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Curated SQL Posts

Azure Networking

Joshua Feierman has an article on how Azure Networking works, particularly from the viewpoint of a DBA:

The connecting thread between an Azure virtual machine and a virtual network is a Virtual Network Interface Card, or VNic for short. These are resources that are separate and distinct from the virtual machine and network itself, which can be assigned to a given virtual machine.

If you go to the “All Resources” screen and sort by the “Type” column, you will find a number of network interface resources.

There’s some good information in here.

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Cloned Database Security

Parikshit Savjani explains what happens when you run DBCC CLONEDATABASE on databases with various security measures activated:

Transparent Data Encryption (TDE)

If you use TDE to encrypt data at rest on the source database, DBCC CLONEDATABASE supports cloning of the source database but the cloned database is not encrypted by TDE. Thus, the backup of the cloned database will be unencrypted. If it is desired to encrypt and protect cloned database backup, you can enable TDE on cloned database before it is backed up as shown below

It’s a good read which covers several technologies.

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Partitioning Basics

Kim Tripp explains partitioning:

So, you’d still need to determine if this is the right approach. But, the main point – partitioning really isn’t designed to give incredible gains to your queries. It’s meant to be better for data management and maintenance. However, some partitioning designs can lead to query performance benefits too.

This is a nice introduction and makes a good point:  performance benefits to partitioning are incidental to the real benefit, which is simplicity of administration.

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Testing SQL Server On Linux Backups

Rob Sewell confirms that Test-DbaLastBackup in the dbatools kit works for Linux:

I have written about Test-DbaLastBackup in posts here, here and here. They have been Windows only posts.

With SQL Server vNext CTP 1.4 now available and providing SQL Agent capability on Linux, I wrote here about using Ola Hallengrens scripts on Linux SQL Servers so can Test-DbaLastBackup work with Linux?

It’s a short post but good to know.

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Connecting To Linux SQL Agent Using Powershell

Slava Murygin shows how to connect to a SQL Agent running on Linux using the SqlServer Powershell module:

From this point we will work directly with SQL Server.
In order to establish connection you have to run following script.
The most important are 2nd and third lines:
– In second line you have to provide your SQL Server Instance address, by replacing “<your_server_instance>” by something like “192.168.58.11” or “192.168.58.11\MSSQLSERVER,1433”
– When second line runs it will ask you for SQL Server credentials !!! So, you have to enter SQL user name and it’s password.

Slava does note some limitations at present, but a lot of the functionality seems to be there.

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Using mrsdeploy To Run R On Azure

John-Mark Agosta shows how to use mrsdeploy to send R batch jobs up to an Azure VM:

Alternately there are other Azure platforms for operationalization using R Server in the Marketplace, with other operating systems and platforms including HDInsight, Microsoft’s Hadoop offering. Or, equivalently one could use the Data Science VM available in the Marketplace, since it has a copy of R Server installed. Configuration of these platforms is similar to the example covered in this posting.

Provisioning an R Server VM, as reference in the documentation, takes a few steps that are detailed here, which consist of configuring the VM and setting up the server account to authorize remote access. To set up the server you’ll use the system account you set up as a user of the Linux machine. The server account is used for client interaction with the R Server, and should not be confused with the Linux system account. This is a major difference with the Windows version of the R Server VM that uses Active Directory services for authentication.

You can also use mrsdeploy to run batch jobs against Microsoft R Server on a local Hadoop cluster.

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The Hive Metastore In HDInsight

Ashish Thapliyal shows how to create a custom Hive metastore in HDInsight:

Custom Metastore – HDInsight lets you pick custom Metastore. It’s a recommended approach for production clusters due to number reasons such as

  • You bring your own Azure SQL database as Metastore

  • As lifecycle of Metastore is not tied to a cluster lifecycle, you can create and delete clusters without worrying about the metadata loss.

  • Custom Metastore lets you attach multiple clusters and cluster types to same Metastore. Example – Single Metastore can be shared across Interactive Hive, Hive and Spark clusters in HDInsight

  • You pay for the cost of Metastore (Azure SQL DB)

Read on to see how to do this.

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The Tidyverse Curse

Bob Muenchen notes a structural conflict between R and its most common set of packages:

There’s a common theme in many of the sections above: a task that is hard to perform using base a R function is made much easier by a function in the dplyr package. That package, and its relatives, are collectively known as the tidyverse. Its functions help with many tasks, such as selecting, renaming, or transforming variables, filtering or sorting observations, combining data frames, and doing by-group analyses. dplyr is such a helpful package that Rdocumentation.org shows that it is the single most popular R package (as of 3/23/2017.) As much of a blessing as these commands are, they’re also a curse to beginners as they’re more to learn. The main packages of dplyr, tibble, tidyr, and purrr contain a few hundred functions, though I use “only” around 60 of them regularly. As people learn R, they often comment that base R functions and tidyverse ones feel like two separate languages. The tidyverse functions are often the easiest to use, but not always; its pipe operator is usually simpler to use, but not always; tibbles are usually accepted by non-tidyverse functions, but not always; grouped tibbles may help do what you want automatically, but not always (i.e. you may need to ungroup or group_by higher levels). Navigating the balance between base R and the tidyverse is a challenge to learn.

Interesting read.  As Bob notes in the comments, he’s still a fan of the tidyverse, but it’s important to recognize that there are pain points there.

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Writing A Better Abstract

Adnan Fiaz reviews conference abstracts for patterns:

Certainly an interesting graph! It may have been better to show the proportions instead of counts as the number of abstracts in each category are not equal. Nevertheless, the conclusion remains the same. The words “r” and “data” are clearly the most common. However, what is more interesting is that abstracts in the “yes” category use certain words significantly more often than abstracts in the “no” category and vice versa (more often because a missing bar doesn’t necessarily mean a zero observation). For example, the words “science”, “production” and “performance” occur more often in the “yes” category. Vice versa, the words “tools”, “product”, “package” and “company(ies)” occur more often in the “no” category. Also, the word “application” occurs in its singular form in the “no” category and in its plural form in the “yes” category. Certainly, at EARL we like our applications to be plural, it is in the name after all.

Granted, this is only abstracts for one conference, but it’s an interesting idea.

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Memory-Optimized Clustered Columnstore Indexes

Ned Otter contrasts memory-optimized clustered columnstore indexes with traditional, disk-based clustered columnstore indexes:

5. Parallelism

On-disk: When you query an on-disk table that has a columnstore index, the database engine can use parallelism to process the results more quickly.

Memory-optimized: When you query a memory-optimized table that has a columnstore index, the database engine can use parallelism to process the results more quickly, BUT, that statement is only true if you use interop. Natively compiled modules are always executed serially.

Click through for the rest of the comparison points as well as a repro script.

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