Steve Catmull
My feedback
-
5 votes
Upvotes: 52
<=-=Oct 2 2015 9:58AM=-=>Niko,
Thanks for submitting this request and I do appreciate you informing us of health industry where computed columns are used commonly. We won’t be able to addess this in SQL 2016 but consider this feedback in the next release
regards
<=-=Apr 15 2016 9:21AM=-=>
SunilCertain problems can only be solved by computed columns, and it would be very nice to use the sweet columnstore index on these tables. For example, a date filter in Tableau will actually write 3 filters on datepart(year,mydate), datepart(month,mydate), and datepart(day,mydate). I can completely solve this performance issue by creating persisted computed columns on those functions (and SQL Server will substitute the persisted column when a query uses a filter on these functions). That’s great. But then, I can’t columnstore index these new columns. So I tried to create an indexed view on the whole table including these…
Steve Catmull supported this idea ·
-
331 votes
Upvotes: 127
<=-=Jun 23 2015 8:37AM=-=>I’m the first to post a useful comment. This must make me special.
Seriously though, this would be an excellent solution to having to create a new “scratchdb” to hold my interim ETL data. This would be a major plus in simplifying design of a high performance app.
<=-=Jul 3 2015 5:04AM=-=>In 2014, memory optimized tables, and delayed durability can be used help mitigate these Issues. However neither of this are always completely viable solutions. Brent’s proposed solution is likely the simplest way to achieve this with the least amount of unwanted impact. It is important to note that other platforms implement similar functionality as well. Notably Oracle.
<=-=Nov 29 2016 3:58PM=-=>There are so many good things about this suggestion. I am amazed that SQL does not have the capability to turn off logging for certain tables that you define as no…
Steve Catmull supported this idea ·
-
449 votes
Steve Catmull supported this idea ·
-
5 votes
Steve Catmull supported this idea ·
-
3 votes
Steve Catmull shared this idea ·
-
115 votes
Steve Catmull supported this idea ·
-
139 votes
Thanks for your suggestion! The feature is currently in our backlog, but has to be prioritized with other ones. So we don’t have an ETA at this point of time. Please don’t hesitate to vote for it if you want to see it’s done sooner.
Steve Catmull supported this idea ·
-
1 vote
Can you help us understand why you would like to return to defaults?
Currently, users can override the lab shutdown.
If you would like to enforce your schedule as the schedule for all lab VMs, you can do so using the Auto shutdown policy blade and selecting ‘Users have no control..’Does that help?
Steve Catmull shared this idea ·
-
190 votes
An error occurred while saving the comment Steve Catmull supported this idea ·
-
53 votes
We do have this feature on our backlog, but do not have a timeline for it as yet.
We will update the status as soon as we have an ETA.Steve Catmull supported this idea ·
-
9 votes
Steve Catmull shared this idea ·
-
79 votes5 comments · Azure Monitor- Alert Management » Configuring Alerts · Flag idea as inappropriate… · Admin →
Steve Catmull supported this idea ·
-
132 votesunder review · 6 comments · Azure Synapse Analytics » SQL/T-SQL · Flag idea as inappropriate… · Admin →
Steve Catmull supported this idea ·
-
252 votes
Steve Catmull supported this idea ·
An error occurred while saving the comment Steve Catmull commented
Hopefully this includes the USE <database> statement too.
-
39 votes
We announced workload isolation via workload groups for public preview at Ignite in Nov., 2019. Workload groups allow you to create your own custom resource classes (among other things). Check out workload classification that allows you to assign requests by more than just login information too!
Steve Catmull supported this idea ·
I could see this being really useful in situations where we often leave dev/test environments open for extended hours (e.g, 5pm-9pm). If it has not been active for x hours before scheduled off time, shut it down early when the idle criteria is met.
As far as what constitutes idle criteria, let's light up some machine learning. Imagine if we could train it through machine-learning to identify idleness. I think it will be hard to define quantitatively but if you could start to use some of the great work in Application Insights Smart Detection that would be great.
Give us some time periods that look like idle based on performance counters / users logins, etc. Then we will help you identify false positive and false negatives. My guess would be that many machines will have unique patterns and maybe we need to opt in for idle detection. Then train on those that opt in.