@inproceedings{sen2021autoexecutor,
author = {Sen, Rathijit and Roy, Abhishek and Jindal, Alekh and Fang, Rui and Zheng, Jeff and Liu, Xiaolei and Li, Ruiping},
title = {AutoExecutor: Predictive Parallelism for Spark SQL Queries},
booktitle = {VLDB},
year = {2021},
month = {August},
abstract = {Right-sizing resources for query execution is important for cost-efficient performance, but estimating how performance is affected by resource allocations, upfront, before query execution is difficult. We demonstrate AutoExecutor, a predictive system that uses machine learning models to predict query run times as a function of the number of allocated executors, that limits the maximum allowed parallelism, for Spark SQL queries running on Azure Synapse.},
url = {http://approjects.co.za/?big=en-us/research/publication/autoexecutor-predictive-parallelism-for-spark-sql-queries/},
}