They entered practical application for search in editors and command-line tools as well as lexical analysis in compilers. Regular expressions emerged from formal language theory where they define a regular language. A solution found in a wide variety of languages is to write a regular expression. The reason this approach doesn't work is because it is element-based while the field separator is a more complex pattern. Another approach is to drop down to low-level index manipulation code.īut it's hard to do right, and even if you know what you're doing, it still takes a lot of code. Splitting on whitespace alone doesn't cut it. We can try to use the element-based algorithms by splitting out the transaction fields, but the field separator being either tab or 2-or-more spaces makes this difficult. These algorithms basically come in two kinds, those that operate over elements, and those that operate over indices. Processing these transactions involves processing strings, and string is a collection, which means we get access to generic collection algorithms. We're just going to hope that it's month/day/year and see what happens. And, yes, that date field is totally ambiguous. Fields are separated by either 2-or-more spaces or a tab for a very important technical reason that no one involved can remember. Here the first field has the transaction kind, the second the transaction date, the third field the individual or institution, the fourth and final field the amount in US dollars. Now, you'd think that for a task this important we'd be processing well-structured data. Let's say we're developers collaborating with some financial investigators on a tool to analyze transactions for irregularities. There's a lot to Swift Regex, and we'll be getting just a taste of everything it has to offer.
Join me as we meet and get to know Regex in Swift. Hello, I am Michael Ilseman and I'm an engineer on the Swift standard library team.
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