"Google大数据论文：Google File System的设计与实现"

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Google公司的三篇著名论文包括"The Google File System"、"MapReduce: Simplified Data Processing on Large Clusters"和"Bigtable: A Distributed Storage System for Structured Data"。这些论文详细介绍了Google在大数据处理方面的重要工作和创新。其中，"The Google File System"由Sanjay Ghemawat、Howard Gobioff和Shun-Tak Leung撰写，介绍了Google设计和实现的可扩展分布式文件系统，用于大规模分布式数据密集型应用。该系统在廉价的通用硬件上实现了容错性，并为大量客户端提供高聚合性能。与先前的分布式文件系统共享许多相同的目标，但Google的设计受到对其他系统的观察和经验的驱动。这个论文提出了一种新的文件系统架构，通过对存储和数据处理的创新，实现了更高的容错性和性能。它为大规模数据处理应用提供了可靠的基础架构。 "MapReduce: Simplified Data Processing on Large Clusters"是由Jeffrey Dean和Sanjay Ghemawat撰写的论文，介绍了Google的MapReduce编程模型和实现。MapReduce提供了一种简化的数据处理模型，适用于大规模集群，能够高效地进行并行化数据处理。它将计算任务分解成一系列的map和reduce操作，并处理了任务调度、容错性等一系列的分布式系统设计问题。MapReduce的创新极大地简化了数据处理任务的开发和管理，成为了大数据处理的重要工具。 "Bigtable: A Distributed Storage System for Structured Data"由Fay Chang等人撰写，介绍了Google的Bigtable分布式存储系统。Bigtable是一种针对结构化数据的分布式存储系统，通过利用Google文件系统和MapReduce，在大规模集群上进行分布式存储和处理。它提供了高可用性、高扩展性的数据存储服务，并广泛应用于Google的各种应用程序中。Bigtable的创新在于使用了一种稀疏、分级的存储结构，以及高效的数据访问和计算模型，能够适用于各种类型的数据处理需求。这三篇论文详细介绍了Google在大数据处理方面的重要工作和创新。它们提出了一系列的分布式系统架构和编程模型，为大规模数据处理应用提供了可靠的基础设施。这些工作不仅在学术界产生了深远的影响，也在工业界推动了大数据处理技术和应用的发展。Google通过这些工作在大数据领域取得了重大成就，为整个行业树立了技术和创新的榜样。

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

into a set of M splits. The input splits can be pro-

cessed in parallel by different machines. Reduce invoca-

tions are distributed by partitioning the intermediate key

space into R pieces using a partitioning function (e.g.,

hash(key) mod R). The number of partitions (R) and

the partitioning function are speciﬁed by the user.

Figure 1 shows the overall ﬂow of a MapReduce op-

eration in our implementation. When the user program

calls the MapReduce function, the following sequence

of actions occurs (the numbered labels in Figure 1 corre-

spond to the numbers in the list below):

1. The MapReduce library in the user program ﬁrst

splits the input ﬁles into M pieces of typically 16

megabytes to 64 megabytes (MB) per piece (con-

trollable by the user via an optional parameter). It

then starts up many copies of the program on a clus-

ter of machines.

2. One of the copies of the program is special – the

master. The rest are workers that are assigned work

by the master. There are M map tasks and R reduce

tasks to assign. The master picks idle workers and

assigns each one a map task or a reduce task.

3. A worker who is assigned a map task reads the

contents of the corresponding input split. It parses

key/value pairs out of the input data and passes each

pair to the user-deﬁned Map function. The interme-

diate key/value pairs produced by the Map function

are buffered in memory.

4. Periodically, the buffered pairs are written to local

disk, partitioned into R regions by the partitioning

function. The locations of these buffered pairs on

the local disk are passed back to the master, who

is responsible for forwarding these locations to the

reduce workers.

5. When a reduce worker is notiﬁed by the master

about these locations, it uses remote procedure calls

to read the buffered data from the local disks of the

map workers. When a reduce worker has read all in-

termediate data, it sorts it by the intermediate keys

so that all occurrences of the same key are grouped

together. The sorting is needed because typically

many different keys map to the same reduce task. If

the amount of intermediate data is too large to ﬁt in

memory, an external sort is used.

6. The reduce worker iterates over the sorted interme-

diate data and for each unique intermediate key en-

countered, it passes the key and the corresponding

set of intermediate values to the user’s Reduce func-

tion. The output of the Reduce function is appended

to a ﬁnal output ﬁle for this reduce partition.

7. When all map tasks and reduce tasks have been

completed, the master wakes up the user program.

At this point, the MapReduce call in the user pro-

gram returns back to the user code.

After successful completion, the output of the mapre-

duce execution is available in the R output ﬁles (one per

reduce task, with ﬁle names as speciﬁed by the user).

Typically, users do not need to combine these R output

ﬁles into one ﬁle – they often pass these ﬁles as input to

another MapReduce call, or use them from another dis-

tributed application that is able to deal with input that is

partitioned into multiple ﬁles.

3.2 Master Data Structures

The master keeps several data structures. For each map

task and reduce task, it stores the state (idle, in-progress,

or completed), and the identity of the worker machine

(for non-idle tasks).

