter derivative event sourcing!

This is a nice article for real-life scenarios where you don’t get to build nice, well-designed services from scratch.

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Tracking Who Changed Data

Published 2019-09-13 by Kevin Feasel

Bert Wagner is on a quest to find out who moved his cheese:

Have you ever wondered who was the last person (or process) to modify a piece of data in your database?

SQL Server offers plenty of system views and functions that provide insight into how your server is running and how your queries are performing. However, it doesn’t offer much information about who last modified your data.

There are a few workarounds, though they aren’t great.

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Prior Year to a Specific Date in DAX

Published 2019-09-13 by Kevin Feasel

Alberto Ferrari lets us compare up to specific dates between years:

Unfortunately, the calculation is not perfect. At the year level, it compares the full previous year against an incomplete current year – in this example there are no sales after September 5th in the current year.

Besides, the problem appears not only at the year level, but also at the month level. Indeed, in September the Previous Year measure returns sales for the entire month of September in the previous year. The comparison is unfair, as there are only five days’ worth of sales in September of the current year.

Read on for a better technique.

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Hot Patching Azure SQL Database

Published 2019-09-13 by Kevin Feasel

Hans Olav Norheim has an interesting paper on a technique Microsoft uses to release SQL Server patches for Azure SQL Database while minimizing downtime:

The SQL Engine we are running in Azure SQL Database is the very latest version of the same engine customers run on their own servers, except we manage and update it. To update SQL Server or the underlying infrastructure (i.e. Service Fabric or the operating system), we must stop the SQL Server process. If that process hosts the primary database replica, we move the replica to another machine (requiring a failover).
 
During failover, the database may be offline for a second and still meet our 99.995% SLA. However,  failover of the primary replica impacts workload because it aborts in-flight queries and transactions. We built features such as resumable index (re)build and accelerated database recovery to address these situations, but not all running operations are automatically resumable. It may be expensive to restart complex queries or transactions that were aborted due to an upgrade. So even though failovers are quick, we want to avoid them.

Read on to see how they do it. There’s no on-prem analogue yet, though perhaps that will come in time.

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Immediate Streaming and the Power BI Data Gateway

Published 2019-09-13 by Kevin Feasel

Gilbert Quevauvilliers doesn’t have time to wait:

Currently as far as I understand it the On-Premise Data Gateway will wait and buffer some data before sending it through to the Power BI Service. By changing the setting below in the On-Premise Data Gateway, it will start streaming the data almost immediately.

I am fortunate enough to be really good mates with Phil Seamark who so part of the Power BI CAT team and he gave me a little nugget of gold that I would like to share with you.

Read on to see how to configure the gateway to stream immediately.

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