A Fine Slice Of SQL Server
This update introduces several enhancements to native incremental copy and change data capture (CDC) capabilities in Copy job, including support for additional data source stores for incremental copy, expanded CDC support for SAP Datasphere Outbound for Amazon S3 and Google Cloud Storage (in addition to Microsoft Cloud Storage), and Column Mapping support for CDC in Copy job.
Click through to see what has changed in the copy job.
Leave a CommentRebecca Lewis has a two-parter. First up is finding instances of long-running I/O Requests:
When diagnosing storage or latency issues, one SQL Server message factors in more than many:
“SQL Server has encountered X occurrence(s) of I/O requests taking longer than 15 seconds to complete on file…”
Where X might be 1, 5 or 50, and it could list a file from any one of your databases. When you see this, the next good question is when did it happen and where.
And then the question is, what do you do about it? Rebecca provides some guidance:
In a previous post, I shared a script to detect the I/O requests taking longer than 15 seconds warning across your SQL Server inventory. Now let’s talk about what to do when you find it.
Here are five of the most common causes with some tips to investigate each:
The neat part is, it’s not always due to slow storage or bad hardware.
Leave a CommentI haven’t participated in a #TSQL Tuesday in quite some time. I saw this topic and said, “That’s perfect!” I love talking about Failure and mistakes! I have a whole presentation about it, and I’m hoping to make a series of blog posts about it very soon.
You do not learn from success in life; you learn from failure. I hope you are making lots of mistakes so that you can learn!
Read on for a story from Pat around creating a trigger and not realizing the consequences. This is why we don’t let just anybody have mogwais triggers.
Elizabeth Christensen lays out an argument:
Lots of us started with a Postgres database that incremented with an id
SERIAL PRIMARY KEY. This was the Postgres standard for many years for data columns that auto incremented. The SERIAL is a shorthand for an integer data type that is automatically incremented. However as your data grows in size,SERIALs andINTs can run the risk of an integer overflow as they get closer to 2 Billion uses.We covered a lot of this in a blog post The Integer at the End of the Universe: Integer Overflow in Postgres a few years ago. Since that was published we’ve helped a number of customers with this problem and I wanted to refresh the ideas and include some troubleshooting steps that can be helpful. I also think that
BIGINTis more cost effective than folks realize.
Click through for Elizabeth’s argument. I’d say that this is very similar for SQL Server, where I’m more inclined to create a BIGINT identity column, especially because I almost automatically apply page-level compression to tables so there’s not really a downside to doing this. Identity columns don’t have a domain, so there’s no domain-specific information like you’d get with a column such as Age; and with page-level compression, you’re not wasting space.
Leave a CommentBrent Ozar notes some problems:
SQL Server 2025 Cumulative Update 1 came out last week, and I was kinda confused by the release notes. They described a couple dozen fixed issues, and the list seemed really short for a CU1.
However, the more I dug into it, the weirder things got. For example, there were several new DMVs added – which is normally a pretty big deal, something to be celebrated in the release notes – but they weren’t mentioned in the release notes. One of the DMVs wasn’t even documented. So I didn’t blog to tell you about CU1, dear reader, because something about it seemed fishy.
Read on for a big deal.
Leave a CommentUmair Shahid digs into the arguments for and against:
“Should PostgreSQL run on Kubernetes too?”
The worst answers are the confident ones:
- “Yes, because everything else is on Kubernetes.”
- “No, because databases are special.”
Both are lazy. The right answer depends on what you’re optimizing for: delivery velocity, platform consistency, latency predictability, operational risk, compliance constraints, and, most importantly, who is on-call when things go sideways.
Click through for a detailed analysis. It’s a similar story in SQL Server:
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