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Month: August 2017

Investigating The OS Workers DMV

Published 2017-08-25 by Kevin Feasel

Ewald Cress continues his DMV internals series:

wait_started_ms_ticks is set in SOS_Task::PreWait(), i.e. just before actually suspending, and again cleared in SOS_Task::PostWait(). For more about the choreography of suspending, see here.

wait_resumed_ms_ticks is set in SOS_Scheduler::PrepareWorkerForResume(), itself called by the mysteriously named but highly popular SOS_Scheduler::ResumeNoCuzz().

start_quantum is set for the Resuming and InstantResuming case within SOS_Scheduler::TaskTransition(), called by SOS_Scheduler::Switch() as the worker is woken up after a wait.

Ewald intends this post as an extension of the official documentation, so it’s best to read that documentation in conjunction with this post.

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Storing Sensitive Information In SSIS

Published 2017-08-25 by Kevin Feasel

Shannon Lowder shows the complex interplay between Biml and SSIS when it comes to handling credentials:

One of the questions I get when teaching others how to use Biml is how do you deal with sensitive information like usernames and passwords in your Biml Solution. No one wants to leave this information in plain text in a solution.  You need access to it while interrogating your sources and destination connections for metadata.  You also need it while Biml creates your SSIS packages since SSIS uses SELECT to read the metadata during design time to gather its metadata.  If you lock away that sensitive information too tightly, you won’t be effective while building your solutions.

In the end, you’ll have to compromise between security and efficacy.

Read on for more.

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AllDefinedSuccessors In Biml

Published 2017-08-25 by Kevin Feasel

Ben Weissman shows how to push a common value to all children which share a certain property:

One great way to introduce default values in Biml would be variables in include files or code files for example. But depending on what you’re trying to achieve or at what point you realize it, it may already be causing some extra work.

For example: You have a couple of diffent ways to create a dataflow task but in the end, they should all share a property like DefaultBufferMaxRows.

In BimlStudio, you could make use of a transformer, but these are not available in BimlExpress.

As a bonus, this is a bilingual post on two fronts, so you can pick up a little English-German translation as well as a little VB.Net-C# translation.

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Regression Trees And Double Seasonal Time Series Trends

Published 2017-08-24 by Kevin Feasel

Peter Laurinec walks us through an example of using regression trees to solve a problem with double-seasonal time series data in R:

Classification and regression tree (or decision tree) is broadly used machine learning method for modeling. They are favorite because of these factors:

  • simple to understand (white box)
  • from a tree we can extract interpretable results and make simple decisions
  • they are helpful for exploratory analysis as binary structure of tree is simple to visualize
  • very good prediction accuracy performance
  • very fast
  • they can be simply tuned by ensemble learning techniques

But! There is always some “but”, they poorly adapt when new unexpected situations (values) appears. In other words, they can not detect and adapt to change or concept drift well (absolutely not). This is due to the fact that tree creates during learning just simple rules based on training data. Simple decision tree does not compute any regression coefficients like linear regression, so trend modeling is not possible. You would ask now, so why we are talking about time series forecasting with regression tree together, right? I will explain how to deal with it in more detail further in this post.

This was a very interesting article.  Absolutely worth reading.  H/T R-Bloggers

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Managing Hive Slowly Changing Dimensions

Published 2017-08-24 by Kevin Feasel

Carter Shanklin shows how to manage Type 1, 2, and 3 slowly changing dimensions in Hive:

The most common SCD update strategies are:

  • Type 1: Overwrite old data with new data. The advantage of this approach is that it is extremely simple, and is used any time you want an easy to synchronize reporting systems with operational systems. The disadvantage is you lose history any time you do an update.

  • Type 2: Add new rows with version history. The advantage of this approach is that it allows you to track full history. The disadvantage is that your dimension tables grow without limit and may become very large. When you use Type 2 SCD you will also usually need to create additional reporting views to simplify the process of seeing only the latest dimension values.

