利用者:Distart/working/MapReduce

MapReduce is a programming model for processing large data sets, and the name of an implementation of the model by Google. MapReduce is typically used to do distributed computing on clusters of computers.

The model is inspired by the map and reduce functions commonly used in functional programming, although their purpose in the MapReduce framework is not the same as their original forms.

MapReduce libraries have been written in many programming languages. A popular free implementation is Apache Hadoop.

Overview

MapReduce is a framework for processing embarrassingly parallel problems across huge datasets using a large number of computers (nodes), collectively referred to as a cluster (if all nodes are on the same local network and use similar hardware) or a grid (if the nodes are shared across geographically and administratively distributed systems, and use more heterogenous hardware). Computational processing can occur on data stored either in a filesystem (unstructured) or in a database (structured). MapReduce can take advantage of locality of data, processing data on or near the storage assets to decrease transmission of data.

"Map" step: The master node takes the input, divides it into smaller sub-problems, and distributes them to worker nodes. A worker node may do this again in turn, leading to a multi-level tree structure. The worker node processes the smaller problem, and passes the answer back to its master node.

"Reduce" step: The master node then collects the answers to all the sub-problems and combines them in some way to form the output – the answer to the problem it was originally trying to solve.

MapReduce allows for distributed processing of the map and reduction operations. Provided each mapping operation is independent of the others, all maps can be performed in parallel – though in practice it is limited by the number of independent data sources and/or the number of CPUs near each source. Similarly, a set of 'reducers' can perform the reduction phase - provided all outputs of the map operation that share the same key are presented to the same reducer at the same time, or if the reduction function is associative. While this process can often appear inefficient compared to algorithms that are more sequential, MapReduce can be applied to significantly larger datasets than "commodity" servers can handle – a large server farm can use MapReduce to sort a petabyte of data in only a few hours. The parallelism also offers some possibility of recovering from partial failure of servers or storage during the operation: if one mapper or reducer fails, the work can be rescheduled – assuming the input data is still available.

Logical view

The Map and Reduce functions of MapReduce are both defined with respect to data structured in (key, value) pairs. Map takes one pair of data with a type in one data domain, and returns a list of pairs in a different domain: Map(k1,v1) → list(k2,v2)

The Map function is applied in parallel to every pair in the input dataset. This produces a list of pairs for each call. After that, the MapReduce framework collects all pairs with the same key from all lists and groups them together, thus creating one group for each one of the different generated keys.

The Reduce function is then applied in parallel to each group, which in turn produces a collection of values in the same domain:

Reduce(k2, list (v2)) → list(v3)

Each Reduce call typically produces either one value v3 or an empty return, though one call is allowed to return more than one value. The returns of all calls are collected as the desired result list.

Thus the MapReduce framework transforms a list of (key, value) pairs into a list of values. This behavior is different from the typical functional programming map and reduce combination, which accepts a list of arbitrary values and returns one single value that combines all the values returned by map.

It is necessary but not sufficient to have implementations of the map and reduce abstractions in order to implement MapReduce. Distributed implementations of MapReduce require a means of connecting the processes performing the Map and Reduce phases. This may be a distributed file system. Other options are possible, such as direct streaming from mappers to reducers, or for the mapping processors to serve up their results to reducers that query them.

Example

The canonical example application of MapReduce is a process to count the appearances of each different word in a set of documents:

function map(String name, String document):
  // name: document name
  // document: document contents
  for each word w in document:
    emit (w, 1)

function reduce(String word, Iterator partialCounts):
  // word: a word
  // partialCounts: a list of aggregated partial counts
  sum = 0
  for each pc in partialCounts:
    sum += pc
  emit (word, sum)

Here, each document is split into words, and each word is counted by the map function, using the word as the result key. The framework puts together all the pairs with the same key and feeds them to the same call to reduce, thus this function just needs to sum all of its input values to find the total appearances of that word.

Dataflow

The frozen part of the MapReduce framework is a large distributed sort. The hot spots, which the application defines, are:

• an input reader
• a Map function
• a partition function
• a compare function
• a Reduce function
• an output writer

Input reader

The input reader divides the input into appropriate size 'splits' (in practice typically 16 MB to 128 MB) and the framework assigns one split to each Map function. The input reader reads data from stable storage (typically a distributed file system) and generates key/value pairs.

A common example will read a directory full of text files and return each line as a record.

Map function

Each Map function takes a series of key/value pairs, processes each, and generates zero or more output key/value pairs. The input and output types of the map can be (and often are) different from each other.

If the application is doing a word count, the map function would break the line into words and output a key/value pair for each word. Each output pair would contain the word as the key and "1" as the value.

