hi Francesc,
wonderful works on blosc2! congrats! this is exactly the direction that
I would hope more data creators/data users would pay attention to.
clearly blosc2 is a well positioned for high performance - msgpack is
one of the most proliferated binary JSON formats out there, with many
extensively optimized libraries; zstd is also a rapidly emerging
compression class that has a well developed multi-threading support.
this combination likely has the best that the current toolchain can
offer to deliver good performance and robustness. The added SIMD and
data chunking features further push the performance bar.
I am aware that msgpack does not currently support packed ND-array data
type (see my PR to add this syntax at
https://github.com/msgpack/msgpack/pull/267), I suppose blosc2 must have
been using customized buffers warped under an ext32 container, is that
the case? or you implemented your own unofficial ext64 type?
I am not surprised to see blosc2 outperforms npz/jdb in compression
benchmarks because zstd supports multi-threading, that makes a huge
difference, as shown clearly in this 2017 benchmark that I found online
https://community.centminmod.com/threads/compression-comparison-benchmarks-zstd-vs-brotli-vs-pigz-vs-bzip2-vs-xz-etc.12764/
using the multi-threaded versions of zlib (pigz) and lzma (pxz, pixz, or
plzip) would be a more apple-to-apple comparison, but I do believe zstd
may still hold an edge in speed (but may trade for less compression
ratio). I also noticed that lbzip2 also gives relatively good speed and
high compression ratio. Nothing beats lzma (lzma/zip/xz) in compression
ratio, even with the highest setting in zstd.
I absolutely agree with you that different flavors of binary JSON
formats (Msgpack vs CBOR vs BSON vs UBJSON vs BJData) matters little
because they are all JSON-convertible and follow the same design
principles as JSON - namely simplicity, generality and lightweight.
I did make some deliberations when deciding whether to use Msgpack vs
UBJSON/BJData as the main binary format for NeuroJSON, there were two
things had steered my decision:
1. there is *no official packed ND array support* in both Msgpack and
UBJSON. ND-array is such a fundamental data structure for scientific
data storage and it has to be the first-class citizen in data
serialization formats - storing an ND array in nested 1D list, as done
in standard msgpack/ubjson, not only lose the dimensional regularity but
also adds overheads and breaks the continuous binary buffer. That was
the main reason that I had to extend UBJSON
<https://groups.google.com/g/universal-binary-json/c/tgMCEbOmhes/m/s7JlCl58hvQJ>
as BJData to natively support ND-array syntax
2. a key belief
<https://pbs.twimg.com/media/FCD_JNtWQAgLq6N?format=png&name=4096x4096>
of the NeuroJSON project is that "human readability" is the single most
important factor to decide the longevity of both codes and data. The
human-readability of codes have been well addressed and reinforced by
open-source/free/libre software licenses (specifically, Freedom 1
<https://www.gnu.org/philosophy/free-sw.en.html#make-changes>). but not
many people have been paying attention to the "readability" of data.
Admittedly, it is a harder problem. storing data in text files results
in much larger size and slow speed, so storing binary data in
application-defined binary files, just like npy, is extremely common.
However, these binary files in most cases are not directly readable;
they depend on a marching parser, which carrys the format spec/schema
separately from the data themselves, to correctly read/write. Because
the data files are not self-contained, usually not self-documenting,
their utility heavily depends on the parser writers - when a parser
phase out an older format, or does not implement the format rigorously,
the data ultimately will no longer able to be opened and become useless.
One feature that really drew my attention to UBJSON/BJData is that they
are "quasi-human-readable
<https://github.com/NeuroJSON/bjdata/blob/Draft_2/Binary_JData_Specification.md#:~:text=quasi%2Dhuman%2Dreadability>".
This is rather *unique* among all binary formats. This is because the
"semantic" elements (data type markers, field names and strings) in
UBJSON/BJData are all human-readable. Essentially one can open such
binary file with a text editor and figure out what's inside - if the
data file is well self-documented (which it permits), then such data can
be quickly understood without depending on a parser.
you can try this command on the lzma.jdb file
*|$ strings -n2 eye5chunk_bjd_lzma.jdb | astyle | sed '/_ArrayZipData_/q'|*|
||[ {U||
|| _ArrayType_SU||
|| doubleU||
|| _ArraySize_[U||
|| ]U||
|| _ArrayZipType_SU||
|| lzmaU||
|| _ArrayZipSize_[U||
|| m@||
|| ]U||
|| _ArrayZipData_[$U#uE||
|
as you can see, the subfields of the data (|_ArraySize_, _ArrayType_|,
...), as well as the data markers (|[,{,U, S, ...|) and string values
(|"double","lzma"|, ...) are all directly readable. There are garbled
text in the binary stream that may also be printed to make it hard to
read, but it's readability is still way better than most other binary
files where the datafield's meaning/format are completely decoupled to
the parser or the semantic markers are not human readable (such as in
msgpack).
again, I applaud the wonderful works from the blosc2 team and have no
doubt it has many advantages to offer to sharing array data, on the
other side, I do want to advocate for considering readability and
portability to the data files. Essentially the NeuroJSON specs
<http://neurojson.org/#specs> (JData
<https://github.com/NeuroJSON/jdata/blob/Draft_2/JData_specification.md>,
BJData
<https://github.com/NeuroJSON/bjdata/blob/Draft_2/Binary_JData_Specification.md>,
etc) are taking the mission of building a "source-code language" for
scientific data storage.
