ShapeLuceneRDD

final def !=(arg0: Any): Boolean

final def ##(): Int

def ++(other: RDD[(K, V)]): RDD[(K, V)]

final def ==(arg0: Any): Boolean

val DefaultFacetNum: Int

val DefaultTopK: Int

Default value for topK queries

val MaxDefaultTopKValue: Int

val SparkDocTopKMonoid: TopKMonoid[SparkScoreDoc]

def aggregate[U](zeroValue: U)(seqOp: (U, (K, V)) ⇒ U, combOp: (U, U) ⇒ U)(implicit arg0: ClassTag[U]): U

final def asInstanceOf[T0]: T0

def bboxSearch(lowerLeft: PointType, upperRight: PointType, k: Int, operationName: String): Iterable[SparkScoreDoc]

Bounding box search with rectangle

def bboxSearch(center: PointType, radius: Double, k: Int, operationName: String = SpatialOperation.Intersects.getName): Iterable[SparkScoreDoc]

Bounding box search with center and radius

def cache(): ShapeLuceneRDD.this.type

def cartesian[U](other: RDD[U])(implicit arg0: ClassTag[U]): RDD[((K, V), U)]

def checkpoint(): Unit

def circleSearch(center: PointType, radius: Double, k: Int): Iterable[SparkScoreDoc]

Search for points within a circle

def clearDependencies(): Unit

def clone(): AnyRef

def close(): Unit

def coalesce(numPartitions: Int, shuffle: Boolean)(implicit ord: Ordering[(K, V)]): RDD[(K, V)]

def collect[U](f: PartialFunction[(K, V), U])(implicit arg0: ClassTag[U]): RDD[U]

def collect(): Array[(K, V)]

def compute(part: Partition, context: TaskContext): Iterator[(K, V)]

RDD compute method.

val config: Config

def context: SparkContext

def count(): Long

def countApprox(timeout: Long, confidence: Double): PartialResult[BoundedDouble]

def countApproxDistinct(relativeSD: Double): Long

def countApproxDistinct(p: Int, sp: Int): Long

def countByValue()(implicit ord: Ordering[(K, V)]): Map[(K, V), Long]

def countByValueApprox(timeout: Long, confidence: Double)(implicit ord: Ordering[(K, V)]): PartialResult[Map[(K, V), BoundedDouble]]

final def dependencies: Seq[Dependency[_]]

def distinct(): RDD[(K, V)]

def distinct(numPartitions: Int)(implicit ord: Ordering[(K, V)]): RDD[(K, V)]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def exists(elem: K): Boolean

def filter(pred: (K, V) ⇒ Boolean): ShapeLuceneRDD[K, V]

def filter(f: ((K, V)) ⇒ Boolean): RDD[(K, V)]

def finalize(): Unit

def first(): (K, V)

def firstParent[U](implicit arg0: ClassTag[U]): RDD[U]

def flatMap[U](f: ((K, V)) ⇒ TraversableOnce[U])(implicit arg0: ClassTag[U]): RDD[U]

def fold(zeroValue: (K, V))(op: ((K, V), (K, V)) ⇒ (K, V)): (K, V)

def foreach(f: ((K, V)) ⇒ Unit): Unit

def foreachPartition(f: (Iterator[(K, V)]) ⇒ Unit): Unit

def getCheckpointFile: Option[String]

final def getClass(): Class[_]

def getDependencies: Seq[Dependency[_]]

final def getNumPartitions: Int

def getPartitions: Array[Partition]

def getPreferredLocations(s: Partition): Seq[String]

def getStorageLevel: StorageLevel

def glom(): RDD[Array[(K, V)]]

def groupBy[K](f: ((K, V)) ⇒ K, p: Partitioner)(implicit kt: ClassTag[K], ord: Ordering[K]): RDD[(K, Iterable[(K, V)])]

def groupBy[K](f: ((K, V)) ⇒ K, numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[(K, V)])]

def groupBy[K](f: ((K, V)) ⇒ K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[(K, V)])]

def hashCode(): Int

val id: Int

def intersection(other: RDD[(K, V)], numPartitions: Int): RDD[(K, V)]

def intersection(other: RDD[(K, V)], partitioner: Partitioner)(implicit ord: Ordering[(K, V)]): RDD[(K, V)]

def intersection(other: RDD[(K, V)]): RDD[(K, V)]

def isCheckpointed: Boolean

def isEmpty(): Boolean

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

final def iterator(split: Partition, context: TaskContext): Iterator[(K, V)]

def keyBy[K](f: ((K, V)) ⇒ K): RDD[(K, (K, V))]

def knnSearch(queryPoint: PointType, k: Int, searchString: String = ...): Iterable[SparkScoreDoc]

