MapReduce_Simplified Data Processing on Large Clusters.pdf

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MapReduce: Simpliﬁed Data Processing on Large Clusters

Jeffrey Dean and Sanjay Ghemawat

jeff@google.com, sanjay@google.com

Google, Inc.

Abstract

MapReduce is a programming model and an associ-

ated implementation for processing and generating large

data sets. Users specify a map function that processes a

key/value pair to generate a set of intermediate key/value

pairs, and a reduce function that merges all intermediate

values associated with the same intermediate key. Many

real world tasks are expressible in this model, as shown

in the paper.

Programs written in this functional style are automati-

cally parallelized and executed on a large cluster of com-

modity machines. The run-time system takes care of the

details of partitioning the input data, scheduling the pro-

gram’s execution across a set of machines, handling ma-

chine failures, and managing the required inter-machine

communication. This allows programmers without any

experience with parallel and distributed systems to eas-

ily utilize the resources of a large distributed system.

Our implementation of MapReduce runs on a large

cluster of commodity machines and is highly scalable:

a typical MapReduce computation processes many ter-

abytes of data on thousands of machines. Programmers

ﬁnd the system easy to use: hundreds of MapReduce pro-

grams have been implemented and upwards of one thou-

sand MapReduce jobs are executed on Google’s clusters

every day.

1 Introduction

Over the past ﬁve years, the authors and many others at

Google have implemented hundreds of special-purpose

computations that process large amounts of raw data,

such as crawled documents, web request logs, etc., to

compute various kinds of derived data, such as inverted

indices, various representations of the graph structure

of web documents, summaries of the number of pages

crawled per host, the set of most frequent queries in a

given day, etc. Most such computations are conceptu-

ally straightforward. However, the input data is usually

large and the computations have to be distributed across

hundreds or thousands of machines in order to ﬁnish in

a reasonable amount of time. The issues of how to par-

allelize the computation, distribute the data, and handle

failures conspire to obscure the original simple compu-

tation with large amounts of complex code to deal with

these issues.

As a reaction to this complexity, we designed a new

abstraction that allows us to express the simple computa-

tions we were trying to perform but hides the messy de-

tails of parallelization, fault-tolerance, data distribution

and load balancing in a library. Our abstraction is in-

spired by the map and reduce primitives present in Lisp

and many other functional languages. We realized that

most of our computations involved applying a map op-

eration to each logical “record” in our input in order to

compute a set of intermediate key/value pairs, and then

applying a reduce operation to all the values that shared

the same key, in order to combine the derived data ap-

propriately. Our use of a functional model with user-

speciﬁed map and reduce operations allows us to paral-

lelize large computations easily and to use re-execution

as the primary mechanism for fault tolerance.

The major contributions of this work are a simple and

powerful interface that enables automatic parallelization

and distribution of large-scale computations, combined

with an implementation of this interface that achieves

high performance on large clusters of commodity PCs.

Section 2 describes the basic programming model and

gives several examples. Section 3 describes an imple-

mentation of the MapReduce interface tailored towards

our cluster-based computing environment. Section 4 de-

scribes several reﬁnements of the programming model

that we have found useful. Section 5 has performance

measurements of our implementation for a variety of

tasks. Section 6 explores the use of MapReduce within

Google including our experiences in using it as the basis

To appear in OSDI 2004 1

for a rewrite of our production indexing system. Sec-

tion 7 discusses related and future work.

2 Programming Model

The computation takes a set of input key/value pairs, and

produces a set of output key/value pairs. The user of

the MapReduce library expresses the computation as two

functions: Map and Reduce.

Map, written by the user, takes an input pair and pro-

duces a set of intermediate key/value pairs. The MapRe-

duce library groups together all intermediate values asso-

ciated with the same intermediate key I and passes them

to the Reduce function.

The Reduce function, also written by the user, accepts

an intermediate key I 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 val-

ues are supplied to the user’s reduce function via an iter-

ator. This allows us to handle lists of values that are too

large to ﬁt in memory.

2.1 Example

Consider the problem of counting the number of oc-

currences of each word in a large collection of docu-

ments. The user would write code similar to the follow-

ing pseudo-code:

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));

The map function emits each word plus an associated

count of occurrences (just ‘1’ in this simple example).

The reduce function sums together all counts emitted

for a particular word.

