Multithread to Accelerate Process Data Sync Using MapReduce Model Programming


Murti Retnowo


Research in the processing of the data is the data shows that the larger Increasingly requires a longer time. Processing of huge amounts of data on a single computer has limitations that can be Overcome by parallel processing. This study utilized the MapReduce programming model synchronization by duplicating data, data from database client to database server. MapReduce is a programming model that was developed to speed up the processing of large data. MapReduce application models on the training process performed on the data sharing that is adapted to the number of sub-process (thread) and data entry into the database server and display data from the synchronization process. The experiments were performed using 1,000, 10,000, 100,000 and 1,000,000 data, and use the thread as much as 1, 5, 10, 15, 20 and 25 threads. The results of this process showed that the use of the MapReduce programming model can result in faster but require a longer time to create many threads. The results of use MapReduce programming model can provide more efficiency time in processing synchronizing data, both on a single database or a distributed database


How to Cite
Retnowo, M. (2018). Multithread to Accelerate Process Data Sync Using MapReduce Model Programming. International Journal of Applied Business and Information Systems, 2(1), 38–45.