K-nearest neighbors search

def linkByKnn[T](that: RDD[T], pointFunctor: (T) ⇒ PointType, topK: Int = DefaultTopK)(implicit arg0: ClassTag[T]): RDD[(T, List[SparkScoreDoc])]

Link entities based on k-nearest neighbors (Knn)

def linkByRadius[T](that: RDD[T], pointFunctor: (T) ⇒ PointType, radius: Double, topK: Int = DefaultTopK, spatialOp: String = SpatialOperation.Intersects.getName)(implicit arg0: ClassTag[T]): RDD[(T, List[SparkScoreDoc])]

Link entities if their shapes are within a distance in kilometers (km)

def linkDataFrameByKnn(other: DataFrame, searchQueryGen: (Row) ⇒ PointType, topK: Int = DefaultTopK): RDD[(Row, List[SparkScoreDoc])]

Link with DataFrame based on k-nearest neighbors (Knn)

def linkDataFrameByRadius(other: DataFrame, pointFunctor: (Row) ⇒ PointType, radius: Double, topK: Int = DefaultTopK, spatialOp: String = SpatialOperation.Intersects.getName): RDD[(Row, List[SparkScoreDoc])]

Link entities if their shapes are within a distance in kilometers (km)

def localCheckpoint(): ShapeLuceneRDD.this.type

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

def map[U](f: ((K, V)) ⇒ U)(implicit arg0: ClassTag[U]): RDD[U]

def mapPartitions[U](f: (Iterator[(K, V)]) ⇒ Iterator[U], preservesPartitioning: Boolean)(implicit arg0: ClassTag[U]): RDD[U]

def mapPartitionsWithIndex[U](f: (Int, Iterator[(K, V)]) ⇒ Iterator[U], preservesPartitioning: Boolean)(implicit arg0: ClassTag[U]): RDD[U]

def max()(implicit ord: Ordering[(K, V)]): (K, V)

def min()(implicit ord: Ordering[(K, V)]): (K, V)

var name: String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def parent[U](j: Int)(implicit arg0: ClassTag[U]): RDD[U]

val partitioner: Option[Partitioner]

final def partitions: Array[Partition]

def persist(newLevel: StorageLevel): ShapeLuceneRDD.this.type

def persist(): ShapeLuceneRDD.this.type

def pipe(command: Seq[String], env: Map[String, String], printPipeContext: ((String) ⇒ Unit) ⇒ Unit, printRDDElement: ((K, V), (String) ⇒ Unit) ⇒ Unit, separateWorkingDir: Boolean): RDD[String]

def pipe(command: String, env: Map[String, String]): RDD[String]

def pipe(command: String): RDD[String]

final def preferredLocations(split: Partition): Seq[String]

def randomSplit(weights: Array[Double], seed: Long): Array[RDD[(K, V)]]

def reduce(f: ((K, V), (K, V)) ⇒ (K, V)): (K, V)

def repartition(numPartitions: Int)(implicit ord: Ordering[(K, V)]): RDD[(K, V)]

def sample(withReplacement: Boolean, fraction: Double, seed: Long): RDD[(K, V)]

def saveAsObjectFile(path: String): Unit

def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]): Unit

def saveAsTextFile(path: String): Unit

def setName(_name: String): ShapeLuceneRDD.this.type

def sortBy[K](f: ((K, V)) ⇒ K, ascending: Boolean, numPartitions: Int)(implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[(K, V)]

def sparkContext: SparkContext

def spatialSearch(point: PointType, k: Int, operationName: String): Iterable[SparkScoreDoc]

