Apache Spark AQE SkewedJoin Optimization and Practice in ByteDance
Description
Almost all distributed computing systems cannot avoid data skew. If data skew is not dealt with, the long-tail task will seriously slow down the execution of the job, or even cause the failure of the job. In this talk, we will introduce how Spark AQE processes SkewedJoin and how we optimize the implementation based on workload in ByteDance. The main points are as follows: 1. Address the risks associated with increasing statistical accuracy to solve the problem of not being able to identify data skew. 2. Optimize the split logic of skew data to achieve a better optimization effect. 3. Compared to the community's implementation, more complex optimization scenarios are supported, which has basically covered all SkewedJoin scenarios. By February 2021, Spark AQE SkewedJoin optimization covers 13000+ Spark jobs per day in ByteDance. The average performance of optimized Spark jobs increased by 35%. Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/
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