I babbled at you on Twitter earlier--I wrote a differ somewhat like this that's at https://github.com/twotwotwo/dltp/blob/master/diff/diff.go https://github.com

I babbled at you on Twitter earlier--I wrote a differ somewhat like this that's at https://github.com/twotwotwo/dltp/blob/master/diff/diff.go https://github.com/twotwotwo/dltp/blob/master/diff/diff.go. First, confession that my code style, interface, etc. are fairly awful (panics, mixing Reader/Writer with passing []bytes around, and there's even a line commented 'why?'). Can't justify it; I just never properly cleaned up the first thing I got working. I also suspect you've got some performance wins over my code--you probably saved overhead by not calling encoding/binary for varints, for example, and your 'radix' constant (257) almost certianly makes the hashing faster (the multiply even optimizes to an 'lea' instr, I think). Also, (de)serializing the dicts is handy and probably crucial for your use case. Here are some things we're doing different--just to document them, not claiming that anything is a win: The rolling hash: We're using similar multiply-add-modulus hashes. I'm relying on uint32 wraparound for the modulus (Go spec says you can rely on wraparound), and I'm doing another multiply when I subtract values out of the hash instead of using a 'save' array. Blocks vs hash bits: I'm stealing a trick from rzip, where instead of saving hashes of non-overlapping blocks, I save hashes whenever a certain number of bits of the hash are zero. I don't know what works better, empirically. Min. match length: I used 24 after trying out various values. Too low and you find short matches when you could get longer ones, too high and you miss matches. The right value is probably data-dependent anyway. The hash table: I'm using a 128k-entry array that's directly indexed by some bits of the rolling hash. Because I'm hashing a lot of documents and only using each hashtable once, I worked out a scheme to reuse the array for multiple diff tasks without garbaging or zeroing it: I made the values in the array indices into all the bytes this MatchState has ever hashed, not into the current document. After fetching an offset out of the hash table, I check if it's before the start of the latest doc (if h < base) and ignore it if so, and otherwise subtract 'base' from it to convert it into an offset into the current doc. Costs something during hashing and matching, but clearing the table was ultimately costing me more. The encoding: I'm encoding each copy/literal as a protobufs-style signed varint. Positive numbers give a number of literal bytes to copy into the output, negative numbers give a number of bytes to be copied from the reference text, zero means end of diff. Copy lengths are followed by another signed varint that gives the location in the reference doc where the copy should start, relative to a "cursor" position. Having that "cursor" allows a copy that starts right after the end of the last copy to have a slightly shorter encoding. Short encoding of matches isn't that critical anyway, compared with doing well at finding matches, so that part I sort of overdid. Things I was intrigued by but haven't actually tried: - rzip separates the 'instructions' ('insert X literal bytes', 'copy Y bytes from position Z in the original') from the text data. For rzip, that seems to improve the secondary compressor (bzip)'s compression ratio. I'm curious if it helps. - I think the Git packfile format makes the literal instruction always a single byte, but the max. literal len is 127. I wonder if that saves output bytes on net. - Lots of other packers look at multiple match candidates for the longest match. It would probably make smaller diffs, but not at all sure that the complexity and CPU-time costs would be worth it. - I could probably eke out a small CPU-time win by matching from "the ends" of the input first, since often one can find longish matches there without hashing. - I wonder if there's any win in checking whether a match can be extended backwards to completely cover a preceding match. It seems complicated, and probably not a huge win. Thanks a lot for open sourcing this. When I get time, may try dropping bm into the program I was playing with. (And both my code and my words above are probably a little fried, forgive--the words are rushed, and the code was nights-and-weekends stuff and my first Go project ever.)