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Month: April 2017

Inline Outsourcing

Published 2017-04-11 by Kevin Feasel

Shane O’Neill coins a term:

There’s never enough hours in the day for everything I need to do!

How many times have we heard a complaint similar to that? Especially now-a-days when DBAs are tasked to look after more and more servers and instances. I cannot remember the last time I heard of a DBA taking care of servers in the single digits.

The work of the DBA keeps increasing but the amount of time that we have remains the same. How do we combat this? How do we make it so we are not sprinting just to keep up?

The only answer I have to this problem is this.

Don’t try to re-invent the wheel…let someone else do it.

It’s an interesting riff on the T-SQL Tuesday theme this month.

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Azure SQL Automation

Published 2017-04-11 by Kevin Feasel

Arun Sirpal thinks about Azure automation in the context of how the job market is changing:

This ultimately maps to Query ID 297 where if you click the bar you can see the actual code.

Now, a debate occurred. This code was pretty awful, implicit conversions, GUIDs as cluster keys etc. I took the above code and analysed the execution plan and made some recommendations. I was quickly shut down; I was told to bump up the DTU of the database! Talk about masking the issue with hardware.

Check it out.

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Backups Causing Transaction Log Growth In Simple Mode

Published 2017-04-11 by Kevin Feasel

Andy Mallon explains why the transaction log will grow during a backup even if you’re in simple recovery mode:

When SQL Server begins backing up data pages, it also starts keeping track of transactions, via the transaction log. After it has backed up the last data page, it then also backs up all of the transactions that occurred during the data backup. Upon restore, it will then roll those transactions forward or backward, as necessary, to ensure a consistent image is restored.

In our librarian metaphor, she would keep an activity log, which would include the changes to books A and D from the first update, then also the changes to D, X, Y, and Z from the second update. She would not “fix” the data within the backup, but simply store those update details along with her mashed-up copy. In the unlikely event she had to recreate the books (ie, a restore), then she would go back and spend the effort to piece it back together. During that restore process, she would look at the first transaction and see that her copy of Book A in her backup was too old, but Book D already had the update, and she would roll forward the update to Book A. Next, she would process the second update and see that Books X, Y, and Z had the updates, but D still needed this second update, and she would roll forward that second update to Book D. At this point, she would have successfully reconstructed an image that is consistent to the time the backup completed.

Great metaphor to describe consistency during backups.

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Logging R Scripts

Published 2017-04-11 by Kevin Feasel

Tomaz Kastrun shows the places where you might be able to track R scripts running on your system:

Extensibility Log will store information about the session but it will not store the R or R environment information or data, just session information and data. Navigate to:

C:\Program Files\Microsoft SQL Server\MSSQL13.MSSQLSERVER\MSSQL\LOG\ExtensibilityLog

to check the content and to see, if there is anything useful for your needs.

It’s not a great answer today.

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Linchpins

Published 2017-04-11 by Kevin Feasel

Bert Wagner on the ongoing “what happens to my tech job?” question:

Seth Godin discusses the concept of a Linchpin in his same-titled book. A Linchpin is someone who is so good at what they do that they become indispensable to their organization. Linchpins are the kind of people who are self-motivated and are able to consistently deliver quality work. They are integral to the operation of a business, even if they don’t get all of the glamour of having VP or Director in their title.

And why are Linchpins always guaranteed jobs? In one scenario, Linchpins will outgrow their role and be promoted or find a better job. They are always learning and growing in addition to delivering, and so this is the natural procession. In the alternate scenario, if the Linchpin has to lose his or her current job (ie. think company buyouts where entire departments close), they will either 1) become promoted to elsewhere in the company because management recognizes their great skills or 2) they will have no problem finding work elsewhere, especially with great recommendations from their former employer.

It’s an interesting read.

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Exporting SQL Server User Permissions

Published 2017-04-10 by Kevin Feasel

Rob Sewell shows how to use the Export-SqlUser cmdlet in dbatools:

There are times when DBA’s are required to export database user permissions to a file. This may be for a number of reasons. Maybe for DR purposes, for auditing, for transfer to another database or instance. Sometimes we need to create a new user with the same permissions as another user or perhaps nearly the same permissions. I was having a conversation with my good friend and MVP Cláudio Silva and we were talking about how Export-SqlUser from dbatools could help in these situations and he suggested that I blogged about it so here it is.

The dbatools module (for those that don’t know) is a PowerShell module written by amazing folks in the community designed to make administrating your SQL Server significantly easier using PowerShell. The instructions for installing it are available here It comprises of 182 separate commands at present

Cláudio wrote Export-SqlUser to solve a problem.

Read on to see what this does and how to use it.

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Probabilistic Record Linking In Spark

Published 2017-04-10 by Kevin Feasel

Tom Lous builds a solution to link similar companies together by address:

Recently a colleague asked me to help her with a data problem, that seemed very straightforward at a glance.
She had purchased a small set of data from the chamber of commerce (Kamer van Koophandel: KvK) that contained roughly 50k small sized companies (5–20FTE), which can be hard to find online.
She noticed that many of those companies share the same address, which makes sense, because a lot of those companies tend to cluster in business complexes.

Read on for the solution.  Like many data problems, it turns out to be a lot more complicated than you’d think at first glance.

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HDInsight 3.6 Available

Published 2017-04-10 by Kevin Feasel

Ashish Thapliyal points out some Hive improvements in HDInsight 3.6:

2 Create a new Hive table from scratch or alter Table

Create a new table by, clicking on the ‘+’ icon, which opens the create table wizard. Enter table name, column name and choose a data type from the dropdown. You can pick folloiwng advanced hive settings directly from the UI

  • Transactional : Turn on transaction support in Hive, by checking this flag. Note that the table must be bucketed and stored using an ACID compliant format (such as ORC).

  • Location : Hive stores the table data for managed tables in the Hive warehouse directory in HDFS which is configured in hive-site.xml with property hive.metastore.warehouse.dir. The default location is /apps/hive/warehouse. The location can be changed using the Location text field.

  • File Format : The default file format for CREATE TABLE statement is ORC. choose a format from the file format dropdown.

  • Row Format : Select a row format such as Field terminator, Lines terminator, and Stored File type.

  • Table can be altered to add new columns or change the column name or column datatype.

  • Tables can also be renames and altred

Read on for more improvements, including a graphical plan viewer and improved autocomplete.

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MERGE In Hive

Published 2017-04-10 by Kevin Feasel

Carter Shanklin notes that Hive now has the ability to run MERGE statements:

As scalable as Apache Hadoop is, many workloads don’t work well in the Hadoop environment because they need frequent or unpredictable updates. Updates using hand-written Apache Hive or Apache Spark jobs are extremely complex.  Not only are developers responsible for the update logic, they must also implement all rollback logic, detect and resolve write conflicts and find some way to isolate downstream consumers from in-progress updates. Hadoop has limited facilities for solving these problems and people who attempted it usually ended up limiting updates to a single writer and disabling all readers while updates are in progress.

This approach is too complicated and can’t meet reasonable SLAs for most applications. For many, Hadoop became just a place for analytics offload — a place to copy data and run complex analytics where they can’t interfere with the “real” work happening in the EDW.

This post mostly describes the gains rather than showing code, but it does show that Hive developers are looking at expanding beyond common Hadoop warehousing scenarios.

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