What exactly is output of mapper and reducer function - hadoop

Question

This is a follow up question of Extracting rows containing specific value using mapReduce and hadoop
Mapper function
public static class MapForWordCount extends Mapper<Object, Text, Text, IntWritable>{
private IntWritable saleValue = new IntWritable();
private Text rangeValue = new Text();
public void map(Object key, Text value, Context con) throws IOException, InterruptedException
{
String line = value.toString();
String[] words = line.split(",");
for(String word: words )
{
if(words[3].equals("40")){
saleValue.set(Integer.parseInt(words[0]));
rangeValue.set(words[3]);
con.write( rangeValue , saleValue );
}
}
}
}
Reducer function
public static class ReduceForWordCount extends Reducer<Text, IntWritable, Text, IntWritable>
{
private IntWritable result = new IntWritable();
public void reduce(Text word, Iterable<IntWritable> values, Context con) throws IOException, InterruptedException
{
for(IntWritable value : values)
{
result.set(value.get());
con.write(word, result);
}
}
}
Output obtained is
40 105
40 105
40 105
40 105
EDIT 1 :
But the Expected output is
40 102
40 104
40 105
What am I doing wrong ?
What exactly is happening here in mapper and reducer function ?

Answer 1

In the context of the original question - you don't need the loop not in the mapper nor in the reducer as you are duplicating entries:
public static class MapForWordCount extends Mapper<Object, Text, Text, IntWritable>{
private IntWritable saleValue = new IntWritable();
private Text rangeValue = new Text();
public void map(Object key, Text value, Context con) throws IOException, InterruptedException
{
String line = value.toString();
String[] words = line.split(",");
if(words[3].equals("40")){
saleValue.set(Integer.parseInt(words[0]));
rangeValue.set(words[3]);
con.write(rangeValue , saleValue );
}
}
}
And in the reducer, as suggested by #Serhiy in the original question you need only one line of code:
public static class ReduceForWordCount extends Reducer<Text, IntWritable, Text, IntWritable>
{
private IntWritable result = new IntWritable();
public void reduce(Text word, Iterable<IntWritable> values, Context con) throws IOException, InterruptedException
{
con.write(word, null);
}
Regrading "Edit 1" - I will leave it a trivial practice :)

Answer 2

What exactly is happening
You are consuming lines of comma-delimited text, splitting the commas, and filtering out some values. con.write() should only be called once per line if all you are doing is extracting only those values.
The mapper will group all the "40" keys that you output and form a list of all the values that were written with that key. And that is what the reducer is reading over.
You should probably try this for your map function.
// Set the values to write
saleValue.set(Integer.parseInt(words[0]));
rangeValue.set(words[3]);
// Filter out only the 40s
if(words[3].equals("40")) {
// Write out "(40, safeValue)" words.length times
for(String word: words )
{
con.write( rangeValue , saleValue );
}
}
If you don't want duplicate values for the length of the split string, then get rid of the for loop.
All your reducer is doing is just printing out what it received from the mapper.

Answer 3

Mapper output would be something like this :
<word,count>
Reducer output would be like this :
<unique word, its total count>
Eg: A line is read and all words in it are counted and put in a <key,value> pair:
<40,1>
<140,1>
<50,1>
<40,1> ..
here 40,50,140, .. are all keys and the value is the count of number of occurrences of that key in a line. This happens in the mapper.
Then, these key,valuepairs are sent to the reducer where similar keys are all reduced to a single key and all the values associates with that key is summed to give a value to the key-value pair. So, the result of the reducer would be something like:
<40,10>
<50,5>
...
In your case, the reducer isn't doing anything. The unique values/words found by the mapper are just given out as the output.
Ideally, you are supposed to reduce & get an output like : "40,150" was found 5 times on the same line.