Linux snappy compression8/5/2023 ![]() 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. It does not aim for maximum compression, or compatibility with any other compression library instead, it aims for very high speeds and reasonable compression. Snappy is a compression/decompression library. For those not familiar with Snappy, additional information is available from its Google Code page. Snappy is a compress/decompression library that Google open-sourced in 2011 after this compression method has already played a vital role within Google with deployments ranging from use in their BigTable and MapReduce to their internal RPC systems. "Should be ready for pulling into the btrfs tree now," Kleen says. snappy is a faster compression algorithm that provides similar compression as LZO, but generally better performance." "Here's a slightly updated version of the BTRFS snappy interface. Do not store plain text files in Snappy compressed form, instead use a container like SequenceFile.New patches have been published for the Btrfs file-system that implement support for Google's Snappy compression algorithm, which promises to deliver better performance beyond LZO compression.Īndi Kleen of Intel has posted his updated Btrfs snappy compression patches, which he says are now ready for merging. Plain text files: Like Gzip, Snappy is not splittable. Permanent Storage: Snappy compression is not efficient space-wise and it is expensive to store data on HDFS (3-way replication) Please do make sure these intermediate files are cleaned up soon enough so we don’t have disk space issues on the cluster. ![]() Temporary Intermediate files (not available currently as of Pig 0.9.2, applicable only to native Map Reduce) : If you have a series of MR jobs chained together, Snappy compression is a good way to store the intermediate files. Map output: Snappy works great if you have large amounts of data flowing from Mappers to the Reducers (you might not see a significant difference if data volume between Map and Reduce is low) Snappy is not CPU intensive – which means MR tasks have more CPU for user operations.Reduce tasks run faster with better decompression speeds.Map tasks begin transferring data sooner compared to Gzip or Bzip (though more data needs to be transferred to Reduce tasks).tCompressOutput(conf, true) Ĭonf.set("",".compress.Snapp圜odec") tOutputCompressionType(conf, CompressionType.BLOCK) //Block level is better than Record level, in most cases Set Configuration parameters for Snappy compressed intermediate Sequence Files tOutputFormat(SequenceFileOutputFormat.class) Set Configuration parameters for Map output compression Configuration conf = new Configuration() Ĭonf.setBoolean("", true) Ĭonf.set(".codec",".compress.Snapp圜odec") There is work being done to be able to use Snappy for creating intermediate/temporary files between multiple MR jobs. You can read and write Snappy compressed files as well, though I would not recommend doing that as its not very efficient space-wise compared to other compression algorithms. This should get you going with using Snappy for Map output compression with Pig. Use Pig’s “set” keyword for per job level configuration.Follow instructions on to set map output compression at a cluster level. ![]() Now you have 2 ways to use map output compression in the Pig scripts: ![]() tools/hadoop/pig-0.9.1/lib/hadoop-snappy-0.0.1-SNAPSHOT.jarĪlso, you need to point PIG to the snappy native export PIG_OPTS="$PIG_OPTS =$HADOOP_HOME/lib/native/Linux-amd64-64" The pig client here is installed at /tools/hadoop and the jar needs to be placed within $PIG_HOME/lib. Pig requires that the snappy jar and native be available on its classpath when a script is run. This is the machine config of my cluster nodes, though the steps that follow could be followed with your installation/machine configs uname -a Assuming you have installed Hadoop on your cluster, if not please follow ![]()
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |