A Fine Slice Of SQL Server
Steven Sanderson puts a halt to things:
When working with time series data, one common challenge is dealing with non-stationary data. Non-stationary time series can be a headache for analysts, but fear not, because we have a handy tool to make your life easier. Say hello to the
auto_stationarize()function from the {healthyR.ts} package.
Read on to learn why you want stationary data for time series analysis and how the auto_stationarize() function works.
Stithi Panigrahi does some troubleshooting:
Due to the potential impact on performance and storage costs, Azure Databricks clusters don’t capture networking logs by default. Follow the below instructions if you need to capture tcpdump to investigate multiple networking issues related to the cluster. These steps will capture a TCP dump on each cluster node–both driver and workers during the entire lifetime of the cluster.
Click through for an initiation script, which generates the actual script, which itself generates the TCP dumps.
Comments closedJosephine Bush builds on prior work:
Yes, you still need to do some work to maintain indexes in Azure SQL Database. This post will walk you through setting up statistic updates and index maintenance using Terraform.
Thanks to Tracy Boggiano for her directions for setting up the runbooks. If you want to do this manually instead of with Terraform, Tracy’s post walks you through it step by step. I only modified the role assignment so it had read to the entire subscription level to loop through every DB in the subscription.
Thanks to Kendra for blogging about index maintenance in Azure SQL. Her post helped me decide on index maintenance thresholds.
Click through for a link to Josephine’s GitHub repo and a walkthrough of how it all works.
Comments closedJohnny Winter takes a look at Data Activator:
It activates data right? Err… not sure that’s even a thing. The one liner I’d give it, is that it acts ON your data.
The concept is that in this day and age, taking action on the insights in your data is still a very manual effort. So why not automate the monitoring of that data and have Data Activator take that action for you? In my mind it’s Microsoft’s attempt to bring Robotic Process Automation (RPA) closer to to your data.
So how does it work and what actions can you take?
That’s where you’ll have to read the whole thing—this post is just a trailer, after all.
Comments closedPaul White explains the performance benefits of fast key optimization and when you can get them:
SQL Server can be called upon to sort a variety of data types. To facilitate this, the sorting code normally calls out to a specific comparator to determine how two compared values should sort: lower, higher, or equal.
Although calling comparator code has low overhead, performing enough of them can cause noticeable performance differences.
To address this, SQL Server has always (since at least version 7) supported a fast key optimization for simple data types.
Click through to learn which data types support fast key optimization and to gain a feeling of the performance impact.
Comments closedOlivier Van Steenlandt performs a hostile takeover:
The end user can save a Power BI report created in the Power BI Service in their own Personal Workspace but then they have no way to share it with their team. It remains a personal report/analysis.
When executing the analysis in Excel, they can distribute the Excel-file as they see fit.
The pitfall of personal workspaces in Power BI, from my point of view, is that if someone leaves the company, there isn’t a straight forward way for other end users to take-over previously created analysis.
This is where the Power BI Administrator can be of importance.
Read on to see how a Power BI Administrator can gain access to a personal workspace and migrate reports out of it.
Comments closedRobert Yokota shows us how to generate data contracts for streaming solutions:
A data contract is a formal agreement between an upstream component and a downstream component on the structure and semantics of data that is in motion. The upstream component enforces the data contract, while the downstream component can assume that the data it receives conforms to the data contract. Data contracts are important because they provide transparency over dependencies and data usage in a streaming architecture. They help to ensure the consistency, reliability, and quality of the data in event streams, and they provide a single source of truth for understanding the data in motion.
Click through for a sample application that uses data contracts.
Comments closedDavid Klee lays out an argument:
SQL Server query developers, listen up! Query execution time is not everything you should be worried about. You need to examine the parse and compilation time for each of your queries too.
Read on for the brunt of David’s argument. There are things you can do about query compilation time, starting with database design (normalize tables and include key constraints, include appropriate indexes, etc.) and continuing with query design (keep queries simple, limit use of functions, limit use of nested views, break complicated queries into multiple steps and use temp tables as intermediaries, etc.). One thing about compilation time, however, is that it doesn’t matter as much if you retain that plan for a while and reuse it a lot.
Comments closedBrent Ozar shares some percentages:
For the long story: ever wonder how fast people are adopting new versions of SQL Server, or what’s “normal” out there for SQL Server adoption rates? Let’s find out in the summer 2023 version of our SQL ConstantCare® population report.
Out of the thousands of monitored SQL Servers, a whopping 44% are SQL Server 2019! That’s the highest percentage we’ve seen for any version in the 3 years that we’ve been doing this analysis.
Standard statistical sampling rules apply, though there is an interesting note in the comments about how EC2 instances break down in AWS by version of SQL Server, and the numbers are reasonably similar.
Comments closedI’ve been pretty busy with work and travel, but I finally got an official Silk Github repository to publish a couple new white papers and sizing assessment worksheets for customer access. These are primarily Oracle and SQL Server to Azure focused white papers, but I will be publishing ones on GCP next, to be followed by AI and other database platforms soon.
Click through for links to the documents.
Comments closed