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What is the MapReduce, please explain in simple terms?

+2 votes
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What is the MapReduce, please explain in simple terms?
posted Jul 27, 2016 by anonymous

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MapReduce is a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster.Conceptually similar approaches have been very well known since 1995 with the Message Passing Interface standard having reduce and scatter operations.
A MapReduce program is composed of a Map() procedure (method) that performs filtering and sorting (such as sorting students by first name into queues, one queue for each name) and a Reduce() method that performs a summary operation (such as counting the number of students in each queue, yielding name frequencies). The "MapReduce System" (also called "infrastructure" or "framework") orchestrates the processing by marshalling the distributed servers, running the various tasks in parallel, managing all communications and data transfers between the various parts of the system, and providing for redundancy and fault tolerance.
The model is inspired by the map and reduce functions commonly used in functional programming, although their purpose in the MapReduce framework is not the same as in their original forms. The key contributions of the MapReduce framework are not the actual map and reduce functions, but the scalability and fault-tolerance achieved for a variety of applications by optimizing the execution engine once. As such, a single-threaded implementation of MapReduce will usually not be faster than a traditional (non-MapReduce) implementation; any gains are usually only seen with multi-threaded implementations.The use of this model is beneficial only when the optimized distributed shuffle operation (which reduces network communication cost) and fault tolerance features of the MapReduce framework come into play. Optimizing the communication cost is essential to a good MapReduce algorithm.
MapReduce libraries have been written in many programming languages, with different levels of optimization. A popular open-source implementation that has support for distributed shuffles is part of Apache Hadoop. The name MapReduce originally referred to the proprietary Google technology, but has since been genericized. By 2014, Google was no longer using MapReduce as their primary Big Data processing model,and development on Apache Mahout had moved on to more capable and less disk-oriented mechanisms that incorporated full map and reduce capabilities.

answer Aug 8, 2016 by Manikandan J
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0 votes

I was trying to implement a Hadoop/Spark audit tool, but l met a problem that I can't get the input file location and file name. I can get username, IP address, time, user command, all of these info from hdfs-audit.log. But When I submit a MapReduce job, I can't see input file location neither in Hadoop logs or Hadoop ResourceManager.

Does hadoop have API or log that contains these info through some configuration ?If it have, what should I configure?

+2 votes
public class MaxMinReducer extends Reducer {
int max_sum=0; 
int mean=0;
int count=0;
Text max_occured_key=new Text();
Text mean_key=new Text("Mean : ");
Text count_key=new Text("Count : ");
int min_sum=Integer.MAX_VALUE; 
Text min_occured_key=new Text();

 public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
       int sum = 0;           

       for (IntWritable value : values) {
             sum += value.get();
             count++;
       }

       if(sum < min_sum)
          {
              min_sum= sum;
              min_occured_key.set(key);        
          }     


       if(sum > max_sum) {
           max_sum = sum;
           max_occured_key.set(key);
       }          

       mean=max_sum+min_sum/count;
  }

 @Override
 protected void cleanup(Context context) throws IOException, InterruptedException {
       context.write(max_occured_key, new IntWritable(max_sum));   
       context.write(min_occured_key, new IntWritable(min_sum));   
       context.write(mean_key , new IntWritable(mean));   
       context.write(count_key , new IntWritable(count));   
 }
}

Here I am writing minimum,maximum and mean of wordcount.

My input file :

high low medium high low high low large small medium

Actual output is :

high - 3------maximum

low - 3--------maximum

large - 1------minimum

small - 1------minimum

but i am not getting above output ...can anyone please help me?

+2 votes

Is it possible to run jobs on Hadoop in batch mode? I have 5 different datasets in HDFS and need to run the same MapReduce application on these datasets sets one after the other.

Right now I am doing it manually How can I automate this? How can I save the log of each execution in text files for later processing?

+3 votes

In KNN like algorithm we need to load model Data into cache for predicting the records.

Here is the example for KNN.

So if the model will be a large file say1 or 2 GB we will be able to load them into Distributed cache.

The one way is to split/partition the model Result into some files and perform the distance calculation for all records in that file and then find the min ditance and max occurance of classlabel and predict the outcome.

How can we parttion the file and perform the operation on these partition ?

ie 1 record  parttition1,partition2,.... 2nd record  parttition1,partition2,... 

This is what came to my thought. Is there any further way. Any pointers would help me.

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