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Category: Error Handling

Read Those Error Messages

Published 2020-10-08 by Kevin Feasel

Randolph West has a public service announcement:

My boss got upset with us one day on The Project From Hell. Tempers were frayed, tensions ran high, and other euphemisms were euphemisming. In short, we were all grumpy, and as expected on a project of this nature we kept making obvious mistakes and wasting our energy chasing our tails.

Obvious? Well, yes. It turns out that the answer to a particularly common issue we were running into was explained in the first line of the stack trace of the code that kept crashing. I’m not exaggerating for the sake of this story. The actual problem was explained in the first sentence of the error, in the very first line.

Microsoft products are fairly notorious about poorly-crafted error messages, but as Randolph mentions, often the solution is there if you take care to read the message.

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GetAllTheErrorLogs: Combining Multiple Log Sources

Published 2020-09-10 by Kevin Feasel

Aaron Bertrand has a new project:

There’s a whole lot of grunt work in there that shouldn’t have to be done by a person. I don’t think you could automate the whole thing, because it is hard to predict exactly what events will be interesting and not, but I think 90% is achievable.

A colleague mentioned that they want to build something that would help, but even when that happens, that would up in proprietary code that only helps us. I saw Drew Furgiuele’s post on Building a Better Get-SQLErrorLog, and that gave me some ideas for what I would build. After reaching out to Drew, I created a GitHub repository with a working name of GetAllTheErrorLogs. Its elevator pitch is a simple sentence:

Powershell to assemble a timeline combining salient events from Windows Event Log, Failover Cluster log, and SQL Server errorlog.

Click through for the details as well as Aaron’s current progress.

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Messy Code and Reasonable Expectations

Published 2020-09-04 by Kevin Feasel

Rachel by the Bay has a doozy of a story:

One day not so long ago, I was in a meeting listening to a team explain why their service had gone down and taken out a big chunk of a business. They were one of those things that has to exist and work in order for the actual “thing that makes money” to go. Think of delivering pizzas, connecting dog walkers with dogs who need to be walked, that kind of thing.

It turned out they had been crashing every time a request came through for a certain part of the country. That is, not all pizzas, dog walkers, or whatever it was were handled identically, so they had their own city or region configurations. Think of differences in pricing, taxes, features, or whatever. Trying to process a request for this one particular region had caused the entire process to die when it hit a new config that was “bad” somehow.

Read on for the story. This sounds like a boundary issue. Boundaries are messy and need thorough examination to handle as many possible points of failure as is reasonable. Taking seriously the point that it makes the code messy, the answer is not “Don’t do the checks,” but rather “Put the checks in a place where their messiness has a minimal impact on the rest of my beautiful code but still does the important work we need them to do.” Failing that, live with the mess and have a working process.

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Dealing with Failing SQL Agent Jobs

Published 2020-09-02 by Kevin Feasel

Garry Bargsley has started a four-part series:

SQL Server Agent Jobs are one of the core features of SQL Server. Agent Jobs perform many actions, from maintenance tasks (server and database), data movement flows, replication, log shipping, data cleanup, health checks and many more. Since Agent Jobs are a critical component in a data organization, it is important to know when Agent Jobs do not succeed. There are several ways to accomplish the monitoring of failed Agent Jobs, from easy to more complex. This four part series is going to cover how you, the DBA, can be notified of failing Agent Jobs in your SQL Server environment.

Click through for part one, which is all about finding failed jobs and filtering down to relevant jobs using dbatools.

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ADF Data Flows and Joins Failing During Debugging

Published 2020-08-20 by Kevin Feasel

Mark Kromer clears up some issues around debugging in Azure Data Factory:

One of the important features built into ADF is the ability to quickly preview your data while designing your data flows and to execute the finished product against a sampling of data prior to finalizing and operationalizing your pipelines.

However, there are a few fundamentals relative to working with Joins that you should keep in mind and a few details below are important to understand at design time and while debugging / testing.

The answer makes sense but it would not have been the first thing to come to mind for me.

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EXTPTR_PTR Error with Rcpp

Published 2020-08-10 by Kevin Feasel

Rick Pack walks us through an error in R:

I experienced a need to update Rcpp when I attempted to install the readxlsb R package, which promised to enable me to read .xlsb files in R.

What happened next has been forgotten: Did the attempted update of Rcpp appear to succeed or fail? I did record that my attempted installation of readxlsb still failed and I now experienced an unfamiliar error when I opened and closed R Studio:

“The procedure entry point EXTPTR_PTR could not be located in the dynamic link library”

Read on to see how Rick solved this problem.

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Issues when Using the Power BI REST API

Published 2020-08-03 by Kevin Feasel

Nicky van Vroenhoven walks us through a few issues discovered when trying to use the Power BI REST API:

Last month when I was trying to follow the excellent video on Building a Power BI Admin View by Parker Stevens ( b | @PowerBIElite | YouTube).

However, I ran into a few things that I’d like to dedicate this quick post on.

Read on to see where Nicky ran into issues and how you might be able to avoid them.

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Downsides to Optimization in Spark SQL

Published 2020-07-13 by Kevin Feasel

Anuj Saxena takes us through some of the pros and cons of using the Catalyst Optimizer in Spark, including a couple of issues:

I am sure the optimizations make the calculation time very short and these optimizations are implemented in such a way that you just have to provide the logic and everything else will be done in abstraction. But as my friend and colleague Ramandeep says “Abstract features come with abstract issues”. So following are the few issues which I have faced in my recent interaction with Spark SQL:

1. Too large of a query to be stored in memory
2. Implicit optimizations interfere with partitioning

Click through for examples of this.

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Ambiguous Columns in Queries when Using One Table

Published 2020-07-06 by Kevin Feasel

Dave Bland shows how easy it is to get the “Ambiguous column name” error message when querying from a single table:

When I added the “*”, this is where I received unexpected results.  All I did was add the “*”.  Looking at the code below, you can see SQL Server is having issues with the Name column in the ORDER BY.

I do wish SQL had a symbol representing “everything else,” where the engine of choice would include all columns except those explicitly named. I know there’d be trickiness around things like “LTRIM(ColumnA) AS TrimmedColumnA” but that’d be for the language designers to figure out…

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