  • Type 3: Add new rows and manage limited version history. The advantage of Type 3 is that you get some version history, but the dimension tables remain at the same size as the source system. You also won’t need to create additional reporting views. The disadvantage is you get limited version history, usually only covering the most recent 2 or 3 changes.

The Hive solution is getting closer and closer to a traditional relational warehouse solution.  And on the whole, that’s a good thing.

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Kafka Connect To Elasticsearch

Published 2017-08-24 by Kevin Feasel

Robin Moffatt shows how to take data from Kafka Connect and feed it into Elasticsearch:

Whilst Kafka Connect is part of Apache Kafka itself, if you want to stream data from Kafka to Elasticsearch you’ll want the Confluent Open Source distribution (or at least, the Elasticsearch connector).

The configuration is pretty simple. As before, see inline comments for details

It’s worth noting that if you’re using the same convertor throughout your pipelines (Avro, in this case) you’d actually put this in the Connect worker config itself rather than repeating it for each connector configuration.

This is a simple example which shows just how easy it can be.

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Query Store And Backups

Published 2017-08-24 by Kevin Feasel

Kendra Little walks us through Query Store portability via database backups:

Query Store was designed to be clever, and to minimize its impact on your performance. Query Store only flushes its data from memory to disk periodically. You get to control this by setting the data flush interval in the Query Store settings for a database. (Read more about this in Microsoft Documentation here.)

The default value for Query Store data flush is 15 minutes. That means that in the case of a crash, you might lose up to around 15 minutes of activity.

Even if you’ve lowered this, you might want to make sure that a backup contains the very latest activity, particularly if you’re taking the backup to get Query Store data for someone to look at.

Read the whole thing.

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Rowgroup Elimination In Stored Procedures

Published 2017-08-24 by Kevin Feasel

Erik Darling notes a parameter sniffing problem when trying to use rowgroup elimination in a stored procedure:

So where are we? Well, we found that Rowgroup Elimination is possible in stored procedures with ColumnStore indexes, but that the cached plan doesn’t change based on feedback from that elimination.

  • Good news: elimination can occur with variables passed in.
  • Bad news: that cached plan sticks with you like belly fat at a desk job

Remember our plan? It used a Stream Aggregate to process the MAX. Stream Aggregates are preferred for small, and/or ordered sets.

Great post, Brent.

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Scale-Up In Azure SQL Database

Published 2017-08-24 by Kevin Feasel

Arun Sirpal walks us through how to bump up the scale of an Azure SQL Database:

We should all be aware that when changing the service tier and/or performance level of a database it creates a replica of the original database at the new performance level and then switches connections over to the replica. No data is lost during this process but during the brief moment when we switch over to the replica, connections to the database are disabled, so some transactions in flight may be rolled back. So it probably best that you do this during a period of low activity.

It’s pretty easy to do, but heed Arun’s warning, as you can cause queries to fail during switch-over.  My experience has been that scaling up is pretty slow, too:  I seem to recall it taking 10-20 minutes for a moderately sized database.

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SSIS In Visual Studio 2017

Published 2017-08-24 by Kevin Feasel

Koen Verbeeck shows how to get SQL Server Integration Services support within Visual Studio 2017:

However, if you wanted to use Visual Studio 2017 you had to wait till today (August 23, 2017). There are several reasons why you want to use VS 2017 over VS 2015:

  • You are one of the cool kids and you use only the latest Visual Studio

  • There’s no TFS Explorer plug-in available for Visual Studio 2015. If you want to install SQL Server Data Tools only (thus without the full-blown Visual Studio), and you wanted TFS integration, you couldn’t use VS 2015. Unless you installed VS 2015 Community Edition (which has its own license issues).

  • You have a brand new laptop and you don’t want to install multiple versions of Visual Studio (the situation I’m currently in).

Read the comments for more details and clarification.

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