hadoop wordcount

Requirements: Count the number of occurrences of all the words in a file.Boilerplate: Hadoop hive hbase Hadoop hive in Word.log fileOutput: Hadoop 2Hive 2HBase 1MapReduce Design Method:First, the map process 1, the text file will be cut into 2, in the map () method to continue to divide a row of data into Second, reduce process 3, here will go through a series of

In the official WordCount program running Hadoop wrongJava.lang.classnotfoundexception:wordcount$tokenizermapperThe tip message is that the Tokenizermapper class could not be found, but the official of the program should be correct.Packaged on Linux to run, OK is not a program error.Then search on the internet, see someone said it might be the eclipse version of the reason, try it okThe version of Eclipse u

1. Introduction to the MapReduce theory1.1. MapReduce Programming ModeMapReduce uses the idea of "divide and conquer", distributes the operation of large data sets to a node under the management of a master node, and then obtains the final result by consolidating the intermediate results of each node. In short, MapReduce is "the decomposition of tasks and the aggregation of results".In Hadoop, there are two machine roles used to perform mapreduce task

DemandCalculates the frequency of each word in the file. The output results are ordered in alphabetical order by word. Each word and its frequency occupy one line, and there is a gap between the word and the frequency.For example, enter a file with the following contents:Hello WorldHello HadoopHello MapReducecorresponding to the input sample given above, the output sample is:Hadoop 1Hello 3MapReduce 1World 1Programme developmentFor this case, the following mapreduce schemes can be designed:1. Ma

Program Analysis 1, Wordcountmap class inherits org.apache.hadoop.mapreduce.mapper,4 generic type is the type of the map function input key, enter the type of value, output key type, output value type. 2. The Wordcountreduce class inherits the same org.apache.hadoop.mapreduce.reducer,4 generic type meaning as the map class. 3. The output type of map is the same as the input type of reduce, and in general, the output type of map is the same as the output type of reduce, so the input type of redu

HDFs = Mypath.getfilesystem (conf);//Get File systemif (Hdfs.isdirectory (MyPath)){//If this output path exists in the file system, delete theHdfs.delete (MyPath, true);} Job Wcjob = new Job (conf, "WC");//Build a Job object named TestanagramSet the jar package for the classes that are used by the entire jobWcjob.setjarbyclass (Wcrunner.class);Mapper and reducer classes used by this jobWcjob.setmapperclass (Wcmapper.class);Wcjob.setreducerclass (Wcreducer.class);Specify the output data kv type

The design idea of MapReduceThe main idea is divide and conquer (divide and conquer), divide and conquer the algorithm. It is a map process to divide a big problem into small problems and then execute them on each node in the cluster. After the map process is over, there is a ruduce process that brings together the results of all the map phase outputs. Steps to write a mapreduce program: 1. Turn the problem into a MapReduce model 2. Set parameters for the run 3. Write the Map Class 4. Write the

1. Program execution Command:Hadoop pipes-d hadoop.pipes.java.recordreader=true-d hadoop.pipes.java.recordwriter=true-input/input/wordcount/ Sample.txt-output/output/wordcount-program/bin/wordcount2, the specific code:#include #include "wordcount.h" Wordcountmapper::wordcountmapper (hadooppipes::taskcontext context) {}void Wordcountmapper::map (hadooppipes::mapcontext context) {int count = 1; string line =

Install SSH Hadoop uses SSH for communication. In this case, we need to set the password to null, that is, no password is required to log on. This eliminates the need to enter a secret during each communication. The installation is as follows: Enter "Y" for installation and wait for the automatic installation to complete. Start the service after installing SSH Run the following command to verify that the service is properly started: You can see

Word count is one of the simplest and most well-thought-capable programs, known as the MapReduce version of "Hello World", and the complete code for the program can be found in the Src/example directory of the Hadoop installation package. The main function of Word counting: count the number of occurrences of each word in a series of text files, as shown in. This blog will be through the analysis of WordCount