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Author: Kevin Feasel

Local Filesystem Source Control

Steve Jones shares a tale of woe related to source control neglect:

One of the first things I found was that all our stored procedures in the production server were encrypted. I wasn’t sure why, since we hosted our machines, but that wasn’t a big deal.

Until it was.

One day we had an issue on one of our SQL Server 2000 servers (we had two, supposedly identical). In troubleshooting and putting some sample data in both systems for a fake customer, we got different results. Hmmm, not what I wanted to see.

I checked the VCS (SourceSafe at the time) and checked out the code. I then loaded my test data and … got a third, different result. Now I was concerned as this was a production bug that was delaying work for a customer.

I’ve become convinced over the past few years that having all of your code in source control (including database scripts!) is a key differentiator between a good work situation and a bad work situation.

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Indexing Woes

Shane O’Neill relates a tale of trying to create an index with a SQL Agent job.  Easy, right?

Now I’m angry too since I count these failures as personal and I don’t like failing, so I get cracking on the investigation.
Straight away, that error message doesn’t help my mood.
I’m not indexing a view!
I’m not including computed columns!
It’s not a filtered index!
The columns are not xml data types, or spatial operations!
And nowhere, nowhere am I using double quotes to justify needing to set QUOTED_IDENTIFIER on!

SO WTF SQL SERVER, WHY ARE YOU GIVING ME THESE ERRORS???

Read the whole thing.

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Coalescing In DocumentDB

Melissa Coates shows how to use the null coalesce operator in DocumentDB to provide default values for missing attributes:

This is a quick post to share how we can use the coalesce operator in Azure DocumentDB (which is a schema-free, NoSQL database) to handle situations when the data structure varies from file to file. Varying data structure is a common issue in big data and analytics projects. A schema-free database like DocumentDB allows us to ingest and store the data with varying structures without a lot of upfront effort. However, accommodating these varying data structures is challenging later when we want to analyze the data. When querying the data (think Schema on Read here), I do need to impose a consistent structure on the data to perform analytics.

Read the whole thing.

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Disk Space Shenanigans

Meagan Longoria writes about an outage due to improper file layout:

One day, a manager asked me if I could help on an urgent matter: the application suddenly could no longer execute transactions on the production database and the database connection was intermittently failing. The system admin was busy with other duties, so I was the closest thing they had to a DBA.  All they could tell me was the production database had crashed and they got an error message about insufficient disk space.

Click through for the rest of the story.

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Replication And TDE

Drew Furgiuele looks at how replication interacts with Transparent Data Encryption:

But what happens if we set up a transactional replication publication on this database and do a snapshot? Remember that when you create a publication, your distributor and subscriber(s) need to know which network share (or FTP server) to drop all the data and schema definitions to so they can be read in by the distribution agent and recreated. In my example, I’m dropping them to a network share. Once the snapshot completes, let’s go check out our subscriber database…

Uh oh. The same query returned zero results at the subscriber. Which means no encryption! Replication won’t replicate encryption, at all. So if you have a requirement to encrypt your data at the source, you’ll need to do it on your subscribers too.

Drew points out a couple important gotchas which might lead to you exposing information you didn’t intend to make available.

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NULL Parameters

Riley Major turns this T-SQL Tuesday into thoughts on procedure parameterization:

But what if the caller wanted the date to be “empty” (i.e. 1900-01-01)? And what if a NULL is passed?

In our environment, we’ve disallowed NULLs from our table fields. We understand that NULL is actually information– it says that the data is unknown– but we believe that for most data fields, there are non-NULL values which just as effectively represent unknown. Typically, 0’s and empty strings (and the “blank” date 1900-01-01) serve that purpose. And those values are more forgiving during coding (they equal things; they don’t make everything else “unknown”), and we accept the risk of paying little attention to which parts of our logic touched “unknown” values.

It’s an interesting look at dealing with optional and default parameters within procedures.

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Supersized Tables

Deborah Melkin tells a story of a design battle she lost:

The programmers came to me and said we need to add a large number of columns to this table for one piece of functionality. It would more than double the total number of columns on the table. Oh, and all of the new columns would be NULL since we would only need to populate them if they were using that functionality and even then, not all of them would require data. The final result would be that 65-75% of the table would end up having nullable fields with the majority of those having NULL for the value.

I said what I think any sane DBA would say to this request: No.

Click through for the rest of the tale.

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The Guru

Adam Machanic wins this T-SQL Tuesday:

Back in my basement hideout, I spent the next couple of hours exploring the network and figuring out which server to connect to. The CTO was right; I did have enough access. I was sysadmin on the production SQL Server and had full admin access to the app server. I logged in to the app and with the help of a Profiler trace managed to figure out one of the main slow stored procedure calls that occurred any time someone saved a change via the user interface.

Pasting the procedure call into SSMS, I turned on Actual Execution Plan, hit F5, and got ready to see indications of a few missing indexes. I was ready to walk back upstairs, gloat to the CTO, and ask for a better workspace so I could continue to help. What I didn’t expect was what actually came back: Not one execution plan, or two, or three, but hundreds and hundreds. The scroll bar become progressively smaller as time clicked by and the elapsed counter did not stop running. All I’d done in the application was change the name of a single field. What was going on?

This was an amazing story full of cringe-worthy moments.

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Creating A Spinlock Convoy

Lonny Niederstadt explains how two performance issues can intertwine in an interesting manner:

I’d seen systems that implemented both trace flags as startup parameters simultaneously.  I’d helped organizations implement first T8048, then T4199 (based on the timing of my research and testing of the trace flags).  This was the first time that there was a desire to implement the trace flags one-at-a-time and we had the choice of which to implement first.

I hadn’t chosen to put T8048 in first previously – that was just the way everything worked out.  If I chose to follow that order – I’d be doing what I’d seen and done before.  But… there was also a reason to choose the reverse order, with T4199 first.  Spinlock issues – especially at that time – were considered more exotic performance issues than many of the “plan-shaping” issues that trace flag 4199 addressed.  Many administrators were much more familiar with that type of performance issue – eliminating significant waits, altering plan shapes, making the logical work of queries more efficient – than with the busy wait/management overhead of spinlocks.  Sometimes demonstrating an improvement that someone is already familiar with evaluating is a plus, helping to gain trust.  I didn’t know of a specific reason NOT to put trace flag T4199 in place, followed by T8048 later.  And in this case it seemed like building up some interpersonal capital might be a good idea.

Thinking through the full ramifications of trace flag changes is hard, even for sharp people like Lonny.  Read on for the details of what happened next.

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