Hi Evan, Seems like we are mostly on the same page. Some more notes below.
> For example, encoding nulls in dictionary values helps reduce the need for both bitmap storage and lookup. I'm not sure if this is provided was provided as an example as something to add, but I believe this is already supported in the spec. In terms of better support for sparseness. I originally had a more complex proposal that in addition to RLE also considered better representation for very sparse data (using indices to represent valid locations and not reserving space in data buffers for null values). I believe, I dropped them before l made the sparseness proposal PR [1] public. My main reasons for doing so was to try to go with a minimal set of enhancements that had the biggest "bang for the buck". In the longer term I think it is potentially something Arrow should include, but given the current number of developers contributing across all languages I'm wary of tacking on too much before we have working reference implementations. If you think there are better encodings to tackle in the short term I'd welcome feedback on the proposal (or a more formal proposal of your own). Thanks, -Micah [1] https://github.com/apache/arrow/pull/4815/files On Wed, Mar 11, 2020 at 9:24 AM Evan Chan <evan_c...@apple.com> wrote: > Sure thing. > > Computation speed needs to be thought about in context.... We might find > something which takes up half the space to be a little more computationally > expensive, but in the grand scheme of things is faster to compute as more > of it can fit in memory, and it saves I/O. I definitely agree that just > making something smaller without other gains might not be worth it. > > Some small changes help both computation and size. For example, encoding > nulls in dictionary values helps reduce the need for both bitmap storage > and lookup. > > So I suppose the process goes something like this? > > 1) Propose designs in this email list > 2) PR for format specification > 3) Start implementing across diff languages > > -Evan > > > On Mar 10, 2020, at 10:31 PM, Micah Kornfield <emkornfi...@gmail.com> > wrote: > > > > +1 to what Wes said. > > > > I'm hoping to have some more time to spend on this end of Q2/beginning of > > Q3 if no progress is made by then. > > > > I still think we should be careful on what is added to the spec, in > > particular, we should be focused on encodings that can be used to improve > > computational efficiency rather than just smaller size. Also, it is > > important to note that any sort of encoding/compression must be > supportable > > across multiple languages/platforms. > > > > Thanks, > > Micah > > > > On Tue, Mar 10, 2020 at 3:12 PM Wes McKinney <wesmck...@gmail.com> > wrote: > > > >> 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 > >>>> > >>>> > >>> > >> > >
