12/30/2023 0 Comments Snappy compression ration![]() S2 can be a drop-in replacement for Snappy but for top performance, it shouldn't compress using the backward compatibility mode. On a single core of a Core i7 processor in 64-bit mode, Snappy compresses at about 250 MB/sec or more and decompresses at about 500 MB/sec or more. Encrypted, random and data that is already compressed are examples that will often cause compressors to waste CPU cycles with little to show for their efforts. inputs, but the resulting compressed files are anywhere from 20 to 100 bigger. S2 is also smart enough to save CPU cycles on content that is unlikely to achieve a strong compression ratio. ![]() S2 aims to further improve throughput with concurrent compression for larger payloads. Additionally, depending on the specifications of the users machine, compression speeds for SNAPPY files are approximately 250 MB/s of compression and 500 MB/s. Snappy has been popular in the data world with containers and tools like ORC, Parquet, ClickHouse, BigQuery, Redshift, MariaDB, Cassandra, MongoDB, Lucene and bcolz all offering support. Each column in flights-1m has a compression ratio between 1 and 2 compared to before snappy compression was applied. Snappy originally made the trade-off going for faster compression and decompression times at the expense of higher compression ratios. S2 is an extension of Snappy, a compression library Google first released back in 2011. Try this: df.write.parquet (outputpath, mode'overwrite', partitionBypartlabels, compression'snappy') Share Improve this answer Follow answered at 8:54 0x26res 11. But, if the payload is already encrypted or wrapped in a digital rights management container, compression is unlikely to achieve a strong compression ratio so decompression time should be the primary goal. Snappy is a compression algorithm reaching over 250MB/s compression and 500MB/s decompression speeds while still providing interesting compression ratio. 2 Answers Sorted by: 2 I think the pyspark API is slightly different from the Java/Scala API. If you're releasing a large software patch, optimising the compression ratio and decompression time would be more in the users' interest. The four major points of measurement are (1) compression time (2) compression ratio (3) decompression time and (4) RAM consumption. Compression algorithms are designed to make trade-offs in order to optimise for certain applications at the expense of others. ![]()
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