hadoop mapreduce example

IdValue:gender->If Gender is female/f/f/0 then converted to Felse if Gender is MALE/M/M/1 then convert to MInput-multiple Maps-reduce-output Input1➜map1➘Reduce➜outputInput2➜map2➚ In this design pattern, we have two input files whose files are in different formats,The format of file one is the prefix of the gender as the name, for example: Ms. Shital Katkar or Mr. Krishna KatkarThe format of document two is a gender format is fixed, but its position

When using MapReduce and HBase, when running the program, it appearsJava.lang.noclassdeffounderror:org/apache/hadoop/hbase/xxx error, due to the lack of hbase supported jar packs in the running environment of Hadoop, you can resolve 1 by following these methods . Turn off the Hadoop process (all) 2. Add in the profile

description and submission classespublic class Flowsumrunner extends configured implements tool{public int run (string[] arg0) throws Exception {Configuration conf = new configuration ();Job Job = job.getinstance (conf);Job.setjarbyclass (Flowsumrunner.class);Job.setmapperclass (Flowsummapper.class);Job.setreducerclass (Flowsumreducer.class);Job.setmapoutputkeyclass (Text.class);Job.setmapoutputvalueclass (Flowbean.class);Job.setoutputkeyclass (Text.class);Job.setoutputvalueclass (Flowbean.clas

Hadoop beginners often have two questions: 1. If a hadoop block is 64 MB by default, will a row of records be divided into two blocks for text in the form of a record row? 2. when a file is read from the block for splitting, will a row of records be divided into two inputsplits? If two inputsplits are split, an inputsplit contains a row of incomplete data, will the ER er processing this inputsplit produce i

Hadoop mapreduce jar File Upload When submitting a job, we often execute a command similar to the following: Hadoop jar Wordcount.jar test. WordCount, and then wait for the job to complete to see the results. In the job execution process, the client uploads the jar file into HDFs, then initializes the job by JT and issues the specific task to TT, where we mainly

Architecture of MapReduce: -Distributed Programming architecture -Data-centric, more emphasis on throughput -Divide and conquer (the operation of large-scale data sets, distributed to a master node under the management of the various nodes together to complete, and then consolidate the intermediate results of each node to get the final output) -map to break a task into multiple subtasks -reduce the decomposed multitasking and summarizes the results

Write the WordCount program data as follows:Hello BeijingHello ShanghaiHello ChongqingHello TianjinHello GuangzhouHello Shenzhen...1, Wcmapper:Package com.hadoop.testHadoop;Import java.io.IOException;Import org.apache.hadoop.io.LongWritable;Import Org.apache.hadoop.io.Text;Import Org.apache.hadoop.mapreduce.Mapper;In 4 generics, the first two are the types that specify mapper input data, Keyin is the type of the input key, and Valuein is the type of the input value.The data input and output of m

Configuring MapReduce>configuration>configuration>Plus this.configuration> property > name>Mapreduce.framework.namename> value>Yarnvalue> Property >configuration>And then configure it inside the yarn-site.xml.configuration> -- property > name>Yarn.resourcemanager.hostnamename> value>Hadoop1value> Property > property > name>Yarn.nodemanager.aux-servicesname> value>Mapreduce_shufflevalue>

overload the protected function, such as issplitable (), which is used to determine whether you can slice a block and return it by default to true, indicating that as long as the data block is larger than the HDFS block size, Then it will be sliced.But sometimes you don't want to slice a file, such as when some binary sequence files cannot be sliced, you need to overload the function to return FALSE. when using Fileinputformat, your primary focus should be on the decomposition of data blocks

Hadoop Reading Notes series article: http://blog.csdn.net/caicongyang/article/category/2166855The role of the 1.MapReduce counter statistics map, reduce, and combiner the number of executions, you can easily judge the code execution Flow 2. MapReduce comes with a counter14/11/26 22:28:51 INFO mapred. JOBCLIENT:COUNTERS:1914/11/26 22:28:51 INFO mapred. Jobclient:f

precedence is higher than the attribute defined by the file resource To override a property by using the JVM parameter -dproperty=value on the command line Second, configure the development environment CONF option: Easy to switch configuration files Genericoptionsparser,tool and Toolrunner: Genericoptionsparser A class that explains common Hadoop command-line options, which can be set in the configuration object depending on th

