hadoop mapreduce example

Prerequisite Preparation: 1.hadoop installation is operating normally. Hadoop installation Configuration Please refer to: Ubuntu under Hadoop 1.2.1 Configuration installation 2. The integrated development environment is normal. Integrated development environment Configuration Please refer to: Ubuntu building Hadoop Sou

Prerequisite Preparation: 1.hadoop installation is operating normally. Hadoop installation Configuration Please refer to: Ubuntu under Hadoop 1.2.1 Configuration installation 2. The integrated development environment is normal. Integrated development environment Configuration Please refer to: Ubuntu building Hadoop Sou

Prerequisite Preparation: 1.hadoop installation is operating normally. Hadoop installation Configuration Please refer to: Ubuntu under Hadoop 1.2.1 Configuration installation 2. The integrated development environment is normal. Integrated development environment Configuration Please refer to: Ubuntu building Hadoop Sou

Prerequisite Preparation: 1.hadoop installation is operating normally. Hadoop installation Configuration Please refer to: Ubuntu under Hadoop 1.2.1 Configuration installation 2. The integrated development environment is normal. Integrated development environment Configuration Please refer to: Ubuntu building Hadoop Sou

Using python to write MapReduce functions -- Taking WordCount as an ExampleAlthough the Hadoop framework is written in java, the Hadoop program is not limited to java, but can be used in python, C ++, ruby, and so on. In this example, write a MapReduce instance using python

In this example, we write a MapReduce instance directly in Python: the word frequency of the words in the statistics input fileThe "trick" of using Python to write MapReduce is to use the API of the Hadoop stream to pass data between the map function and the reduce function via stdin (standard input), STDOUT (standard

Data de-weight * * *Target: Data that occurs more than once in the original data appears only once in the output file.Algorithm idea: According to the process characteristics of reduce, the input value set is calculated automatically according to key, and the data is output as key to reduce, no matter how many times the data appears, the key can only be output once in the final result of reduce.1. Each data in the instance represents a single line in the input file, and the map stage uses the

BlogThat Yi-Wipe smilecsdn Blog Address:http://blog.csdn.net/u012185296 itdog8 address link: http://www.itdog8.com/thread-203-1-1.html blog post title:Hbase-mapreduce-hbase As an example of an input source | That Yi-wipe smile Personality Signature:The furthest distance in the world is not the horizon, nor the cape, but I stand in front of you, but you do not feel my presenceTechnical Direction:Flume+kafk

BlogThat Yi-Wipe smilecsdn Blog Address:http://blog.csdn.net/u012185296 itdog8 address link: http://www.itdog8.com/thread-204-1-1.html blog post title:Hbase-mapreduce-hbase As an example of an output source | That Yi-wipe smile Personality Signature:The furthest distance in the world is not the horizon, nor the cape, but I stand in front of you, but you do not feel my presenceTechnical Direction:Flume+kaf

=" Wkiol1mnb53d9fosaafs3ffxzew450.jpg-wh_50 "/>Implementing the Reduce classThis class implements the reduce method in the Reducer interface, the key in the input parameter, and values is the intermediate result of the map task output.Values is a iterator, and traversing this iterator, you can get all the value that belongs to the same key.Here, key is a word, and value is the frequency of words. Just add all of the value and you can get the total number of occurrences of the word.650) this.widt

Hadoop version 1.2.1 Jdk1.7.0 Example 3-1: Use the urlstreamhandler instance to display files of the hadoop File System in standard output mode hadoop fs -mkdir input Create two files, file1, file2, and file1, as Hello world, and file2 as Hello hadoop, and then upload the f

Immediately after the completion of the installation and running of Hadoop, it is time to run the relevant example, and the simplest and most straightforward example is the HelloWorld-wordcount example. Follow the blog to run: http://xiejianglei163.blog.163.com/blog/static/1247276201443152533684/ First create a folde

name including the package path needs to be specified after ***.jar when running the Hadoop jar command For example, Hadoop jar/home/hadoop/documents/hadooptest.jar hadoop.test.maxtemperature/user/hadoop/temperature output ） 4 data that will be analyzed is sent to HDFs

after the debug analysis of the previous code, In the database where the map process is processed, hbase data needs to be placed under the Lib package of Hadoop when it is processed in MapReduce. The result of the processing, see view: Note that the key in the row in the HBase database cannot be the same, or the next one will overwrite the previous value. Need to keep it unique. Name1 and Score1 is a data

Today began to MapReduce design patterns this book on the MapReduce example, I think this book on learning MapReduce programming very well, the book finished, basically can meet the mapreduce problems can also be dealt with. Let's start with the first piece. This procedure i

can make the final reduce ... This step is quite simple, according to the content of each file to do a final statistic, the results are as follows:Reducer Node 1Company 2EMC 1is 4Reducer Node 2Lisa 1My 4Name 2Pivotal 1Tony 1That's it! We successfully counted the number of each word in the two files, and deposited them in two output files, the two output files are the legendary part-r-00000 and part-r-00001, look at the contents of two files, and then look back to the first partition, Should be

the long type in Java, similar to text ~ String, intwritable ~ Int, but the former has been optimized for serialization during network transmission. 2. Reduce stage Likewise, the four type parameters of the reducer class also specify the input (Key, value) type and output (Key, value) Type of the reducer task. The input type must match the output type of the Mapper task (text, intwritable in this example )). 1 import java.io.IOException; 2 import or

. mapreduce package (and sub-package. Earlier versions of APIs are stored in org. Apache. hadoop. mapred.3. The new API uses context object extensively and allows user code to communicate with the mapreduce system. For example, mapcontext basically acts as outputcollector and reporter of jobconf.4. The new API supports

#pi值示例hadoop Jar/app/cdh23502/share/hadoop/mapreduce2/hadoop-mapreduce-examples-2.3.0-cdh5.0.2. Jar PI - $#生成数据 The first parameter is the number of rows the second parameter is the location of the Hadoop jar/app/cdh23502/share/hadoop