Use Fast Data Algorithms | Joey Lynch's Site
Disclaimer: There are lies, damn lies, and benchmarks from some random person on the internet. If you are considering taking some of the advice in this post please remember to test your specific workloads, which might have different bottlenecks. Also the implementation quality in your particular software stack for your particular hardware matters a lot. For this post I’ll be playing with a ~5 GiB
spliceを使ã£ã¦é«˜é€Ÿãƒ»çœãƒ¡ãƒ¢ãƒªã§Gzipã‹ã‚‰ZIPを作る - knqyf263's blog
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How to speed up LZ4 decompression in ClickHouse?
When you run queries in ClickHouse, you might notice that the profiler often shows the LZ_decompress_fast function near the top. What is going on? This question had us wondering how to choose the best compression algorithm. ClickHouse stores data in compressed form. When running queries, ClickHouse tries to do as little as possible, in order to conserve CPU resources. In many cases, all the potent
zstandard-overview.html
I recently realised that, while there is a specification for Zstandard, which describes in great details what is encoded where, there is no “overview†of the format, which would be neither too detailed nor too vague for programmers with a casual interest in data compression to understand its inner working. This blog post is an attempt to correct that. Introduction Zstandard is an LZ77-class compre
2016å¹´ã®OSS圧縮ツールé¸æŠžã‚«ã‚¿ãƒã‚° - Qiita
ã¾ã gzipã§æ¶ˆè€—ã—（略） 2016å¹´ã€äººé¡žãŒå¾…ã¡æœ›ã‚“ã§ã„ãŸã€gzipを圧倒ã™ã‚‹OSS圧縮ツールzstd(Zstandard)ãŒãƒªãƒªãƒ¼ã‚¹ã•ã‚ŒãŸã«ã‚‚é–¢ã‚らãšã€ãªã‚“ã‹ã‚ã‚“ã¾ã‚Šè©±é¡Œã«ãªã£ã¦ã„ãªãã¦å¯‚ã—ã„ã®ã§ã€ã¡ã‚‡ã‚ã„ã‹ã‚“ã˜ã®è³‘ã‚„ã‹ã—比較記事を書ãã¾ã—ãŸã€‚圧縮ツールã®ã‚«ã‚¿ãƒã‚°çš„ã«çœºã‚ã¦ã„ãŸã ã‘ã‚‹ã‹ã¨æ€ã„ã¾ã™ã€‚ ã¯ã˜ã‚㫠（ã“ã®è¨˜äº‹ã§è¨€ã†ï¼‰åœ§ç¸®ãƒ„ールã¨ã¯ä½•ã‹ 圧縮ツールã¨ã„ã†å‘¼ã³åã¯æ£ç¢ºã§ã¯ãªã„（ã¯ãšï¼‰ã§ã™ã€‚å¹³ãŸã言ãˆã°ã€gzipã‚„bzip2ã€xzã€lz4ãªã©ã§ã™ãŒã€äººã«ã‚ˆã£ã¦ã¯ã€tarã®è£å´ã¨ã—ã¦ã—ã‹ä½¿ã£ã¦ãªãã¦ã€èžã„ãŸã“ã¨ã‚‚ãªã„ã‹ã‚‚ã—ã‚Œã¾ã›ã‚“ã。ãã†ã„ã†ã¨ãã¯ã¾ãšgzipã®manpageã¨ã‹èªã‚“ã§ãã ã•ã„。 ã—ã‹ã—ã€ãã†ã„ã†ãƒ„ールを何ã¨å‘¼ã¹ã°ã„ã„ã®ã‹ã‚ã‹ã‚‰ãªã„ã®ã§ã€ã“ã“ã§ã¯åœ§ç¸®ãƒ„ールã¨å‘¼ã‚“ã§ã„ã¾ã™ã€‚ ã‚„ã‚„ã“ã—ã„ã§ã™ãŒã€ã‚¢ãƒ¼ã‚«ã‚¤ãƒã§ã¯ã‚ã‚Šã¾ã›ã‚“。アーカイブã¨ã¯å®Ÿæ…‹ãŒä¸€ã¤ã®ãƒ•ã‚¡ã‚¤ãƒ«ã«ãªã£ã¦ã„るフォル
GitHub - ebiggers/libdeflate: Heavily optimized library for DEFLATE/zlib/gzip compression and decompression
libdeflate is a library for fast, whole-buffer DEFLATE-based compression and decompression. The supported formats are: DEFLATE (raw) zlib (a.k.a. DEFLATE with a zlib wrapper) gzip (a.k.a. DEFLATE with a gzip wrapper) libdeflate is heavily optimized. It is significantly faster than the zlib library, both for compression and decompression, and especially on x86 and ARM processors. In addition, libde
LZ4 explained
At popular request, this post tries to explain the LZ4 inner workings, in order to allow any programmer to develop its own version, potentially using another language than the one provided on Google Code (which is C). The most important design principle behind LZ4 has been simplicity. It allows for an easy code, and fast execution. Let's start with the compressed data format. The compressed block