Partition function

Each Map function output is allocated to a particular reducer by the application's partition function for sharding purposes. The partition function is given the key and the number of reducers and returns the index of the desired reduce.

A typical default is to hash the key and modulo the number of reducers. It is important to pick a partition function that gives an approximately uniform distribution of data per shard for load balancing purposes, otherwise the MapReduce operation can be held up waiting for slow reducers to finish.

Between the map and reduce stages, the data is shuffled (parallel-sorted / exchanged between nodes) in order to move the data from the map node that produced it to the shard in which it will be reduced. The shuffle can sometimes take longer than the computation time depending on network bandwidth, CPU speeds, data produced and time taken by map and reduce computations.

Comparison function

The input for each Reduce is pulled from the machine where the Map ran and sorted using the application's comparison function.

Reduce function

The framework calls the application's Reduce function once for each unique key in the sorted order. The Reduce can iterate through the values that are associated with that key and produce zero or more outputs.

In the word count example, the Reduce function takes the input values, sums them and generates a single output of the word and the final sum.

Output writer

The Output Writer writes the output of the Reduce to stable storage, usually a distributed file system.

Distribution and reliability

MapReduce achieves reliability by parceling out a number of operations on the set of data to each node in the network. Each node is expected to report back periodically with completed work and status updates. If a node falls silent for longer than that interval, the master node (similar to the master server in the Google File System) records the node as dead and sends out the node's assigned work to other nodes. Individual operations use atomic operations for naming file outputs as a check to ensure that there are not parallel conflicting threads running. When files are renamed, it is possible to also copy them to another name in addition to the name of the task (allowing for side-effects).

The reduce operations operate much the same way. Because of their inferior properties with regard to parallel operations, the master node attempts to schedule reduce operations on the same node, or in the same rack as the node holding the data being operated on. This property is desirable as it conserves bandwidth across the backbone network of the datacenter.

Implementations are not necessarily highly-reliable. For example, in Hadoop the NameNode is a single point of failure for the distributed filesystem.

Uses

MapReduce is useful in a wide range of applications, including distributed pattern-based searching, distributed sort, web link-graph reversal, term-vector per host, web access log stats, inverted index construction, document clustering, machine learning, and statistical machine translation. Moreover, the MapReduce model has been adapted to several computing environments like multi-core and many-core systems, desktop grids, volunteer computing environments, dynamic cloud environments, and mobile environments.

At Google, MapReduce was used to completely regenerate Google's index of the World Wide Web. It replaced the old ad hoc programs that updated the index and ran the various analyses.

MapReduce's stable inputs and outputs are usually stored in a distributed file system. The transient data is usually stored on local disk and fetched remotely by the reducers.

Criticism

David DeWitt and Michael Stonebraker, experts in parallel databases and shared-nothing architectures, have been critical of the breadth of problems that MapReduce can be used for. They called its interface too low-level and questioned whether it really represents the paradigm shift its proponents have claimed it is. They challenged the MapReduce proponents' claims of novelty, citing Teradata as an example of prior art that has existed for over two decades. They also compared MapReduce programmers to Codasyl programmers, noting both are "writing in a low-level language performing low-level record manipulation." MapReduce's use of input files and lack of schema support prevents the performance improvements enabled by common database system features such as B-trees and hash partitioning, though projects such as Pig (or PigLatin), Sawzall, Apache Hive, YSmart, HBase and BigTable are addressing some of these problems.

Another article, by Greg Jorgensen, rejects these views. Jorgensen asserts that DeWitt and Stonebraker's entire analysis is groundless as MapReduce was never designed nor intended to be used as a database.

DeWitt and Stonebraker have subsequently published a detailed benchmark study in 2009 comparing performance of Hadoop's MapReduce and RDBMS approaches on several specific problems. They concluded that databases offer real advantages for many kinds of data use, especially on complex processing or where the data is used across an enterprise, but that MapReduce may be easier for users to adopt for simple or one-time processing tasks. They have published the data and code used in their study to allow other researchers to do comparable studies.

Google has been granted a patent on MapReduce. However, there have been claims that this patent should not have been granted because MapReduce is too similar to existing products. For example, map and reduce functionality can be very easily implemented in Oracle's PL/SQL database oriented language.

Conferences and users groups

• The First International Workshop on MapReduce and its Applications (MAPREDUCE'10) was held with the HPDC conference and OGF'29 meeting in Chicago, IL.
• MapReduce Users Groups around the world.

See also

• Hadoop, Apache's free and open source implementation of MapReduce.

References

Specific references:

General references:

• Dean, Jeffrey & Ghemawat, Sanjay (2004). "MapReduce: Simplified Data Processing on Large Clusters". Retrieved Nov. 23, 2011.
• Matt WIlliams (2009). "Understanding Map-Reduce". Retrieved Apr. 13, 2011.

