Abstract: A large-scale data processing system and method including a plurality of processes, wherein a master process assigns input data blocks to respective map processes and partitions of intermediate data are assigned to respective reduce processes. In each of the plurality of map processes an application-independent map program retrieves a sequence of input data blocks assigned thereto by the master process and applies an application-specific map function to each input data block in the sequence to produce the intermediate data and stores the intermediate data in high speed memory of the interconnected processors. Each of the plurality of reduce processes receives a respective partition of the intermediate data from the high speed memory of the interconnected processors while the map processes continue to process input data blocks an application-specific reduce function is applied to the respective partition of the intermediate data to produce output values.

Abstract: A computer-implemented method for processing input data in a mapreduce framework includes: receiving, in the mapreduce framework, a data processing request for input data; initiating, based on the data processing request, a map operation on the input data by multiple mappers in the mapreduce framework, each of the mappers using an aggregator to partially aggregate the input data into one or more intermediate key/value pairs; initiating a reduce operation on the intermediate key/value pairs by multiple reducers in the mapreduce framework, wherein, without sorting the intermediate key/value pairs, those of the intermediate key/value pairs with a common key are handled by a same one of the reducers, each of the reducers using the aggregator to aggregate the intermediate key/value pairs into one or more output values; and providing the output values in response to the data processing request.