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Author: Kevin Feasel

Azure ML Updates

Published 2016-08-03 by Kevin Feasel

David Smith walks us through new language engines supported in Azure ML:

ML studio now gives you even more flexibility, with new language engines supported in the language modules. Within the Execute Python Script module, you can now choose to use Python 2.7.11 or Python 3.5, both of which run within the Acaconda 4.0 distribution. And within the Execute R Script module, you can now choose Microsoft R Open 3.2.2 as your R engine, in addition to the existing CRAN R 3.1.0 engine. Microsoft R Open 3.2.2 not only gives you a newer R language engine, it also gives you access to a wealth of new R packages for use within ML Studio. Over 400 packages are pre-installed for use with the R Script module, and you can install and use any other R package (including CRAN packages and your own R packages) via the Script Bundle input port.

I’m interested in the Microsoft R Open language support, as Azure ML’s still using a relatively older version of R (3.1.0).

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vNet Peering Within An Azure Region

Published 2016-08-03 by Kevin Feasel

Denny Cherry reports that there is a public preview of a feature to allow vNet peering without setting up a site-to-site VPN connection:

Up until August 1st if you had 2 vNets in the same Azure region (USWest for example) you needed to create a site to site VPN between them in order for the VMs within each vNet to be able to see each other.  I’m happy to report that this is no longer the case (it is still the default configuration).  On August 1st, 2016 Microsoft released a new version of the Azure portal which allows you to enable vNet peering between vNets within an account.

Now this feature is in public preview (aka. Beta) so you have to turn it on, which is done through Azure PowerShell. Thankfully it uses the Register-AzureRmProviderFeature cmdlet so you don’t need to have the newest Azure PowerShell installed, just something fairly recent (I have 1.0.7 installed). To enable the feature just request to be included in the beta like so (don’t forget to login with add-AzureRmAccount and then select-AzureRmSubscription).

Read the whole thing for details on how to enroll in this feature and how to set it up.

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Thinking About Index Design

Published 2016-08-03 by Kevin Feasel

Jeremiah Peschka looks at a scenario in which a heap might be superior to a clustered index:

In this case, we have to assume that Event IDs may be coming from anywhere and, as such, may not arrive in order. Even though we’re largely appending to the table, we may not be appending in a strict order. Using a clustered index to support the table isn’t the best option in this case – data will be inserted somewhat randomly. We’ll spend maintenance cycles defragmenting this data.

Another downside to this approach is that data is largely queried by Owner ID. These aren’t unique, and one Owner IDcould have many events or only a few events. To support our querying pattern we need to create a multi-column clustering key or create an index to support querying patterns.

This result is not intuitive to me, and I recommend reading the whole thing.

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Don’t Use Cron For Scheduling Hadoop Jobs

Published 2016-08-02 by Kevin Feasel

Matthew Rathbone explains why cron is not a great choice for scheduling Hadoop and Spark jobs:

Reason 3: Poor transparency for teammates

Which jobs are running right now? Which are going to run today? How long do these jobs take? How do I schedule my job? What machine should I schedule it on? These are all questions that are impossible to answer without building custom orchestration around your Cron process – time you’d be better off spending on building a better system.

Matthew then gives us four alternative products.

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Plotting Variables Against One Another

Published 2016-08-02 by Kevin Feasel

Simon Jackson shows how to plot multiple variables against one another using R:

This post is an extension of a previous one that appears here:https://drsimonj.svbtle.com/quick-plot-of-all-variables.

In that prior post, I explained a method for plotting the univariate distributions of many numeric variables in a data frame. This post does something very similar, but with a few tweaks that produce a very useful result. So, in general, I’ll skip over a few minor parts that appear in the previous post (e.g., how to use purrr::keep() if you want only variables of a particular type).

Read on for code, including a good bit of tidyr.

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Multi-Column, Auto-Created Statistics

Published 2016-08-02 by Kevin Feasel

Shaun J. Stuart looks into a scenario in which it appears that multi-column, auto-created statistics were generated:

Wow.. That sure looks like three auto-created, multi-column statistics! We have three stats: stats_ids 3, 4, and 5. The sys.stats_column table contains one row for each column that is in a statistic, so multiple rows for a single statistic (i.e., a single stats_id value), indicate multiple columns in that stat. Indeed, the column_id values indicate the table columns contained the stat. So stats_id 3 contains columns VersionMajor and ApplicationID (column_ids 3 and 1), stats_id 4 contains columns VersionMinor and ApplicationID (column_ids 4 and 1), and stats_id 5 contains columns VersionRevision and ApplicationID (column_ids 5 and 1). And, clearly, the auto_created flag is true, so these three stats were auto-created. What’s going on?

Read on for the answer.

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Structured Streaming

Published 2016-08-02 by Kevin Feasel

Andrew Ray explains streaming solutions using Spark 2.0:

If you are familiar with traditional Spark streaming you may notice that the above example is lacking an explicit batch duration. In structured streaming the equivalent feature is a trigger. By default it will run batches as quickly as possible, starting the next batch as soon as more data is available and the previous batch is complete. You can also set a more traditional fixed batch interval for your trigger. In the future more flexible trigger options will be added.

A related consequence is that windows are no longer forced to be a multiple of the batch duration. Furthermore, windows needn’t be only on processing time anymore, we can rearrange events that may have been delayed or arrived out of order and window by event time. Suppose our input stream had a column event_time that we wanted to do windowed counts on. Then we could do something like the following to get counts of events in a 1 minute window:

Right now, there are some pretty strict limitations on this new streaming, but I imagine they’ll loosen up quite soon.

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RetainSameConnection

Published 2016-08-02 by Kevin Feasel

I explain what the RetainSameConnection property on an Integration Services connection does:

My co-worker had set up a dynamic connection (see Rafael Salas or Hari Bagra for details on how to do this), but something weird was happening:  the package was trying to push everything to the same server.  I confirmed that if all relevant customers loaded were for the same server, that the process would work correctly, and that I could run each server load one at a time, so there weren’t any problems connecting to particular servers or parameters overriding this choice.  It’s like the connection was “sticky,” connecting successfully to the first server and then ignoring the later changes.

RetainSameConnection allows for certain benefits, but has specific limitations.  Click through to see those details.

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Shredding Event Data

Published 2016-08-02 by Kevin Feasel

Jason Brimhall has a script to shred extended events:

In the following script, I have tried to accomplish just that – a single script to create the entire XML parser for me, for you, and for anybody wishing to use it. I don’t want to have to remember the subtle nuances of how to parse each of the events each time I need to parse the session data. I want something that is quick, easy, and repeatable.

With all of that said, here is the script that I now use to parse my session data. You should notice that it has been simplified and is more extensive now.

Jason also has sample usage.  Check this out for sure.

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