mapreduce algorithm in hadoop

;import Org.apache.hadoop.mapreduce.lib.input.fileinputformat;import Org.apache.hadoop.mapreduce.lib.output.fileoutputformat;import Org.apache.hadoop.mapreduce.lib.output.textoutputformat;import org.apache.hadoop.mapreduce.lib.partition.hashpartitioner;/** * * Welcome everybody to discuss the study together! Useful Self-collection!Record and share, let you and I grow together!Welcome to see my other blogs;My personal blog: http://blog.caicongyang.com;My csdn Blog address: Http://blog.csdn.net/ca

Original: http://xiaoxia.org/2011/12/18/map-reduce-program-of-rmm-word-count-on-hadoop/Running a MapReduce program based on RMM Chinese word segmentation algorithm on Hadoop 23 repliesI know the title of this article is very "academic", very vulgar, people seem to be a very cow B or a very loaded paper! In fact, it is

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 the other sample, then again compute the clust

Hadoop Reading Notes series article:http://blog.csdn.net/caicongyang/article/category/21668551. Description:Finds the maximum value from the given file2. Code:Topapp.javaPackage Suanfa;import Java.io.ioexception;import Java.net.uri;import org.apache.hadoop.conf.configuration;import Org.apache.hadoop.fs.filesystem;import Org.apache.hadoop.fs.path;import Org.apache.hadoop.io.longwritable;import Org.apache.hadoop.io.nullwritable;import Org.apache.hadoop.

comment. It is also worth mentioning that snappy, which is developed by Google and open source compression algorithm, is the Cloudera official strongly advocated in mapreduce used in the compression algorithm. It is characterized by: in the case of similar compression rate as the Lzo file, the compression and decompression performance can also be greatly improve

Basic information of hadoop technology Insider: in-depth analysis of mapreduce architecture design and implementation principles by: Dong Xicheng series name: Big Data Technology series Publishing House: Machinery Industry Press ISBN: 9787111422266 Release Date: 318-5-8 published on: July 6,: 16 webpage:: Computer> Software and program design> distributed system design more about "

Preface A few weeks ago, when I first heard about the first two things about Hadoop and MapReduce, I was slightly excited to think they were mysterious, and the mysteries often brought interest to me, and after reading about their articles or papers, I felt that Hadoop was a fun and challenging technology. , and it also involved a topic I was more interested i

use and its use of restrictions3. Best practices for using Partitioner4, Hadoop built-in sorting algorithm analysis5. Custom sorting algorithm6. Hadoop built-in grouping algorithm7. Custom Grouping algorithm8. MapReduce Common scenario and algorithm implementation4th topic:

use and its use of restrictions3. Best practices for using Partitioner4, Hadoop built-in sorting algorithm analysis5. Custom sorting algorithm6. Hadoop built-in grouping algorithm7. Custom Grouping algorithm8. MapReduce Common scenario and algorithm implementation4th topic:

algorithms cannot be simply represented as one-step map-reduce operations. In this case, you need to carefully design the algorithm and divide it into multiple map-Reduce steps. The output results of the reduce stage in the previous step can be used as the input of the next map.. Many algorithms are iterative: An algorithm performs an Operation repeatedly before the termination condition is met. Iteration

The first 2 blog test of Hadoop code when the use of this jar, then it is necessary to analyze the source code. It is necessary to write a wordcount before analyzing the source code as follows Package mytest; Import java.io.IOException; Import Java.util.StringTokenizer; Import org.apache.hadoop.conf.Configuration; Import Org.apache.hadoop.fs.Path; Import org.apache.hadoop.io.IntWritable; Import Org.apache.hadoop.io.Text; Import Org.apache.hadoop.map

3.1 local Aggregation) In a data-intensive distributed processing environment, interaction of intermediate results is an important aspect of synchronization from processes that generate them to processes that consume them at the end. In a cluster environment, except for the embarrassing parallel problem, data must be transmitted over the network. In addition, in hadoop, the intermediate result is first written to the local disk and then sent over the

In Hadoop, data processing is resolved through the MapReduce job. Jobs consist of basic configuration information, such as the path of input files and output folders, which perform a series of tasks by the MapReduce layer of Hadoop. These tasks are responsible for first performing the map and reduce functions to conver

user data. After years of development, hadoop has become a popular data warehouse. Hammerbacher [68], talked about Facebook's building of business intelligence applications on Oracle databases, and later gave up, because he liked to use his own hadoop-based hive (now an open-source project ). Pig [114] is a platform built with hadoop for massive data analysis an

Hadoop's support for compressed files Hadoop supports transparent identification of compression formats, and execution of our mapreduce tasks is transparent. hadoop can automatically decompress the compressed files for us without worrying about them. If the compressed file has an extension (such as lzo, GZ, and Bzip2) of the corresponding compression format,

Algorithm detailed explanation> How to implement PageRank algorithm with MapReduceIntroduction to Hive> Hive's architecture> CLI, Hive Server, Hwi Introduction> Configure hive to use MySQL to store meta data> Basic use of the CLI+, hive App-Search tips (1)> Tomcat Log Parsing> Using regular expressions to parse the Tomcat log> Using regular Expressions in queriesHive Application-Search tips (2)> Calling Py

level of fault tolerance and is designed to be deployed on inexpensive (low-cost) hardware, and it provides high throughput (hi throughput) to access application data for applications with very large datasets (large data set). HDFs relaxes the requirements of (relax) POSIX and can access data in a stream (streaming access) file system. The core design of the Hadoop framework is: HDFs and MapReduce. HDFS pr

Apache Hadoop yarn:moving beyond MapReduce and Batch processing with Apache Hadoop 2Apache Hadoop yarn:moving beyond MapReduce and Batch processing with Apache Hadoop 2. mobi:http://www.t00y.com/file/7949 7801Apache

description of the Status message, especially the Counter) attribute check. The transfer process of status update in the MapReduce system is as follows: F. job completion When JobTracker receives the message that the last Task of the Job is completed, it sets the Job status to "complete". After JobClient knows it, it returns the result from the runJob () method. 2). Yarn (MapReduce 2.0) Yarn is available

This section mainly analyzes the principles and processes of mapreduce. Complete release directory of "cloud computing distributed Big Data hadoop hands-on" Cloud computing distributed Big Data practical technology hadoop exchange group:312494188Cloud computing practices will be released in the group every day. welcome to join us! You must at least know