External links

Papers

• "A Hierarchical Framework for Cross-Domain MapReduce Execution" — paper by Yuan Luo, Zhenhua Guo, Yiming Sun, Beth Plale, Judy Qiu; from Indiana University and Wilfred Li; from University of California, San Diego
• "Interpreting the Data: Parallel Analysis with Sawzall" — paper by Rob Pike, Sean Dorward, Robert Griesemer, Sean Quinlan; from Google Labs
• "Evaluating MapReduce for Multi-core and Multiprocessor Systems" — paper by Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, and Christos Kozyrakis; from Stanford University
• "Why MapReduce Matters to SQL Data Warehousing" — analysis related to the August, 2008 introduction of MapReduce/SQL integration by Aster Data Systems and Greenplum
• "MapReduce for the Cell B.E. Architecture" — paper by Marc de Kruijf and Karthikeyan Sankaralingam; from University of Wisconsin–Madison
• "Mars: A MapReduce Framework on Graphics Processors" — paper by Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang; from Hong Kong University of Science and Technology; published in Proc. PACT 2008. It presents the design and implementation of MapReduce on graphics processors.
• "A Peer-to-Peer Framework for Supporting MapReduce Applications in Dynamic Cloud Environments" — paper by Fabrizio Marozzo, Domenico Talia, Paolo Trunfio; from University of Calabria; published in Cloud Computing: Principles, Systems and Applications, N. Antonopoulos, L. Gillam (Editors), chapt. 7, pp. 113–125, Springer, 2010, ISBN 978-1-84996-240-7.
• "Map-Reduce-Merge: Simplified Relational Data Processing on Large Clusters" — paper by Hung-Chih Yang, Ali Dasdan, Ruey-Lung Hsiao, and D. Stott Parker; from Yahoo and UCLA; published in Proc. of ACM SIGMOD, pp. 1029–1040, 2007. (This paper shows how to extend MapReduce for relational data processing.)
• FLuX: the Fault-tolerant, Load Balancing eXchange operator from UC Berkeley provides an integration of partitioned parallelism with process pairs. This results in a more pipelined approach than Google's MapReduce with instantaneous failover, but with additional implementation cost.
• "A New Computation Model for Rack-Based Computing" — paper by Foto N. Afrati; Jeffrey D. Ullman; from Stanford University; Not published as of Nov 2009. This paper is an attempt to develop a general model in which one can compare algorithms for computing in an environment similar to what map-reduce expects.
• FPMR: MapReduce framework on FPGA -- paper by Yi Shan, Bo Wang, Jing Yan, Yu Wang, Ningyi Xu, Huazhong Yang (2010), in FPGA '10, Proceedings of the 18th annual ACM/SIGDA international symposium on Field programmable gate arrays.
• "Tiled-MapReduce: Optimizing Resource Usages of Data-parallel Applications on Multicore with Tiling" -- paper by Rong Chen, Haibo Chen and Binyu Zang from Fudan University; published in Proc. PACT 2010. It presents the Tiled-MapReduce programming model which optimizes resource usages of MapReduce applications on multicore environment using tiling strategy.
• "Scheduling divisible MapReduce computations " -- paper by Joanna Berlińska from Adam Mickiewicz University and Maciej Drozdowski from Poznan University of Technology; Journal of Parallel and Distributed Computing 71 (2011) 450-459, doi:10.1016/j.jpdc.2010.12.004. It presents scheduling and performance model of MapReduce.
• "Nephele/PACTs: A Programming Model and Execution Framework for Web-Scale Analytical Processing" -- paper by D. Battré, S. Ewen, F. Hueske, O. Kao, V. Markl, and D. Warneke from TU Berlin published in Proc. of ACM SoCC 2010. The paper introduces the PACT programming model, a generalization of MapReduce, developed in the Stratosphere research project.
• "MapReduce and PACT - Comparing Data Parallel Programming Models" -- paper by A. Alexandrov, S. Ewen, M. Heimel, F. Hueske, O. Kao, V. Markl, E. Nijkamp, and D. Warneke from TU Berlin published in Proc. of BTW 2011.

Books

• Jimmy Lin and Chris Dyer. "Data-Intensive Text Processing with MapReduce" (manuscript)

Educational courses

• Cluster Computing and MapReduce course from Google Code University contains video lectures and related course materials from a series of lectures that was taught to Google software engineering interns during the Summer of 2007.
• MapReduce in a Week course from Google Code University contains a comprehensive introduction to MapReduce including lectures, reading material, and programming assignments.
• MapReduce course, taught by engineers of Google Boston, part of 2008 Independent Activities Period at MIT.