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Deep Dive: Spark Memory Management
Memory management is at the heart of any data-intensive system. Spark, in particular, must arbitrate memory allocation between two main use cases: buffering intermediate data for processing (execution) and caching user data (storage). This talk will take a deep dive through the memory management designs adopted in Spark since its inception and discuss their performance and usability implications for the end user.
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companion_to · incomingDBTB INT Andrew Or rtalk ↗representedDatabrickscompany ↗affiliated withDatabrickscompany ↗documented byData-2016-05-18photo ↗presented · incomingAndrew Orperson ↗presented atData by the Bayevent ↗recorded asdata.bythebay.io: Andrew Or - Deep Dive: Spark Memory Managementvideo ↗