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Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
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Why do we need RDDs in Apache Spark?
RDDs allow Spark to outperform existing models by up to 100x in multi-pass analytics. We showed that RDDs can support a wide variety of iterative algorithms, as well as interactive data mining and a highly efficient SQL engine ( Shark ). You can find more about the research behind Spark in the following papers:
Where did the idea for Apache Spark come from?
Apache Spark started as a research project at UC Berkeley in the AMPLab, which focuses on big data analytics. Our goal was to design a programming model that supports a much wider class of applications than MapReduce, while maintaining its automatic fault tolerance.
What's the difference between Apache Spark and MapReduce?
Our goal was to design a programming model that supports a much wider class of applications than MapReduce, while maintaining its automatic fault tolerance. In particular, MapReduce is inefficient for multi-pass applications that require low-latency data sharing across multiple parallel operations.
Last Update: Oct 2021
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