how to write mapreduce program in hadoop

Write the MapReduce program to implement the Kmeans algorithm. Our idea may be1. centroid after the second iteration2. Map. Calculates the distance between each centroid and sample, obtains the centroid with the shortest distance from the sample, takes this centroid as the key, the sample as value, the output3. In reduce, the input key is the centroid, value is t

1.1 Chaining MapReduce jobs in a sequenceThe MapReduce program is capable of performing some complex data processing, typically by splitting the task tasks into smaller subtask, then each subtask is run through the job in Hadoop, and then the lesson plan subtask results are collected. Complete this complex task.The sim

(tokenizermapper.class); Job.setcombinerclass (intsumreducer.class); Job.setreducerclass (intsumreducer.class); Job.setoutputkeyclass (Text.class); Job.setoutputvalueclass (intwritable.class); Fileinputformat.addinputpath (Job,NewPath (args[0])); Fileoutputformat.setoutputpath (Job,NewPath (args[1])); System.exit (Job.waitforcompletion (true) ? 0:1); }}4. Compile Wordcount.java and package as Jar$ bin/WC. Jar Wordcount*.class5. Running the MapReduce

(Intsortreducer.class);Job.setoutputkeyclass (Intwritable.class);Job.setoutputvalueclass (Intwritable.class);Fileinputformat.addinputpath (Job, New Path (Args[0]));Fileoutputformat.setoutputpath (Job, New Path (Args[1]));Boolean success = Job.waitforcompletion (true);return success?0:1;}}Package com.wy.hadoop.sort;public class Jobmain {/*** @param args*/public static void Main (string[] args) {if (args.length==2) {New Thread (New Intsortjob (Args[0],args[1])). Start ();}}}7. Create 3 files loca

MapReduce is the core framework for completing data computing tasks in Hadoop1. MapReduce constituent Entities(1) Client node: The MapReduce program and the Jobclient instance object are run on this node, and the MapReduce job is submitted.(2) Jobtracker: Coordinated schedul

, and is pre-sorted for efficiency considerations.Each map task has a ring memory buffer that stores the output of the task. By default,Buffer size is 100MB, once the buffered content reaches the threshold (default is 80%), a background threadThe content is then written to a new overflow file in the disk-specified directory. In the process of writing to disk,The map output continues to be written to the buffer, but if the buffer is filled during this time, the map will block,Until the

intermediate files. A problem to be noted when playing jar packs is that when Maven runas is used, the resulting jar packages are all under Lib and only their contents in the current program's jar package. So you need to use a compression program to open the jar package, create a new lib directory inside it, and then put the jar package you need (Hadoop's jar pack) so that you can just put the jar package on the server and start. Since Java-classpath

Tags: hadoop mapreduceFirst, to print logs without using log4j, you can directly use system. Out. println. The log information output to stdout can be found at the jobtracker site.Second, if you use system. Out. println to print the log when the main function is started, you can see it directly on the console.Second, the jobtracker site is very important.Http: // your_name_node: 50030/jobtracker. jspNote: here we can see that map 100% is not necessari

Learn to sort by yourself and sort the two times with the following knowledge. Description of the serialization format for 1.Hadoop: Writable2.hadoop key sort logic 3. Full sort 4. How to customize your own writable Type 5. How to implement a two-order 1.hadoop serialization Format Description: Writable the first knowledge point you must know to understand and

map task, and then compare it to the assumed maximum value in turn, and then output the maximum value by using the cleanup method after all the reduce methods have been executed.The final complete code is as follows:View Code3.3 Viewing implementation results　　As you can see, our program has calculated the maximum value: 32767. Although the example is very simple, the business is very simple, but we introduced the idea of distributed computing, the u

Hadoop's automated distributed cache Distributedcache (the new version of the API) is often used in the write MapReduce program, but executes in eclipse under Windows, with an error similar to the following:2016-03-03 10:53:21,424 WARN [main] util. Nativecodeloader (nativecodeloader.java:2016-03-03 10:53:22,152 INFO [main] Configuration.deprecation (Configuration

emphasize the fulcrum of fast sequencing.2) HDFs is a file system with very asymmetric reading and writing performance. As far as possible the use of its high-performance characteristics of reading. Reduce reliance on write files and shuffle operations. For example, when data processing needs to be determined based on the statistics of the data. Dividing statistics and data processing into two rounds of map-reduce is much faster than combining statis

/bin/hadoop Fs-cat./OUT/PART-XXX (successfully running a mapreduce job) Note:(If error: Org.apache.hadoop.mapred.SafeModeException:JobTracker is in safe mode, turn off safety)Hadoop Dfsadmin-safemode LeaveHadoop2.8.1 Lab Environment Operation Sample algorithm Note:It looks like a mapreduce sample, such as a

Some of the pictures and text in this article come from HKU COMP7305 Cluster and Cloud Computing,professor:c.l.wang Hadoop Official Document: HTTP://HADOOP.APACHE.ORG/DOCS/R2.7.5/ Topology and hardware configuration First talk about the underlying structure of Hadoop, we are 4 people a group, each person a machine, install Xen, and then use Xen to open two VMs, is a total of 8 VMS, the configuration of the

System: Ubuntu14.04Hadoop version: 2.7.2Learn to run the first Hadoop program by referencing share in http://www.cnblogs.com/taichu/p/5264185.html.Create the input folder under the installation folder/usr/local/hadoop of Hadoop[Email protected]:/usr/local/hadoop$ mkdir./inpu

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

The previous article describedhadOOPone of the core contentHDFS, isHadoopDistributed Platform Foundation, and this speaks ofMapReduceis to make the best useHdfsdistributed, improved algorithm model for operational efficiency ,Map(Mapping)and theReduce (return to about)the two main stages areKey-value pairs as inputs and outputs, all we need to do is to,value>do the processing we want. Seemingly simple but troublesome, because it is too flexible. First, OK, Let's take a look at the two graphs be

(implementing the Writablecomparable interface or calling the Setsortcomparatorclass function). In this way, the result of reduce acquisition is first sorted by key, followed by the value of the results, it should be noted that the user needs to implement Paritioner, so that only according to key data division. Hadoop explicitly supports two-time sorting, and in the configuration class there is a Setgroupingcomparatorclass () method that can be used

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 o

(conf, new Path ("/bar"), Keyvaluetextinputformat.class, Mapclass2.class); Related articles June 27, 2014 optimization of a Hadoop program – implemented Combinefileinputformat based on the actual size of the file January 9, 2012 using Sequecefile+lzo format data in Hadoop MapReduce and Hive March 11, 2014