What Is MapReduce?

In this era of Big Data and the rise of a new species called Data Scientist, us "mere" Software Developers might have heard of a thing called MapReduce, but what is it really? And why has it been a game-changing tool in the last decade? This is my attempt to explain the concept in simple terms.

To illustrate the concepts I will use Python, one of the most popular languages especially when it comes to data science, and LISP (in particular Clojure) to honor the language that introduced the concepts used for MapReduce.

So what is MapReduce? It is an approach to solve some problems that allow us to process huge amounts of data in a very simple way. It was described in a Google whitepaper in 2004, and it has been the basis for tools like Apache Hadoop, among others.

The old reliable way

But let's not lose ourselves with abstract descriptions and let us start from the very beginning - with a simple loop:

def powers(numbers):
  results = []
  for number in numbers:
    results.append(number * number)
  return results

Ok that's not very idiomatic, is it? Here is a better version:

def powers(numbers):
  return [number * number for number in numbers]

I'm sure everyone who can write the above code understands what is going on here: we have an array of some sorts and we cycle through the elements. For each of those elements we calculate the power of 2 and place that result in a new array.

It is not hard to see how this will work at a lower level, in the machine itself: the array is an area of memory, we start by pointing at the beginning this memory area, read the first value into the CPU register, multiply it by itself and store that value in another memory area that corresponds to the new array. Then proceed to the next element until the end of the array.

Since we are telling exactly what the computer should do, we call this way of writing programs imperative programming.

The other old reliable way

How would the same thing be written in LISP? It's not that complicated:

(defn powers [numbers] (map (fn [number] (* number number)) numbers))

Whoa! What's up with all the parentheses? Okay, let's break it down in easy-to-digest pieces, shall we?

(defn powers [numbers] ...)

Here we define a function named powers that takes an argument, numbers.

(fn [number] ...)

This creates an anonymous function that takes an argument number. In python you would accomplish the same with lambda number: ....

(* number number)

This looks a bit weird at first, but it just means that we multiply number by itself!

Now for the juicy part:

(map (fn [number] (* number number)) numbers)

Meet our new friend map! What this function does is simple: return a list of values that are the result of the function passed in as the first argument applied to every item of the list passed in as the second argument.

Or to put it differently, map will loop through every value in numbers, apply our function to each value, and return the result of this function in a new list. It is the equivalent of our for loop!

But let's reflect for a second on what happens at a lower level: from the code we see we actually can't know how this will play out! Will we walk through the memory in the same order as with the for loop? Or in reverse order? Or with some random order? We can't be sure because the detail of how to produce the result is not in our hands, but it is taken care by the implementation of our programming language.

This is what is called functional programming, which is a form of declarative programming: we state what we want, not how to produce our result. But why is this important? Is it just a matter of style and personal taste?

Thinking Big Data

Our example is small enough that the way we loop through the data doesn't matter. But what if instead we had a very big list of numbers, greater than what can be held in memory? What if instead of multiplying we had to perform a more CPU-intensive function? How would this play out?

This is a scenario where you might want to introduce some form of parallelism, to use all of your CPU cores. How would that look with our Python example?1

# To use Threads, we would use ThreadPoolExecutor
with concurrent.futures.ProcessPoolExecutor(max_workers=NUM_WORKERS) as executor:
  futures = [executor.submit(expensive_function, number) for number in numbers]
  concurrent.futures.wait(futures)
  results = [future.result() for future in futures]

The complexity went definitely up! And this is taking advantage of the concurrent.futures module, which simplifies already a lot of details.2

What about LISP?

Here it is:

(pmap expensive-function numbers)

The only relevant change is that we use pmap instead of map, and the only reason we have to change function at all is because it might not always be desirable to run the function in separate threads. That's not just a matter of programming styles or syntactic sugar, this is actually a very big deal!

Now let's extend the reasoning even further: what if we have so much data to process, maybe from a file of a few gigabytes or even terabytes, and we can't possibly process all of that on one computer. We have to distribute the load over hundreds or thousands of servers! How can we do that?

Distributing work

How could we split our work across various servers, instead of having it in separate threads or processes? This is where it starts to get hairy. We have to manage somehow our cluster of servers, ensure they are reachable, distribute the work, handle failures and retries, and ideally also scale them automatically. Seems complicated? Well it is, and since we are talking about a distributed system there are many ways things can fail and countless pitfalls to avoid. Distributed systems are hard to implement correctly.

So how would our code look like in Python? Honestly, I don't even want to start with it. It would be an immense effort to accomplish this and it would certainly not fit in this blog post. A possible idea could be to use Celery to enqueue the slices of computations and use that to distribute the load across different hosts.

How would it look in LISP? Theoretically, it doesn't need to change: we could have a dmap function that distributes work across multiple nodes. In practice it is a bit more complicated than that just because we would need to setup our cluster and control its behaviour.

I don't want to leave you without an example, though, so here is one written in Python, using a library called Scoop:

from scoop import futures

list(futures.map(expensive_function, numbers))

Can you tell from the code that this might actually run on multiple machines?

But what about reduce?

We talked at length about the map operation, but we haven't mentioned the reduce yet. What is it about?

I'm sure you encountered this operation quite a few times before, just look at this imperative approach to implement the operation:

def sum_nums(numbers):
    count = 0
    for number in numbers:
        count += number
    return count

You've done this or a variation of this a hundred times, I'm sure. It is often useful to turn an array into an hash map of some sort, for example this:

def group_by_id(customers):
    customers_map = {}
    for customer in customers:
        customers_map[customer.id] = customer
    return customers_map

How would this look in the functional world? Here it is:

(defn sum_nums [numbers]
  (reduce + 0 numbers))

Again the details are hidden and we only provide a combining operation (the + in our case), an initial value (0 which could be omitted in this particular case) and the collection.

What will happen is that it will take an element from the collection, combine it with our accumulator (that is the initial 0), use this combination as the new accumulator and proceed with the next element in our collection.

If we combine it with the map operation we can do some pretty powerful stuff:

(defn sum_squares [numbers]
  (reduce + (map * numbers)))

The imperative equivalent is this:

def sum_squares(numbers):
    final_sum = 0
    for number in numbers:
        final_sum += number * number
    return final_sum

MapReduce at last

Now that we clarified the building blocks and the concepts behind them, it is easy to understand what MapReduce is all about. Let's take the example straight from Google's Whitepaper, which will count the occurrences of the words in a text:

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);
  Emit(AsString(result));

We first define a map operation, that for each document it receives, it will split it into words and for each word emit a key/value pair, where the key is the word itself an the value is 1 .

The reduce function will then receive the key and the list of values that correspond to that key. In our case for each word we would receive a list of ones, one for each repetition of the word.

We then take all of those ones and we sum them together. This is then the value that we emit.

All of this actually happens in a distributed manner, but the code doesn't really need to know. How cool is that?

Is this the end?

Is that all there is to it? Mostly, yes. Of course there is much more to know, but the basic idea is that simple. From this idea other cool things were built, to the point that No one at Google uses MapReduce anymore!

I hope that this will allow you to understand better what the concepts are behind MapReduce and to have a general feeling about what it allows you to do. Between this and actually using it there are many more steps to make, but maybe now it looks less intimidating.

Was this helpful? Let me know in the comments!


  1. Due to the Global Interpreter Lock, we are actually spawning multiple processes here. Python threads are useless for CPU-intensive operations. [return]
  2. With the executors you can use executor.map, which is a functional approach to the problem. This highlights how powerful this approach really is. [return]

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