On 8/30/22 06:29, Francesc Alted wrote:
Not exactly. What we've done is to encode the header and the trailer (i.e. where the metadata is) of the frame with msgpack. Thechunks section <https://github.com/Blosc/c-blosc2/blob/main/README_CFRAME_FORMAT.rst#chunks>is where the actual data is; this section does not follow a msgpack structure as such, but it is rather a sequence of data chunks and an index (for quickly locating the chunks). You can easily access the header or trailer sections reading from the start or the end of the frame. This way you don't need to update the indexes of chunks in msgpack, which can be expensive during data updates.
This indeed prevents data to be dumped by using typical msgpack tools, but our sense is that users should care mostly about metainfo, and let the libraries to deal with the actual data in the most efficient way.
thanks for your detailed reply. I spent the past few days reading the links/documentations, as well as experimenting the blosc2 meta-compressors, I was quite impressed by the performance of blosc2. I was also happy to see great alignments behind the drives for Caterva those of NeuroJSON. I have a few quick updates 1. I added blosc2 as a codec in my jdata module, as an alternative compressor to zlib/lzma/lz4 https://github.com/NeuroJSON/pyjdata/commit/ce25fa53ce73bf4cbe2cff9799b5a616... 2. as I mentioned, jdata/bjdata were not optimized for speed, they contain many inefficient handling of numpy arrays (as I discovered); after some profiling, I was able to remove most of those, the run-time is now nearly entirely spent in compression/decompression (see attached profiler outputs for the `zlib` compressor benchmark) 3. the new jdata that supports blosc2, v0.5.0, has been tagged and uploaded (https://pypi.org/project/jdata) 4. I wrote a script and compared the run times of various codecs (using BJData and JSON as containers) , the code can be found here https://github.com/NeuroJSON/pyjdata/blob/master/test/benchcodecs.py the save/load times tested on a Ryzen 9 3950X/Ubuntu 18.04 box (at various threads) are listed below (similar to your posted before) *|- Testing npy/npz|*| || 'npy', 'save' 0.2914195 'load' 0.1963226 'size' 800000128|| || 'npz', 'save' 2.8617918 'load' 1.9550347 'size' 813846||||| *|- Testing text-based JSON files (.jdt)|**|*|(nthread=8)|*...|*| || 'zlib', 'save' 2.5132861 'load' 1.7221164 'size' 1084942|| || 'lzma', 'save' 9.5481696 'load' 0.3865211 'size' 150738|| || 'lz4', 'save' 0.3467197 'load' 0.5019965 'size' 4495297|| || 'blosc2blosclz'save' 0.0165646 'load' 0.1143934 'size' 1092747|| || 'blosc2lz4', 'save' 0.0175058 'load' 0.1015181 'size' 1090159|| || 'blosc2lz4hc','save' 0.2102167 'load' 0.1053235 'size' 4315421|| || 'blosc2zlib', 'save' 0.1002635 'load' 0.1188845 'size' 1270252|| || 'blosc2zstd', 'save' 0.0463817 'load' 0.1017909 'size' 253176| || *||**|- Testing binary JSON (BJData) files (.jdb) (nthread=8)...|*| || 'zlib', 'save' 2.4401443 'load' 1.6316463 'size' 813721|| || 'lzma', 'save' 9.3782029 'load' 0.3728334 'size' 113067|| || 'lz4', 'save' 0.3389360 'load' 0.5017435 'size' 3371487|| || 'blosc2blosclz'save' 0.0173912 'load' 0.1042985 'size' 819576|| || 'blosc2lz4', 'save' 0.0133688 'load' 0.1030941 'size' 817635|| || 'blosc2lz4hc','save' 0.1968047 'load' 0.0950071 'size' 3236580|| || 'blosc2zlib', 'save' 0.1023218 'load' 0.1083922 'size' 952705|| || 'blosc2zstd', 'save' 0.0468430 'load' 0.1019175 'size' 189897|||| ||| *||||- Testing binary JSON (BJData) files (.jdb) ||*|*||(nthread=1)|...|*| | 'blosc2blosclz'save' 0.0883078 'load' 0.2432985 'size' 819576 'blosc2lz4', 'save' 0.0867996 'load' 0.2394990 'size' 817635 'blosc2lz4hc','save' 2.4794559 'load' 0.2498981 'size' 3236580 'blosc2zlib', 'save' 0.7477457 'load' 0.4873921 'size' 952705 'blosc2zstd', 'save' 0.3435547 'load' 0.3754863 'size' 189897 | |*||||- Testing binary JSON (BJData) files (.jdb) ||*|*||(nthread=32)|...|*| || 'blosc2blosclz'save' 0.0197186 'load' 0.1410989 'size' 819576 'blosc2lz4', 'save' 0.0168068 'load' 0.1414074 'size' 817635 'blosc2lz4hc','save' 0.0790011 'load' 0.0935394 'size' 3236580 'blosc2zlib', 'save' 0.0608818 'load' 0.0985531 'size' 952705 'blosc2zstd', 'save' 0.0370790 'load' 0.0945577 'size' 189897 | a few observations: 1. single-threaded zlib/lzma are relatively slow, reflected by npz, zlib and lzma results 2. for simple data structure like this one, using JSON/text-based wrapper vs a binary wrapper has a marginal difference in speed; the only penalty is that text/JSON is ~33% larger than binary in size due to base64 3. blosc2 overall delivered very impressive speed - even in single thread, it can be than faster than uncompressed npz or other standard compression methods 4. several blosc2 compressors scaled well with more threads 5. it is a bit strange that blosc2lz4hc yielded larger file size, similar to that from a standard lz4, but blosc2lz4 produces a size comparable to zlib; I expected reverted findings, because lz4hc is supposed to give "higher-compression" one question I have is: how stable is your format spec? do you see the buffers compressed by your current blosc2 library be still opened/parsed by your future releases (at least with an intent to)? | | ||
Not quite. Blosc2 does not use the multi-threaded version of zstd; it rather implements its own internal multi-threading engine and hence all the codecs (and filters) benefit from it, so no need to trust on a multi-threaded codec for speed. Also, as filters execute prior to codecs, they can reuse the same internal buffers, avoiding copies (which is critical for achieving high I/O performance).
