MapReduce: Simplified Data Processing on Large Clusters
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.
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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
Google, Inc.
To appear in OSDI 2004
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.
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)
reduce (k2,list(v2))
→ list(k2,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 ⟨URL, 1⟩. The reduce function adds together all values for the same URL and emits a ⟨URL, total count⟩ pair.
⟨target, list(source)⟩
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 ⟨word, f requency⟩ pairs. The map function emits a ⟨hostname, term vector⟩ 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 ⟨hostname, term vector⟩ pair.
Reverse Web-Link Graph:
The map function outputs ⟨target, source⟩ 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:
To appear in OSDI 2004
User Program
(2) assign map
(4) local write
assign reduce
(5) remote read
worker worker
output file 0
output file 1
Input files
Intermediate files (on local disks)
Output files
Reduce phase
Inverted Index: The map function parses each docu- ment, and emits a sequence of ⟨word, document ID⟩ pairs. The reduce function accepts all pairs for a given word, sorts the corresponding document IDs and emits a ⟨word,list(document ID)⟩pair.Thesetofalloutput 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 ⟨key, record⟩ 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
Figure 1: Execution overview
To appear in OSDI 2004
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
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 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 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 sp
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