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Author: Kevin Feasel

SQL Server Isolation Levels

Published 2019-04-03 by Kevin Feasel

Pamela M. takes us through a humorous journey covering transaction isolation levels in SQL Server:

READ COMMITTED:  One step up (and the default for SQL Server).    A query in the current transaction can’t read data modified by someone else that hasn’t yet committed.  No dirty reads.  BUT….data could be changed by others between statements in the current transaction, so the data may not look the same twice.  READ COMMITTED uses shared locks to prevent dirty reads, but that’s about all you get.  You still get non-repeatable reads and phantom reads here (more on phantom reads below).

Click through for the full list.

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CLR Assembly Failure After Availability Group Failover

Published 2019-04-03 by Kevin Feasel

Paul Randal walks us through a permissions issue after an Availability Group failover:

Jonathan was working with a client recently who experienced a CLR assembly failure after an AG failover and needed to figure out why. They’d been testing their AG disaster recovery strategy and ran into an unexpected problem with their application which relies heavily on SQLCLR and an UNSAFE assembly that calls a web service from inside SQL Server.  When they failed over their AG to their DR server, the CLR assembly failed with the following error:

An error occurred in the Microsoft .NET Framework while trying to load assembly id 65546. The server may be running out of resources, or the assembly may not be trusted with PERMISSION_SET = EXTERNAL_ACCESS or UNSAFE. Run the query again, or check documentation to see how to solve the assembly trust issues. For more information about this error: System.IO.FileLoadException: Could not load file or assembly ‘sqlclr_assemblyname, Version=1.0.0.0, Culture=neutral, PublicKeyToken=fa39443c11b12591’ or one of its dependencies. Exception from HRESULT: 0x80FC80F1

Read on to see the root cause and what you can do to correct it.

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The SparkSession Object

Published 2019-04-02 by Kevin Feasel

Achilleus explains SparkSession and why it’s useful even if you know about SparkContext:

Spark session is a unified entry point of a spark application from Spark 2.0. It provides a way to interact with various spark’s functionality with a lesser number of constructs. Instead of having a spark context, hive context, SQL context, now all of it is encapsulated in a Spark session.

Read on to learn more about SparkSession and how you can use it.

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Kafka Streams: Streams and Tables

Published 2019-04-02 by Kevin Feasel

Neha Bhardwaj explains a couple of the key abstractions in Kafka Streams:

In this blog, we’ll move one step forward to get an understanding of the Dual streaming model to see what abstractions does KSQL use to process the data.

All the data that we are working on with KSQL is produced to Kafka topics by some client. This client can be any Application, Kafka connectors etc., which produces continuous never-ending data to the topics.

KSQL does not directly interact with these topics, it rather introduces a couple of abstractions in between to process the data, which are known as Streams and Tables.

Read on to learn what these are and why it’s useful to think in these terms.

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Request: Add Support for Row Pattern Recognition

Published 2019-04-02 by Kevin Feasel

Itzik Ben-Gan would like to see Row Pattern Recognition make it into T-SQL:

The ISO/IEC 9075:2016 standard (aka SQL:2016) introduces support for Row Pattern Recognition (RPR) in SQL. Similar to using regular expressions to identify patterns in a string, RPR allows you to use regular expressions to identify patterns in a sequence of rows.

To me, it’s the next step in the evolution of window functions. If you think that window functions are profound and useful, RPR is really going to bake your noodle.

RPR has limitless practical applications, including identifying patterns in stock market activity, handling time series, fraud detection, material handling, shipping applications, DNA sequencing, gaps and islands, top N per group, and many others. 

I’ve voted it up and recommend you do so too. This is a great way to think of streams of data sitting in a database. If you have business use cases where this could help, adding those as comments would be great too.

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Using Semi-Additive Measures with DAX

Published 2019-04-02 by Kevin Feasel

Alberto Ferrari explains what semi-additive measures are and how we can work with them in DAX:

First things first: what is a semi-additive calculation? Any calculation can be either additive, non-additive or semi-additive. An additive measure uses SUM to aggregate over any attribute. The sales amount is a perfect example of an additive measure. Indeed, the sales amount for all customers is the sum of the individual sales for each customer; at the same time, the amount over a year is the sum of the amounts for each month.

A non-additive measure does not use SUM over any dimension. Distinct count is the simplest example: the distinct count of products sold over a month is not the sum of the distinct counts of individual days. The same happens with any other dimension: a distinct count of products sold in a country is not the sum of the distinct counts of the products sold in each city in the country.

Semi-additive calculations are the hardest ones: a semi-additive measure uses SUM to aggregate over some dimensions and a different aggregation over other dimensions – a typical example being time.

Semi-additive measures are probably the trickiest of the three, as you can easily work with additive measures and you know you won’t be able to do much with non-additive measures.

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Adding SSIS Catalog to a Docker Container

Published 2019-04-02 by Kevin Feasel

Andy Leonard takes two shots at adding the SSIS Catalog to a Docker container. First up is the version which doesn’t work:

I have been working on getting an SSIS Catalog running in a container for a couple years.
I share this post not to discourage you. 
I share it to let you know one way I failed. 
I thought I had succeeded when the PowerShell in this post worked. The PowerShell works, by the way – just not in a container configured thus.
This is but one failure. 
I failed more than once, I promise.

Andy perseveres and succeeds in part 4 of the series:

I can hear some of you thinking, “How do we accomplish this, Andy?”
I’m glad you asked. 
The answer is “We modify our container.”
Disclaimer: I’m about 100% certain there’s another way to do this and about 99% sure there’s a better way. I’m going to show you what I did. Cool?
Cool.

Read on to see how Andy did it.

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Capturing Execution Plans with Extended Events

Published 2019-04-02 by Kevin Feasel

Pedro Lopes shows us how we can get actual execution plans using Extended Events:

Query execution plans, otherwise known as actual execution plans or just Showplan, provide a map of all the required operations to get the query output, which includes runtime performance statistics.

Query Store (QS) is a valuable tool for troubleshooting workload trends via specific queries – this is because QS aggregates performance data on queries. However, sometimes we need to get the specific, singleton query execution plan to analyze and troubleshoot – this is where xEvents come in.

Since SQL Server 2012 we’ve had the query_post_execution_showplan xEvent for this. As the name suggests, it gets you the actual query plan – because it is *after* execution – when we have the runtime statistics available. However this xEvent is based on the standard query execution statistics profile infrastructure(quite a mouthful) – or standard profiling for short (read more about it here). This has a very high overhead (75%+ with a TPC-C like workload) which is why its use needs to be seriously considered, and most likely not used unless in last resort.

In more recent releases we have other alternatives for these requirements to get the singleton actual execution plans, based on the lightweight query execution statistics profile infrastructure – or lightweight profiling for short (read more about it here). These xEvents are listed below, where we’ll see examples on how to use them.

Pedro does a good job of taking us through the available events and what we get from them.

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