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  2. MapReduce - Wikipedia

    en.wikipedia.org/wiki/MapReduce

    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. [1] [2] [3]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 ...

  3. Parallelization contract - Wikipedia

    en.wikipedia.org/wiki/Parallelization_contract

    Second-order Functions: PACT provides more second-order functions. Currently, five second-order functions called Input Contracts are supported. This set might be extended in the future. Program structure: PACT allows the composition of arbitrary acyclic data flow graphs. In contract, MapReduce programs have a static structure (Map -> Reduce).

  4. Map (parallel pattern) - Wikipedia

    en.wikipedia.org/wiki/Map_(parallel_pattern)

    Some parallel programming systems, such as OpenMP and Cilk, have language support for the map pattern in the form of a parallel for loop; [2] languages such as OpenCL and CUDA support elemental functions (as "kernels") at the language level. The map pattern is typically combined with other parallel design patterns.

  5. Apache Pig - Wikipedia

    en.wikipedia.org/wiki/Apache_Pig

    Pig Latin abstracts the programming from the Java MapReduce idiom into a notation which makes MapReduce programming high level, similar to that of SQL for relational database management systems. Pig Latin can be extended using user-defined functions (UDFs) which the user can write in Java , Python , JavaScript , Ruby or Groovy [ 3 ] and then ...

  6. Data-intensive computing - Wikipedia

    en.wikipedia.org/wiki/Data-intensive_computing

    The MapReduce architecture allows programmers to use a functional programming style to create a map function that processes a key–value pair associated with the input data to generate a set of intermediate key–value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Since the system ...

  7. Collective operation - Wikipedia

    en.wikipedia.org/wiki/Collective_operation

    Information flow of Reduce operation performed on three nodes. f is the associative operator and α is the result of the reduction. The reduce pattern [4] is used to collect data or partial results from different processing units and to combine them into a global result by a chosen operator.

  8. Monoid - Wikipedia

    en.wikipedia.org/wiki/Monoid

    Function f : [Z] 3 → [Z] 6 given by [k] 3 ↦ [3k] 6 is a semigroup homomorphism, since [3k ⋅ 3l] 6 = [9kl] 6 = [3kl] 6. However, f([1] 3) = [3] 6 ≠ [1] 6, so a monoid homomorphism is a semigroup homomorphism between monoids that maps the identity of the first monoid to the identity of the second monoid and the latter condition cannot be ...

  9. MongoDB - Wikipedia

    en.wikipedia.org/wiki/MongoDB

    MongoDB provides three ways to perform aggregation: the aggregation pipeline, the map-reduce function and single-purpose aggregation methods. [40] Map-reduce can be used for batch processing of data and aggregation operations. However, according to MongoDB's documentation, the aggregation pipeline provides better performance for most ...