Map. Reduce - Wikipedia, the free encyclopedia. Map. Reduce is a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster. As such, a single- threaded implementation of Map. Reduce will usually not be faster than a traditional (non- Map. Reduce) implementation; any gains are usually only seen with multi- threaded implementations. Optimizing the communication cost is essential to a good Map.
Reduce algorithm. A popular open- source implementation that has support for distributed shuffles is part of Apache Hadoop. The name Map. Reduce originally referred to the proprietary Google technology, but has since been genericized. By 2. 01. 4, Google was no longer using Map. Reduce as their primary Big Data processing model. Processing can occur on data stored either in a filesystem (unstructured) or in a database (structured).
MapReduce Design Patterns Building Effective Algorithms and Analytics for Hadoop and Other Systems.
Map. Reduce can take advantage of the locality of data, processing it near the place it is stored in order to reduce the distance over which it must be transmitted. A master node ensures that only one copy of redundant input data is processed.
Provided that each mapping operation is independent of the others, all maps can be performed in parallel . 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.
Flow-based programming defines applications using the metaphor of a 'data factory'. It views an application not as a single, sequential process, which starts at a point in time, and then does one thing at a time.
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. En informatique, et plus particuli
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While this process can often appear inefficient compared to algorithms that are more sequential (because multiple rather than one instance of the reduction process must be run), Map. Reduce can be applied to significantly larger datasets than . 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. Map takes one pair of data with a type in one data domain, and returns a list of pairs in a different domain: Map(k. This produces a list of pairs (keyed by k. After that, the Map. Reduce framework collects all pairs with the same key (k.
The Reduce function is then applied in parallel to each group, which in turn produces a collection of values in the same domain: Reduce(k. The returns of all calls are collected as the desired result list. Thus the Map. Reduce 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 Map. Reduce. Distributed implementations of Map.
Reduce 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 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. In SQL, such a query could be expressed as: SELECTage,AVG(contacts)FROMsocial. GROUPBYage. ORDERBYage. Using Map. Reduce, the K1 key values could be the integers 1 through 1. K2 key value could be a person. The Map step would produce 1. Y,(N,1)) records, with Y values ranging between, say, 8 and 1.
The Map. Reduce System would then line up the 9. 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 9. Y,A), which would be put in the final result file, sorted by Y. 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.
If we reduce files #1 and #2, we will have a new file with an average of 9 contacts for a 1. If we reduce it with file #3, we lose the count of how many records we've already seen, so we end up with an average of 9. The correct answer is 9. Dataflow. The hot spots, which the application defines, are: an input readera Map functiona partition functiona compare functiona Reduce functionan output writer. Input reader. 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. 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. Partition function.
The partition function is given the key and the number of reducers and returns the index of the desired reducer. A typical default is to hash the key and use the hash value 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 Map. Reduce operation can be held up waiting for slow reducers (reducers assigned more than their share of data) 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 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 main benefit of this programming model is to exploit the optimized shuffle operation of the platform, and only having to write the Map and Reduce parts of the program. In practice, the author of a Map. Reduce program however has to take the shuffle step into consideration; in particular the partition function and the amount of data written by the Map function can have a large impact on the performance and scalability. Additional modules such as the Combiner function can help to reduce the amount of data written to disk, and transmitted over the network. Map. Reduce applications can achieve sub- linear speedups under specific circumstances.
Communication cost often dominates the computation cost. The amount of data produced by the mappers is a key parameter that shifts the bulk of the computation cost between mapping and reducing. Reducing includes sorting (grouping of the keys) which has nonlinear complexity. Hence, small partition sizes reduce sorting time, but there is a trade- off because having a large number of reducers may be impractical. The influence of split unit size is marginal (unless chosen particularly badly, say < 1.
MB). The gains from some mappers reading load from local disks, on average, is minor. Since these frameworks are designed to recover from the loss of whole nodes during the computation, they write interim results to distributed storage. This crash recovery is expensive, and only pays off when the computation involves many computers and a long runtime of the computation.
A task that completes in seconds can just be restarted in the case of an error, and the likelihood of at least one machine failing grows quickly with the cluster size. On such problems, implementations keeping all data in memory and simply restarting a computation on node failures or . 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 older versions of Hadoop the Name. Node was a single point of failure for the distributed filesystem. Later versions of Hadoop have high availability with an active/passive failover for the .
Moreover, the Map. Reduce model has been adapted to several computing environments like multi- core and many- core systems. It replaced the old ad hoc programs that updated the index and ran the various analyses.
The transient data is usually stored on local disk and fetched remotely by the reducers. Criticism. They also compared Map.
Reduce programmers to CODASYL programmers, noting both are . For example, map and reduce functionality can be very easily implemented in Oracle's. PL/SQL database oriented language.