hadoop file formats

Currently in Hadoop used more than lzo,gzip,snappy,bzip2 these 4 kinds of compression format, the author based on practical experience to introduce the advantages and disadvantages of these 4 compression formats and application scenarios, so that we in practice according to the actual situation to choose different compression format. 1 gzip compression Advantages: The compression ratio is high, and the comp

as the output of a mapreduce job and the input of another mapreduce job. 4 bzip2 Compression Advantages: Support Split, with a high compression rate, than the gzip compression rate is high; Hadoop itself supports, but does not support native, the Linux system with bzip2 command, easy to use. Disadvantage: Compression/decompression speed is slow; native is not supported. Application scenario: Suitable for the speed requirements are not high, but need

, but does not support native; it is easy to use with BZIP2 commands in Linux systems. Disadvantage: Compression/decompression speed is slow; native is not supported. Application scenario: Suitable for the speed requirements are not high, but need high compression rate, can be used as the output format of the MapReduce job, or the data after the output is larger, the data after processing need to compress the archive to reduce disk space and later data used relatively small situation , or you wa

not support native; Bzip2 command is provided in Linux for ease of use. Disadvantages: the compression/Decompression speed is slow; Native is not supported. Application Scenario: Suitable for scenarios where the speed requirement is not high, but the compression ratio is high, it can be used as the output format of mapreduce jobs; or the output data is large, after processing, the data needs to be compressed and archived to reduce disk space and reduce data usage in the future. Or, if you wan

Hadoop provides multioutputformat to output data to different directories and Fileinputformat to read multiple directories at once, but the default one job can only use Job.setinputformatclass Set up to process data in one format using a inputfomat. If you need to implement the ability to read different format files from different directories at the same time in a job, you will need to implement a multiinputformat to read the files in different

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Comparison of the six most common prototype file formats and six prototype file formats Internet product partners will not be unfamiliar with the term "prototype. Like "User Experience", it is often spoken by various people. Prototype is a way for users to experience products, exchange design ideas, and display compl

Mapmap read files in different formats This problem has always been, the previous reading method is to get the name of the file in the map, according to the name of different ways to read, such as the following wayFetch file name Inputsplit Inputsplit = Context.getinputsplit (); String FileName = ((filesplit) inputsplit). GetPath (). toString (), if (Filename.con

calculate the checksum again when the data is transmitted through an unreliable channel, in this way, you can see whether the data is damaged. If the two calculation checksum does not match, you think the data is damaged. However, this technology cannot repair the data and can only detect errors. Common error detection code is CRC-32 (cyclic redundancy check), any size of data input is calculated to get a 32-bit integer checksum. 6. Compress and input parts

Algorithm File name extension Multiple files Severability DEFLATE No DEFLATE . Deflate No No Gzip Gzip DEFLATE . gz No No Zip Zip DEFLATE . zip Is Yes, within the scope of the file Bzip2 Bzip2 Bzip2 . bz2 No Is LZO

Algorithm File name extension Multiple files Severability DEFLATE No DEFLATE . Deflate No No Gzip Gzip DEFLATE . gz No No Zip Zip DEFLATE . zip Is Yes, within the scope of the file Bzip2 Bzip2 Bzip2 . bz2 No Is LZO

This document describes how to operate a hadoop file system through experiments. Complete release directory of "cloud computing distributed Big Data hadoop hands-on" Cloud computing distributed Big Data practical technology hadoop exchange group:312494188Cloud computing practices will be released in the group every

............... ........................................ ........................................ ........................................ ........................................ ........................................ setrep: `/fish/1.txt ': No such file or directory [[email protected] bin] # hadoop fs -stat "% b% F% u:% g% o% y% n% r" /fish/1.txt Return value: Return 0 for success, 1 to

use: Hadoop fs-rmr uri [uri ...]The recursive version of Delete.Example: Hadoop Fs-rmr/user/hadoop/dir Hadoop FS-RMR Hdfs://host:port/user/hadoop/dir return value:Successful return 0, Failure returns-1.21:setrepHow to use:

specified, the trash, if enabled, will be bypassed and the specified file (s) deleted immediately. this can be useful when it is necessary to delete files from an over-quota directory.Example: Hadoop FS-RMR/user/hadoop/Dir Hadoop FS-rmr hdfs: // nn.example.com/user/hadoop