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Month: October 2021

Comparing OPENJSON vs Other List-Parsing Methods in SQL Server

Published 2021-10-25 by Kevin Feasel

Aaron Bertrand splits a string:

I have long advocated avoiding splitting strings by using table-valued parameters (TVPs), but this is not always a valid option; for example, PHP drivers do not handle this feature yet. A new pattern I’ve seen emerging is to replace splitting comma-separated strings with the new OPENJSON functionality introduced in SQL Server 2016. I wanted to explore why this is not an improvement in the typical case, unless you are using a client application platform that doesn’t support TVPs and your application data already happens to be in JSON format.

Read on for the solution, which has a somewhat surprising outcome.

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Four DBA ToDos in a New Role

Published 2021-10-25 by Kevin Feasel

Lee Markum has a starting point for DBAs in a new role:

You’ve just been hired into a DBA role at a new company, or you’ve been given the DBA keys at your current company. Maybe you’re a SysAdmin and your boss has informed you that you are now supposed to manage the SQL Servers as well as everything else on your plate. In any of these situations, you may have some confidence in your skills, but especially in the case of being a new hire, you have absolutely no true idea of what you’re walking into.

In these scenarios, where do you start? Start with these four areas.

Click through for the four areas. I completely agree with Lee on these for DBAs, including the order.

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Grouping on a Substring

Published 2021-10-25 by Kevin Feasel

Kevin Hill tries something out:

I was chatting with Jeff (b|t) on my team yesterday and the context escapes me but I had this thought:

“Can you Group By the beginning characters, or a subset, of a field?”

I’m not a developer, so this question never comes my way. Except yesterday.

Click through for the answer. Mind you, this is unlikely to perform well, but if you’re looking at a small enough dataset or need a one-time query and can afford a full scan of the data, so be it.

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Storage Testing for Azure SQL Managed Instances

Published 2021-10-22 by Kevin Feasel

Joe Obbish busts out the slide rule:

Lately I’ve been doing some exploratory performance testing on Azure SQL Managed Instances in preparation for a migration to that platform. This blog post documents some storage testing results and may even have practical advice near the end. All testing was done on a gen5 general purpose instance with 8 vCores.

Read on for Joe’s findings. Spoiler alert: there is practical advice at the end.

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Real-Time Change Detection via Cumulative Sums

Published 2021-10-22 by Kevin Feasel

Nithin Sankar tracks deviations with cumulative sums:

With the advent of Internet of Things (IOT) and the proliferation of connected devices, comes the challenge of monitoring parts for maintenance before they break down. A common approach revolves around getting data from connected devices and performing a statistical test to determine the likelihood of the device failing. While this common approach is robust, it typically involves a significant time investment in exploratory data analysis, feature engineering, training, and testing to build a predictive model. It, therefore, often lacks the agility required to keep up with the monitoring demands of increasingly time-sensitive initiatives. 

In this context, the question becomes: how can we ensure a similar degree of rigor, but also improve the timeliness and responsiveness of being able to perform predictive maintenance? 

Click through for the process, as well as an example using Azure Stream Analytics and Power BI.

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Optimizing Blob Storage Query Performance

Published 2021-10-22 by Kevin Feasel

Dennes Torres compares several strategies for querying data stored in Azure Blob Storage:

In the third part of the series Querying Blob Storage with SQL, I will focus on the performance behaviour of queries: What makes them faster, slower, and some syntax beyond the basics.

The performance tests in this article are repeated, and the best time of the queries is recorded. This doesn’t mean you will always achieve the same timing. Many architectural details will affect the timing, such as cache, first execution, and so on. The timing exposed on each query is only a reference pointing to the differences of the query methods that can affect the time and the usual result for better or worse performance.

Click through to see which patterns perform well and which don’t.

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Automatic Backups on a Data Lake or Lakehouse

Published 2021-10-22 by Kevin Feasel

Dave Ruijter backs that thing up:

Out of the box, Azure Data Lake Storage Gen2 provides redundant storage. Therefore, the data in your Data Lake(house) is resilient to transient hardware failures within a datacenter through automated replicas. This ensures durability and high availability. In this blog post, I provide a backup strategy on how to further protect your data from accidental deletions, data corruption, or any other data failures. This strategy works for Data Lake as well as Data Lakehouse implementations. It uses native Azure services, no additional tools, software, or licenses are required.

Read on for a detailed strategy.

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