第1页 / 共13页
第2页 / 共13页
第3页 / 共13页
第4页 / 共13页
第5页 / 共13页
第6页 / 共13页
第7页 / 共13页
第8页 / 共13页
Google三篇论文-MapReduce英文版.pdf
MapReduce: Simplied 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
nd 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 ve 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 nish 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-
specied 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 renements 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 t 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 ll in a mapreduce
specication object with the names of the input and out-
put les, and optional tuning parameters. The user then
invokes the MapReduce function, passing it the speci-
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
reduce (k2,list(v2)) ! list(v2)
(k1,v1)
! list(k2,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-dened 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; f requencyi 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 nal
hhostname; term vectori pair.
To appear in OSDI 2004
2
User
Program
(1) fork
(1) fork
(1) fork
Master
(2)
assign
reduce
(2)
assign
map
worker
(3) read
worker
(4) local write
(5) remote read
worker
worker
(6) write
output
file 0
output
file 1
split 0
split 1
split 2
split 3
split 4
Input
files
worker
Map
phase
Intermediate files
(on local disks)
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.
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 le
system [8] developed in-house is used to manage the data
stored on these disks. The le 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
This section describes an implementation targeted
to the computing environment in wide use at Google:
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 specied by the user.
Figure 1 shows the overall ow 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 rst
splits the input les 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-dened 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 notied 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 t 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 nal output le 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 les (one per
reduce task, with le names as specied by the user).
Typically, users do not need to combine these R output
les into one le they often pass these les 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 les.
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 le 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 le 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 le system.
When a map task is executed rst 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 notied 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 les. A reduce task
produces one such le, and a map task produces R such
les (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 les 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 les in a master data structure.
When a reduce task completes, the reduce worker
atomically renames its temporary output le to the nal
output le. If the same reduce task is executed on multi-
ple machines, multiple rename calls will be executed for
the same nal output le. We rely on the atomic rename
operation provided by the underlying le system to guar-
antee that the nal le 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 R1 is equivalent to
the output for R1 produced by a sequential execution of
the non-deterministic program. However, the output for
a different reduce task R2 may correspond to the output
for R2 produced by a different sequential execution of
the non-deterministic program.
Consider map task M and reduce tasks R1 and R2.
Let e(Ri) be the execution of Ri that committed (there
is exactly one such execution). The weaker semantics
arise because e(R1) may have read the output produced
by one execution of M and e(R2) 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
le 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 les 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 signicant 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
Furthermore, R is often constrained by users because
the output of each reduce task ends up in a separate out-
put le. In practice, we tend to choose M so that each
individual task is roughly 16 MB to 64 MB of input data
(so that the locality optimization described above is most
effective), and we make R a small multiple of the num-
ber of worker machines we expect to use. We often per-
form MapReduce computations with M = 200; 000 and
R = 5; 000, using 2,000 worker machines.
3.6 Backup Tasks
One of the common causes that lengthens the total time
taken for a MapReduce operation is a straggler: a ma-
chine that takes an unusually long time to complete one
of the last few map or reduce tasks in the computation.
Stragglers can arise for a whole host of reasons. For ex-
ample, a machine with a bad disk may experience fre-
quent correctable errors that slow its read performance
from 30 MB/s to 1 MB/s. The cluster scheduling sys-
tem may have scheduled other tasks on the machine,
causing it to execute the MapReduce code more slowly
due to competition for CPU, memory, local disk, or net-
work bandwidth. A recent problem we experienced was
a bug in machine initialization code that caused proces-
sor caches to be disabled: computations on affected ma-
chines slowed down by over a factor of one hundred.
We have a general mechanism to alleviate the prob-
lem of stragglers. When a MapReduce operation is close
to completion, the master schedules backup executions
of the remaining in-progress tasks. The task is marked
as completed whenever either the primary or the backup
execution completes. We have tuned this mechanism so
that it typically increases the computational resources
used by the operation by no more than a few percent.
We have found that this signicantly reduces the time
to complete large MapReduce operations. As an exam-
ple, the sort program described in Section 5.3 takes 44%
longer to complete when the backup task mechanism is
disabled.
4 Renements
Although the basic functionality provided by simply
writing Map and Reduce functions is sufcient for most
needs, we have found a few extensions useful. These are
described in this section.
4.1 Partitioning Function
The users of MapReduce specify the number of reduce
tasks/output les that they desire (R). Data gets parti-
tioned across these tasks using a partitioning function on
the intermediate key. A default partitioning function is
provided that uses hashing (e.g. hash(key) mod R).
This tends to result in fairly well-balanced partitions. In
some cases, however, it is useful to partition data by
some other function of the key. For example, sometimes
the output keys are URLs, and we want all entries for a
single host to end up in the same output le. To support
situations like this, the user of the MapReduce library
can provide a special partitioning function. For example,
using hash(Hostname(urlkey)) mod R as the par-
titioning function causes all URLs from the same host to
end up in the same output le.
