This chart shows the file size in bytes (lower numbers are better). Test Case 5 – Disk space analysis (narrow) I like the comment from David (2014, before ZLib Update) "SNAPPY for time based performance, ZLIB for resource performance (Drive Space)." Thus well-crafted JavaScript code can have competitive performance even compared to native C++ code. while achieving comparable compression ratios. Zstandard library is provided as open source software using a BSD license. E. Using More Memory. Snappy is Google’s 2011 answer to LZ77, offering fast runtime with a fair compression ratio. The source code also contains a formal format specification , as well as a specification for a framing format useful for higher-level framing and encapsulation of Snappy data, e.g. I have found out these algorithms to be suitable for my use. Body. Snappy can be used to benchmark itself against a number of other compression libraries - zlib, LZO, LZF, FastLZ and QuickLZ –, if they are installed on the same machine. Benchmarks against a few other compression libraries (zlib, LZO, LZF, FastLZ, and QuickLZ) are included in the source code distribution. While Snappy compression is faster, you might need to factor in slightly higher storage costs. Enable Snappy Compression for Improved Performance in Big SQL and Hive - Hadoop Dev. Zstandard is a fast compression algorithm, providing high compression ratios. lzbench is an in-memory benchmark of open-source LZ77/LZSS/LZMA compressors. ORC+ZLib seems to have the better performance. Also released in 2011, LZ4 is another speed-focused algorithm in the LZ77 family. 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). TreeDB's performance on the other hand is better without compression than with compression. ZLib is also the default compression option, however there are definitely valid cases for Snappy. The last comparison is the amount of disk space used. This benchmark only uses the default backend because I wanted to avoid the setup effort — sorry; Snappy. LZO, LZF, QuickLZ, etc.) lzbench Compression Benchmark. The benchmark currently consists of 36 datasets, tested against 40 codecs at every compression level they offer. In our tests, Snappy usually is faster than algorithms in the same class (e.g. Presumably this is because TreeDB's compression library (LZO) is more expensive than LevelDB's compression library (Snappy). Parquet was able to generate a smaller dataset than Avro by 25%. The distinction of what type of file format is to be used is done during table creation. I have a large file of size 500 mb to compress in a minute with the best possible compression ratio. Benchmark. It is run on 1 test machine, yielding a grand total of 7200 datapoints. It does not aim for maximum compression, or compatibility with any other compression library; instead, it aims for very high speeds and reasonable compression. We increased the overall cache size for each database to 128 MB. I benchmark SnappyJS against node-snappy (which is Node.js binding of native implementation). Command for benchmark is node benchmark. The job was configured so Avro would utilize Snappy compression codec and the default Parquet settings were used. Using GZip or Snappy compression for storage of data in kafka brokers uses many cpu cycles and increases overhead on servers. Read this paper for more information on the different file formats supported by Big SQL. For instance, compared to the fastest mode of zlib, Snappy is an order of magnitude faster for most inputs, but the resulting compressed files are anywhere from 20% to 100% bigger. 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