Autoplay Next video from Max length

Spark SQL Adaptive Execution Unleashes The Power of Cluster (Carson Wang and Yuanjian Li)

833 views · Published 24 September 2018 · 25:46 · Indexed 18 October 2026

Channel: Databricks · 2018 · Science & Technology

Carson Wang is a big data software engineer at Intel, focusing on developing and improving new big data technologies. Yuanjian is a senior engineer and the lead for distributed computing team at Baidu.

In this talk, we will explore Intel and Baidu’s joint efforts to address challenges in large scale and offer an overview of an adaptive execution mode we implemented for Baidu’s Big SQL platform which is based on Spark SQL. At runtime, adaptive execution can change the execution plan to use a better join strategy and handle skewed join automatically. It can also change the number of reducer to better fit the data scale. In general, adaptive execution decreases the effort involved in tuning SQL query parameters and improves the execution performance by choosing a better execution plan and parallelism at runtime.

To learn more: https://databricks.com/product/getting-started-guide
About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
Read more here: https://databricks.com/product/unified-data-analytics-platform

Connect with us:
Website: https://databricks.com
Facebook: https://www.facebook.com/databricksinc
Twitter: https://twitter.com/databricks
LinkedIn: https://www.linkedin.com/company/databricks
Instagram: https://www.instagram.com/databricksinc/ Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. https://databricks.com/databricks-named-leader-by-gartner

Spark SQL Adaptive Execution Unleashes The Power of Cluster (Carson Wang and Yuanjian Li)

More from this channel