As said, we are not using packed ND array in msgpack, but rather, using our own schema. Blosc2 supports the concept of metalayers for adding new meaning to the stored data (seehttps://www.blosc.org/docs/Caterva-Blosc2-SciPy2019.pdf, slide 17). One of these layers is Caterva, where we have added support forMD arrays <https://github.com/Blosc/caterva/blob/master/CATERVA_METALAYER.rst>. Note that our implementation for supporting ND arrays uses two levels of partitioning (chunks and blocks) for:
1. Allowfiner granularity <https://www.blosc.org/posts/caterva-slicing-perf/>in retrieving data.
2. Better adapt to the memory hierarchies (i.e. main memory and cache levels in CPU)for efficiency <https://www.blosc.org/posts/breaking-memory-walls/>.
OTOH, I have noticed thatyour patch for msgpack <https://github.com/msgpack/msgpack/pull/267/files#diff-bc6661da34ecae62fbe724bb93fd69b91a7f81143f2683a81163231de7e3b545R334>only suggest to use uint32 as the type for array shape. This would prevent to use creating arrays where some dim is larger than 2^32. Is that intended?
see the last part of this post https://github.com/msgpack/msgpack/issues/268#issuecomment-495050845 in BJData, the ND-array dimensional vector supports different integer types <https://github.com/NeuroJSON/bjdata/blob/Draft_2/Binary_JData_Specification....>
I see your point, and your intent is really appreciated. It is just in the 10's GB and up domain that I see BJData a bit lacking in that text handling tools (strings, sed, not to mention editors, where you can run out of memory very soon) can become unnecessarily slow for retrieving the metainfo. We really feel that such metainfo should go either at the beginning or at the end of the frame, where it can be found and processed way more efficiently.
regardless which serialization format is chosen, I think both projects see the needs to store hierarchical metadata along-side with the data. I agree with you that if reading/searching metadata is desired, header&trailer are the best places. For efficient search of metadata while accommodating large amount of binary data in scales, CouchDB/MongoDB use "attachments" to hold large binary data. The metadata tree and the attachment can be linked using a simple UUID or JSON-reference string
OTOH, I agree in that msgpack is not human readable directly, but the format is becoming so ubiquitous that you can find standard tools for introspecting metadata quite easily
it would be nice to store the header data in a map so it can be self-explanatory (with just a small cost of size). I am even willing go as far as adding non-essential metadata that can help make the data file as self-explained as possible, such as spec, schemas and parsers, just because the format can and it costs almost nothing https://github.com/rordenlab/dcm2niix/blob/v1.0.20220720/console/nii_dicom_b...
:
$ msgpack2json -di eye5_blosc2_blosclz.b2frame [ ... ]
And, as there are msgpack libraries for almost all of the currently used languages, I think that formats based on it are as open and transparent as we can get.
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 theNeuroJSON 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....>, etc) are taking the mission of building a "source-code language" for scientific data storage.
Thanks, I concur with your work too! It is always nice to discuss with people that has put a lot of thought in how to pack data efficiently, and as simply as possible (but not any simpler!). Actually, we might be adopting some aspects ofJData <https://github.com/fangq/jdata>to be able to store different objects (arrays, tables, graphs, trees...) in the same frame in a future possible extension of Blosc2. Or, maybe using JData as the external container for existing Blosc2 frames. Very interesting discussion indeed; many possibilities are open now!
will be absolutely happy to explore collaboration possibilities. will reach out offline. Qianqian
Cheers, Francesc
