A Fine Slice Of SQL Server
Nicky van Vroenhoven calls a timeout:
After a while it got me thinking:
- What does it actually mean when I pause a Fabric capacity?
- What will stop working?
- What can I still do and won’t stop working?
Read on for the answer to this question.
Comments closedAmit Khandelwal disambiguates two options:
Recently, in one of the meetings with our CSS team (support) we realized that our customers might not be selecting the right Enterprise edition for their SQL Servers deployed on Linux. To learn more about different SQL Server editions please refer: Editions and supported features of SQL Server 2022 – SQL Server | Microsoft Learn.
Thus, through this blog we would like to provide guidance on how you can identify and update the SQL Server to use the right Enterprise edition which should show up in the error log as : “Enterprise Edition: Core-based Licensing (64-bit)” and not “Enterprise Edition (64-bit)” which is only needed in case you need to follow historical licensing agreements that limits to a maximum 20 cores per SQL Server instance.
Read on to learn how you can change the edition if you did indeed goof on the installation.
Comments closedItzik Ben-Gan lays out some fundamentals:
When people start learning a new field, for example T-SQL, it’s tempting to spend very little time trying to understand the fundamentals of the field so that you can quickly get to the advanced parts. You might think that you already understand what certain ideas, concepts and terms mean, so you don’t necessarily see the value in dwelling on them. That’s often the case with newcomers to T-SQL, especially because soon after you start learning the language, you can already write queries that return results, giving you a false impression that it’s a simple or easy language. However, without a good understanding of the foundations and roots of the language, you’re bound to end up writing code that doesn’t mean what you think it means. To be able to write robust and correct T-SQL code, you really want to spend a lot of energy on making sure that you have an in-depth understanding of the fundamentals.
No matter your knowledge level, there’s a really good chance you’ll learn at least one new thing in this article.
Comments closedThis week, one of my colleagues at Microsoft, Nat Van Gulck, showed me a cool new open-source tool he’s been working on to make VisOps for Power BI much easier: PBI Inspector. What is VisOps? I’ll admit I didn’t really know either, so being lazy I asked Nat to write a few paragraphs describing the project and why it will be useful:
Read on for Nat’s description and an example of PBI Inspector in action.
Comments closedMeagan Longoria adds some widgets:
Widgets provide a way to parameterize notebooks in Databricks. If you need to call the same process for different values, you can create widgets to allow you to pass the variable values into the notebook, making your notebook code more reusable. You can then refer to those values throughout the notebook.
Click through to learn more about the four types of widgets and how they work.
Comments closedSteven Sanderson adds points to those lines:
In this blog post, we’ll explore how to overlay points or lines on a plot using Base R. We’ll use the
plot()function to create the initial plot and then show how to overlay points withpoints()and lines withlines(). We’ll provide several examples, explaining each code block in simple terms, and encourage you to try them out on your own datasets.
Read on to see how. It’s also pretty easy to do in ggplot2 or other visualization libraries.
Comments closedSalman Ahmed builds a cluster:
pg_cirrus is a simple and automated solution to deploy highly available 3-node PostgreSQL clusters with auto failover. It is built using Ansible and to perform auto failover and load balancing we are using pgpool.
We understand that setting up 3-node HA cluster using pg_cirrus on cloud environment isn’t as simple as setting it up on VMs. In this blog we will guide you in setting up a 3-node HA cluster using pg_cirrus on AWS EC2 instances.
Read on for the step-by-step instructions.
Comments closedMarc Bushong doesn’t want to copy and paste:
Developing ETLs/ELTs can be a complex process when you add in business logic, large amounts of data, and the high volume of table data that needs to be moved from source to target. This is especially true in analytical workloads involving Azure SQL when there is a need to either fully reload a table or incrementally update a table. In order to handle the logic to incrementally update a table or fully reload a table in Azure SQL (or Azure Synapse), we will need to create the following assets:
- Metadata table in Azure SQL
- This will contain the configurations needed to load each table end to end
- Metadata driven pipelines
- Parent and child pipeline templates that will orchestrate and execute the ETL/ELT end to end
- Custom SQL logic for incremental processing
- Dynamic SQL to perform the delete and insert based on criteria the user provides in the metadata table
Read on for the demonstration, which reads from one Azure SQL DB into another.
Comments closedMarc Lelijveld breaks out the thermometer:
As part of Microsoft Fabric, a new storage mode to connect from Power BI to data in OneLake has been introduced. Direct Lake it makes to possible to use your data from OneLake in Power BI without taking an additional copy of the data. Where Direct Lake promises to deliver the performance of Import-mode with the real-time capabilities of Direct query, it is time to have a closer look how data gets loaded into memory and delving into the concept of data dictionary temperature.
In this blog I will explain when data gets loaded into memory, elaborate on how you can measure the dictionary temperature of your data and the effect of queries on the temperature.
Click through to see what affects this measure and how.
Comments closedErik Darling recommends caution:
Like in yesterday’s post about Cost Threshold For Parallelism, changing MAXDOP settings will have a universal effect on the workload.
This is true whether you change it at the server level for all databases, or at the database level using a database scoped configuration for a single database.
It is a guardrail to prevent unwanted conditions as a whole, like excessive concurrent parallel queries causing worker thread starvation (THREADPOOL waits), or just pushing CPU to 100% for extended periods of time.
Read on to see what Erik recommends you think about after any MaxDOP change.
Comments closed