Example of MapReduce Program

In this video we are discussing example of MapReduce program

So, here we are going to take a very common program that is a what count problem, which

is the Hallow Old Program in our MapReduce. So, let us go through the steps

The practical demonstration of what count problem. To run a Map �ase program, we need to start the Hadoop Demons first

So, please initialize the Hadoop Demons so that it can, you can run this

this MapReduce program under it. Create a copy a file into the SDFS and put the file inside

a separate directory in this case we are considering slash input directory. So here we are having

one file on which this MapReduce program will be working. The MapReduce will create another

directory in this case it will be slash output. We have mentioned this one you can change it

And in that directory, there will be a part file and the file will hold the final MapReduce

program output. So, in the output directory, the file will be there, which is a part file containing

the output of the MapReduce program and in the input folder will be having our respective file on which the MapReduce program will be working on that And for that purpose you are supposed to use the put command to put the file from the

Unique system to the SDFS. The WordCound program is one of the default programs and that comes with Hadoop itself

And we can use the JAR file to execute the MapReduce task accordingly

The syntax of using the jar file to start the map produce task will be like this

So, Hadoop, JAR, JAR path, class name and the respective parameters. In this case, the parameters are the input and the output directories are to be mentioned

in this particular command. So let us go for one practical demonstration for the easy understanding of this concept

Whenever we install Hadoop, always you know that HADU comes with the default one map produce

program one on the what count so this what count program we are going to execute right now we

can show you that how this particular what count program can get executed it comes with the

had do by default automatically so in that case we opening one terminal here so control alter t is a terminal we having we going for eco dollar home to show you the corresponding value of this variable system variable that is a home

so there is a slash home slash big data so eco dollar hadup install so this particular variable

is having the value the path is slash home slash big data slash hadoop so remember this one so now

what we are trying to do is that we shall execute our wordcon program on a on a certain file so now

here i'm going to show you the file here see i'm just going to show you the file so i'm just

opening this one so i'm at the home home means in which path that is a slash home slash big data

so that is a home so under this home we are having a hadub my files and under this hard of my files

we're having this student underscore info c sb and also so sample underscore file dot txd so we shall execute our map produce what can program on this sample

underscore file dot txd okay so we'll be executing on it so let me see the content of it so we shall go

for eco we shall go for cat rather so cat we can go for that is our home we can write this home in this way putting a dollar in front we can go for home and then slash there is a haduk my files and the name of

the file is sample file dot tx t okay so that is a way we have seen the content of the

file you say the same comment can also be written in this way the same command

instead of writing this home we can also put tilt symbol here so that will also do

the same others we can write into details that is a slash home big data so that is

another way to get the same file content so this is a content on which my

what count map produce program will be working now let me tell you where this

particular map produce program is is residing here so in that case I shall come to home

then I shall come to Hadoop remember this path home Hadoop then I shall go for share

home Hadoop share Hadoop again map reduce so home Hadoop share Hadoop MapReduce