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Author: Kevin Feasel

Azure Container Service Supports Kubernetes

Published 2017-02-24 by Kevin Feasel

Serdar Yegulalp reports that Azure Container Service now supports the Kubernetes container management system:

Microsoft emphasized “choice” when it originally introduced Azure Container Service. Although it launched without Kubernetes, Azure initially supported Mesosphere DC/OS and Docker Swarm because the majority of Microsoft’s customers used them and the company believed they would be well served by the support.

Since then, Kubernetes has emerged as a clear leader among container orchestration solutions. It is used as an underpinning for deep learning frameworks and the basis for an open source serverless/“lambda” app framework, as well as offered as a managed on-premise service by one company.

Kubernetes on Azure is strictly focused on running Kubernetes within Azure, not providing it as a service elsewhere. But the GA release includes additions meant to appeal to a broad audience of both Linux and Windows Server users, such as support for the latest version of DC/OS (1.8.8).

It’s an interesting world out there.

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RTVS RC1

Published 2017-02-24 by Kevin Feasel

David Smith alerts us that R Tools for Visual Studio Release Candidate 1 is available:

We’ll cover the features in detail with the general availability release of RTVS 1.0, but in summary the new features include:

  • Remote Execution: type R code in your local RTVS instance, but have the computations performed on a remote R server. You can also switch between local and remote workspaces at will.

  • SQL Server Integration: work with database connections and SQL queries, and create stored procedures with embedded R code.

  • Enhanced R Graphics Support: multiple floating and dockable plot windows, each with plot history.

I’ve been using RTVS more frequently lately and it’s definitely growing on me.

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Hadoop 3.0.0-alpha2

Published 2017-02-23 by Kevin Feasel

Andrew Wang and Ray Chaing note that a new Hadoop 3 alpha is now available:

YARN introduces the notion of opportunistic containers in addition to the current guaranteed containers. An opportunistic container is queued at the NodeManager waiting for resources to become available, and run opportunistically so long as resources are available. They are preempted, if and when needed, to make room for guaranteed containers. Running opportunistic containers between the completion of a guaranteed container and the allocation of a new one should improve cluster utilization.

There are a couple other new features, including support for Azure Data Lake Store.

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Using popt To Switch Tabs In Powershell ISE

Published 2017-02-23 by Kevin Feasel

Jana Sattainathan shows how to open a specific Powershell ISE tab without moving your hands off the keyboard:

This is a function that activates a tab based on a partial search string that you type in the CLI portion of the ISE. Among the numerous tabs you have open in the ISE, to activate a tab that has the word “register”, you would just type in

1
popt register

That’s it. The tab that has the word “register” will become active.

Why “popt” instead of a standard name?

“popt” is the alias you can create for the real “search and activate function” Pop-FileInISETab. It stands for “poptab”. You don’t want to trade one problem with another by having to type that long name, so, there is functionality to optionally create an alias named “popt” for the function.

At first I said to myself, “Why would anybody have this many tabs open?”  Then I looked at my instance of SQL Server Management Studio.  Then I decided I wanted this for SSMS.

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SQL Server Error Handling Scenarios

Published 2017-02-23 by Kevin Feasel

Dan Guzman goes into some detail about error handling in SQL Server:

T-SQL and ADO.NET data access code must work in concert with one another to ensure SQL errors are detected in application code. The T-SQL constructs used in multi-statement batches can affect if and how when errors are reported by ADO.NET during batch execution. I’ll start by citing core T-SQL error handling objectives, which can be summarized as:

1) Ensure a multi-statement T-SQL batch doesn’t continue after an error occurs.
2) Rollback transaction after errors.
3) Raise error so that the client application is aware a problem occurred.

Read the whole thing.

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Thinking About Contexts

Published 2017-02-23 by Kevin Feasel

Ewald Cress has started a new series on system internals:

Per-processor partitioning of certain thread management functions makes perfect sense, since we’d aim to minimise the amount of global state. Thus each processor would have its own dispatcher state, its own timer list… And hang on, this is familiar territory we know from SQLOS! The only difference is that SQLOS operates on the premise of virtualising a CPU in the form of a Scheduler, whereas the OS kernel deals with physical CPUs, or at least what it honestly believes to be physical CPUs even in the face of CPU virtualisation.

This is a start to a very interesting series.

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Python + knitr

Published 2017-02-23 by Kevin Feasel

Steph Locke shows how to integrate Python code into knitr:

One of the nifty things about using R is that you can use it for many different purposes and even other languages!

If you want to use Python in your knitr docs or the newish RStudio R notebook functionality, you might encounter some fiddliness getting all the moving parts running on Windows. This is a quick knitr Python Windows setup checklist to make sure you don’t miss any important steps.

Between knitr, Zeppelin, and Jupyter, you should be able to find a cross-compatible notebook which works for you.

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Getting Started With Azure Cognitive Services

Published 2017-02-23 by Kevin Feasel

Rolf Tesmer has a demo app showing what Azure Cognitive Services Text Analytics can do:

Each execution of the application on any input file will generate 3 text output files with the results of the assessment.  The application runs at a rate of about 1-2 calls per second (the max send rate cannot exceed 100/min as this is the API limit).

  • File 1 [AzureTextAPI_SentimentText_YYYYMMDDHHMMSS.txt] – the sentiment score between 0 and 1 for each individual line in the Source Text File.  The entire line in the file is graded as a single data point.  0 is negative, 1 is positive.

  • File 2 [AzureTextAPI_SentenceText_YYYYMMDDHHMMSS.txt] – if the “Split Document into Sentences” option was selected then this contains each individual sentence in each individual line with the sentiment score of that sentence between 0 and 1.  0 is negative, 1 is positive.  RegEx is used to split the line into sentences.

  • File 3 [AzureTextAPI_KeyPhrasesText_YYYYMMDDHHMMSS.txt] – the key phrases identified within the text on each individual line in the Source Text File.

Rolf has also put his code on GitHub, so read on and check out his repo.

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Pattern Matching With LIKE

Published 2017-02-23 by Kevin Feasel

Shane O’Neill looks at different ways to match patterns with the LIKE operator:

So let’s test out this bad boy using the WideWorldImporters database, see if we can find everyone with the first name of Leyla.

Simple right? And because [Sales].[Customers] uses the full name, we have to use LIKE.

Now a developer comes along and says “Wait a second, my sister is Leila”. So we try to cheat and add a wildcard in there.

Leonardo!? Well I suppose he does count in this situation, but there’s 2 characters between the ‘e’ and the ‘a’ and I only wanted one.

Click through for a couple pattern matching tricks and look for ways to avoid Lejla in your life.

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