hadoop mapreduce example

: (K1, V1), List (K2, v2)Reduce (K2, List (v2)), list (K3, v3)Hadoop data type:The MapReduce framework supports only serialized classes that act as keys or values in the framework.Specifically, the class that implements the writable interface can be a value, and the class that implements the WritablecomparableThe keys are sorted in the reduce phase, and the values are simply passed.Classes that implement th

Looking at the trends in the industry's use of distributed systems and the long-term development of the Hadoop framework, MapReduce's jobtracker/tasktracker mechanism requires massive tweaks to fix its flaws in scalability, memory consumption, threading model, reliability, and performance. The Hadoop development team has done some bug fixes over the past few years, but the cost of these fixes has increased

. getnumreducetasks (); jobcontext context = New Jobcontext (job, jobid ); // Check whether the output directory exists. If yes, an error is returned. Org. Apache. hadoop. mapreduce. outputformat // Create the splits for the job Log. debug (" Creating splits "+ FS. makequalified (submitsplitfile )); Int Maps = writenewsplits (context, submitsplitfile ); /// Determine the split Information Job. Set (" Mapred

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

handle key first, the data corresponding to the key is divided into different partitions. In this way, the same value of first in key will be placed in the same reduce, then the second order in reduce C (code is not implemented, in fact, there is processing). Key comparison function class, Key's second order, is a comparator that inherits Writablecomparator. Setsortcomparatorclass can be implemented. Why not use Setsortcomparatorclass () is because of the

In general, we need to use small datasets to unit test the map and reduce functions we have written. Generally, we can use the Mockito framework to simulate the OutputCollector object (Hadoop version earlier than 0.20.0) and Context object (greater than or equal to 0.20.0 ). The following is a simple WordCount example: (using a new API) at the beginning In general, we need to use small datasets to unit test

Build a Hadoop cluster environment or stand-alone environment, and run the MapReduce process to get up1. Assume that the following environment variables have been configuredExport java_home=/usr/java/defaultexport PATH= $JAVA _home/bin: $PATHexport Hadoop_classpath = $JAVA _home/lib/tools.jar2. Create 2 test files and upload them to Hadoop HDFs[email protected] O

The command to run the MapReduce jar package is the Hadoop jar **.jar The command to run the jar package for the normal main function is Java-classpath **.jar Because I have not known the difference between the two commands, so I stubbornly use Java-classpath **.jar to start the MapReduce. Until today there are errors. Java-classpath **.jar is to make the jar pac

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 scheduling, master node, one

Reprint Please specify the Source: http://blog.csdn.net/lastsweetop/article/details/9187721 as input when the compressed file as a mapreduce input, MapReduce will automatically find the appropriate codec by extension to decompress it. as output when the output file of the MapReduce needs to be compressed, you can change mapred.output.compress to True, Mapped.

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

MapReduce is a computational model and a related implementation of an algorithmic model for processing and generating very large datasets. The user first creates a map function that processes a data set based on the key/value pair, outputs the middle of the data collection based on the Key/value pair, and then creates a reduce function that merges all intermediate value values with the same intermediate key value. The main two parts are the map proces

First run MapReduce, recorded several problems encountered, Hadoop cluster is CDH version, but my Windows local jar package is directly with hadoop2.6.0 version, and did not specifically look for CDH version of the1.Exception in thread "main" Java.lang.NullPointerException Atjava.lang.ProcessBuilder.startDownload Hadoop2 above version, in the Hadoop2 bin directory without Winutils.exe and Hadoop.dll, find t

Both Spark and Hadoop MapReduce are open-source cluster computing systems, but the scenarios for both are not the same. Among them, Spark is based on memory calculation, can be calculated by memory speed, optimize workload iteration process, speed up data analysis processing speed; Hadoop mapreduce processes data in ba

(Deletedataduplicationreducer.class);Job.setoutputkeyclass (Text.class);Job.setoutputvalueclass (Text.class); Fileinputformat.addinputpath (Job,new Path (otherargs[0]));Fileoutputformat.setoutputpath (Job,new Path (otherargs[1]));System.exit (Job.waitforcompletion (true)? 0:1);}} 3 Execution procedures For information on how to execute a program, you can refer to the implementation procedure in the article "Application II of the Hadoop

rules Job.setgroupingcomparatorclass (mygroupingcomparator. Class);(3) Now look at the results of the operation:Resources(1) Chao Wu, "in Layman's Hadoop": http://www.superwu.cn/(2) Suddenly, "Hadoop diary day18-mapreduce Sorting and Grouping": http://www.cnblogs.com/sunddenly/p/4009751.htmlZhou XurongSource: http://edisonchou.cnblogs.com/The copyright of thi