understanding mapreduce

The shuffle process is the core of mapreduce, also known as a miracle. To understand mapreduce, shuffle must be understood. I have read a lot of related materials, but every time I read them, it is difficult to clarify the general logic, but it is more and more confusing. Some time ago, the output preprocessing of mahout requires in-depthCodeAfter studying the running mechanism of

The shuffle process is the core of mapreduce, also known as a miracle. To understand mapreduce, shuffle must be understood. I have read a lot of related materials, but every time I read them, it is difficult to clarify the general logic, but it is more and more confusing. Some time ago, when I was doing mapreduce job performance tuning, I needed to go deep into t

Transferred from:http://blog.csdn.net/Androidlushangderen/article/details/41051027After analyzing the Redis source code for a period of time, I am about to start the next technical learning journey, the technology is currently very hot Hadoop, but a hadoop ecosystem is very large, so first of all my intention is to select one of the modules, to learn, research, I chose MapReduce. MapReduce was first develop

;combiner->merge->reducer->hdfs1, HDFs input data is divided into split, is read by the Mapper class,2, mapper read the data, the task is partion (allocated)3, if the map operation memory overflow, need to spill (overflow) to disk4, Mapper to do sort (sort) operation5. Combine (merge key) operation after sorting, can be understood as local mode reduce6, combine will also be the overflow file merge (merge)7, all tasks completed after the data to reducer for processing, processing completed writin

file path Fileinputformat.addinputpath (Job, New Path (otherargs[0)); Set the output file path: Fileoutputformat.setoutputpath (Job, New Path (otherargs[1]); Wait for the program to run Complete: System.exit (Job.waitforcompletion (true) 0:1); You can see that main just started a job, Then set the job-related parameters to implement MapReduce is the Mapper class and Reducer class. The map function in the Tokenizermapper class splits a row into This

take a look at the specific program: In the main function, the first job is a job object, job = new job (Conf, "Word Count"); then, set mapperclass and reducerclass of the job, and set fileinputformat in the input file path. addinputpath (job, new path (otherargs [0]); set the output file path: fileoutputformat. setoutputpath (job, new path (otherargs [1]); wait until the program runs successfully: system. exit (job. waitforcompletion (true )? 0: 1). It can be seen that the main only starts a

to solve big computing problems. The idea of "divide and conquer" is the core of the understanding of MapReduce, and we use a few money scenarios to explain the idea of "divide and conquer". With a table full of 100, 50, and 20 banknotes, how can we quickly know how much money this table has? The usual practice is to ask 103 people, 100 of whom put the banknotes in front of them in the denominations of 1

of the file, parse to key, value pair 2. Before reduce, there is a shuffle process to merge and sort the output of multiple map tasks3, write the reduce function's own logic, the input key, value processing, converted to a new key, value output4. Save the output of reduce to a fileThe above is an understanding of the work flow of mapreduce after I finished my study, and the WordCount algorithm is implement

Wen/VincentzhOriginal link: http://www.cnblogs.com/vincentzh/p/6055850.htmlLast weekend was supposed to write this, the result did not think of last weekend, their environment did not set up, the operation of the problem, dragged until Monday to solve the problem. Just this week will also review the content of the previous reading, while reviewing the code understanding, the impression is very deep, to see the things understand also more deeply.Catalo

Original link: http://www.cnblogs.com/vincentzh/p/6055850.htmlLast weekend was supposed to write this, the result did not think of last weekend, their environment did not set up, the operation of the problem, dragged until Monday to solve the problem. Just this week will also review the content of the previous reading, while reviewing the code understanding, the impression is very deep, to see the things understand also more deeply.Directory

Absrtact: MapReduce is another core module of Hadoop, from what MapReduce is, what mapreduce can do and how MapReduce works. MapReduce is known in three ways. Keywords: Hadoop MapReduce distributed processing In the face of big da

Abstract: MapReduce is another core module of Hadoop. It understands MapReduce from three aspects: What MapReduce is, what MapReduce can do, and how MapReduce works. Keywords: Hadoop MapReduce Distributed Processing In the face of

MongoDB is a large data environment for the birth of a large amount of data to save the relational database, for a large number of data, how to do statistical operations is very important, then how to count some data from the MongoDB? In MongoDB, we provide three ways of aggregating data: (1) Simple user aggregation function; (2) using aggregate for statistics; (3) using MapReduce for statistics; Today we first talk about how

Legends of the rivers and lakes: Google technology has "three treasures", GFS, MapReduce and Big Table (BigTable)!Google has published three influential articles in the past 03-06 years, namely the gfs,04 of the 03 Sosp osdi, and 06 Osdi bigtable. Sosp and OSDI are top conferences in the field of operating systems and belong to Class A in the Computer Academy referral Conference. SOSP is held in singular years, and OSDI is held in even-numbered years.

MapReduce and Spark compare the current big data processing can be divided into the following three types:1, complex Batch data processing (Batch data processing), the usual time span of 10 minutes to a few hours;2, based on the historical Data Interactive query (interactive query), the usual time span of 10 seconds to a few minutes;3, data processing based on real-time data stream (streaming data processing), the usual time span of hundreds of millis

about how the MapReduce program runs on yarn memory allocation has always been a let me circle of things, alone to check any information can not be well understood. So, recently looked up a lot of information, comprehensive explanations, finally understand a relatively clear degree, here will understand the things to make a simple record, in case of forgetting.First, paste the parameters about the memory allocation of