I am working a bit in applied mathematics, but not in theoretical mathematics. I dabble a bit in general topology, but if possible would like to ask you to consider my level of know-how and add some intuition into your answer. I can handle some formulas, but since I am not a professional mathematician I am not fluent in all the lingo... so basic terminology is appreciated.
In data analytics / data science, we have premetrics, like Dynamic time warping, which can be calculated between two real-valued sequences, even if the sequences do not have the same length (so they are not vectors). As such, we can say that the space created by the premetric is a topological space.
When we are dealt with nominal-valued sequences (aka strings), like human words, we can have only equality as an operation at our disposal for comparing elements of the sequences. To calculate a metric distance between such, we have e.g. the Levenshtein distance. This metric only outputs integer-values as distances. This would create a metric space, I guess, however not in the normal sense that we have infinitesimal close neighbors for each sequence/point.
Usually a metric space would induce a topological space. Usually topological spaces concretize the notion of what "the closest next point" is. Here, the "closest next point" is not infinitesimally close. Can we still talking about topological spaces in this context?
I have not read anywhere that the codomain of the metric of a metric space needs to be a field (which integers are not), but are we still talking about a metric space for the Levenshtein distance?
How should I think about the fact that the Levenshtein distance have integers as output?