Extending SQL Server monitoring with Raspberry PI and Lametric

Mis en avant

First blog of this new year 2021 and I will start with a fancy and How-To Geek topic

In my last blog post, I discussed about monitoring and how it should help to address quickly a situation that is going degrading. Alerts are probably the first way to raise your attention and, in my case, they are often in the form of emails in a dedicated folder. That remains a good thing, at least if you’re not focusing too long in other daily tasks or projects. In work office, I know I would probably better focus on new alerts but as I said previously, telework changed definitely the game.

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Why we moved SQL Server monitoring on Prometheus and Grafana

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During this year, I spent a part of my job on understanding the processes and concepts around monitoring in my company. The DevOps mindset mainly drove the idea to move our SQL Server monitoring to the existing Prometheus and Grafana infrastructure. Obviously, there were some technical decisions behind the scene, but the most important part of this write-up is dedicated to explaining other and likely most important reasons of this decision.

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Interesting use case of using dummy columnstore indexes and temp tables

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Columnstore indexes are a very nice feature and well-suited for analytics queries. Using them for our datawarehouse helped to accelerate some big ETL processing and to reduce resource footprint such as CPU, IO and memory as well. In addition, SQL Server 2016 takes columnstore index to a new level and allows a fully updateable non-clustered columnstore index on a rowstore table making possible operational and analytics workloads. Non-clustered columnstore index are a different beast to manage with OLTP workload and we got both good and bad experiences on it. In this blog post, let’s talk about good effects and an interesting case where we use them for reducing CPU consumption of a big reporting query.

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SQL Server index rebuid online and blocking scenario

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A couple of months ago, I experienced a problem about index rebuild online operation on SQL Server. In short, the operation was supposed to be online and to never block concurrent queries. But in fact, it was not the case (or to be more precise, it was partially the case) and to make the scenario more complex, we experienced different behaviors regarding the context. Let’s start the story with the initial context: in my company, we usually go through continuous deployment including SQL modification scripts and because we usually rely on daily pipeline, we must ensure related SQL operations are not too disruptive to avoid impacting the user experience.

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dbachecks and AlwaysOn availability group checks

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When I started my DBA position in my new company, I was looking for a tool that was able to check periodically the SQL Server database environments for several reasons. First, as DBA one of my main concern is about maintaining and keeping the different mssql environments well-configured against an initial standard. It is also worth noting I’m not the only person to interact with databases and anyone in my team, which is member of sysadmin server role as well, is able to change any server-level configuration settings at any moment. In this case, chances are that having environments shifting from our initial standard over the time and my team and I need to keep confident by checking periodically the current mssql environment configurations, be alerting if configuration drifts exist and obviously fix it as faster as possible.

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SQL Server on Linux and new FUA support for XFS filesystem

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I wrote a (dbi services) blog post concerning Linux and SQL Server IO behavior changes before and after SQL Server 2017 CU6. Now, I was looking forward seeing some new improvements with Force Unit Access (FUA) that was implemented with Linux XFS enhancements since the Linux Kernel 4.18.

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Availability Group 2017 Direct seeding and updated default path policy

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A couple of days ago, I ran into an issue when adding a new database in direct seeding mode that led me to reconsider refreshing my skills on this feature. Going through the AG database wizard for adding database, I faced the following error message …

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Mitigating Scalar UDF’s procedural code performance with SQL 2019 and Scalar UDF Inlining capabilities

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A couple of days ago, I read the write-up of my former colleague @FranckPachot about refactoring procedural code to SQL. This is recurrent subject in the database world and I was interested in transposing this article to SQL Server because it was about refactoring a Scalar-Valued function to a SQL view. The latter one is a great alternative when it comes performance but something new was shipped with SQL Server 2019 and could address (or at least could mitigate) this recurrent scenario.

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