The master is the conduit through which the location

of intermediate ﬁle regions is propagated from map tasks

to reduce tasks. Therefore, for each completed map task,

the master stores the locations and sizes of the R inter-

mediate ﬁle regions produced by the map task. Updates

to this location and size information are received as map

tasks are completed. The information is pushed incre-

mentally to workers that have in-progress reduce tasks.

3.3 Fault Tolerance

Since the MapReduce library is designed to help process

very large amounts of data using hundreds or thousands

of machines, the library must tolerate machine failures

gracefully.

Worker Failure

The master pings every worker periodically. If no re-

sponse is received from a worker in a certain amount of

time, the master marks the worker as failed. Any map

tasks completed by the worker are reset back to their ini-

tial idle state, and therefore become eligible for schedul-

ing on other workers. Similarly, any map task or reduce

task in progress on a failed worker is also reset to idle

and becomes eligible for rescheduling.

Completed map tasks are re-executed on a failure be-

cause their output is stored on the local disk(s) of the

failed machine and is therefore inaccessible. Completed

reduce tasks do not need to be re-executed since their

output is stored in a global ﬁle system.

When a map task is executed ﬁrst by worker A and

then later executed by worker B (because A failed), all

To appear in OSDI 2004 4

workers executing reduce tasks are notiﬁed of the re-

execution. Any reduce task that has not already read the

data from worker A will read the data from worker B.

MapReduce is resilient to large-scale worker failures.

For example, during one MapReduce operation, network

maintenance on a running cluster was causing groups of

80 machines at a time to become unreachable for sev-

eral minutes. The MapReduce master simply re-executed

the work done by the unreachable worker machines, and

continued to make forward progress, eventually complet-

ing the MapReduce operation.

Master Failure

It is easy to make the master write periodic checkpoints

of the master data structures described above. If the mas-

ter task dies, a new copy can be started from the last

checkpointed state. However, given that there is only a

single master, its failure is unlikely; therefore our cur-

rent implementation aborts the MapReduce computation

if the master fails. Clients can check for this condition

and retry the MapReduce operation if they desire.

Semantics in the Presence of Failures

When the user-supplied map and reduce operators are de-

terministic functions of their input values, our distributed

implementation produces the same output as would have

been produced by a non-faulting sequential execution of

the entire program.

We rely on atomic commits of map and reduce task

outputs to achieve this property. Each in-progress task

writes its output to private temporary ﬁles. A reduce task

produces one such ﬁle, and a map task produces R such

ﬁles (one per reduce task). When a map task completes,

the worker sends a message to the master and includes

the names of the R temporary ﬁles in the message. If

the master receives a completion message for an already

completed map task, it ignores the message. Otherwise,

it records the names of R ﬁles in a master data structure.

When a reduce task completes, the reduce worker

atomically renames its temporary output ﬁle to the ﬁnal

output ﬁle. If the same reduce task is executed on multi-

ple machines, multiple rename calls will be executed for

the same ﬁnal output ﬁle. We rely on the atomic rename

operation provided by the underlying ﬁle system to guar-

antee that the ﬁnal ﬁle system state contains just the data

produced by one execution of the reduce task.

The vast majority of our map and reduce operators are

deterministic, and the fact that our semantics are equiv-

alent to a sequential execution in this case makes it very

easy for programmers to reason about their program’s be-

havior. When the map and/or reduce operators are non-

deterministic, we provide weaker but still reasonable se-

mantics. In the presence of non-deterministic operators,

the output of a particular reduce task R

is equivalent to

the output for R

produced by a sequential execution of

the non-deterministic program. However, the output for

a different reduce task R

may correspond to the output

for R

produced by a different sequential execution of

the non-deterministic program.

Consider map task M and reduce tasks R

and R

Let e(R

) be the execution of R

that committed (there

is exactly one such execution). The weaker semantics

arise because e(R

) may have read the output produced

by one execution of M and e(R

) may have read the

output produced by a different execution of M.

3.4 Locality

Network bandwidth is a relatively scarce resource in our

computing environment. We conserve network band-

width by taking advantage of the fact that the input data

(managed by GFS [8]) is stored on the local disks of the

machines that make up our cluster. GFS divides each

ﬁle into 64 MB blocks, and stores several copies of each

block (typically 3 copies) on different machines. The

MapReduce master takes the location information of the

input ﬁles into account and attempts to schedule a map

task on a machine that contains a replica of the corre-

sponding input data. Failing that, it attempts to schedule

a map task near a replica of that task’s input data (e.g., on

a worker machine that is on the same network switch as

the machine containing the data). When running large

MapReduce operations on a signiﬁcant fraction of the

workers in a cluster, most input data is read locally and

consumes no network bandwidth.

3.5 Task Granularity

We subdivide the map phase into M pieces and the re-

duce phase into R pieces, as described above. Ideally, M

and R should be much larger than the number of worker

machines. Having each worker perform many different

tasks improves dynamic load balancing, and also speeds

up recovery when a worker fails: the many map tasks

it has completed can be spread out across all the other

worker machines.

There are practical bounds on how large M and R can

be in our implementation, since the master must make

O(M + R) scheduling decisions and keeps O(M ∗ R)

state in memory as described above. (The constant fac-

tors for memory usage are small however: the O(M ∗ R)

piece of the state consists of approximately one byte of

data per map task/reduce task pair.)

To appear in OSDI 2004 5

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