MapReduce and Hadoop:
How the data sharing is done in MapReduce? In MapReduce, whatever data set we have. We are going to store this data set in to HDFS or Hadoop distributed file system. It is a kind of file system which is provided and provisioned by Hadoop environment, Hadoop ecosystem. It is a kind of parallel processing database. Whatever operations you are going to do. Whatever the dataset you are going to use for performing. MapReduce programming that you are going to do through Hadoop distributed file system. All the data is first being inputted in to the HDFS. This is done with HDFS command. After that, this data is written on to be as HDFS data store. And from they can be red. Then you can perform right. operations on the different nodes of HDFS. Whenever you are going to make any query using MapReduce.
MapReduce with Large Data Set:
What is going to happen is your data set will be partition? If you're using Hadoop, it will be partition and your queries will be processed on four different modes. Then after the query is process, you get a result. This result will be further consolidated by a concept called as a reducer concept. You will be getting the output of the data. One advantage is that, it provides you with high availability because the data is highly available. So, It is replicated across different nodes. You can say sometimes that it is slow. Afterward, If you are using MapReduce for an app dataset which is very small in that kind of scenario. What you will notice? The output you get will take much more time than you can do it using a traditional normal program.
That kind of cases, MapReduce can be slow. But if you're going for a dataset which is very high. In case, you are using big data analytics, the dataset is very large. In this case, MapReduce will provide you with a much faster option.