Below, I'll provide a brief description of how you might approach a MapReduce word count task in each of the requested languages without translating the exact Java code.
JavaScript
In JavaScript, you could use a library like Hadoop-Streaming to run MapReduce jobs. A possible mapper and reducer written in Node.js might look like:
Mapper
1process.stdin.on('data', function(chunk) {
2 chunk.toString().split(/\s+/).forEach(word => {
3 console.log(word + "\t1");
4 });
5});Reducer
1var counts = {};
2process.stdin.on('data', function(chunk) {
3 chunk.toString().trim().split("\n").forEach(line => {
4 var parts = line.split("\t");
5 var word = parts[0];
6 var count = parseInt(parts[1]);
7 counts[word] = (counts[word] || 0) + count;
8 });
9});
10
11process.stdin.on('end', function() {
12 for (var word in counts) {
13 console.log(word + "\t" + counts[word]);
14 }
15});Python
Python provides Hadoop Streaming for MapReduce. You could write your mapper and reducer like:
Mapper
1#!/usr/bin/env python
2import sys
3for line in sys.stdin:
4 words = line.strip().split()
5 for word in words:
6 print(f"{word}\t1")Reducer
1#!/usr/bin/env python
2import sys
3
4current_word = None
5current_count = 0
6word = None
7
8for line in sys.stdin:
9 line = line.strip()
10 word, count = line.split('\t', 1)
11 count = int(count)
12 if current_word == word:
13 current_count += count
14 else:
15 if current_word:
16 print(f"{current_word}\t{current_count}")
17 current_word = word
18 current_count = count
19
20if current_word == word:
21 print(f"{current_word}\t{current_count}")C++
C++ doesn't have a direct equivalent for Hadoop MapReduce, but you could use MPI (Message Passing Interface) for parallel processing or find a specific library tailored for distributed computing.
Go
Similarly, Go doesn't have a direct MapReduce framework like Hadoop for Java. However, you could build a distributed system using Go's native concurrency features or use a library like Glow for MapReduce-style processing.
Please note that these examples are quite high-level and may require additional code, configuration, and dependencies to run in a real distributed environment.