4.2 Ordering Guarantees
We guarantee that within a given partition, the interme-
diate key/value pairs are processed in increasing key or-
der. This ordering guarantee makes it easy to generate
a sorted output le per partition, which is useful when
the output le format needs to support efcient random
access lookups by key, or users of the output nd it con-
venient to have the data sorted.
4.3 Combiner Function
In some cases, there is signicant repetition in the inter-
mediate keys produced by each map task, and the user-
specied Reduce function is commutative and associa-
tive. A good example of this is the word counting exam-
ple in Section 2.1. Since word frequencies tend to follow
a Zipf distribution, each map task will produce hundreds
or thousands of records of the form . All of
these counts will be sent over the network to a single re-
duce task and then added together by the Reduce function
to produce one number. We allow the user to specify an
optional Combiner function that does partial merging of
this data before it is sent over the network.
The Combiner function is executed on each machine
that performs a map task. Typically the same code is used
to implement both the combiner and the reduce func-
tions. The only difference between a reduce function and
a combiner function is how the MapReduce library han-
dles the output of the function. The output of a reduce
function is written to the nal output le. The output of
a combiner function is written to an intermediate le that
will be sent to a reduce task.
Partial combining signicantly speeds up certain
classes of MapReduce operations. Appendix A contains
an example that uses a combiner.
4.4 Input and Output Types
The MapReduce library provides support for reading in-
put data in several different formats. For example, text
To appear in OSDI 2004
6
mode input treats each line as a key/value pair: the key
is the offset in the le and the value is the contents of
the line. Another common supported format stores a
sequence of key/value pairs sorted by key. Each input
type implementation knows how to split itself into mean-
ingful ranges for processing as separate map tasks (e.g.
text mode’s range splitting ensures that range splits oc-
cur only at line boundaries). Users can add support for a
new input type by providing an implementation of a sim-
ple reader interface, though most users just use one of a
small number of predened input types.
A reader does not necessarily need to provide data
read from a le. For example, it is easy to dene a reader
that reads records from a database, or from data struc-
tures mapped in memory.
In a similar fashion, we support a set of output types
for producing data in different formats and it is easy for
user code to add support for new output types.
4.5 Side-effects
In some cases, users of MapReduce have found it con-
venient to produce auxiliary les as additional outputs
from their map and/or reduce operators. We rely on the
application writer to make such side-effects atomic and
idempotent. Typically the application writes to a tempo-
rary le and atomically renames this le once it has been
fully generated.
We do not provide support for atomic two-phase com-
mits of multiple output les produced by a single task.
Therefore, tasks that produce multiple output les with
cross-le consistency requirements should be determin-
istic. This restriction has never been an issue in practice.
4.6 Skipping Bad Records
Sometimes there are bugs in user code that cause the Map
or Reduce functions to crash deterministically on certain
records. Such bugs prevent a MapReduce operation from
completing. The usual course of action is to x the bug,
but sometimes this is not feasible; perhaps the bug is in
a third-party library for which source code is unavail-
able. Also, sometimes it is acceptable to ignore a few
records, for example when doing statistical analysis on
a large data set. We provide an optional mode of execu-
tion where the MapReduce library detects which records
cause deterministic crashes and skips these records in or-
der to make forward progress.
Each worker process installs a signal handler that
catches segmentation violations and bus errors. Before
invoking a user Map or Reduce operation, the MapRe-
duce library stores the sequence number of the argument
in a global variable. If the user code generates a signal,
the signal handler sends a last gasp UDP packet that
contains the sequence number to the MapReduce mas-
ter. When the master has seen more than one failure on
a particular record, it indicates that the record should be
skipped when it issues the next re-execution of the corre-
sponding Map or Reduce task.
4.7 Local Execution
Debugging problems in Map or Reduce functions can be
tricky, since the actual computation happens in a dis-
tributed system, often on several thousand machines,
with work assignment decisions made dynamically by
the master. To help facilitate debugging, proling, and
small-scale testing, we have developed an alternative im-
plementation of the MapReduce library that sequentially
executes all of the work for a MapReduce operation on
the local machine. Controls are provided to the user so
that the computation can be limited to particular map
tasks. Users invoke their program with a special ag and
can then easily use any debugging or testing tools they
nd useful (e.g. gdb).
4.8 Status Information
The master runs an internal HTTP server and exports
a set of status pages for human consumption. The sta-
tus pages show the progress of the computation, such as
how many tasks have been completed, how many are in
progress, bytes of input, bytes of intermediate data, bytes
of output, processing rates, etc. The pages also contain
links to the standard error and standard output les gen-
erated by each task. The user can use this data to pre-
dict how long the computation will take, and whether or
not more resources should be added to the computation.
These pages can also be used to gure out when the com-
putation is much slower than expected.
In addition, the top-level status page shows which
workers have failed, and which map and reduce tasks
they were processing when they failed. This informa-
tion is useful when attempting to diagnose bugs in the
user code.
