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A MapReduce program is composed of a Map() procedure that performs filtering and sorting (such as sorting students by first name into queues, one queue for each name) and a Reduce() procedure that performs a summary operation (such as counting the number of students in each queue, yielding name frequencies).
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by marshalling the distributed servers, running the various tasks in parallel, managing all communications and data transfers between the various parts of the system, and providing for redundancy and fault tolerance.
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but the scalability and fault-tolerance achieved for a variety of applications by optimizing the execution engine once.
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Only when the optimized distributed shuffle operation (which reduces network communication cost) and fault tolerance features of the MapReduce framework come into play, is the use of this model beneficial.
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A popular open-source implementation is Apache Hadoop.
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framework for processing parallelizable 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).
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n data stored either in a filesystem (unstructured) or in a database (structured).
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advantage of locality of data, processing it on or near the storage assets in order to reduce the distance over which it must be transmitted.
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The master node takes the input, divides it into smaller sub-problems, and distributes them to worker nodes.
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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.
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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.
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distributed processing of the map and reduction operatio
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all maps can be performed in parallel – though in practice this is limited by the number of independent data sources and/or the number of CPUs near each source.
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a set of 'reducers' can perform the reduction phase, provided that all outputs of the map operation that share the same key are presented to the same reducer at the same time, or that the reduction function is associative
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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
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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.
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Prepare the Map() input – the "MapReduce system" designates Map processors, assigns the input key value K1 that each processor would work on, and provides that processor with all the input data associated with that key value.
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Run the user-provided Map() code – Map() is run exactly once for each K1 key value, generating output organized by key values K2.
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"Shuffle" the Map output to the Reduce processors – the MapReduce system designates Reduce processors, assigns the K2 key value each processor should work on, and provides that processor with all the Map-generated data associated with that key value.
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Run the user-provided Reduce() code – Reduce() is run exactly once for each K2 key value produced by the Map step.
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Produce the final output – the MapReduce system collects all the Reduce output, and sorts it by K2 to produce the final outcome.
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each step starts only after the previous step is completed – though in practice they can be interleaved, as long as the final result is not affected.
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he input data might already be distributed ("sharded") among many different servers, in which case step 1 could sometimes be greatly simplified by assigning Map servers that would process the locally present input data
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up by assigning Reduce processors that are as close as possible to the Map-generated data they need to process.
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are both defined with respect to data structured in (key, value) pairs
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one pair of data with a type in one data domain, and returns a list of pairs in a different domain:
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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, creating one group for each key.
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he Reduce function is then applied in parallel to each group, which in turn produces a collection of values in the same domain
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typically produces either one value v3 or an empty return, though one call is allowed to return more than one value.
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as the desired result list.
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list of (key, value) pairs into a list of value
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Distributed implementations of MapReduce require a means of connecting the processes performing the Map and Reduce phases.
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split into words, and each word is counted by the map function, using the word as the result key
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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.
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t for a database of 1.1 billion people, one would like to compute the average number of social contacts a person has according to age
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would line up the 1100 Map processors, and would provide each with its corresponding 1 million input records
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would then line up the 96 Reduce processors by performing shuffling operation of the key/value pairs due to the fact that we need average per age, and provide each with its millions of corresponding input records
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billion (Y,(N,1)) records, with Y values ranging between, say, 8 and 10
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the much reduced set of only 96 output records (Y,A), which would be put in the final result file, sorted by Y.
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The count info in the record is important if the processing is reduced more than one time
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11 Sep 14
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05 Sep 14
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Map() procedure that performs filtering and sorting (such as sorting students by first name into queues, one queue for each name) and a Reduce() procedure that performs a summary operation (such as counting the number of students in each queue, yielding name frequencies).
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24 Jul 14
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aster node then c
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with a parallel, distributed algorithm
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29 Apr 14
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'MapReduce' is a framework for processing parallelizable 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.
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"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.
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"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.
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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.
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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:
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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, creating one group for each key.
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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.
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Thus the MapReduce framework transforms a list of (key, value) pairs into a list of values.
