Name
Abstract | ||
|---|---|---|
AbstractRight-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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.14778/3476311.3476362 | Hosted Content |
DocType | Volume | Issue |
Journal | 14 | 12 |
ISSN | Citations | PageRank |
2150-8097 | 1 | 0.36 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rathijit Sen | 1 | 4 | 5.50 |
| Abhishek Roy | 2 | 2 | 3.08 |
| Alekh Jindal | 3 | 1 | 1.37 |
| Rui Fang | 4 | 1 | 0.36 |
| Jeff Zheng | 5 | 1 | 0.70 |
| Xiaolei Liu | 6 | 11 | 8.70 |
| Ruiping Li | 7 | 4 | 3.15 |