Hi John, Not Wes, but my thoughts on this are as follows: 1. Alternate bit/byte arrangements can also be useful for processing [1] in addition to compression. 2. I think they are quite a bit more complicated then the existing schemes proposed in [2], so I think it would be more expedient to get the integration hooks necessary to work with simpler encodings before going with something more complex. I believe the proposal is generic enough to support this type of encoding. 3. For prototyping, this seems like a potential use of the ExtensionType [3] type mechanism already in the specification. 4. I don't think these should be new types or part of the basic Array data structure. I think having a different container format in the form of "SparseRecordBatch" (or perhaps it should be renamed to EncodedRecordBatch) and keeping the existing types with alternate encodings is a better option.
That being said if you have bandwidth to get this working for C++ and Java we can potentially setup a separate development branch to see how it evolves. Personally, I've not brought my proposal up for discussion again, because I haven't had bandwidth to work on it, but I still think introducing some level of alternate encodings is a good idea. Cheers, Micah [1] https://15721.courses.cs.cmu.edu/spring2018/papers/22-vectorization2/p31-feng.pdf [2] https://github.com/apache/arrow/pull/4815 [3] https://github.com/apache/arrow/blob/master/docs/source/format/Columnar.rst#extension-types On Thu, Jan 23, 2020 at 11:36 AM John Muehlhausen <j...@jgm.org> wrote: > Wes, what do you think about Arrow supporting a new suite of fixed-length > data types that unshuffle on column->Value(i) calls? This would allow > memory/swap compressors and memory maps backed by compressing > filesystems (ZFS) or block devices (VDO) to operate more efficiently. > > By doing it with new datatypes there is no separate flag to check? > > On Thu, Jan 23, 2020 at 1:09 PM Wes McKinney <wesmck...@gmail.com> wrote: > > > On Thu, Jan 23, 2020 at 12:42 PM John Muehlhausen <j...@jgm.org> wrote: > > > > > > Again, I know very little about Parquet, so your patience is > appreciated. > > > > > > At the moment I can Arrow/mmap a file without having anywhere nearly as > > > much available memory as the file size. I can visit random place in > the > > > file (such as a binary search if it is ordered) and only the locations > > > visited by column->Value(i) are paged in. Paging them out happens > > without > > > my awareness, if necessary. > > > > > > Does Parquet cover this use-case with the same elegance and at least > > equal > > > efficiency, or are there more copies/conversions? Perhaps it requires > > the > > > entire file to be transformed into Arrow memory at the beginning? Or > on a > > > batch/block basis? Or to get this I need to use a non-Arrow API for > data > > > element access? Etc. > > > > Data has to be materialized / deserialized from the Parquet file on a > > batch-wise per-column basis. The APIs we provide allow batches of > > values to be read for a given subset of columns > > > > > > > > IFF it covers the above use-case, which does not mention compression or > > > encoding, then I could consider whether it is interesting on those > > points. > > > > My point really has to do with Parquet's design which is about > > reducing file size. In the following blog post > > > > https://ursalabs.org/blog/2019-10-columnar-perf/ > > > > I examined a dataset which is about 4GB as raw Arrow stream/file but > > only 114 MB as a Parquet file. A 30+X compression ratio is a huge deal > > if you are working with filesystems that yield < 500MB/s (which > > includes pretty much all cloud filesystems AFAIK). In clickstream > > analytics this kind of compression ratio is not unusual. > > > > > > > > -John > > > > > > On Thu, Jan 23, 2020 at 12:06 PM Francois Saint-Jacques < > > > fsaintjacq...@gmail.com> wrote: > > > > > > > What's the point of having zero copy if the OS is doing the > > > > decompression in kernel (which trumps the zero-copy argument)? You > > > > might as well just use parquet without filesystem compression. I > > > > prefer to have compression algorithm where the columnar engine can > > > > benefit from it [1] than marginally improving a file-system-os > > > > specific feature. > > > > > > > > François > > > > > > > > [1] Section 4.3 http://db.csail.mit.edu/pubs/abadi-column-stores.pdf > > > > > > > > > > > > > > > > > > > > On Thu, Jan 23, 2020 at 12:43 PM John Muehlhausen <j...@jgm.org> > wrote: > > > > > > > > > > This could also have utility in memory via things like zram/zswap, > > right? > > > > > Mac also has a memory compressor? > > > > > > > > > > I don't think Parquet is an option for me unless the integration > with > > > > Arrow > > > > > is tighter than I imagine (i.e. zero-copy). That said, I confess I > > know > > > > > next to nothing about Parquet. > > > > > > > > > > On Thu, Jan 23, 2020 at 11:23 AM Antoine Pitrou < > anto...@python.org> > > > > wrote: > > > > > > > > > > > > > > > > > > Le 23/01/2020 à 18:16, John Muehlhausen a écrit : > > > > > > > Perhaps related to this thread, are there any current or > proposed > > > > tools > > > > > to > > > > > > > transform columns for fixed-length data types according to a > > > > "shuffle?" > > > > > > > For precedent see the implementation of the shuffle filter in > > hdf5. > > > > > > > > > > > > > > > > > > > https://support.hdfgroup.org/ftp/HDF5//documentation/doc1.6/TechNotes/shuffling-algorithm-report.pdf > > > > > > > > > > > > > > For example, the column (length 3) would store bytes 00 00 00 > 00 > > 00 > > > > 00 > > > > > 00 > > > > > > > 00 00 01 02 03 to represent the three 32-bit numbers 00 00 00 > 01 > > 00 > > > > 00 > > > > > 00 > > > > > > > 02 00 00 00 03 (I'm writing big-endian even if that is not > > actually > > > > the > > > > > > > case). > > > > > > > > > > > > > > Value(1) would return 00 00 00 02 by referring to some metadata > > flag > > > > > that > > > > > > > the column is shuffled, stitching the bytes back together at > call > > > > time. > > > > > > > > > > > > > > Thus if the column pages were backed by a memory map to > something > > > > like > > > > > > > zfs/gzip-9 (my actual use-case), one would expect approx 30% > > savings > > > > in > > > > > > > underlying disk usage due to better run lengths. > > > > > > > > > > > > > > It would enable a space/time tradeoff that could be useful? > The > > > > > filesystem > > > > > > > itself cannot easily do this particular compression transform > > since > > > > it > > > > > > > benefits from knowing the shape of the data. > > > > > > > > > > > > For the record, there's a pull request adding this encoding to > the > > > > > > Parquet C++ specification. > > > > > > > > > > > > Regards > > > > > > > > > > > > Antoine. > > > > > > >
