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Month: October 2017

More On The New Service Model

Published 2017-10-05 by Kevin Feasel

Randolph West summarizes the new SQL Server patching model:

  • Every twelve months after GA, the installation files will be updated to contain all the Cumulative Updates in what is effectively now a service pack, but won’t be called that. This will also become the slipstream update. In other words, you’re more likely to be up to date when installing from scratch, later in the release cycle.

  • Customers on the GDR (General Distribution Release) release cycle will only get important security and corruption fixes, as before. You can switch to the standard CU release cadence any time, but once you do, you can’t switch back to GDR.

Brent Ozar thinks CU12 might become the new SP1 in the minds of managers:

So now fast forward to late 2018, early 2019. You’re about to build a new SQL Server for a project, and you have two choices:

  • SQL Server 2018 – which is basically the new dev branch, getting monthly updates, or
  • SQL Server 2017 (or 2016, or 2014) – which is the stable branch, getting quarterly updates

Once a version has hit CU12, and it only gets updates once a quarter, it might be considered Good Enough For Our Apps. Managers might see 2017/2016/2014 interchangeably at that point – which might be great for the second most recent version’s adoption.

It will be interesting to see how companies adopt this new model.

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Active Directory On CentOS

Published 2017-10-05 by Kevin Feasel

Drew Furgiuele shows how to configure a box running CentOS to work with Active Directory:

Before we start though, there’s a few things you’re going to need to have already set up:

  • An Active Directory Domain to test in, and rights to administer it. Since we’re going to be creating (and possibly deleting, if there are errors) computer objects and a service account, you’ll need a domain account with adequate permissions.

  • My example assumes you have a Microsoft DNS server running alongside your domain services. It is possible to use a separate DNS server to get this to work, but you might need some additional network configuration (see below). Also, depending on your environment, you might need a reverse lookup zone defined. If you notice long ping times or other weird lookups, I’d set one up in your DNS.

  • A machine (virtual or otherwise) that is running CentOS 7 or later (and this guide was written and tested against CentOS 7). For this demo, we’ll be using the Server (minimal install) installation option.  If you’re new to Linux, you might opt a desktop version (server with a GUI). When you download a CentOS disk image to install it, you get all these options on the default media; you won’t need separate downloads

There are a few more prereqs, so read the whole thing.  This route is easier than Ubuntu, as Drew notes.

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Unattended Installation Of SQL Server 2017 On Linux

Published 2017-10-05 by Kevin Feasel

Denzil Ribeiro walks us through an unattended installation and configuration of SQL Server 2017 on Linux:

SQL 2017 bits are generally available to customers today. One of the most notable milestones in the 2017 release is SQL Server on Linux. Setup has been relatively simple for SQL Server on Linux, but often there are questions around unattended install. For SQL Server on Linux, there are several capabilities that are useful in unattended install scenarios:

  • You can specify environment variables prior to the install that are picked up by the install process, to enable customization of SQL Server settings such as TCP port, Data/Log directories, etc.

  • You can pass command line options to Setup.

  • You can create a script that installs SQL Server and then customizes parameters post-install with the mssql-conf

The sample script link seems like it’s broken, but you can see it all on Denzil’s Github repo.

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New Docker Tag For SQL Server 2017

Published 2017-10-05 by Kevin Feasel

Hamish Watson notes that there are new Docker tags for SQL Server 2017:

I have been using SQL Server 2017 running on Linux for a while now (blog post pending) and use the official images from:

https://hub.docker.com/r/microsoft/mssql-server-linux/

To get the latest I used to run

docker pull microsoft/mssql-server-linux:latest

However today I noticed that the :latest tag had been removed:

Click through to see the tag you probably want to use.

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Using XPath To Shred HTML

Published 2017-10-05 by Kevin Feasel

Shannon Lowder shows off the HTML Agility Path project to help him parse the contents of webpages:

Let’s say we wanted the table.  We could use the XPath /html/body/table to retrieve it. We can also use XPath to refer to a collection.  Let’s say we wanted all the rows. We would use the XPath /html/body/table/tr. We would get a collection of three rows.  Notice the XPath looks a lot like a Linux or windows folder path.  That’s the idea of XPath!

I would like to point out a couple of extra points.  First, XPath is case sensitive.  So if I had tried to use /html/body/table/TR, I would find no nodes.

Second, you can use “short hand” in your XPath queries.  //body/table/tr would get you to the same place /html/body/table/tr did.

This intro is part of a series Shannon has started on scraping data from websites.

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Online Dashboard Taxonomy

Published 2017-10-04 by Kevin Feasel

Tim Bock has a lot of examples of online dashboards:

The classic dashboards are designed to report key performance indicators (KPIs). Think of the dashboard of a car or the cockpit of an airplane. The KPI dashboard is all about dials and numbers. Typically, these dashboards are live and show the latest numbers. In a business context, they typically show trend data as well.

A very simple example of a KPI Dashboard is below. Such dashboards can, of course, be huge. Huge dashboards have lots of pages crammed with numbers and charts, looking at all manner of operational and strategic data.

The single most important question I think you can ask about dashboards is, what does the intended audience need to see (and do, once they’ve seen)?  That will drive the kind of dashboard elements you want to use.  If you need people to react and perform some maintenance operation, you probably want a KPI chart.  If you want to influence readers’ opinions, infographic elements might be the trick.

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Custom Snippets In RStudio

Published 2017-10-04 by Kevin Feasel

Mara Averick shows how to create a custom code snippet in RStudio:

The RStudio Support Code Snippets post is a great step-by-step for adding snippets of your own. The gist of it for a markdown snippet is as follows:

  1. Open RStudio Preferences

  2. Go to the Code section

  3. Click the Edit Snippets button3

  4. Select Markdown

  5. Add your snippet and Save

Click through for a demo of how to embed tweets into your RMarkdown documentation.

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Timeline Storyteller Custom Visual

Published 2017-10-04 by Kevin Feasel

Devin Knight continues his Power BI custom visuals series:

In this module you will learn how to use the Timeline Storyteller.  The Timeline Storyteller is a great way to tell a story about your data. It gives you the ability to create multiple representations of your data and then pull them together by creating multiple scenes.

This is a flashy visual and I think Devin’s set is an excellent example of where you might want to use it.

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Limiting Docker Container Resources

Published 2017-10-04 by Kevin Feasel

Andrew Pruski shows how to cap the resources available to a container:

What I’ve done here is use the cpus and memory switches to limit that container to a maximum of 2 CPUs and 2GB of RAM. There are other options available, more info is available here.

Simple, eh? But it does show something interesting.

I’m running Docker on my Windows 10 machine, using Linux containers. The way this works is by spinning up a Hyper-V Linux VM to run the containers (you can read more about this here).

Read on to learn more.

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Finding Adaptive Join Inefficiencies

Published 2017-10-04 by Kevin Feasel

Joe Obbish walks us through a scenario with adaptive joins in SQL Server 2017:

The estimated costs for the two queries are very close to each other: 74.6842 and 74.6839 optimizer units. However, we saw earlier that the tipping point for an adaptive join on this query can vary between 22680 and 80388.3 rows. This inconsistency means that we can find a query that performs worse with adaptive joins enabled.

Click through to see the queries Joe is using.  Based on this, I’d guess that this is probably a knife-edge problem:  most of the time, adaptive join processing is better, but if you hit the wrong query, it’s worse.

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