As part of Project Tungsten, we started an ongoing effort to substantially improve the memory and CPU efficiency of Apache Spark's backend execution and push performance closer to the limits of modern hardware. In this talk, we'll take a deep dive into Apache Spark's unified memory model and discuss how Spark exploits memory hierarchy and leverages application semantics to manage memory explicitly (both on and off-heap) to eliminate the overheads of JVM object model and garbage collection. Session hashtag: #SFdev23

About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
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