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one node in the case of numPartitions = 1). This may result in your computation taking place on fewer nodes than However, if you're doing a drastic coalesce, e.g. Of partitions is requested, it will stay at the current number of partitions. New partitions will claim 10 of the current partitions. To 100 partitions, there will not be a shuffle, instead each of the 100 This results in a narrow dependency, e.g. Return a new RDD that is reduced into numPartitions partitions. compute public abstract compute( Partition split,.numericRDDToDoubleRDDFunctions public static DoubleRDDFunctions numericRDDToDoubleRDDFunctions( RDD rdd,.doubleRDDToDoubleRDDFunctions public static DoubleRDDFunctions doubleRDDToDoubleRDDFunctions( RDD rdd).rddToOrderedRDDFunctions public static OrderedRDDFunctions> rddToOrderedRDDFunctions( RDD> rdd,.
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rddToSequenceFileRDDFunctions public static SequenceFileRDDFunctions rddToSequenceFileRDDFunctions( RDD> rdd,.rddToAsyncRDDActions public static AsyncRDDActions rddToAsyncRDDActions( RDD rdd,.rddToPairRDDFunctions public static PairRDDFunctions rddToPairRDDFunctions( RDD> rdd,.Scala.Function4,> f,Ĭonstruct an RDD with just a one-to-one dependency on one parent Zip this RDD's partitions with one (or more) RDD(s) and return a new RDD byĪpplying a function to the zipped partitions. Please refer to theįor more details on RDD internals. Reading data from a new storage system) by overriding these functions. Indeed, users can implement custom RDDs (e.g. To implement its own way of computing itself. block locations forĪll of the scheduling and execution in Spark is done based on these methods, allowing each RDD Optionally, a list of preferred locations to compute each split on (e.g. Optionally, a Partitioner for key-value RDDs (e.g. Internally, each RDD is characterized by five main properties: SequenceFileRDDFunctions contains operations available on RDDs thatĪll operations are automatically available on any RDD of the right type (e.g. PairRDDFunctions contains operations available only on RDDs of key-valueĭoubleRDDFunctions contains operations available only on RDDs of This class contains theīasic operations available on all RDDs, such as map, filter, and persist. Partitioned collection of elements that can be operated on in parallel. A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.