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Examples[edit]
The prototypical MapReduce example counts the appearance of each word in a set of documents:[5]
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 += ParseInt(pc) emit (word, sum)
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As another example, imagine that for a database of 1.1 billion people, one would like to compute the average number of social contacts a person has according to age. In SQL such a query could be expressed as:
SELECT age, AVG(contacts) FROM social.person GROUP BY age ORDER BY age
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Using MapReduce, the K1 key values could be the integers 1 through 1,100, each representing a batch of 1 million records, the K2 key value could be a person’s age in years, and this computation could be achieved using the following functions:
function Map is input: integer K1 between 1 and 1100, representing a batch of 1 million social.person records for each social.person record in the K1 batch do let Y be the person's age let N be the number of contacts the person has produce one output record (Y,(N,1)) repeat end function function Reduce is input: age (in years) Y for each input record (Y,(N,C)) do Accumulate in S the sum of N*C Accumulate in Cnew the sum of C repeat let A be S/Cnew produce one output record (Y,(A,Cnew)) end function
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The MapReduce System would line up the 1,100 Map processors, and would provide each with its corresponding 1 million input records.
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The Map step would produce 1.1 billion (Y,(N,1)) records, with Y values ranging between, say, 8 and 103. The MapReduce System would then line up the 96 Reduce processors by performing shuffling operation of the key/value pairs due to the fact that we need average per age, and provide each with its millions of corresponding input records. The Reduce step would result in the much reduced set of only 96 output records (Y,A), which would be put in the final result file, sorted by Y.
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Input reader[edit]
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.
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Map function[edit]
The 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 the number of instances of that word in the line as the value.
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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.
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Comparison function[edit]
The input for each Reduce is pulled from the machine where the Map ran and sorted using the application's comparison function.
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Reduce function[edit]
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.
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Output writer[edit]
The Output Writer writes the output of the Reduce to the stable storage, usually a distributed file system.
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When designing a MapReduce algorithm, the author needs to choose a good tradeoff[6] between the computation and the communication costs. Communication cost often dominates the computation cost,[6] and many MapReduce implementations are designed to write all communication to distributed storage for crash recovery.
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Distribution and reliability[edit]
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.
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Implementations are not necessarily highly reliable. For example, in older versions of Hadoop the NameNode was a single point of failure for the distributed filesystem. Later versions of Hadoop have high availability with an active/passive failover for the "NameNode."
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Uses[edit]
MapReduce is useful in a wide range of applications, including distributed pattern-based searching, distributed sorting, web link-graph reversal, term-vector per host, web access log stats, inverted index construction, document clustering, machine learning,[7] and statistical machine translation. Moreover, the MapReduce model has been adapted to several computing environments like multi-core and many-core systems,[8][9][10] desktop grids,[11] volunteer computing environments,[12] dynamic cloud environments,[13] and mobile environments.[14]
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.[15]
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.
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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,[18] YSmart,[19] HBase[20] and BigTable[20][21] are addressing some of these problems.
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MapReduce was never designed nor intended to be used as a database.
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MapReduce may be easier for users to adopt for simple or one-time processing tasks.
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Google has been granted a patent on MapReduce
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MapReduce tasks must be written as acyclic dataflow programs, i.e. a stateless mapper followed by a stateless reducer, that are executed by a batch job scheduler. This paradigm makes repeated querying of datasets difficult and imposes limitations that are felt in fields such as machine learning, where iterative algorithms that revisit a single working set multiple times are the norm.[28]
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28 Apr 14
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procedure that performs filtering and sorting (such as sorting students by first name into queues, one queue for each name
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but the scalability and fault-tolerance achieved for a variety of applications by optimizing the execution engine once
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"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.
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"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.
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Map() is run exactly once for each K1 key value, generating output organized by key values K2.
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Map(k1,v1)→list(k2,v2) -
Reduce(k2, list (v2))→list(v3) -
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 += ParseInt(pc) emit (word, sum)
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09 Dec 13
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MapReduce is a programming model for processing large data sets with a parallel, distributed algorithm on a cluster.[1]
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19 Nov 13
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21 Oct 13
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27 Sep 13
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19 Aug 13
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Map() procedure that performs filtering and sorting (such as sorting students by first name into queues, one queue for each name
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Reduce() procedure that performs a summary operation (such as counting the number of students in each queue, yielding name frequencies)
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"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.
