MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster.
A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary operation (such as counting the number of students in each queue, yielding name frequencies). The "MapReduce System" (also called "infrastructure" or "framework") orchestrates the processing 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.
The model is a specialization of the split-apply-combine strategy for data analysis. It is inspired by the map and reduce functions commonly used in functional programming,"Our abstraction is inspired by the map and reduce primitives present in Lisp and many other functional languages." -"MapReduce: Simplified Data Processing on Large Clusters", by Jeffrey Dean and Sanjay Ghemawat; from Google Research although their purpose in the MapReduce framework is not the same as in their original forms. The key contributions of the MapReduce framework are not the actual map and reduce functions (which, for example, resemble the 1995 Message Passing Interface standard'shttp://www.mcs.anl.gov/research/projects/mpi/mpi-standard/mpi-report-2.0/mpi2-report.htm MPI 2 standard reduce and scatter operations), but the scalability and fault-tolerance achieved for a variety of applications due to parallelization. As such, a single-threaded implementation of MapReduce is usually not faster than a traditional (non-MapReduce) implementation; any gains are usually only seen with multi-threaded implementations on multi-processor hardware. The use of this model is beneficial only when the optimized distributed shuffle operation (which reduces network communication cost) and fault tolerance features of the MapReduce framework come into play. Optimizing the communication cost is essential to a good MapReduce algorithm.
MapReduce libraries have been written in many programming languages, with different levels of optimization. A popular open-source implementation that has support for distributed shuffles is part of Apache Hadoop. The name MapReduce originally referred to the proprietary Google technology, but has since become a generic trademark. By 2014, Google was no longer using MapReduce as its primary big data processing model, and development on Apache Mahout had moved on to more capable and less disk-oriented mechanisms that incorporated full map and reduce capabilities.
Overview MapReduce is a framework for processing parallelizable problems across large 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 heterogeneous hardware). Processing can occur on data stored either in a filesystem (unstructured) or in a database (structured). MapReduce can take advantage of the locality of data, processing it near the place it is stored in order to minimize communication overhead.
A MapReduce framework (or system) is usually composed of three operations (or steps):
Map: each worker node applies the map function to the local data, and writes the output to a temporary storage. A master node ensures that only one copy of the redundant input data is processed. Shuffle: worker nodes redistribute data based on the output keys (produced by the map function), such that all data belonging to one key is located on the same worker node. Reduce: worker nodes now process each group of output data, per key, in parallel.
MapReduce allows for the distributed processing of the map and reduction operations. Maps can be performed in parallel, provided that each mapping operation is independent of the others; in practice, this 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 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. While this process often appears inefficient compared to algorithms that are more sequential (because multiple instances of the reduction process must be run), MapReduce can be applied to significantly larger datasets than a single "commodity" server 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 are still available.
Another way to look at MapReduce is as a 5-step parallel and distributed computation:
Prepare the Map() input – the "MapReduce system" designates Map processors, assigns the input key K1 that each processor would work on, and provides that processor with all the input data associated with that key. Run the user-provided Map() code – Map() is run exactly once for each K1 key, generating output organized by key K2. "Shuffle" the Map output to the Reduce processors – the MapReduce system designates Reduce processors, assigns the K2 key each processor should work on, and provides that processor with all the Map-generated data associated with that key. Run the user-provided Reduce() code – Reduce() is run exactly once for each K2 key 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.
These five steps can be logically thought of as running in sequence – each step starts only after the previous step is completed – although in practice they can be interleaved as long as the final result is not affected.
In many situations, the input data might have already been 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. Similarly, step 3 could sometimes be sped up by assigning Reduce processors that are as close as possible to the Map-generated data they need to process.
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 (keyed by k1) in the input dataset. This produces a list of pairs (keyed by k2) for each call. After that, the MapReduce framework collects all pairs with the same key (k2) from all lists and groups them together, creating one group for each key.
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((k3, v3))
Each Reduce call typically produces either one key value pair or an empty return, though one call is allowed to return more than one key value pair. The returns of all calls are collected as the desired result list.
Thus the MapReduce framework transforms a list of (key, value) pairs into another list of (key, value) pairs. 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.
Examples The canonical MapReduce example counts the appearance of each 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.
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
Using MapReduce, the key values could be the integers 1 through 1100, each representing a batch of 1 million records, the 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
Note that in the function, is the count of people having in total N contacts, so in the function it is natural to write , since every output pair is referring to the contacts of one single person.
The MapReduce system would line up the 1100 Map processors, and would provide each with its corresponding 1 million input records. The Map step would produce 1.1 billion records, with 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 , which would be put in the final result file, sorted by .
The count info in the record is important if the processing is reduced more than one time. If we did not add the count of the records, the computed average would be wrong, for example:
-- map output #1: age, quantity of contacts 10, 9 10, 9 10, 9
-- map output #2: age, quantity of contacts 10, 9 10, 9
-- map output #3: age, quantity of contacts 10, 10
If we reduce files and , we will have a new file with an average of 9 contacts for a 10-year-old person ((9+9+9+9+9)/5):
-- reduce step #1: age, average of contacts 10, 9
If we reduce it with file , we lose the count of how many records we've already seen, so we end up with an average of 9.5 contacts for a 10-year-old person ((9+10)/2), which is wrong. The correct answer is 9.166 = 55 / 6 = (9×3+9×2+10×1)/(3+2+1).
Dataflow