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Month: February 2017

Superheat

Published 2017-02-06 by Kevin Feasel

David Smith shows off a very cool heatmap package called superheat:

While the superheat pacakge uses the ggplot2 package internally, it doesn’t itself follow the grammar of graphics paradigm: the function is more like a traditional base R graphics function with a couple of dozen options, and it creates a plot directly rather than returning a ggplot2 object that can be further customized. But as long as the options cover your heatmap needs (and that’s likely), you should find it a useful tool next time you need to represent data on a grid.

The superheat package apparently works with any R version after 3.1 (and I can confirm it works on the most recent, R 3.3.2). This arXiv paper provides some details and several case studies, and you can find more examples here. Check out the vignette for detailed usage instructions, and download it from its GitHub repository linked below.

Click through for some great-looking examples.

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Spark UDFs

Published 2017-02-06 by Kevin Feasel

Curtis Howard explains how to create a user-defined function in Apache Spark:

It’s important to understand the performance implications of Apache Spark’s UDF features.  Python UDFs for example (such as our CTOF function) result in data being serialized between the executor JVM and the Python interpreter running the UDF logic – this significantly reduces performance as compared to UDF implementations in Java or Scala.  Potential solutions to alleviate this serialization bottleneck include:

  1. Accessing a Hive UDF from PySpark as discussed in the previous section.  The Java UDF implementation is accessible directly by the executor JVM.  Note again that this approach only provides access to the UDF from the Apache Spark’s SQL query language.
  2. Making use of the approach also shown to access UDFs implemented in Java or Scala from PySpark, as we demonstrated using the previously defined Scala UDAF example.

In general, UDF logic should be as lean as possible, given that it will be called for each row.  As an example, a step in the UDF logic taking 100 milliseconds to complete will quickly lead to major performance issues when scaling to 1 billion rows.

Definitely worth a read.  UDFs in Spark can come at a performance penalty, so they aren’t free.

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Parsing Text Fragments

Published 2017-02-06 by Kevin Feasel

Aaron Bertrand looks at a way of speeding up LIKE %Something% queries and builds a fragment table:

It’s clear that in this specific case – with an address column of nvarchar(60) and a max length of 26 characters – breaking up each address into fragments can bring some relief to otherwise expensive “leading wildcard” searches. The better payoff seems to happen when the search pattern is larger and, as a result, more unique. I’ve also demonstrated why EXISTS is better in scenarios where multiple matches are possible – with a JOIN, you will get redundant output unless you add some “greatest n per group” logic.

Read the whole thing.  If you’re interested in the concept, I recommend reading up on n-grams, like Alan Burstein’s series and this TechNet article on implementing N-Grams in SQL Server.

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Agent Schedule Unique IDs

Published 2017-02-06 by Kevin Feasel

Chris Sommer has a gripe about the schedule_uid column in SQL Agent jobs:

When you script out a SQL Agent Job you’ll notice that the job schedule will have a schedule_uid parameter (providing your job has a schedule). The gottcha lies in that schedule_uid. If you create another job schedule with the same schedule_uid, it will overwrite the schedule for any jobs that are using it. i.e. Any other jobs that are using that schedule_uid will start using the new schedule. Normally I consider UID’s as very unique and chances of a collision are low, but if you do a fair amount of copying jobs between SQL Servers there’s a good chance this will bite you eventually. That’s what happened to us (more than once).

Lets see if I can explain it better in an example.

Something to think about when scripting SQL Agent jobs.

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Testing Nested Sets

Published 2017-02-06 by Kevin Feasel

Nate Johnson continues his nested set series:

Now the complex.  To validate that our tree is properly structured, the following statements need to be true:

  1. Each node’s Right value is greater than its Left.

  2. More to the point, each node’s Right value is greater than all of its ancestors’ Left values.

  3. Similarly, each node’s Left value is less than all of its descendants’ Left values (and Right values, obviously!)

  4. Leaf nodes have no gaps between Left & Right: Right = Left + 1

  5. Depth is easy to verify because we already wrote the rCTE to calculate it!

  6. And of course, no orphans – all ParentIDs lead to an actual parent node, except of course if they’re NULL (root nodes).