Javaã®åœ§ç¸®ãƒ»è§£å‡ãƒ©ã‚¤ãƒ–ラリ（JDK標準GZIPã€Commons Compressã€snappy-javaã€LZ4 Java）を試㙠- CLOVERðŸ€
ã¡ã‚‡ã£ã¨åœ§ç¸®ãƒ»è§£å‡ãƒ©ã‚¤ãƒ–ラリを調ã¹ã¦ã„ã¾ã—ã¦ã€ç°¡å˜ã«ä»¥ä¸‹ã‚ãŸã‚Šã‚’比較ã—ãŸã‚Šã€ç¢ºèªã™ã‚‹ãŸã‚㮠下地を作ã£ã¦ã¿ã‚ˆã†ã‹ãªã¨æ€ã„ã¾ã—ã¦ã€‚ å‚考） 2016å¹´ã®OSS圧縮ツールé¸æŠžã‚«ã‚¿ãƒã‚° Snappyã¨ã‹åå‰ã‚’ã¡ã‚‰ã£ã¨è¦‹ã‹ã‘ãŸã“ã¨ãŒã‚ã‚‹ãらã„ã ã£ãŸã®ã§ã€ã“ã†ã„ã†æ©Ÿä¼šã«1度見ã¦ãŠã“ã†ã‹ã¨ã€‚ ピックã—ã¦ã„ã‚‹ã‚‚ã®ã¯ã‘ã£ã“ã†é©å½“ã ã£ãŸã‚Šé›°å›²æ°—ã ã£ãŸã‚Šã—ã¾ã™ãŒã€ã”愛嬌。 ãªãŠã€ã¨ã‚Šã‚ãˆãšç¢ºèªã«ã¯ãƒ†ã‚¹ãƒˆã‚³ãƒ¼ãƒ‰ã‚’使ã†ãŸã‚ã€JUnitã¨AssertJã‚’Mavenä¾å˜é–¢ä¿‚ã«åŠ ãˆã¦ã„ã‚‹ ã‚‚ã®ã¨ã—ã¾ã™ã€‚ <dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>4.12</version> <scope>test</scope> </dependency> <dependency> <groupId>org.assertj
Introducing Brotli: a new compression algorithm for the internet
The latest news from Google on open source releases, major projects, events, and student outreach programs. At Google, we think that internet users’ time is valuable, and that they shouldn’t have to wait long for a web page to load. Because fast is better than slow, two years ago we published the Zopfli compression algorithm. This received such positive feedback in the industry that it has been in
Engineering fast indexes
Contemporary computing hardware offers massive new performance opportunities. Yet high-performance programming remains a daunting challenge. We present some of the lessons learned while designing faster indexes, with a particular emphasis on compressed bitmap indexes. Compressed bitmap indexes accelerate queries in popular systems such as Apache Spark, Git, Elastic, Druid and Apache Kylin.Read les
Roaring Bitmaps
A better compressed bitset Roaring bitmaps are compressed bitmaps. They can be hundreds of times faster. Grab one of our research papers Roaring Bitmaps on GitHub Widely used Roaring is found in Google Procella: YouTube’s SQL Engine, Apache Lucene and derivative systems such as Solr and Elasticsearch, Apache Druid, Apache Spark, Apache Hive, Apache Tez, Apache Zeppelin, Apache Doris, Apache Carbon
Open Ecosystem
Using Intel.com Search You can easily search the entire Intel.com site in several ways. Brand Name: Core i9 Document Number: 123456 Code Name: Emerald Rapids Special Operators: “Ice Lakeâ€, Ice AND Lake, Ice OR Lake, Ice* Quick Links You can also try the quick links below to see results for most popular searches. Product Information Support Drivers & Software
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Standard compression algorithm could revolutionize physical and biological computations, researchers say
Fuzz-Testing the Snappy Compression Algorithm
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INTEL コード & ダウンãƒãƒ¼ãƒ‰
Design of Hardware Accelerator for Lempel-Ziv 4 (LZ4) Compression
lz4/doc/lz4_Frame_format.md at dev · lz4/lz4
GitHub - facebook/zstd: Zstandard - Fast real-time compression algorithm
Facebook Open-Sources New Compression Algorithm Outperforming Zlib
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