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Month: August 2016

Joins Versus NOT IN Clause

Published 2016-08-10 by Kevin Feasel

Kevin Hill explains a potential performance difference between using NOT IN and using a left join:

Basic stuff, right?   Both will return 951 records (books) that I do not own.  And, very quickly…because the tables are tiny.   Sub-1 second is fast.

The issue here is HOW the rows are compared.

English version now, techy stuff later:

In the first query, this is equivalent to you standing at the bookstore and calling home to have someone check to see if the book in your hand is already in your collection.  EVERY time.  One by one.

In the second, you got really smart and brought a list with you, which you are comparing to the books on the shelf at the store.   You’ve got both “lists” in one place, so it is far more efficient.

Even in the case with a few hundred records, you can see why there’d be a performance difference.

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Power BI Embedded

Published 2016-08-09 by Kevin Feasel

Reza Rad looks at Power BI Embedded:

Power BI Embedded is the reporting and analysis solution for mobile and web applications. Power BI Embedded is an Azure service that integrates Power BI solution into mobile and web applications. The report still has to be authored and created in Power BI Desktop. After creating the report it can be published into Power BI workspace in Azure, and using API Keys of Power BI workspace and embedding Power BI report frame into the web/mobile application it will be integrated into the application.

Reza walks through the process step-by-step.  The upshot is that you can take this report you created in Power BI and embed it into your own application, where you can apply your own in-app access controls.  There are limitations, which Reza spells out at the end of the post, so check it out.

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Compression Delay

Published 2016-08-09 by Kevin Feasel

Rob Farley digs into Compression Delay as part of real-time operational analytics:

The thing with Operational Analytics is that the analytical data, reporting data, warehouse-style data, is essentially the same data as the transactional data. Now, it doesn’t look quite the same, because it’s not been turned into a star-schema, or have slowly changing dimension considerations, but for the purposes of seeing what’s going on, it’s data that’s capable of handling aggregations over large amounts of data. It’s columnstore.

Now, columnstore data isn’t particularly suited to transactional data. Finding an individual row within columnstore data can be tricky, and it’s much more suited to rowstore. So when data is being manipulated quite a lot, it’s not necessarily that good to be using columnstore. Rowstore is simply better for this.

But with SQL 2016, we get updateable non-clustered columnstore indexes. Data which is a copy of the underlying table (non-clustered data is a copy – clustered data or heap data is the underlying table). This alone presents a useful opportunity, as we can be maintaining a columnstore copy of the data for analytics, while handling individual row updates in the rowstore.

Read the whole thing.

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Pitfalls Of DIY Hadoop

Published 2016-08-09 by Kevin Feasel

Ben Davis discusses considerations when rolling your own Hadoop cluster:

5. Security hardening
I find it is easier to deploy Hadoop in a fairly low security configuration. This is because there are a range of ports that Hadoop talks on and having an incorrectly configured firewall can cause you problems. So after deployment, set aside time to identify how to customise your firewalls, user and group settings, Kerberos and ssl settings.

I think the article makes some good points.  DIY is great for a proof of concept or for playing around with a technology, but if you don’t already have a good amount of experience with a technology, you’ll probably make costly mistakes in development and administration.  This is not Hadoop-specific:  I’ve seen companies do terrible things to SQL Server because they didn’t know the correct way to do it but needed to get work done.  As part of a proof of concept, do all the terrible things you’d like; they’re how you’ll learn.  But if this is going to production, it’s a good idea to have people who know what they’re doing involved.

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Credentials In Azure Automation

Published 2016-08-09 by Kevin Feasel

Mark Vaillancourt explains Azure automation credentials:

With that String parameters, it is easy to just type what I want in the textbox. But, for the PSCredential parameter at the bottom, I was unsure what to do. I figured, since this is just a textbox, I couldn’t just magically pass in a Credential Asset. So, I tried passing in an expression that used the Get-AzureRmAutomationCredential cmdlet that returns the Credential Asset specified. That didn’t work very well. I then started digging through documentation, figuring I would see references and examples of passing a Credential Asset in the Test Pane. It took me a bit, but I finally landed on an Azure.com blog post titled Azure Automation: Runbook Input, Output, and Nested Runbooks. While this post didn’t match all that closely with the search criteria around the Test Pane, it did contain my answer in a section on starting Runbooks:

The answer turns out to be pretty simple.

