hadoop mapreduce tutorial

Overview: This is a brief introduction to the hadoop ecosystem, from its origins to relative application technical points: 1. hadoop core includes Common, HDFS and MapReduce; 2.Pig, Hbase, Hive, Zookeeper; 3. hadoop log analysis tool Chukwa; 4. problems solved by MR: massive input data, simple task division and cluster

First, the basic conceptIn MapReduce, an application that is ready to commit execution is called a job, and a unit of work that is divided from one job to run on each compute node is called a task. In addition, the Distributed File System (HDFS) provided by Hadoop is responsible for the data storage of each node and achieves high throughput data reading and writing.Hadoop is a master/slave (Master/slave) ar

: (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

Hadoop Streaming is a tool for Hadoop that allows users to write MapReduce programs in other languages, and users can perform map/reduce jobs simply by providing mapper and reducer For information, see the official Hadoop streaming document. 1, the following to achieve wordcount as an example, using C + + to write map

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

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 simplest is "order" executed. The programming mo

Tags: style blog http color using OS IO fileTransferred from: http://www.cnblogs.com/liqizhou/archive/2012/05/16/2503458.html http://www.cnblogs.com/ liqizhou/archive/2012/05/15/2501835.html This blog describes how mapreduce read relational database data, select the relational database for MySQL, because it is open source software, so we use more. Used to go to school without using open source software, directly with piracy, but also quite with

Hadoop mapreduce sequencing principle Hadoop Case 3 Simple problem----sorting data (Entry level)"Data Sorting" is the first work to be done when many actual tasks are executed,such as student performance appraisal, data indexing and so on. This example and data deduplication is similar to the original data is initially processed, for further data operations to la

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

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 use of M