-
- Prepare the Map() input – the "MapReduce system" designates Map processors, assigns the K1 input key value each processor would work on, and provides that processor with all the input data associated with that key value.
- Run the user-provided Map() code – Map() is run exactly once for each K1 key value, generating output organized by key values K2.
- "Shuffle" the Map output to the Reduce processors – the MapReduce system designates Reduce processors, assigns the K2 key value each processor would work on, and provides that processor with all the Map-generated data associated with that key value.
- Run the user-provided Reduce() code – Reduce() is run exactly once for each K2 key value produced by the Map step.
- Produce the final output – the MapReduce system collects all the Reduce output, and sorts it by K2 to produce the final outcome.
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02 Jul 13
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a Map() procedure that performs filtering and sorting (such as sorting students by first name into queues, one queue for each name)
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a Reduce() procedure that performs a summary operation (such as counting the number of students in each queue, yielding name frequencies).
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20 Jun 13
Selenite Vingt-NeufMapReduce is a programming model for processing large data sets with a parallel, distributed algorithm on a cluster. The model is inspired by the map and reduce functions commonly used in functional programming, although their purpose in the MapReduce fra
data analysis definition parallel programming database distributed application big mapreduce
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19 May 13
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MapReduce is a programming model for processing large data sets with a parallel, distributed algorithm on a cluster.[1]
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28 Apr 13
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multi-level tree structure
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all maps can be performed in parallel
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reducers'
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some possibility of recovering from partial failure of servers or storage
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the work can be rescheduled
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K1 input key value
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the input data associated with that key value
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generating output organized by key values K2.
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Map servers that would process the locally present input data
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(key, value) pairs
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given the key and the number of reducers and returns the index of the desired reduce.
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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
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27 Mar 13
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MapReduce can take advantage of locality of data, processing data on or near the storage assets to decrease transmission of data
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04 Feb 13
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n of the model by G
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15 Nov 12
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12 Nov 12
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Computational processing can occur on data stored either in a filesystem (unstructured) or in a database (structured).
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The master node takes the input, divides it into smaller sub-problems, and distributes them to worker nodes
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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.
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(key, value)
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24 Sep 12
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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.[1]
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MapReduce libraries have been written in many programming languages. A popular free implementation is Apache Hadoop.
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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).
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Computational processing can occur on data stored either in a filesystem (unstructured) or in a database (structured)
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"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.
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"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.
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Provided each mapping operation is independent of the others, all maps can be performed in parallel
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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) -
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.
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The Map function is applied in parallel to every pair in the input dataset
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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) -
Thus the MapReduce framework transforms a list of (key, value) pairs into a list of values.
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Example
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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.
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The frozen part of the MapReduce framework is a large distributed sort
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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,[4] and statistical machine translation
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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
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03 Jun 12
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22 Apr 12
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MapReduce is a framework for processing highly distributable problems across huge datasets using a large number of computers (nodes), collectively referred to as a cluster (if all nodes use the same hardware) or a grid (if the nodes use different hardware). Computational processing can occur on data stored either in a filesystem (unstructured) or in a database (structured).
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"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.
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12 Apr 12
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18 Mar 12
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MapReduce is a software framework introduced by Google in 2004 to support distributed computing on large data sets on clusters of computers.[1] Parts of the framework are patented in some countries.[2]
The framework is inspired by the map and reduce functions commonly used in functional programming,[3] although their purpose in the MapReduce framework is not the same as their original forms.[4]
MapReduce libraries have been written in many programming languages.
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12 Mar 12
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MapReduce libraries have been written in many programming languages.
