They are sorted by increasing the compression ratio using plain CSVs as a baseline.
Snappy is a compression library developed by Google. Snappy compression is designed to be fast and efficient regarding memory usage, making it a good fit for MongoDB workloads. Using a sample of 35 random symbols with only integers, here are the aggregate data sizes under various storage formats and compression codecs on Windows. By default, MongoDB provides a snappy block compression method for storage and network communication. Both took a similar amount of time for the compression, but Parquet files are more easily ingested by Hadoop HDFS.
#Snappy compression zip#
The bzip2, tar and zip support came from Avalon's Excalibur, but originally from Ant, as far as life in Apache goes. Total count of records a little bit more than 8 billions with 84 columns. Snappy compression on list features in Redis hash: 377MiB: 44s: 2.5ms: 1.9ms: Table 5. Splittablity : If you need your compressed data to be splittable, BZip2, LZO, and Snappy formats are splittable, but GZip is not. A compression codec value of 0 indicates an uncompressed message. It is worth running tests to see if you detect a significant difference. Each column type (like string, int etc) get different Zlib compatible algorithms for compression (i.e different trade-offs of RLE/Huffman/LZ77). 1 byte compression-attributes 4 byte CRC32 of the payload The lowest 2 bits in the attributes byte will select the compression codec used for the compressing data. As result of import, I have 100 files with total 46.4 G du, files with diffrrent size (min 11MB, max 1.5GB, avg 500MB). Snappy or LZO are a better choice for hot data, which is accessed frequently. Compressed CSVs achieved a 78% compression. The Apache Commons Compress library defines an API for working with ar, cpio, Unix dump, tar, zip, gzip, XZ, Pack200, bzip2, 7z, arj, lzma, snappy, DEFLATE, lz4, Brotli, Zstandard, DEFLATE64 and Z files. I have dataset, lets call it product on HDFS which was imported using Sqoop ImportTool as-parquet-file using codec snappy. Parquet v2 with internal GZip achieved an impressive 83% compression on my real data and achieved an extra 10 GB in savings over compressed CSVs. My goal this weekend is to experiment with and implement a compact and efficient data transport format. I have an experimental cluster computer running Spark, but I also have access to AWS ML tools, as well as partners with their own ML tools and environments (TensorFlow, Keras, etc.). LZ4 - Extremely fast compression LZ4 is lossless compression algorithm, providing compression speed > 500 MB/s per core, scalable with multi-cores CPU. My financial time-series data is currently collected and stored in hundreds of gigabytes of SQLite files on non-clustered, RAIDed Linux machines. Goal: Efficiently transport integer-based financial time-series data to dedicated machines and research partners by experimenting with the smallest data transport format(s) among Avro, Parquet, and compressed CSVs.
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