Abstract
In this paper, we introduce the fault-tolerant Distributed Analytics System (DAS) for analyzing big data collected from search engines in Arabic. This system consists of three main subsystems: Logging and Archiving Subsystem (LAS), Analytics Subsystem (AS), and a User Interface (UI). We used the data provided by opensooq.com, an online market with Arabic content, and compiled four datasets with sizes: 50 Million, 100 Million, 150 Million, and 200 Million events, in order to assess DAS. The experiments showed that DAS outperformed its sequential counterpart at datasets of 100 Million events and more, with the best speedup being 3.5 at 200 Million events. Additionally, DAS outperformed the well-known analytics system ElasticSearch (ES) in terms of response time for input sizes of 70 Million events and more, as the time per request achieved by DAS was 21% faster than ES's time. Moreover, DAS turned out to be more energy-efficient in terms of CPU utilization, as ES's CPU utilization was 2.4 times more than DAS's utilization, on average.
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