This article aims to cope with the problem of high memory usage and low processing efficiency when computers process a large amount of data in the era of big data, and conducts research on MapReduce based on a serverless platform and finds that MapReduce is able to accurately measure the processing time, communication time, and running time.
This study builds a Serverless MapReduce prototype system, and analyzes the impact of the number of Map Workers on the task time by designing the Map/Reduce function process, deploying the computing service, and verifying the system performance.
A novel hybrid approach for efficient big data query processing through a two-phase approach that combines the parallel processing capabilities of HDFS Map-Reduce with the optimization power of PSO, contributing to an overall improvement in query processing efficiency for large-scale datasets.
S. Selvan, Gowri R. Punitha, V. V. Nathan et al.· ITEGAM- Journal of Engineeri...· 0 citations
The analyzed characteristics of Cloudera Impala and Apache Hive allow us to conclude that these SQL tools for analytical data processing, in the Hadoop ecosystem, complement each other.
S. A. Reznichenko, A. N. Alexeeva· Herald of Dagestan State Tec...· 0 citations
. The core part of distributed text processing is the MapReduce framework. Alibaba Cloud Function Compute and Object Storage Service can realize its serverless deployment, but the parameter matching rules for data sharding and memory configuration still stay unclear. This study builds a MapReduce distributed architectu...
Jing-Qiao Zhang· Proceedings of the 4th Inter...· 0 citations