Qianqian
On 8/27/22 04:32, Francesc Alted wrote:
Hi Qianqian,
Your work in bjdata's is very interesting. Our team (Blosc) has been
working on something along these lines, and I was curious on how the
different approaches compares. In particular, Blosc2 uses the msgpack
format to store binary data in a flexible way, but in my experience,
using binary JSON or msgpack is not that important; the real thing is
to be able to compress data in chunks that fits in CPU caches, and
then trust in fast codecs and filters for speed.
I have setup a small benchmark
(https://gist.github.com/FrancescAlted/e4d186404f4c87d9620cb6f89a03ba0d)
based on your setup and here are my numbers (using an AMD 5950X
processor, and a fast SSD here):
(python-blosc2) faltet2@ryzen16:~/blosc/python-blosc2/bench$
PYTHONPATH=.. python read-binary-data.py save
time for creating big array (and splits): 0.009s (86.5 GB/s)
** Saving data **
time for saving with npy: 0.450s (1.65 GB/s)
time for saving with np.memmap: 0.689s (1.08 GB/s)
time for saving with npz: 1.021s (0.73 GB/s)
time for saving with jdb (zlib): 4.614s (0.161 GB/s)
time for saving with jdb (lzma): 11.294s (0.066 GB/s)
time for saving with blosc2 (blosclz): 0.020s (37.8 GB/s)
time for saving with blosc2 (zstd): 0.153s (4.87 GB/s)
** Load and operate **
time for reducing with plain numpy (memory): 0.016s (47.4 GB/s)
time for reducing with npy (np.load, no mmap): 0.144s (5.18 GB/s)
time for reducing with np.memmap: 0.055s (13.6 GB/s)
time for reducing with npz: 1.808s (0.412 GB/s)
time for reducing with jdb (zlib): 1.624s (0.459 GB/s)
time for reducing with jdb (lzma): 0.255s (2.92 GB/s)
time for reducing with blosc2 (blosclz): 0.042s (17.7 GB/s)
time for reducing with blosc2 (zstd): 0.070s (10.7 GB/s)
Total sum: 10000.0
So, it is evident that in this scenario compression can accelerate
things a lot, specially for compression. Here are the sizes:
(python-blosc2) faltet2@ryzen16:~/blosc/python-blosc2/bench$ ll -h eye5*
-rw-rw-r-- 1 faltet2 faltet2 989K ago 27 09:51 eye5_blosc2_blosclz.b2frame
-rw-rw-r-- 1 faltet2 faltet2 188K ago 27 09:51 eye5_blosc2_zstd.b2frame
-rw-rw-r-- 1 faltet2 faltet2 121K ago 27 09:51 eye5chunk_bjd_lzma.jdb
-rw-rw-r-- 1 faltet2 faltet2 795K ago 27 09:51 eye5chunk_bjd_zlib.jdb
-rw-rw-r-- 1 faltet2 faltet2 763M ago 27 09:51 eye5chunk-memmap.npy
-rw-rw-r-- 1 faltet2 faltet2 763M ago 27 09:51 eye5chunk.npy
-rw-rw-r-- 1 faltet2 faltet2 785K ago 27 09:51 eye5chunk.npz
Regarding decompression, I am quite pleased on how jdb+lzma performs
(specially with the compression ratio). But in order to provide a
better idea on the actual read performance, it is better to evict the
files from the OS cache. Also, the benchmark performs some operation
on data (in this case a reduction) to make sure that all the data is
processed.
