DHive: Query Execution Performance Analysis via Dataflow in Apache Hive

Abstract

Nowadays, Apache Hive has been widely used for large-scale data analysis applications in many organizations. Various visual analytical tools are developed to help Hive users quickly analyze the query execution process and identify the performance bottleneck of executed queries. However, existing tools mostly focus on showing the time usage of query sub-components (jobs and operators) but fail to provide enough evidence to analyze the root reasons for the slow execution progress. To tackle this problem, we develop a visual analytical system DHive to visualize and analyze the query execution progress via dataflow analysis. DHive shows the dataflow during query execution at multiple levels: query level, job level and task level, which enable users to identify the key jobs/tasks and explain their time usage by linking them to the auxiliary information such as the system configuration and hardware status. We demonstrate the effectiveness of DHive by two cases in a production cluster. DHive is open-source at https://github.com/DBGroup-SUSTech/DHive.git.