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Author: Kevin Feasel

Checking for Missing Failover Cluster Dependencies

Published 2021-02-01 by Kevin Feasel

Chad Callihan ran into an error creating a new database:

A tool that restores a model type database and does a bit of configuration work was failing. I took a look at the stores procedures and started to go step by step. It didn’t take long before getting this error message when attempting to restore/create a database:

Msg 5184, Level 16, State 2, Line 3
Cannot use file ‘D:\sql_log\CC_Test_name_4.ldf’ for clustered server. Only formatted files on which the cluster resource of the server has a dependency can be used. Either the disk resource containing the file is not present in the cluster group or the cluster resource of the Sql Server does not have a dependency on it.

Click through for the solution.

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Using Active Directory Authentication for SQL Server on Linux

Published 2021-02-01 by Kevin Feasel

Jamie Wick takes us through a lengthy process:

SQL Server has been supported on several Linux distributions for a couple of years now. For some people, the primary stumbling block to implementing SQL Server on Linux is the need to retain Active Directory (ie Windows-based) authentication for their database users and applications. Below we’ll go over how to join a Linux server (Ubuntu release 20.04) with SQL Server 2019 to an Active Directory domain, and then configure SQL Server to allow Windows-based logins.

There are quite a few steps here and I appreciate Jamie providing us an image-filled, step-by-step process.

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Optimizing a SQL Server 2019 Project for a Dedicated SQL Pool

Published 2021-02-01 by Kevin Feasel

Kevin Chant shows us how we can modify a database schema intended for SQL Server 2019 to work best with an Azure Synapse Analytics dedicated SQL pool:

In this post I want to cover how you can transform your SQL Server database schema for a dedicated SQL Pool if you are using Azure DevOps. Because I covered it at Data Toboggan over the weekend and it can be very useful.

By the end of this post, you will know one way you can transform the schema of a database project for SQL Server 2019 if you are using Azure DevOps. So that you can make it optimal for dedicated SQL Pools.

Click through for the process and an example. Note that this isn’t a quick “check this box and you’re done” type of solution, but if you already have a proper star schema, this will help you think through some of the things you’ll need to do.

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Performance Comparison of ISNULL and COALESCE

Published 2021-02-01 by Kevin Feasel

Erik Darling notes the edge cases where ISNULL() can be faster than COALESCE():

Sometimes there are very good reasons to use either coalesce or isnull, owing to them having different capabilities, behaviors, and support across databases.

But isnull has some particular capabilities that are interesting, despite its limitations: only two arguments, specific to SQL Server, and uh… well, we can’t always get three reasons, as a wise man once said.

There is one thing that makes isnull interesting in certain scenarios. Let’s look at a couple.

Read the whole thing. You (hopefully) won’t be in this situation often, but if you do happen to find yourself in it for whatever reason (and you can’t re-write the query to something better), it’s good to keep this in mind.

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Reading Delta Lake Tables from Power BI

Published 2021-02-01 by Kevin Feasel

Gerhard Brueckl checks out the Apache Parquet connector in Power BI, reading from a Delta Lake:

“Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.”

However, Parquet is just a file format and does not really support you when it comes to data management. Common data manipulation operations (DML)  like updates and deletes still need to be handled manually by the data pipeline. This was one of the reasons why Delta Lake (delta.io) was developed besides a lot of other features like ACID transactions, proper meta data handling and a lot more. If you are interested in the details, please follow the link above.

Click through for a demo.

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Default Format String for Power BI Calculation Groups

Published 2021-02-01 by Kevin Feasel

Gilbert Quevauvilliers figures out how to display a calculation group’s default format string in Power BI:

Recently I was working on a customer’s data and one thing that was missed was when something was not selected the Calculation Group did not return any default Format String.

Below is how I solved this, with the tricky part being formatting of the measure to be displayed correctly.

This follows on from my previous blog posts with regards to Calculation Groups:

Power BI Visuals now support Custom Formatting for measures using Calculation Groups!

Create Currency Formatting Strings using Calculation Groups in Power BI Pro & Premium / Azure Analysis Services / SQL Server Analysis Services 2019

Click through for the process.

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A Review of AWS Athena

Published 2021-01-29 by Kevin Feasel

John McCormack updates an older review:

AWS’s own documentation is the best place for full details on the Athena offering, this post hopes to serve as further explanation and also act as an anchor to some more detailed information. As it is a managed service, Athena requires no administration, maintenance or patching. It’s not designed for regular querying of tables in a way that you would with an RDBMS. Performance is geared around querying large data sets which may include structured data or semi-structured data. There are no licensing costs like you may have with some Relational Database Management Systems (RDBMS) such as SQL Server and costs are kept low, as you only pay when you run queries in AWS Athena.

Click through for an overview of product benefits.

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Using Spark Pools in Azure Synapse Analytics

Published 2021-01-29 by Kevin Feasel

Rahul Mehta shows how to create and use an Apache Spark pool in Azure Synapse Analytics:

In the last part of the Azure Synapse Analytics article series, we learned how to create a dedicated SQL pool. Azure Synapse support three different types of pools – on-demand SQL pool, dedicated SQL pool and Spark pool. Spark provides an in-memory distributed processing framework for big data analytics, which suits many big data analytics use-cases. Azure Synapse Analytics provides mechanisms to use SQL on-demand pool to query data as a service, SQL dedicated pool for data warehousing using distributed data processing engine, and Spark pool for analytics using in-memory big data processing engine. This article shows how to create a Spark pool in Azure Synapse Analytics and further how to process the data using it.

Click through for a demo on setup and a sample notebook to get started.

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Resource Governor and Memory Grants

Published 2021-01-29 by Kevin Feasel

Deepthi Goguri explains how you can use Resource Governor to control memory utilization:

Queries doesn’t start unless available memory is 150% of requested query memory because sql server will always allocate a little bit of additional buffer memory. Lets take the 100MB query which will get 150MB with additional buffer. What if this query only get 100MB and doesn’t get that additional buffer in the beginning to run the query? In that case, the query will wait until the 25x the query cost which is in seconds. When the timeout occurs and if the exact grant (100MB) is available without the buffer amount then the query starts to run this time. In other scenario, even after waiting for so long and after the timeout, the exact memory (100MB) without the memory grant is not available and less than 100MB is available, in that case the grant can be decreased to the minimum required grant. What if even the minimum required grant is not available, in that case we will receive an error 8645. We can configure the timeout by using the resource governor.

Read on for more detail.

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