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a framework for processing highly distributable problems across huge datasets using a large number of computers (nodes)
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cluster
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distributes them to worker nodes
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do this again in turn, leading to a multi-level tree structure
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partitions it up into smaller sub-problems
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master node
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combines them in some way to form the output
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master node
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the answers to all the sub-problems
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15 Feb 12
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21 Jan 12
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04 Nov 11
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MapReduce allows for distributed processing of the map and reduction operations
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27 Sep 11
Jochen FrommMapReduce is a software framework introduced by Google to support parallel computations over large data sets on clusters of computers
performance mapreduce technology software programming map reduce
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23 Sep 11
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18 Jul 11
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27 Jun 11
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MapReduce is a software framework introduced by Google in 2004 to support distributed computing on large data sets on clusters of computers.[1] Parts of the framework are patented in some countries.
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The framework is inspired by the map and reduce functions commonly used in functional programming,[3] although their purpose in the MapReduce framework is not the same as their original forms.
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"Map" step: The master node takes the input, partitions it up 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.
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"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.
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The advantage of MapReduce is that it 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 data source and/or the number of CPUs near that data.
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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.
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"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.
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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.
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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.
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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.
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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.
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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 output 0 or more values.
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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.
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Implementations are not necessarily highly-reliable.
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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.
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15 Jun 11
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a large server farm can use MapReduce to sort a petabyte of data in only a few hours
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06 Mar 11
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MapReduce is that it 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 data source and/or the number of CPUs near that data
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Map function is applied in parallel to every item in the input dataset. This produces a list of (k2,v2) 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.
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MapReduce framework transforms a list of (key, value) pairs into a list of values
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Here, each document is split into words, and each word is counted initially with a "1" value 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.
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17 Feb 11
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MapReduce is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes), collectively referred to as a cluster (if all nodes use the same hardware) or as a grid (if the nodes use different hardware). Computational processing can occur on data stored either in a filesystem (unstructured) or within a database (structured).
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"Map" step: The master node takes the input, chops it up into smaller sub-problems, and distributes those to worker nodes.
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"Reduce" step: The master node then takes the answers to all the sub-problems and combines them in some way to get the output -
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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.
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11 Feb 11
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processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes), collectively referred to as a cluster (if all nodes use the same hardware) or as a grid (if the nodes use different hardware)
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(key, value)
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Map(k1,v1) -> list(k2,v2)
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09 Feb 11
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28 Dec 10
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Dante-Gabryell MonsonMapReduce is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes), collectively referred to as a cluster. Computational processing can occur on data stored either in a filesystem (un
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rufous"Map" step: The master node takes the input, chops it up into smaller sub-problems, and distributes those to worker nodes. A worker node may do this again in turn, leading to a multi-level tree structure.The worker node processes that smaller problem, and passes the answer back to its master node."Reduce" step: The master node then takes the answers to all the sub-problems and combines them in a way to get the output - the answer to the problem it was originally trying to solve.
mapreduce distributed_systems cloud_computing google search algorithms
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MapReduce is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes), collectively referred to as a cluster. Computational processing can occur on data stored either in a filesystem (unstructured) or within a database (structured)
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MapReduce
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10 Nov 09
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MapReduce is a software framework introduced by Google to support distributed computing on large data sets on clusters of computers
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04 Nov 09
Damon WeissMapReduce is a software framework introduced by Google to support distributed computing on large data sets on clusters of computers.[1]
The framework is inspired by map and reduce functions commonly used in functional programming,[2] although their purposwikipedia mapreduce google programming distributed algorithm parallel architecture algorithms
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15 Oct 09
viniciusjlMapReduce is a software framework introduced by Google to support distributed computing on large data sets on clusters of computers.[1] The framework is inspired by map and reduce functions commonly used in functional programming,[2] although their purpos
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Holger Abelsoftware framework by Google to support distributed computing on large data sets on clusters of computers
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Map takes one pair of data with a type on a data domain, and returns a list of pairs in a different domain:
Map(k1,v1) -> list(k2,v2) -
ct to data stru
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defined with respect to data structured in (key, value) pairs.
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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.
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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(v2) -
This behavior is different from the 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.
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count the appearances of each different word in a set of documents
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each document is split in words, and each word is counted initially with a "1" value 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.
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The master node takes the input, chops it up into smaller sub-problems, and distributes those to worker nodes. (A wor
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