7/23/2023 0 Comments Snappy compression format![]() In simple words, this means that Snappy blocks themselves are not splittable but in container file formats like SequenceFile (which contains key and a value pair) and Avro data files (which stores metadata of schema directly in the file) if each record inside the file is compressed using either Snappy or even Gzip then we can split the file as a whole thereby turning the non-splittable into splittable. Splittability is not relevant to HBase data." Snappy is intended to be used with a container format, like SequenceFiles or Avro data files, rather than being used directly on plain text, for example, since the latter is not splittable and cannot be processed in parallel using MapReduce. Snappy and GZip blocks are not splittable, but files with Snappy blocks inside a container file format such as SequenceFile or Avro can be split. Even this blog by IBM states that Snappy is splittable (link below).īut thankfully the new documentation by CDH (link below) clarifies the statements and gives a precise answer to our question "For MapReduce, if you need your compressed data to be splittable, BZip2 and LZO formats can be split. While Hadoop's book 'Hadoop the Definitive Guide' states that it isn't Splittable. Splittability is not relevant to HBase data." (Link to the documentation). This blog by Cloudera states that " For MapReduce, if you need your compressed data to be splittable, BZip2, LZO, and Snappy formats are splittable, but GZip is not. There are common misconceptions about it being Splittable or not. And when talking about compression codecs we talk about qualities like speed, compression ratio, splittable or not (especially in case of MapReduce/Hadoop) which means that after using the codec the file could be split into chunks for different Mappers as this helps in parallel processing and can really affect the throughput. The actual file size is approximated when the file is being written, so it might not be exactly equal to the. You can use MAXFILESIZE to specify a file size of 5 MB6.2 GB. If MAXFILESIZE isnt specified, the default maximum file size is 6.2 GB. Each row group is compressed with SNAPPY. Speaking of its uses, Snappy is used in MapReduce along with Big Table, MongoDB etc. 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. Unload to Parquet doesnt use file level compression. This makes it a good choice in case you're not quite experienced enough to know which codec to use. Snappy is a compression/decompression library developed by Google and unlike other libraries that work at extremes of the tradeoff i.e either focusing on speed of compression or the compression efficiency, Snappy works somewhere in between as it significantly improves the speed of compression while also maintaining a reasonable compression which is crucial for improving the throughput for Hadoop jobs. While ((readCount = inStream.If you're reading this you probably already know what Snappy is and can skip this next part. InputStream inStream = new BufferedInputStream(new FileInputStream( createOutputStream(new BufferedOutputStream( newInstance(Snapp圜odec.class, new Configuration()) *This program compresses the given file in snappy formatĬompressionCodec codec = (CompressionCodec) ReflectionUtils INSTALL Makefile.am NEWS README README.md autogen.sh clearcap.map cmakeconfig.h.in comment-43-format.c configure.ac crc32.c crc32.h crc32sse42.c framing-format.c framing2-format. Published + Follow When we are working with big data files like Parquet, ORC, Avro etc then you will mostly come across different compression codec like snappy, lzo, gzip, bzip2. ![]() You can download the code directly from github This is available in your hadoop installation. This code requires the following dependent jars. The code shown below will help you in creating snappy compressed file which will work perfectly in hadoop. I compressed a file in snappy using the google snappy library and the snappy codecs present in hadoop. I verified the file size and checksum of both the files and found that It is having difference. The compressed file created using hadoop snappy is having some bytes more than that of the compressed file created using google snappy. It is some extra metadata that is consuming the extra bytes. I did a little research on this and found the workaround for that. The method I followed for finding the solution was as follows. But it gave me an error that the file is missing the Snappy identifier. Snappy is one among the compression formats supported by hadoop. I created a snappy compressed file using the google snappy library and used in hadoop. You can perform algorithmic compression on DATASET data using. Hadoop supports various compression formats. It also defines certain processes for operating on and compressing the storage format data.
0 Comments
Leave a Reply. |