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That was a challenge! There are probably ways to do it with less tools than I used....I'll have to examine some of the other submissions!
This was more fiddly than difficult - spent a lot of time aligning fields, changing types etc.
Great practice with the XML parsing tool!
My original workflow treated each instance of the program (e.g., same program, different days), as a record. Simple Unique tool at the end of the workflow took care of that.
Fun fact. I could verify this list with one of my dear friend's father -- he's been the librarian of the NY Phil for many many years!
When this challenge first came out I had no idea how to solve it, but now I'm slowly starting to feel comfortable with XML parse :) could probably do with a few less tools!
I took into account all concerts, so I have a few more rows (at least that's what I think is the reason!)
I also initially got one less record than the published results, relating to Program 12104 Piece 7955* which is a second intermission.
My filter condition Isnull([Interval]) excluded this, but when I change the filter condition to [Interval] != "Intermission", I got the same as the official results.
It really helps to open the xml file in a browser before you start to just see which headings contain the data that you want and how many levels you have to parse to get to them.
Ah! The joys of running on an under powered (8GB) machine. I had originally built my workflow linearly, parsing each layer in-line with the previous one. By the time I was parsing the work info, it taking upwards of 3 minutes and generating 10GB data sets. And this was before had even started on the soloists. I changed tactics and parsed the major node types in parallel. Now, complete run time is under 10 seconds, including the download and the largest data set is only 30MB
Divide and conquer saves the day