mapreduce tutorial

Hadoop is getting more and more hot, and the sub-projects around Hadoop are growing fast, with more than 10 of them listed on the Apache website, but original aim, most of the projects are based on Hadoop Common.MapReduce is the core of the core. So what exactly is MapReduce, and how does it work in particular?About its principle, said simple also simple, casually draw a picture to spray a map and reduce two stages seems to be over. But it also contai

Transferred from:http://www.cnblogs.com/forfuture1978/archive/2010/11/19/1882279.htmlTransfer note: Originally wanted in the Hadoop Learning Summary series detailed analysis HDFs and map-reduce, but find the information, found this article, and found that Caibinbupt has been the source code of Hadoop has been detailed analysis, recommended everyone read.Transfer from http://blog.csdn.net/HEYUTAO007/archive/2010/07/10/5725379.aspxReference:1 Caibinbupt Source Code Analysis http://caibinbupt.javae

1. mapreduce Mapreduce is a concept that is hard to understand or understand. It is hard to understand because it is really hard to learn and understand theoretically. It is easy to understand because, if you have run several mapreduce jobs on hadoop and learn a little about the working principle of hadoop, you will basically understand the concept of

Mapreduce Mapreduce is a programming model for data processing. The model is simple, yet not too simple to express useful programs in. hadoop can run mapreduce programs writtenIn various versions; In this chapter, we shall look at the same program expressed in Java, Ruby, Python, and C ++. most important, mapreduce pr

We know that if you want to run a mapreduce job on yarn, you only need to implement a applicationmaster component, and Mrappmaster is the implementation of MapReduce applicationmaster on yarn, It controls the execution of the Mr Job on yarn. So, one of the problems that followed was how Mrappmaster controlled the mapreduce operation on yarn, in other words, what

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 Source Reading environment MapReduce Programming Examples: MapReduce Programming Example (i)

article from my personal blog: MongoDB mapreduce Usage Summary As we all know, MongoDB is a non-relational database, that is, each table in the MongoDB database is independent, there is no dependency between the table and the table. In MongoDB, in addition to the various CRUD statements, we also provide aggregation and mapreduce statistics, this article mainly to talk about MongoDB's

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.mapreduce.Job; Import Org.apache.hadoop.mapreduc

Tags: MongoDB database mapreducearticle from my personal blog: MongoDB mapreduce use summary ?As we all know, MongoDB is a non-relational database, that is, each table in the MongoDB database is independent, there is no dependency between the table and the table. In MongoDB, in addition to the various CRUD statements, we also provide aggregation and mapreduce statistics, this article mainly to talk about Mo

This article is not intended for HDFS or MapReduce configuration, but for Hadoop development. The premise for development is to configure the development environment, that is, to obtain the source code and first to build smoothly. This article records the process of configuring eclipse to compile Hadoop source code on Linux (Ubuntu10.10. Which version of the source code should be used to develop Hadoop? One option is to track the latest source code sy

The shuffle process is the core of MapReduce, also known as the place where miracles occur. To understand mapreduce, shuffle must be understood. I have seen a lot of relevant information, but every time I read the foggy around, it is difficult to sort out the general logic, but the more stirred mixed. The first time in the work of the MapReduce job performance tu

The MapReduce in Hadoop is a simple software framework based on the applications it writes out to run on a large cluster of thousands of commercial machines, and to process terabytes of data in parallel in a reliable, fault-tolerant way.A MapReduce job (job) typically divides the input dataset into separate pieces of data that are processed by the map task in a parallel manner. The framework sorts the outpu

The shuffle process is the core of MapReduce, also known as the place where miracles occur. To understand mapreduce,shuffle, you have to understand. I have seen a lot of relevant information, but every time I read the foggy around, it is difficult to clarify the general logic, but the more confused. Front-end time in the work of the MapReduce job performance tuni

Sometimes we use it, but we don't know why. Just likeIt may have been natural for the apples to hit us, but Newton discovered the gravitational force of the Earth. OK, hopefully by understanding MapReduce, we can write better examples of MapReduce.Part I: How MapReduce works MapReduce Roleclient: Job submission initiator.Jobtracker: Initializes the job, allocates

Mapreduce: Google's Human Cannon The most authoritative introduction to mapreduce on the network is Jeffrey Dean.And Sanjay Ghemawat: mapreduce: Simpli ed data processing on large clustersYou can download it from labs.google.com. For companies such as Goole who need to analyze and process massive data, ordinary programming methods are not enough. So Google

PageRank algorithm has long been interested, but has always been the concept of contour, no specific in-depth study. To learn and summarize the examples of MapReduce recently, the PageRank algorithm was re-studied again and implemented based on MapReduce.1. What is PageRank?PageRank, page rank, right foot page level. Is named after the name of Larry Page, Google's founder. PageRank calculates the PageRank v

Hadoop written questions: Identify common friends of different people (consider data deduplication) Example: Zhang San: John Doe, Harry, Zhao Liu John Doe: Zhang San, tianqi, Harry The actual work, the data to reuse is still quite a lot of, including the empty value of the filter and so on, this article on data deduplication and inverted index detailed explanation. first, data deduplication [simulation of a carrier call detail to weight] The number of statistics data sets in the project, the sit

Use MultipleOutputs in MapReduce to output multiple files When you use Mapreduce, the part-* name is used by default. MultipleOutputs can output different key-value pairs to different custom files. The implementation process is to call output. write (key, new IntWritable (total), key. toString ()); The third parameter is public void write (KEYOUT key, VALUEOUT value, String baseOutputPath), which specifies

Why is mapreduce on HBase required? HBase itself does not provide a well-indexed two-level approach. If you directly use the Scan Direct scan provided by HBase, it will be very slow in large amounts of data.The HBase database can be manipulated using the MapReduce method. Hadoop MapReduce provides APIs that can be seamlessly connected to the HBase database.API Li

The mapreduce processing process is divided into two stages: Map stage and reduce stage. When you want to count the number of occurrences of all words in a specified file, In the map stage, each keyword is written to one row and separated by commas (,), and the initialization quantity is 1 (the map in the same word hadoop is automatically placed in one row) The reduce stage counts the frequency of occurrence of each word and writes it back. Such as c