Now that we know the start position and the length, we can append the segment to the buffer itself: It is important to understand that the end of the segment might not be initializied before the rest of the segment is appended, because overlaps are allowed. The hash table size must be a power of two with a maximum size of 32768 bytes and is passed as the log2() of the table size. lz4 0m56.506s 207M lz4 c -I"lz4 -12" -f Supports levels -[1-12]. I have found out these algorithms to be suitable for my use. Appendix: Compressing Time of … This means that you can only look up the latest sequence given some 4-byte prefix. Use COMPRESSION _LZ4 if speed is critical, and you are willing to sacrifice compression ratio to achieve it. Worst compression king. if there's no more compressed bytes to read. Zstd, short for Zstandard, is a new lossless compression algorithm, aiming at providing both great compression ratio and speed for your standard compression needs. If the dictionary is filled, the cache replacement policy should determine which match should be replaced. If you need a portable and efficient compression algorithm which can be implement in only a few hundreds of lines, LZ4 would be my go-to. But when using e.g. An LZ4 stream is divided into segments called "blocks". There are 9 levels of ZLIB supported (1 to 9), mapping 1:1 from the mount option to the algorithm defined level. It is important to understand that the offset is not the starting position of the copied buffer. When you can't go any longer, you encode the literals section until another duplicate 4-byte is found. And while there's a small chance of random data containing runs of compressible patterns, if that were the case, I should see some variance in output sizes, as different random streams would have slightly different compression ratios. We'll use the name "LSIC" for convinience. It is a form of addition code, in which we read a byte. The reason we add 4 is because copying less than 4 bytes would result in a negative expansion of the compressed buffer. What if we found no match or a bad match (a match that shares less than some threshold)? I did GZIP compression through the gzip package in Python, which uses a compression level 9 (best compression, slowest speed) by default, so I needed to make sure that LZ4 used the same setting. This process is repeated until a byte below 255 is reached, which will be added to the sum, and the sequence will then end. The encoded format that the Compression library produces and consumes is compatible with the open source version, apart from the addition of a very simple frame to the raw stream to allow some additional validation and functionality. Note that COMPRESSION _LZMA is an order of magnitude slower for both compression and decompression than other choices. In this case, it is 4 bytes. Copying from the old stream allows deduplication and runs-length encoding. For example, LZ4 is super fast but doesn't yield great compression ratios. lzip: 4m42.017s 116M lzip c --lzip -f v1.21. The default is level 3, which provides the highest compression ratio and is still reasonably fast. Blocks contains a literal which is to be copied directly to the output stream, and then a back reference, which tells us to copy some number of bytes from the already decompressed stream. The only explanation (not spec, explanation) can be found on the author's blog, but I think it is less of an explanation and more of an informal specification. LZ4 is lossless compression algorithm, providing compression speed > 500 MB/s per core (>0.15 Bytes/cycle). Well, then we write it as literal until a good match is found. As you may notice, the dictionary grows linearly. It allows you to modify the compression ratio (and the corresponding compression speed). Looking up allows you to progress and see how long the duplicate sequence match. It features an extremely fast decoder, with speed in multiple GB/s per core (~1 Byte/cycle). In general, there are two classes of such compression algorithms: We will focus on the FC-class algorithms. However, Zstandard, at the default setting, shows substantial improvements in both compression speed and decompression speed, while compressing at the same ratio as zlib. In short, we just keep adding bytes and stop when we hit a non-0xFF byte. It also offers a special mode for small data, called dictionary compression.The reference library offers a very wide range of speed / compression trade-off, and is backed by an extremely fast decoder (see benchmarks below). The original LZ4 compression algorithm is modified for real-time hardware implementation. Strictly speaking, LZ4 data decompression (typically 3 GB/sec) is slower than memcpy (typically 12 GB/sec). A high compression derivative, called LZ4_HC, is available, trading customizable CPU time for compression ratio. The fastest algorithm, lz4, results in lower compression ratios; xz, which has the highest compression ratio, suffers from a slow compression speed. To put it simplistically, if you have a file which is a (good) random mix of an equal number A and B characters, LZ4 won't be able to compress it significantly, while Zstd will compress it 8:1 converging to an encoding where a '1' bit is A, and a '0' bit is B. reply. LZ4HC is the "high compression" version of LZ4 that improves the compression ratio at slightly lower compression speed. As such, it is important that you reduce memory once in a while, by trimming it. I used the command-line tool for Windows that Yann Collet—the creator of the LZ4 algorithm—provides. \[\overbrace{\underbrace{t_1}_\text{4 bits}\ \underbrace{t_2}_\text{4 bits}}^\text{Token} \quad \underbrace{\overbrace{e_1}^\texttt{LISC}}_\text{If $t_1 = 15$} \quad \underbrace{\overbrace{L}^\text{Literal}}_{t_1 + e_1\text{ bytes }} \quad \overbrace{\underbrace{O}_\text{2 bytes}}^\text{Little endian} \quad \underbrace{\overbrace{e_2}^\texttt{LISC}}_\text{If $t_2 = 15$}\]. In particular, every byte iterated over will add a pointer to the rest of the buffer to a B-tree, we call the "duplicate tree". Just last year Kafka 0.11.0 came out with the new improved protocol and log format. ZFS Get Compressratio Results LZ4. This can be a severely limiting factor for very small files - there is just too little history ("old data") to achieve a proper compression. However, compression speeds are similar to LZO and several times faster than DEFLATE, while decompression speeds can be significantly higher than LZO. Compression ratio: The original size (numerator) compared with the compressed size (denominator), measured in unitless data as a size ratio of 1.0 or greater. Discussion. I've got a decent feel for how things compress with the way I want now. JavaScript is disabled. https://www.truenas.com/community/t...pression-ratio-numbers-in-freenas-mean.42330/. For a better experience, please enable JavaScript in your browser before proceeding. This starting point is calculated by \(l - O\) with \(l\) being the number of bytes already decoded. - Method 1 - compress better and faster, decompress up to 1.8x faster than Lz4. Okay, so from reading another thread, my current understanding of the compression ratio shown in storage view, is basically summed up by this quote from danb35: Short answer: Yes, you need to use sparse zvols and oversubscribe your pool storage. These are my thoughts on designing fast, high-quality non-cryptographic hash functions.…, Some notes and ideas on block-based rolling compression.…. You must log in or register to reply here. Until now, we have only considered decoding, not the reverse process. Binary Search Trees (often B-trees) are often used for searching for duplicates. Got an SLOG device? it's not possible to be more precise. This parameter is fully implementation specific. If we search for cddda, we'll get a partial match, namely cdddd => 2. Right now I have a bunch of 80GB thick provisioned volumes, I likely will keep the same structure with thinly provisioned ones. Zstandard library is provided as open source software using a BSD license. 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