In addition, the user writes code to ﬁll in a mapreduce

speciﬁcation object with the names of the input and out-

put ﬁles, and optional tuning parameters. The user then

invokes the MapReduce function, passing it the speciﬁ-

cation object. The user’s code is linked together with the

MapReduce library (implemented in C++). Appendix A

contains the full program text for this example.

2.2 Types

Even though the previous pseudo-code is written in terms

of string inputs and outputs, conceptually the map and

reduce functions supplied by the user have associated

types:

map (k1,v1) → list(k2,v2)

reduce (k2,list(v2)) → list(v2)

I.e., the input keys and values are drawn from a different

domain than the output keys and values. Furthermore,

the intermediate keys and values are from the same do-

main as the output keys and values.

Our C++ implementation passes strings to and from

the user-deﬁned functions and leaves it to the user code

to convert between strings and appropriate types.

2.3 More Examples

Here are a few simple examples of interesting programs

that can be easily expressed as MapReduce computa-

tions.

Distributed Grep: The map function emits a line if it

matches a supplied pattern. The reduce function is an

identity function that just copies the supplied intermedi-

ate data to the output.

Count of URL Access Frequency: The map func-

tion processes logs of web page requests and outputs

hURL, 1i. The reduce function adds together all values

for the same URL and emits a hURL, total counti

pair.

Reverse Web-Link Graph: The map function outputs

htarget, sourcei pairs for each link to a target

URL found in a page named source. The reduce

function concatenates the list of all source URLs as-

sociated with a given target URL and emits the pair:

htarget, list(source)i

Term-Vector per Host: A term vector summarizes the

most important words that occur in a document or a set

of documents as a list of hword, frequencyi pairs. The

map function emits a hhostname, term vectori

pair for each input document (where the hostname is

extracted from the URL of the document). The re-

duce function is passed all per-document term vectors

for a given host. It adds these term vectors together,

throwing away infrequent terms, and then emits a ﬁnal

hhostname, term vectori pair.

To appear in OSDI 2004 2

User

Program

Master

(1) fork

worker

(1) fork

worker

(1) fork

(2)

assign

map

(2)

assign

reduce

split 0

split 1

split 2

split 3

split 4

output

file 0

(6) write

worker

(3) read

worker

(4) local write

Map

phase

Intermediate files

(on local disks)

worker

output

file 1

Input

files

(5) remote read

Reduce

phase

Output

files

Figure 1: Execution overview

Inverted Index: The map function parses each docu-

ment, and emits a sequence of hword, document IDi

pairs. The reduce function accepts all pairs for a given

word, sorts the corresponding document IDs and emits a

hword, list(document ID)i pair. The set of all output

pairs forms a simple inverted index. It is easy to augment

this computation to keep track of word positions.

Distributed Sort: The map function extracts the key

from each record, and emits a hkey, recordi pair. The

reduce function emits all pairs unchanged. This compu-

tation depends on the partitioning facilities described in

Section 4.1 and the ordering properties described in Sec-

tion 4.2.

3 Implementation

Many different implementations of the MapReduce in-

terface are possible. The right choice depends on the

environment. For example, one implementation may be

suitable for a small shared-memory machine, another for

a large NUMA multi-processor, and yet another for an

even larger collection of networked machines.

This section describes an implementation targeted

to the computing environment in wide use at Google:

large clusters of commodity PCs connected together with

switched Ethernet [4]. In our environment:

(1) Machines are typically dual-processor x86 processors

running Linux, with 2-4 GB of memory per machine.

(2) Commodity networking hardware is used – typically

either 100 megabits/second or 1 gigabit/second at the

machine level, but averaging considerably less in over-

all bisection bandwidth.

(3) A cluster consists of hundreds or thousands of ma-

chines, and therefore machine failures are common.

(4) Storage is provided by inexpensive IDE disks at-

tached directly to individual machines. A distributed ﬁle

system [8] developed in-house is used to manage the data

stored on these disks. The ﬁle system uses replication to

provide availability and reliability on top of unreliable

hardware.

(5) Users submit jobs to a scheduling system. Each job

consists of a set of tasks, and is mapped by the scheduler

to a set of available machines within a cluster.

3.1 Execution Overview

The Map invocations are distributed across multiple

machines by automatically partitioning the input data

To appear in OSDI 2004 3

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