hadoop streaming example

If the executable file, script, or configuration file required for the program to run does not exist on the compute nodes of the Hadoop cluster, you first need to distribute the files to the cluster for a successful calculation. Hadoop provides a mechanism for automatically distributing files and compressing packages by simply configuring the appropriate parameters when you start the

The streaming framework allows programs implemented in any program language to be used in hadoopmapreduce to facilitate the migration of existing programs to the Hadoop platform. So it can be said that the scalability of Hadoop is significant. Next we use C + +, PHP, Python language to implement Hadoopwordcount.　Combat one: C + + language implementation WordCount

PreviousArticleThis section describes various streaming parameters. Example of submitting a hadoop task: $ Hadoop_home/bin/hadoop streaming \ -Input/user/test/input-output/user/test/output \ -Mapper "mymapper. Sh"-reducer "myreducer. Sh "\ -File/Home/work/mymapper.

large files and archives in Hadoop streaming Tasks use the-cachefile and-cachearchive options to distribute files and archives in the cluster, and the parameters of the options are the URIs of the files or archives that the user has uploaded to HDFs. These files and archives are cached between different jobs. The user can configure the parameters by Fs.default.name.config the host and fs_port that the file

First, steamingMap tasks:#!/bin/Bashawk 'begin{FS ="[ ,. ]"OFS="\ t"}{ for(i =1; I i) {dict[$i]+=1}}end{ for(Keyinchdict) {print Key,dict[key]}}'Reducer tasks:#!/bin/bashawk'begin{' \ t'} { dict[$ 1] + = $2}end{ for in dict) { Print Key,dict[key] }}'Startup script:#!/bin/Bashhadoop FS-RM-r/data/apps/zhangwenchao/mapreduce/streaming/wordcount/Outputhadoop Jar/data/tools/

Put the command first:Hadoop jar/usr/hadoop-1.2. 1/contrib/streaming/hadoop-streaming-1.2. 1. Jar-mapper mapper.py- file mapper.py-reduce reduce.py- file reduce.py-file Params.txt-file params2.txt-input/data/*-output/outputWhere output does not exist can only be.The output of the mapper.py is passed directly to reduce.

We know that the hadoop streaming framework uses '/t' as the Separator by default, and takes the part before the first'/t' in each line as the key, and the remaining content as the value, if no '/t' separator exists, the entire line is used as the key.Key/TValueAnd serves as the reduce input. Hadoop provides configuration for you to set separators.-DStream. Map.

If the executable file, script, or configuration file required for the program to run does not exist on the compute nodes of the Hadoop cluster, you first need to distribute the files to the cluster for a successful calculation. Hadoop provides a mechanism for automatically distributing files and compressing packages by simply configuring the appropriate parameters when you start the

Streaming supports using scripts as map and reduceProgram. The following describes a program for calculating the total number of rows of all files in a distributed manner. 1. Put the data to be retrieved into HDFS$ Hadoop FS-put localfile/user/hadoop/hadoopfile 2. Write the map and reduce scripts. Remember to add executable permissions to the scripts.Mapper.

/samples/cachefile/input.txtcache/file (cache is the extracted directory name, with the # redefined alias, participate in the following) Cache/file2hadoop_ home=/home/hadoop/hadoop-2.3.0-cdh5.1.3$hadoop_home/bin/hadoopfs-rmr/cacheout/$HADOOP _home/ bin/hadoopjar $HADOOP _home/share/

. Look at the submit job script, which is also important: #!/bin/bash export hadoop_home=/home/q/hadoop-2.2.0 sudo-u flightdev HADOOP jar $HADOOP _home/share/hadoop/ Tools/lib/hadoop-streaming-2.2.0.jar \ D-mapred.job.

Streaming Http://hadoop.apache.org/common/docs/r0.21.0/cn/streaming.html#Hadoop+Streaming Http://dongxicheng.org/mapreduce/hadoop-streaming-programming/ Http://dongxicheng.org/mapreduce/hadoop-

Recently, I want to briefly learn streaming, mainly using python. Python + hadoop also has an exception in the previous blog post. It's interesting. If C ++ has the opportunity to try it. Record some webpages that you see as memos Http://hadoop.apache.org/docs/r0.19.2/cn/streaming.html#Hadoop+Streaming Chinese, alt

Hadoop Streaming usage Usage: $HADOOP _home/bin/hadoop jar \ $HADOOP _home/hadoop-streaming.jar [Options] Options (1)-input: Input file path (2)-output: Output file path (3)-mapper: User-written mapper program, can be executable f

Reprint http://www.cnblogs.com/shapherd/archive/2012/12/21/2827860.htmlHadoop supports the functionality of the reduce multi-output, where a reduce can be exported to multiple part-xxxxx-x files, where X is one of a-Z letters, and the program appends the "#X" suffix to the value after the output How to useYou need to specify-outputformat Org.apache.hadoop.mapred.lib.SuffixMultipleTextOutputFormat or-outputformat in the startup script Org.apache.hadoop.mapred.lib.SuffixMultipleSequenceFileOutputF

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divided (the above explanation does not find the relevant document, nor the original) Example1output output (keys) because-D stream.num.map.output.key.fields=4Specify the first 4 output lines of the map as key, followed by value11.12.1.2 11.14.2.3 11.11.4.1 11.12.1.1 11.14.2.2divided into 3 reducer (the first 2 fields as the keys of partition)11.11.4.1-----------11.12.1.2 11.12.1.1-----------11.14.2.3 11.14.2.2The reducer is sorted within each division (4 fields are used for sorting at the same

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There have also been recent studies using spark streaming for streaming. This article is a simple example of how to do spark streaming programming with the flow-based count of word counts.1. Dependent jar PackagesRefer to the article "Using Eclipse and idea to build the Scala+spark development environment," which speci