Hadoop Reading Notes series article:http://blog.csdn.net/caicongyang/article/category/2166855 (series of articles will be gradually trimmed to complete, add data file format expected related comments)1. Description:From the given file, find the maximum of 100 values, given the data file format as follows:5331656517800292911374982668522067918224212228227533691229525338221001067312284316342740518015 ...2. Use the TreeMap class in the code below, so writ

0 Basic Learning Hadoop to get started work line guide beginner: Hive and MapReduce: http://www.aboutyun.com/thread-7567-1-1.htmlMapReduce Learning Catalog SummaryMApreduce Learning Guide and Troubleshooting summary : http://www.aboutyun.com/thread-7091-1-1.htmlWhat is map/reduce:http://www.aboutyun.com/thread-5541-1-1.htmlMapreduce whole working mechanism diagram: http://www.aboutyun.com/thread-5641-1-1.h

As input When the compressed file is MapReduce input, MapReduce will automatically extract the corresponding codec from the extension. As output When the MapReduce output file requires compression, you can change mapred.output.compress to True, Mapped.output.compression.codec the class name for the codec you want to use Yes, of course you can specify in the c

Original article link Mapreduce has gone through a thorough overhaul in the hadoop-0.23, and now we have a new framework called mapreduce2.0 (mrv2) or yarn. The basic concept of mrv2 is to split two main functions (resource management and Job Scheduling/monitoring) in jobtracker into separate daemon processes. The idea is to have a global resourcemaager (RM) and the applicationmaster (AM) corresponding to

/memory footprint, if two large memory consumption task is dispatched to a piece, it is easy to appear OOM. 4 at the Tasktracker end, the resource is forced to be divided into map task slot and reduce task slot, which can be a waste of resources when only a map task or a reduce task is available, which is the previously mentioned cluster resource benefit Use of the problem. 5 Source code Level analysis, you will find the code is very difficult to read, often because one class did too many thin

Reason:Hadoop-eclipse-plugin-2.7.3.jar compiled JDK versions are inconsistent with the JDK version used by Eclipse startup.Solution One :Modify the Myeclipse.ini file to resolve it. D:/java/myeclipse/common/binary/com.sun.java.jdk.win32.x86_1.6.0.013/jre/bin/client/jvm.dll to: D:/Program Files ( x86)/java/jdk1.7.0_45/jre/bin/client/jvm.dlljdk1.7.0_45 version of the JDK for your own installationIf it is not valid, check that the Hadoop version set in t

Configuring MapReduceconfiguration>configuration>Plus this.configuration> property > name>Mapreduce.framework.namename> value>Yarnvalue> Property >configuration>And then configure it inside the yarn-site.xml.configuration> -- property > name>Yarn.resourcemanager.hostnamename> value>Hadoop1value> Property > property > name>Yarn.nodemanager.aux-servicesname> value>Mapreduce_shufflevalue> Property > property > name>Yarn.n

(Firstpartitioner.class);//partition functionJob.setsortcomparatorclass (Keycomparator.class);//This course does not have custom sortcomparator, but instead uses Intpair's own sortJob.setgroupingcomparatorclass (Groupingcomparator.class);//Group functionJob.setmapoutputkeyclass (Intpair.class);Job.setmapoutputvalueclass (Intwritable.class);Job.setoutputkeyclass (Text.class);Job.setoutputvalueclass (Intwritable.class);Job.setinputformatclass (Textinputformat.class);Job.setoutputformatclass (Text

Tags: hadoop Business Logic In fact, it is very simple to input two files, one as the basic data (student information file) and the other is the score information file.Student Information File: stores student data, including student ID and Student name Score data: stores scores of students, including student IDs, subjects, and scores. We will use M/R to associate data based on student IDs. The final result is student name, subject, and score. Analog d