We had a situation in which application users were not checking the customer list before adding new customers, which was resulting in multiple entries for the same customer. We wanted to make it so that when they tried to add a new customer, they would be shown a list of possible duplicates.
It turned out that finding duplicates wasn’t as easy as I thought. I ended up with a helper class that used Metaphones to find similar customers. This post describes the class, and shows how to use it.
I was working with a third-party API, and had to specify a date range object, with a start and date. The problem was, their server was throwing intermittent exceptions when I passed this date filter object as a parameter, claiming that the start date was after the end date. I checked the data, and I was definitely sending a valid date range.
The answer to the problem turned out to involve a little-known (to me at least) part of DateTime. Not the sort of thing you encounter every day, but worth knowing about for the times when it hits you.
I had an odd problem, the resolution of which exposed an interesting bit of information about what goes on under the C# covers that we never usually know.
I had a Linq query that worked fine on its own, but failed at run time when I extracted it into a method and passed in a lambda.
Whilst trying to work out why this was happening, I came to an understanding of the difference between a Func and an Expression, and why it (sometimes) matters.
A quick and simple way to find out which method called the current one.
I discovered a gem of a class hidden away in the .NET framework that makes parsing CSV files very simple.
Read more for details, and some sample code.
Like most of us, my applications usually have a global unhandled exception handler, in order that the inevitable unhandled exception won’t crash the system.
The problem with this is that by the time you get to the exception handler, you’ve completely lost the context of what was happening. You’re in an isolated, global context, away from any window.
What you really want is to get immediate feedback when there’s been a problem, so you can react appropriately for the situation. For example, failure to load a customer list could be handled by trying again, up to a maximum number of attempts, before informing the user that the list couldn’t be loaded. By contrast, if they were trying to save an individual customer (presumably from a customer details window), you’d react differently.
Around a year ago, whilst contemplating this issue, I had an idea that turned out to be an excellent answer to the problem. It turned out that this wasn’t an original idea (it was too obvious for that), but as I hadn’t come across it before, I didn’t know that at the time.
In this post, I describe the Fallible type, and how it can be used (really easily) to simplify and improve your exception handling.
As part of some overall auditing in one of my projects, we recently added a LastUpdatedByUserID column to all of the major tables. As the name implies, this column stores the ID of the user who last updated the row. In order to keep an audit trail of the changes, for each table in the database, we have a corresponding TableName_Audit table that is updated (by triggers on the main table) every time the main table is modified.
I noticed that for one of the tables that had had the LastUpdatedByUserID column added, the script to regenerate the audit table hadn’t been run, so the audit table was missing the LastUpdatedByUserID column. This would entirely nullify the point of the column.
This led me to wonder if there were any other tables in the same state. rather than check this manually, I decided to write a script to do it, as this would be useful for future tables. Being a Linq type of person (I rarely write SQL any more), I decided to see if I could do this in LinqPad.
It took a bit of fiddling, but the end result was what I wanted, and paved the way for future queries.
A while back I blogged about a situation where thinking out of the box helped me write some code more quickly. In a nutshell, I needed to extract an exchange rate from an XML feed, and found that by using F# to do it, then translating the code back to C#, it was much easier than trying to write the code in C# in the first place.
I thought it might be interesting to revisit this in both languages, and compare the two experiences. I found this to be a significantly better sales pitch for F# than anything else I’ve seen.
If you’ve ever had to write or read a regular expression, you won’t need any convincing that a tool to make this easier would be useful!
If you aren’t familiar with regexs (as they are colloquially known) then it’s worth getting to know, as they can be amazingly useful at times. Sadly, they can also be amazingly difficult to write, and impossible to read afterwards!
Here are a few tips for taking away the pain.
Does this sound familiar…
User: Your app just crashed
You: What happened?
User: It crashed
You: (sigh) What were you doing at the time?
User: Using the app
You: (sigh) What specifically were you doing?
User: I was clicking in that box
You: Which box?
User: You know, the one on the window you get when you click the other button
…and so it goes on
Read how we managed to get useful information about errors without having to suffer speaking to the users!
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