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Resilient Distributed Datasets: A Fault-Tolerant ...

Resilient Distributed Datasets: A Fault-Tolerant Abstraction forIn-Memory Cluster ComputingMatei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma,Murphy McCauley, Michael J. Franklin, Scott Shenker, Ion StoicaUniversity of California, BerkeleyAbstractWe present Resilient Distributed Datasets (RDDs), a dis-tributed memory abstraction that lets programmers per-form in-memory computations on large clusters in afault-tolerant manner. RDDs are motivated by two typesof applications that current computing frameworks han-dle inefficiently: iterative algorithms and interactive datamining tools. In both cases, keeping data in memorycan improve performance by an order of achieve fault tolerance efficiently, RDDs provide arestricted form of shared memory, based on coarse-grained transformations rather than fine-grained updatesto shared state.

derived from other datasets (its lineage) to compute its partitions from data in stable storage. This is a power-ful property: in essence, a program cannot reference an RDD that it cannot reconstruct after a failure. Finally, users can control two other aspects of RDDs: persistence and partitioning. Users can indicate which

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  Distributed, Dataset, Resilient, Resilient distributed datasets

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