Abstract
Hive and Impala queries are used to process a big amount of data. The overwriting amount of information requires an efficient data processing system. When we deal with a long-term batch query and analysis Hive will be more suitable for this query. Impala is the most powerful system suitable for real-time interactive Structured Query Language (SQL) query which are added a massive parallel processing to Hadoop distributed cluster. The data growth makes a problem with SQL Cluster because the execution processing time is increased. In this paper, a comparison is demonstrated between the performance time of Hive, Impala and SQL on two different data models with different queries chosen to test the performance. The results demonstrate that Impala outperforms Hive and SQL cluster when it comes to analyze data and processing tasks. Using two benchmark datasets, TPC-H and statistical computing, we compare the performance of Hive, Impala, and SQL clusters 2009 Statistical Graphics Data Expo.
Highlights
A massive quantity of data is produced daily from every part of the world [1]
In TPC-H Dataset, we tested the queries which contained the most joins in specific query to test the performance of Hive and Impala when multiple tables exist compared with Structured Query Language (SQL) query
In SQL and Hive queries, there are a big difference in the execution time as compared with Impala framework
Summary
A massive quantity of data is produced daily from every part of the world [1]. This Inflation in the data comes from the advances of technology like the arise of cloud computing, internet of things and smart and sending devices which exist nowadays in [2]. The source of data is heterogenous and diversified It could be means of communication like social media sites, sensor networks, health care reports, security camera, hospitals, governments and so on [3]. One of the many techniques to analyze a big amount of data is Hadoop framework [5,6,7]. In the two sections we are explained the Hive and Impala frameworks
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