So, let's evict the files:
(python-blosc2) faltet2@ryzen16:~/blosc/python-blosc2/bench$ vmtouch
-ev eye5*
Evicting eye5_blosc2_blosclz.b2frame
Evicting eye5_blosc2_zstd.b2frame
Evicting eye5chunk_bjd_lzma.jdb
Evicting eye5chunk_bjd_zlib.jdb
Evicting eye5chunk-memmap.npy
Evicting eye5chunk.npy
Evicting eye5chunk.npz
Files: 7
Directories: 0
Evicted Pages: 391348 (1G)
Elapsed: 0.084441 seconds
And then re-run the benchmark (without re-creating the files indeed):
(python-blosc2) faltet2@ryzen16:~/blosc/python-blosc2/bench$
PYTHONPATH=.. python read-binary-data.py
time for creating big array (and splits): 0.009s (80.4 GB/s)
** Load and operate **
time for reducing with plain numpy (memory): 0.065s (11.5 GB/s)
time for reducing with npy (np.load, no mmap): 0.413s (1.81 GB/s)
time for reducing with np.memmap: 0.547s (1.36 GB/s)
time for reducing with npz: 1.881s (0.396 GB/s)
time for reducing with jdb (zlib): 1.845s (0.404 GB/s)
time for reducing with jdb (lzma): 0.204s (3.66 GB/s)
time for reducing with blosc2 (blosclz): 0.043s (17.2 GB/s)
time for reducing with blosc2 (zstd): 0.072s (10.4 GB/s)
Total sum: 10000.0
In this case we can notice that the combination of blosc2+blosclz
achieves speeds that are faster than using a plain numpy array.
Having disk I/O going faster than memory is strange enough, but if we
take into account that these arrays compress extremely well (more than
1000x in this case), then the I/O overhead is really low compared with
the cost of computation (all the decompression takes place in CPU
cache, not memory), so in the end, this is not that surprising.
Cheers!
On Fri, Aug 26, 2022 at 4:26 AM Qianqian Fang <fan...@gmail.com> wrote:
On 8/25/22 18:33, Neal Becker wrote:
the loading time (from an nvme drive, Ubuntu 18.04, python
3.6.9, numpy 1.19.5) for each file is listed below:
|0.179s eye1e4.npy (mmap_mode=None)||
||0.001s eye1e4.npy (mmap_mode=r)||
||0.718s eye1e4_bjd_raw_ndsyntax.jdb||
||1.474s eye1e4_bjd_zlib.jdb||
||0.635s eye1e4_bjd_lzma.jdb|
clearly, mmapped loading is the fastest option without a
surprise; it is true that the raw bjdata file is about 5x
slower than npy loading, but given the main chunk of the
data are stored identically (as contiguous buffer), I
suppose with some optimization of the decoder, the gap
between the two can be substantially shortened. The longer
loading time of zlib/lzma (and similarly saving times)
reflects a trade-off between smaller file sizes and time for
compression/decompression/disk-IO.
I think the load time for mmap may be deceptive, it isn't
actually loading anything, just mapping to memory. Maybe a
better benchmark is to actually process the data, e.g., find
the mean which would require reading the values.
yes, that is correct, I meant to metion it wasn't an
apple-to-apple comparison.
the loading times for fully-loading the data and printing the
mean, by running the below line
|t=time.time(); newy=jd.load('eye1e4_bjd_raw_ndsyntax.jdb');
print(np.mean(newy)); t1=time.time() - t; print(t1)|
are summarized below (I also added lz4 compressed BJData/.jdb file
via |jd.save(..., {'compression':'lz4'})|)
|0.236s eye1e4.npy (mmap_mode=None)||- size: 800000128 bytes
||0.120s eye1e4.npy (mmap_mode=r)||
||0.764s eye1e4_bjd_raw_ndsyntax.jdb||(with C extension _bjdata
in sys.path) - size: 800000014 bytes|
||0.599s eye1e4_bjd_raw_ndsyntax.jdb||(without C extension _bjdata)|
||1.533s eye1e4_bjd_zlib.jdb|||(without C extension _bjdata)|||
- size: 813721
||0.697s eye1e4_bjd_lzma.jdb|||(without C extension _bjdata) -
size: 113067
|||||0.918s eye1e4_bjd_lz4.jdb|||(without C extension _bjdata) -
size: 3371487 bytes||
||||
the mmapped loading remains to be the fastest, but the run-time is
more realistic. I thought the lz4 compression would offer much
faster decompression, but in this special workload, it isn't the case.
It is also interesting to see that the bjdata's C extension
<https://github.com/NeuroJSON/pybj/tree/master/src> did not help
when parsing a single large array compared to the native python
parser, suggesting rooms for further optimization|.|||
||
||
||
||Qianqian||
||
||
_______________________________________________
NumPy-Discussion mailing list -- numpy-discussion@python.org
To unsubscribe send an email to numpy-discussion-le...@python.org
https://mail.python.org/mailman3/lists/numpy-discussion.python.org/
Member address: fal...@gmail.com
--
Francesc Alted
_______________________________________________
NumPy-Discussion mailing list --numpy-discussion@python.org
To unsubscribe send an email tonumpy-discussion-le...@python.org
https://mail.python.org/mailman3/lists/numpy-discussion.python.org/
Member address:fan...@gmail.com
_______________________________________________
NumPy-Discussion mailing list -- numpy-discussion@python.org
To unsubscribe send an email to numpy-discussion-le...@python.org
https://mail.python.org/mailman3/lists/numpy-discussion.python.org/
Member address: arch...@mail-archive.com
