FastPFor
corpuscompression
FastPFor | corpuscompression | |
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4 | 2 | |
839 | 7 | |
- | - | |
8.5 | 0.0 | |
about 1 month ago | over 3 years ago | |
C++ | Java | |
Apache License 2.0 | - |
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FastPFor
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Making CRDTs 98% More Efficient
Well if your integers are sequential you can encode huge numbers of them using diff + RLE in just a few bytes, likely far fewer than 1/2 a byte on average, for the right dataset (in theory you can store 1,2,3,4,5...10_000 in 2 bytes).
But for other integer datasets there's FastPFOR
https://github.com/lemire/FastPFor
The linked papers there will talk about techniques that can be used to store multiple 32bit integers into a single byte, etc. Integer compression is pretty powerful if your data isn't random. The thing with UUIDs is that your data is pretty random - even a UUIDv7 contains a significant amount of random data.
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Time-Series Compression Algorithms
One notable omission from this piece is a technique to compress integer time series with both positive and negative values.
If you naively apply bit-packing using the Simple8b algorithm, you'll find that negative integers are not compressed. This is due to how signed integers are represented in modern computers: negative integers will have their most significant bit set [1].
Zigzag encoding is a neat transform that circumvents this issue. It works by mapping signed integers to unsigned integers so that numbers with a small absolute value can be encoded using a small number of bits. Put another way, it encodes negative numbers using the least significant bit for sign. [2]
If you're looking for a quick way to experiment with various time series compression algorithm I highly recommend Daniel Lemire's FastPFor repository [3] (as linked in the article). I've used the Python bindings [4] to quickly evaluate various compression algorithms with great success.
Finally I'd like to humbly mention my own tiny contribution [5], an adaptation of Lemire's C++ Simple8b implementation (including basic methods for delta & zigzag encoding/decoding).
I used C++ templates to make the encoding and decoding routines generic over integer bit-width, which expands support up to 64 bit integers, and offers efficient usage with smaller integers (eg 16 bit). I made a couple other minor tweaks including support for arrays up to 2^64 in length, and tweaking the API/method signatures so they can be used in a more functional style. This implementation is slightly simpler to invoke via FFI, and I intend to add examples showing how to compile for usage via JS (WebAssembly), Python, and C#. I threw my code up quickly in order to share with you all, hopefully someone finds it useful. I intend to expand on usage examples/test cases/etc, and am looking forward to any comments or contributions.
[1] https://en.wikipedia.org/wiki/Signed_number_representation
[2] https://en.wikipedia.org/wiki/Variable-length_quantity#Zigza...
[3] https://github.com/lemire/FastPFor
[4] https://github.com/searchivarius/PyFastPFor
[5] https://github.com/naturalplasmoid/simple8b-timeseries-compr...
- The big-load anti-pattern
- FastPFOR: Fast Integer Compression in C++
corpuscompression
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Time-Series Compression Algorithms
I've always liked this kind of thing. I've also done some experiments with automatically memorizing better starting dictionaries for a corpus eras where the data is small:
https://github.com/spullara/corpuscompression
Another interesting case is using a compressed stream to indicate anomalies in the data when the compression ratio spikes down like in log analysis.
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Text Classification by Data Compression
Was going to come here to say that. Played around with this a bit for compressing small fields using a learned dictionary:
https://github.com/spullara/corpuscompression
What are some alternatives?
Code-used-on-Daniel-Lemire-s-blog - This is a repository for the code posted on my blog
simple8b-timeseries-compression
simple8b-timeseries-compr
interpolative_coding - A flexible and efficient C++ implementation of the Binary Interpolative Coding algorithm.