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Novel tuning method for Spark SQL applications

An outline of LOCAT. Credit: YU Zhibin

Spark SQL is a Spark module for structured knowledge processing. It has been extensively deployed in business however it’s difficult to tune its efficiency.

Current machine studying tuning strategies are troublesome to use in observe as a result of excessive time price and failure to adapt to the modifications within the quantity of knowledge to be processed.

To handle these issues, a analysis group led by Prof. Yu Zhibin from the Shenzhen Institute of Superior Know-how (SIAT) of the Chinese language Academy of Sciences proposed a low-time-cost automated configuration optimization technique named Low-Overhead On-line Configuration Auto-Tuning (LOCAT), which may scale back the optimization time and enhance efficiency of Spark SQL.

The outcomes had been revealed at SIGMOD 2022, a world discussion board for database researchers, practitioners, builders, and customers. The related paper could be present in Proceedings of the 2022 Worldwide Convention on Administration of Information.

The researchers first designed question and configuration parameter sensitivity evaluation methods for LOCAT. Queries that had been insensitive to configuration parameters had been recognized and faraway from a given workload when coaching samples had been collected.

“For the remaining queries, LOCAT calculated correlation coefficients to identify important configuration parameters,” mentioned Prof. Yu. “Then, it applies kernel principal component analysis to cut back the dimension of configuration parameter search.”

Lastly, the researchers designed Bayesian optimization for LOCAT, which is conscious of the dataset measurement to seek for the optimum configuration in order that its efficiency could be robotically optimized primarily based on the scale of the dataset.

The experimental outcomes on the ARM cluster (a cluster of servers for large knowledge computing, during which every server makes use of CPU primarily based on the ARM instruction) confirmed that the LOCAT accelerated the optimization procedures of the state-of-the-art approaches by not less than 4.1x and as much as 9.7x. Furthermore, the LOCAT improved the appliance efficiency by not less than 1.9x and as much as 2.4x. On the x86 cluster, LOCAT confirmed related outcomes to these on the ARM cluster.

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Extra data:
Jinhan Xin et al, LOCAT: Low-Overhead On-line Configuration Auto-Tuning of Spark SQL Purposes, Proceedings of the 2022 Worldwide Convention on Administration of Information (2022). DOI: 10.1145/3514221.3526157

Novel tuning technique for Spark SQL functions (2022, June 16)
retrieved 16 June 2022

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