Map Reduce

In 2004, engineers at Google introduced a new paradigm for large-scale parallel data processing known as MapReduce (see the original paper here, and make sure to look in the citations at the end). One key aspect of MapReduce is that it makes programming such tasks on large-scale clusters easy for developers; instead of worrying about how to manage parallelism, handle machine crashes, and many other complexities common within clusters of machines, the developer can instead just focus on writing little bits of code (described below) and the infrastructure handles the rest.

In this project, you'll be building a simplified version of MapReduce for just a single machine. While somewhat easier than with a single machine, there are still numerous challenges, mostly in building the correct concurrency support. Thus, you'll have to think a bit about how to build the MapReduce implementation, and then build it work efficiently and correctly.

There are three specific objectives to this assignment:

To learn bout the general nature of the MapReduce paradigm.
To implement a correct and efficient MapReduce framework using threads and related functions.
To gain more experience writing concurrent code.

Background

To understand how to make progress on this project, you should understand the basics of thread creation, mutual exclusion (with locks), and signaling/waiting (with condition variables). These are described in the following book chapters:

Read these chapters carefully in order to prepare yourself for this project.

General Idea

Let's now get into the exact code you'll have to build. The MapReduce infrastructure you will build supports the execution of user-defined Map() and Reduce() functions.

As from the original paper: "Map(), written by the user, takes an input pair and produces a set of intermediate key/value pairs. The MapReduce library groups together all intermediate values associated with the same intermediate key K and passes them to the Reduce() function."

"The Reduce() function, also written by the user, accepts an intermediate key K and a set of values for that key. It merges together these values to form a possibly smaller set of values; typically just zero or one output value is produced per Reduce() invocation. The intermediate values are supplied to the user's reduce function via an iterator."

A classic example, written here in pseudocode, shows how to count the number of occurrences of each word in a set of documents:

map(String key, String value):
  // key: document name
  // value: document contents
  for each word w in value:
    EmitIntermediate(w, "1");

reduce(String key, Iterator values):
  // key: a word
  // values: a list of counts
  int result = 0;
  for each v in values:
    result += ParseInt(v);
  print key, result;

What's fascinating about MapReduce is that so many different kinds of relevant computations can be mapped onto this framework. The original paper lists many examples, including word counting (as above), a distributed grep, a URL frequency access counters, a reverse web-link graph application, a term-vector per host analysis, and others.

What's also quite interesting is how easy it is to parallelize: many mappers can be running at the same time, and later, many reducers can be running at the same time. Users don't have to worry about how to parallelize their application; rather, they just write Map() and Reduce() functions and the infrastructure does the rest.

Details

We give you here mapreduce.h file that specifies exactly what you must build in your MapReduce library:

#ifndef __mapreduce_h__
#define __mapreduce_h__

// Various function pointers
typedef char *(*Getter)();
typedef void (*Mapper)(char *file_name);
typedef void (*Reducer)(char *key, Getter get_func, int partition_number);
typedef unsigned long (*Partitioner)(char *key, int num_buckets);

// Key functions exported by MapReduce
void MR_Emit(char *key, char *value);
unsigned long MR_DefaultHashPartition(char *key, int num_buckets);
void MR_Run(int argc, char *argv[],
            Mapper map, int num_mappers,
            Reducer reduce, int num_reducers,
            Partitioner partition);

The most important function is MR_Run, which takes the command line parameters of a given program, a pointer to a Map function (type Mapper, called map), the number of mapper threads your library should create (num_mappers), a pointer to a Reduce function (type Reducer, called reduce), the number of reducers (num_reducers), and finally, a pointer to a Partition function (partition, described below).

Thus, when a user is writing a MapReduce computation with your library, they will implement a Map function, implement a Reduce function, possibly implement a Partition function, and then call MR_Run(). The infrastructure will then create threads as appropriate and run the computation.

Here is a simple (but functional) wordcount program, written to use this infrastructure:

#include <assert.h>
#include <stdio.h>
#include <string.h>
#include "mapreduce.h"

void Map(char *file_name) {
    FILE *fp = fopen(file_name, "r");
    assert(fp != NULL);

    char *line = NULL;
    size_t size = 0;
    while (getline(&line, &size, fp) != -1) {
        char *token, *dummy = line;
        while ((token = strsep(&dummy, " \t\n\r")) != NULL) {
            MR_Emit(token, "1");
        }
    }

    fclose(fp);
}

void Reduce(char *key, Getter get_next, int partition_number) {
    int count = 0;
    char *value;
    while ((value = get_next(partition_number)) != NULL)
        count++;
    printf("%s %d\n", key, count);
}

int main(int argc, char *argv[]) {
    MR_Run(argc, argv, Map, 10, Reduce, 10, MR_DefaultHashPartition);
}

Let's walk through this code, in order to see what it is doing. First, notice that Map() is called with a file name. In general, we assume that this type of computation is being run over many files; each invocation of Map() is thus handed one file name and is expected to process that file in its entirety.

In this example, the code above just reads through the file, one line at a time, and uses strsep() to chop the line into tokens. Each token is then emitted using the MR_Emit() function, which takes two strings as input: a key and a value. The key here is the word itself, and the token is just a count, in this case, 1 (as a string). It then closes the file.

The MR_Emit() function is thus another key part of your library; it needs to take key/value pairs from the many different mappers and store them in a way that later reducers can access them, given constraints described below. Designing and implementing this data structure is thus a central challenge of the project.

After the mappers are finished, your library should have stored the key/value pairs in such a way that the Reduce() function can be called. Reduce() is invoked once per key, and is passed the key along with a function that enables iteration over all of the values that produced that same key. To iterate, the code just calls get_next() repeatedly until a NULL value is returned; get_next returns a pointer to the value passed in by the MR_Emit() function above.

Considerations

Memory Management. yyy.

Grading

Your code should turn in mapreduce.c which implements the above functions correctly and efficiently. It will be compiled with test applications with the -Wall -Werror -pthread -O flags; it will also be valgrinded to check for memory errors.

Your code will first be measured for correctness, ensuring that it performs the maps and reductions correctly. If you pass the correctness tests, your code will be tested for performance; higher performance will lead to better scores.

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