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Quality Graphics With R

Published 2016-08-09 by Kevin Feasel

David Smith discusses building high-quality visuals with R:

Note the use of an attractive colour pallette, style-compatible fonts, and even the official Olympic icons for the sports. I just took a screenshot here, but if you click through to the actual site you’ll notice that these graphics are also scale-independent (you can zoom in on your browser and they’ll look better, not worse) and even interactive (pop-ups appear with country-specific data when you hover over a bar).

Duc-Quang has been generous enough to provide the R code behind these charts if you’d like to try your hand at something similar. The data themselves were scraped from the official Rio 2016 site. The bar charts were created using a standard geom_bar plot using ggplot2, with a custom theme to set the font to OpenSans Condensed. The interactive elements were added using the ggiraph package and the geom_bar_interactive function. The chart titles (including the icons) were created as HTML headers directly, which was then exported along with the interactive charts using the save_html function.

I’m impressed that this all comes from R.  There’s a good bit of work involved in getting this going, but you can get professional-grade graphics quality with R, and that’s pretty cool.

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Issues With SSISDB In An Availability Group

Published 2016-08-09 by Kevin Feasel

Andrea Allred has some lessons learned from a troublesome service pack upgrade:

Here are a few of the fun errors that we saw.

“Script level upgrade for database ‘master’ failed because upgrade step ‘SSIS_hotfix_install.sql’ encountered error 942, state 4, severity 25. This is a serious error condition which might interfere with regular operation and the database will be taken offline. If the error happened during upgrade of the ‘master’ database, it will prevent the entire SQL Server instance from starting. Examine the previous errorlog entries for errors, take the appropriate corrective actions and re-start the database so that the script upgrade steps run to completion.”

There are some good lessons here.

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Getting Started With Hadoop

Published 2016-08-09 by Kevin Feasel

Jon Morisi is looking at Hadoop:

From here I think I’ll start playing around on a sandbox.  Each of the distributions offers a way to spin up a VM or log into a cloud based environment.  There are also docker images out there. (search hadoop, cloudera, or hortonworks).  Most of these docker images look fairly new, so don’t cut yourself.

I’m looking at the HortonWorks distro, so I’ll probably setup a Hortonworks Sandbox.

Jon includes some resources he’s used to learn a bit about the topic.  I think he’s going down the right path with videos and mailing lists—the ecosystem changes too quickly for books to have much long-term value, and mailing lists & forums tend to be better for keeping up to date.  My biggest suggestion is to get case studies and play around.  Check out studies on e-mail ingest, real-time data analysis, and analyzing fantasy sports for starters.

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Audit Select Statements

Published 2016-08-09 by Kevin Feasel

Jason Brimhall shows how to build an extended event session which audits all SELECT statements:

I have to be a little honest here. Prior to somebody asking how they could possibly achieve a statement audit via extended events, I had not considered it as a tool for the job. I would have relied on Audit (which is Extended Event related), or some home grown set of triggers. In this particular request, Audit was not fulfilling the want and custom triggers was not an option. Another option might have included the purchase of third party software but there are times when budget does not allow for nice expensive shiny software.

So, with a little prodding, I hopped into the metadata and poked around a bit to see what I could come up with to achieve this low-budget audit solution.

Read the whole thing.

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Duplicate Statistics

Published 2016-08-09 by Kevin Feasel

Shaun J. Stuart discusses removing duplicate statistics:

I puzzled on this for a bit and got sidetracked by the strange way SSMS displays statistics columns on the Property page. Then it got to be the end of the day and I went home. The next day, I had a comment on my previous post from Aaron Bertrand who mentioned there is a related bug with the stats_column_id column of the sys.stats_columns view. It does not contain what the MSDN documentation says it contains. The Connect item for this, along with a workaround, can be found here.

The script I was using did not reference that column, but it did get me thinking that perhaps the script was not correctly identifying the first column in an index.

Shaun has an updated version of a duplicate statistics checker script that you may want to check out.

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