On Tue, Mar 10, 2020 at 5:01 PM Evan Chan <evan_c...@apple.com.invalid> wrote: > > Martin, > > Many thanks for the links. > > My main concern is not actually FP and integer data, but sparse string data. > Having many very sparse arrays, each with a bitmap and values (assume > dictionary also), would be really expensive. I have some ideas I’d like to > throw out there, around something like a MapArray (Think of it essentially as > dictionaries of keys and values, plus List<List<u8>> for example), but > something optimized for sparseness. > > Overall, while I appreciate the design of Arrow arrays to be super fast for > computation, I’d like to be able to keep more of such data in memory, thus > I’m interested in more compact representations, that ideally don’t need a > compressor. More like encoding. > > I saw a thread middle of last year about RLE encodings, this would be in the > right direction I think. It could be implemented properly such that it > doesn’t make random access that bad. > > As for FP, I have my own scheme which is scale tested, SIMD friendly and > should perform relatively well, and can fit in with different predictors > including XOR, DFCM, etc. Due to the high cardinality of most such data, I > wonder if it wouldn’t be simpler to stick with one such scheme for all FP > data. > > Anyways, I’m most curious about if there is a plan to implement RLE, the FP > schemes you describe, etc. and bring them to Arrow. IE, is there a plan for > space efficient encodings overall for Arrow?
It's been discussed many times in the past. As Arrow is developed by volunteers, if someone volunteers their time to work on it, it can happen. The first step would be to build consensus about what sort of protocol level additions (see the format/ directory and associated documentation) are needed. Once there is consensus about what to build and a complete specification for that, then implementation can move forward. > Thanks very much, > Evan > > > On Mar 10, 2020, at 1:41 AM, Radev, Martin <martin.ra...@tum.de> wrote: > > > > Hey Evan, > > > > > > thank you for the interest. > > > > There has been some effort for compressing floating-point data on the > > Parquet side, namely the BYTE_STREAM_SPLIT encoding. On its own it does not > > compress floating point data but makes it more compressible for when a > > compressor, such as ZSTD, LZ4, etc, is used. It only works well for > > high-entropy floating-point data, somewhere at least as large as >= 15 bits > > of entropy per element. I suppose the encoding might actually also make > > sense for high-entropy integer data but I am not super sure. > > For low-entropy data, the dictionary encoding is good though I suspect > > there can be room for performance improvements. > > This is my final report for the encoding here: > > https://github.com/martinradev/arrow-fp-compression-bench/blob/master/optimize_byte_stream_split/report_final.pdf > > > > Note that at some point my investigation turned out be quite the same > > solution as the one in https://github.com/powturbo/Turbo-Transpose. > > > > > > Maybe the points I sent can be helpful. > > > > > > Kinds regards, > > > > Martin > > > > ________________________________ > > From: evan_c...@apple.com <evan_c...@apple.com> on behalf of Evan Chan > > <evan_c...@apple.com.INVALID> > > Sent: Tuesday, March 10, 2020 5:15:48 AM > > To: dev@arrow.apache.org > > Subject: Summary of RLE and other compression efforts? > > > > Hi folks, > > > > I’m curious about the state of efforts for more compressed encodings in the > > Arrow columnar format. I saw discussions previously about RLE, but is > > there a place to summarize all of the different efforts that are ongoing to > > bring more compressed encodings? > > > > Is there an effort to compress floating point or integer data using > > techniques such as XOR compression and Delta-Delta? I can contribute to > > some of these efforts as well. > > > > Thanks, > > Evan > > > > >