4.9 Counters
The MapReduce library provides a counter facility to
count occurrences of various events. For example, user
code may want to count total number of words processed
or the number of German documents indexed, etc.
To use this facility, user code creates a named counter
object and then increments the counter appropriately in
the Map and/or Reduce function. For example:
To appear in OSDI 2004
7
Counter* uppercase;
uppercase = GetCounter("uppercase");
map(String name, String contents):
for each word w in contents:
if (IsCapitalized(w)):
uppercase->Increment();
EmitIntermediate(w, "1");
The counter values from individual worker machines
are periodically propagated to the master (piggybacked
on the ping response). The master aggregates the counter
values from successful map and reduce tasks and returns
them to the user code when the MapReduce operation
is completed. The current counter values are also dis-
played on the master status page so that a human can
watch the progress of the live computation. When aggre-
gating counter values, the master eliminates the effects of
duplicate executions of the same map or reduce task to
avoid double counting. (Duplicate executions can arise
from our use of backup tasks and from re-execution of
tasks due to failures.)
Some counter values are automatically maintained
by the MapReduce library, such as the number of in-
put key/value pairs processed and the number of output
key/value pairs produced.
Users have found the counter facility useful for san-
ity checking the behavior of MapReduce operations. For
example, in some MapReduce operations, the user code
may want to ensure that the number of output pairs
produced exactly equals the number of input pairs pro-
cessed, or that the fraction of German documents pro-
cessed is within some tolerable fraction of the total num-
ber of documents processed.
5 Performance
In this section we measure the performance of MapRe-
duce on two computations running on a large cluster of
machines. One computation searches through approxi-
mately one terabyte of data looking for a particular pat-
tern. The other computation sorts approximately one ter-
abyte of data.
These two programs are representative of a large sub-
set of the real programs written by users of MapReduce
one class of programs shufes data from one representa-
tion to another, and another class extracts a small amount
of interesting data from a large data set.
5.1 Cluster Conguration
All of the programs were executed on a cluster that
consisted of approximately 1800 machines. Each ma-
chine had two 2GHz Intel Xeon processors with Hyper-
Threading enabled, 4GB of memory, two 160GB IDE
)
s
/
B
M
(
t
u
p
n
I
30000
20000
10000
0
20
40
60
80
100
Seconds
Figure 2: Data transfer rate over time
disks, and a gigabit Ethernet link. The machines were
arranged in a two-level tree-shaped switched network
with approximately 100-200 Gbps of aggregate band-
width available at the root. All of the machines were
in the same hosting facility and therefore the round-trip
time between any pair of machines was less than a mil-
lisecond.
Out of the 4GB of memory, approximately 1-1.5GB
was reserved by other tasks running on the cluster. The
programs were executed on a weekend afternoon, when
the CPUs, disks, and network were mostly idle.
5.2 Grep
The grep program scans through 1010 100-byte records,
searching for a relatively rare three-character pattern (the
pattern occurs in 92,337 records). The input is split into
approximately 64MB pieces (M = 15000), and the en-
tire output is placed in one le (R = 1).
Figure 2 shows the progress of the computation over
time. The Y-axis shows the rate at which the input data is
scanned. The rate gradually picks up as more machines
are assigned to this MapReduce computation, and peaks
at over 30 GB/s when 1764 workers have been assigned.
As the map tasks nish, the rate starts dropping and hits
zero about 80 seconds into the computation. The entire
computation takes approximately 150 seconds from start
to nish. This includes about a minute of startup over-
head. The overhead is due to the propagation of the pro-
gram to all worker machines, and delays interacting with
GFS to open the set of 1000 input les and to get the
information needed for the locality optimization.
5.3 Sort
The sort program sorts 1010 100-byte records (approxi-
mately 1 terabyte of data). This program is modeled after
the TeraSort benchmark [10].
The sorting program consists of less than 50 lines of
user code. A three-line Map function extracts a 10-byte
sorting key from a text line and emits the key and the
To appear in OSDI 2004
8
相关推荐
2023年江西萍乡中考道德与法治真题及答案.doc
2012年重庆南川中考生物真题及答案.doc
2013年江西师范大学地理学综合及文艺理论基础考研真题.doc
2020年四川甘孜小升初语文真题及答案I卷.doc
2020年注册岩土工程师专业基础考试真题及答案.doc
2023-2024学年福建省厦门市九年级上学期数学月考试题及答案.doc
2021-2022学年辽宁省沈阳市大东区九年级上学期语文期末试题及答案.doc
2022-2023学年北京东城区初三第一学期物理期末试卷及答案.doc
2018上半年江西教师资格初中地理学科知识与教学能力真题及答案.doc
2012年河北国家公务员申论考试真题及答案-省级.doc
2020-2021学年江苏省扬州市江都区邵樊片九年级上学期数学第一次质量检测试题及答案.doc
2022下半年黑龙江教师资格证中学综合素质真题及答案.doc