Read on for further explanation of these points.

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Azure VM Encryption

Published 2017-02-06 by Kevin Feasel

Melissa Coates looks at different encryption methods available for Azure Virtual Machines:

Initially I opted for Storage Service Encryption due to its sheer simplicity. This is done by enabling encryption when you initially provision the storage account. After having set it up, I had proceeded onto other configuration items, one of which is setting up backups via the Azure Recovery Vault. Turns out that encrypted backups in the Recovery Vault are not (yet?) supported for VMs encrypted with Storage Service Encryption (as of Feb 2017).

Next I decided to investigate Disk Encryption because it supports encrypted backups in the Recovery Vault. It’s more complex to set up because you need a Service Principal in AAD, as well as Azure Key Vault integration. (More details on that in my next post.)

Click through for a point-by-point comparison between the two methods.

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Replication Error When Listing Directory Contents

Published 2017-02-06 by Kevin Feasel

Andrew Peterson troubleshoots a replication issue:

You’re trying to setup SQL Server Replication on a server, and it fails. Looking thru the error message you find this:

        An exception occurred while executing a Transact-SQL statement or batch.
        (Microsoft.SQLServer.ConnectionInfo)

        Destination path ………….is not valid. Unable to list directory contents. Specify a valid
            destination path.
            Changed database context to ‘master’. (Microsoft SQL Server, Error: 14430)

Read on for the solution.

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Thoughts On Deprecated Technologies

Published 2017-02-06 by Kevin Feasel

Dan Guzman discusses a couple deprecated components which are still hanging around:

The message is loud and clear that ODBC is the supported and preferred path for native applications going forward. The Data Access Technologies Road Map provides an overview and history of Microsoft data access technologies, which I recommend you peruse to ensure you are not inadvertently using deprecated or unsupported technologies for new development and, for existing applications, consider moving from legacy data access technologies to current ones when practical.

The current Microsoft ODBC Driver for SQL Server as of this writing is ODBC Driver 13 for SQL Server. Note that that both the 13.0 and 13.1 versions of this driver have the same “ODBC Driver 13 for SQL Server” display name listed under installed programs and ODBC Data Source Administrator. If installed, the driver will be listed under installed programs along with the corresponding driver version (when viewed detail mode). The 13.1 version adds support for the Always Encrypted feature. These ODBC Drivers are available from the link I mentioned earlier.

They will pry OLE DB from my cold, dead hands.  For my money, it’s still the best SQL Server data access technology for Integration Services, beating out ODBC both in terms of ease of use and performance.

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Encryption In ElasticMapReduce

Published 2017-02-03 by Kevin Feasel

Sai Sriparasa shows how to enable encryption in an ElasticMapReduce cluster:

In this post, I go through the process of setting up the encryption of data at multiple levels using security configurations with EMR. Before I dive deep into encryption, here are the different phases where data needs to be encrypted.

Data at rest

  • Data residing on Amazon S3—S3 client-side encryption with EMR
  • Data residing on disk—the Amazon EC2 instance store volumes (except boot volumes) and the attached Amazon EBS volumes of cluster instances are encrypted using Linux Unified Key System (LUKS)

Data in transit

  • Data in transit from EMR to S3, or vice versa—S3 client side encryption with EMR

  • Data in transit between nodes in a cluster—in-transit encryption via Secure Sockets Layer (SSL) for MapReduce and Simple Authentication and Security Layer (SASL) for Spark shuffle encryption

  • Data being spilled to disk or cached during a shuffle phase—Spark shuffle encryption or LUKS encryption

Turns out this is rather straightforward.

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Data Frame Serialization In R

Published 2017-02-03 by Kevin Feasel

David Smith shows a new contender for serializing data frames in R, fst:

And now there’s a new package to add to the list: the fst package. Like the data.table package (the fast data.frame replacement for R), the primary focus of the fst package is speed. The chart below compares the speed of reading and writing data to/from CSV files (with fwrite/fread), feather, fts, and the native R RDS format. The vertical axis is throughput in megabytes per second — more is better. As you can see, fst outperforms the other options for both reading (orange) and writing (green).

These early numbers look great, so this is a project worth keeping an eye on.

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