Spatial search with a single Point

def spatialSearch(shapeWKT: String, k: Int, operationName: String = SpatialOperation.Intersects.getName): Iterable[SparkScoreDoc]

Spatial search with arbitrary shape

def subtract(other: RDD[(K, V)], p: Partitioner)(implicit ord: Ordering[(K, V)]): RDD[(K, V)]

def subtract(other: RDD[(K, V)], numPartitions: Int): RDD[(K, V)]

def subtract(other: RDD[(K, V)]): RDD[(K, V)]

final def synchronized[T0](arg0: ⇒ T0): T0

def take(num: Int): Array[(K, V)]

def takeOrdered(num: Int)(implicit ord: Ordering[(K, V)]): Array[(K, V)]

def takeSample(withReplacement: Boolean, num: Int, seed: Long): Array[(K, V)]

def toDebugString: String

def toJavaRDD(): JavaRDD[(K, V)]

def toLocalIterator: Iterator[(K, V)]

def toString(): String

def top(num: Int)(implicit ord: Ordering[(K, V)]): Array[(K, V)]

def treeAggregate[U](zeroValue: U)(seqOp: (U, (K, V)) ⇒ U, combOp: (U, U) ⇒ U, depth: Int)(implicit arg0: ClassTag[U]): U

def treeReduce(f: ((K, V), (K, V)) ⇒ (K, V), depth: Int): (K, V)

def union(other: RDD[(K, V)]): RDD[(K, V)]

def unpersist(blocking: Boolean = true): ShapeLuceneRDD.this.type

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

def zip[U](other: RDD[U])(implicit arg0: ClassTag[U]): RDD[((K, V), U)]

def zipPartitions[B, C, D, V](rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D])(f: (Iterator[(K, V)], Iterator[B], Iterator[C], Iterator[D]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[D], arg3: ClassTag[V]): RDD[V]

def zipPartitions[B, C, D, V](rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D], preservesPartitioning: Boolean)(f: (Iterator[(K, V)], Iterator[B], Iterator[C], Iterator[D]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[D], arg3: ClassTag[V]): RDD[V]

def zipPartitions[B, C, V](rdd2: RDD[B], rdd3: RDD[C])(f: (Iterator[(K, V)], Iterator[B], Iterator[C]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[V]): RDD[V]

def zipPartitions[B, C, V](rdd2: RDD[B], rdd3: RDD[C], preservesPartitioning: Boolean)(f: (Iterator[(K, V)], Iterator[B], Iterator[C]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[C], arg2: ClassTag[V]): RDD[V]

def zipPartitions[B, V](rdd2: RDD[B])(f: (Iterator[(K, V)], Iterator[B]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[V]): RDD[V]

def zipPartitions[B, V](rdd2: RDD[B], preservesPartitioning: Boolean)(f: (Iterator[(K, V)], Iterator[B]) ⇒ Iterator[V])(implicit arg0: ClassTag[B], arg1: ClassTag[V]): RDD[V]

def zipWithIndex(): RDD[((K, V), Long)]

def zipWithUniqueId(): RDD[((K, V), Long)]

def filterWith[A](constructA: (Int) ⇒ A)(p: ((K, V), A) ⇒ Boolean): RDD[(K, V)]

def flatMapWith[A, U](constructA: (Int) ⇒ A, preservesPartitioning: Boolean)(f: ((K, V), A) ⇒ Seq[U])(implicit arg0: ClassTag[U]): RDD[U]

def foreachWith[A](constructA: (Int) ⇒ A)(f: ((K, V), A) ⇒ Unit): Unit

def mapPartitionsWithContext[U](f: (TaskContext, Iterator[(K, V)]) ⇒ Iterator[U], preservesPartitioning: Boolean)(implicit arg0: ClassTag[U]): RDD[U]

def mapPartitionsWithSplit[U](f: (Int, Iterator[(K, V)]) ⇒ Iterator[U], preservesPartitioning: Boolean)(implicit arg0: ClassTag[U]): RDD[U]

def mapWith[A, U](constructA: (Int) ⇒ A, preservesPartitioning: Boolean)(f: ((K, V), A) ⇒ U)(implicit arg0: ClassTag[U]): RDD[U]

def toArray(): Array[(K, V)]