I am curious as to how one might very compactly compress the domain of an arbitrary IDN hostname (as defined by RFC5890) and suspect this could become an interesting challenge. A Unicode host or domain name (U-label) consists of a string of Unicode characters, typically constrained to one language depending on the top-level domain (e.g. Greek letters under
.gr), which is encoded into an ASCII string beginning with
xn-- (the corresponding A-label).
One can build data models not only from the formal requirements that
each non-Unicode label be a string matching
each A-label be a string matching
the total length of the entire domain (A-labels and non-IDN labels concatenated with '.' delimiters) not exceed 255 characters
but also from various heuristics, including:
lower-order U-labels are often lexically, syntactically and semantically valid phrases in some natural language including proper nouns and numerals (unpunctuated except hyphen, stripped of whitespace and folded per Nameprep), with a preference for shorter phrases; and
higher-order labels are drawn from a dictionary of SLDs and TLDs and provide context for predicting which natural language is used in the lower-order labels.
I fear that achieving good compression of such short strings will be difficult without considering these specific features of the data and, furthermore, that existing libraries will produce unnecessary overhead in order to accomodate their more general use cases.
Reading Matt Mahoney's online book Data Compression Explained, it is clear that a number of existing techniques could be employed to take advantage of the above (and/or other) modelling assumptions which ought to result in far superior compression versus less specific tools.
By way of context, this question is an offshoot from a previous one on SO.
It strikes me that this problem is an excellent candidate for offline training and I envisage a compressed data format along the following lines:
A Huffman coding of the "public suffix", with probabilities drawn from some published source of domain registration or traffic volumes;
A Huffman coding of which (natural language) model is used for the remaining U-labels, with probabilities drawn from some published source of domain registration or traffic volumes given context of the domain suffix;
Apply some dictionary-based transforms from the specified natural language model; and
An arithmetic coding of each character in the U-labels, with probabilities drawn from contextually adaptive natural language models derived from offline training (and perhaps online too, although I suspect the data may well be too short to provide any meaningful insight?).