Class

org.apache.spark.sql

SampleDataFrame

Related Doc: package sql

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final class SampleDataFrame extends DataFrame with Serializable

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Dataset[Row], Serializable, Serializable, AnyRef, Any
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  1. SampleDataFrame
  2. Dataset
  3. Serializable
  4. Serializable
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Instance Constructors

  1. new SampleDataFrame(snappySession: SnappySession, logicalPlan: LogicalPlan)

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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def agg(expr: Column, exprs: Column*): DataFrame

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    Definition Classes
    Dataset
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    @varargs()

  5. def agg(exprs: Map[String, String]): DataFrame

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    Definition Classes
    Dataset

  6. def agg(exprs: Map[String, String]): DataFrame

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    Definition Classes
    Dataset

  7. def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame

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    Definition Classes
    Dataset

  8. def alias(alias: Symbol): Dataset[Row]

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    Definition Classes
    Dataset

  9. def alias(alias: String): Dataset[Row]

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    Dataset

  10. def apply(colName: String): Column

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    Definition Classes
    Dataset

  11. def as(alias: Symbol): Dataset[Row]

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    Definition Classes
    Dataset

  12. def as(alias: String): Dataset[Row]

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    Definition Classes
    Dataset

  13. def as[U](implicit arg0: Encoder[U]): Dataset[U]

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    Definition Classes
    Dataset
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    @Experimental() @Evolving()

  14. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  15. def cache(): SampleDataFrame.this.type

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    Definition Classes
    Dataset

  16. def checkpoint(eager: Boolean): Dataset[Row]

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    Definition Classes
    Dataset
    Annotations
    @Experimental() @Evolving()

  17. def checkpoint(): Dataset[Row]

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    Definition Classes
    Dataset
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    @Experimental() @Evolving()

  18. def clone(): AnyRef

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    Attributes
    protected[java.lang]
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    AnyRef
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    @throws( ... )

  19. def coalesce(numPartitions: Int): Dataset[Row]

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    Definition Classes
    Dataset

  20. def col(colName: String): Column

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    Definition Classes
    Dataset

  21. def collect(): Array[Row]

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    Dataset

  22. def collectAsList(): List[Row]

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    Definition Classes
    Dataset

  23. def columns: Array[String]

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    Dataset

  24. def count(): Long

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    Dataset

  25. def createGlobalTempView(viewName: String): Unit

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    @throws( ... )

  26. def createOrReplaceTempView(viewName: String): Unit

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    Dataset

  27. def createTempView(viewName: String): Unit

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    Dataset
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    @throws( ... )

  28. def crossJoin(right: Dataset[_]): DataFrame

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    Dataset

  29. def cube(col1: String, cols: String*): RelationalGroupedDataset

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    Dataset
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    @varargs()

  30. def cube(cols: Column*): RelationalGroupedDataset

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    Dataset
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    @varargs()

  31. def describe(cols: String*): DataFrame

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    @varargs()

  32. def distinct(): Dataset[Row]

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    Dataset

  33. def drop(col: Column): DataFrame

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    Dataset

  34. def drop(colNames: String*): DataFrame

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    @varargs()

  35. def drop(colName: String): DataFrame

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    Definition Classes
    Dataset

  36. def dropDuplicates(col1: String, cols: String*): Dataset[Row]

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    Dataset
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    @varargs()

  37. def dropDuplicates(colNames: Array[String]): Dataset[Row]

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    Dataset

  38. def dropDuplicates(colNames: Seq[String]): Dataset[Row]

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    Dataset

  39. def dropDuplicates(): Dataset[Row]

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    Dataset

  40. def dtypes: Array[(String, String)]

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    Dataset

  41. final def eq(arg0: AnyRef): Boolean

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  42. def equals(arg0: Any): Boolean

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  43. def errorEstimateAverage(columnName: String, confidence: Double, groupByColumns: Set[String] = Set.empty): Map[Row, ErrorRow]

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  44. def errorStats(columnName: String, groupBy: Set[String] = Set.empty): MultiColumnOpenHashMap[StatCounter]

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  45. def except(other: Dataset[Row]): Dataset[Row]

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    Definition Classes
    Dataset

  46. def explain(): Unit

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    Dataset

  47. def explain(extended: Boolean): Unit

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    Dataset

  48. def filter(func: FilterFunction[Row]): Dataset[Row]

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    Dataset
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    @Experimental() @Evolving()

  49. def filter(func: (Row) ⇒ Boolean): Dataset[Row]

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    Dataset
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    @Experimental() @Evolving()

  50. def filter(conditionExpr: String): Dataset[Row]

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  51. def filter(condition: Column): Dataset[Row]

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    Dataset

  52. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )

  53. def first(): Row

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    Definition Classes
    Dataset

  54. def flatMap[U](f: FlatMapFunction[Row, U], encoder: Encoder[U]): Dataset[U]

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    @Experimental() @Evolving()

  55. def flatMap[U](func: (Row) ⇒ TraversableOnce[U])(implicit arg0: Encoder[U]): Dataset[U]

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    @Experimental() @Evolving()

  56. def foreach(func: ForeachFunction[Row]): Unit

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  57. def foreach(f: (Row) ⇒ Unit): Unit

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  58. def foreachPartition(func: ForeachPartitionFunction[Row]): Unit

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  59. def foreachPartition(f: (Iterator[Row]) ⇒ Unit): Unit

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  60. final def getClass(): Class[_]

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  61. def groupBy(col1: String, cols: String*): RelationalGroupedDataset

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    Dataset
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    @varargs()

  62. def groupBy(cols: Column*): RelationalGroupedDataset

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    @varargs()

  63. def groupByKey[K](func: MapFunction[Row, K], encoder: Encoder[K]): KeyValueGroupedDataset[K, Row]

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    Dataset
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    @Experimental() @Evolving()

  64. def groupByKey[K](func: (Row) ⇒ K)(implicit arg0: Encoder[K]): KeyValueGroupedDataset[K, Row]

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    @Experimental() @Evolving()

  65. def hashCode(): Int

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  66. def head(): Row

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  67. def head(n: Int): Array[Row]

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  68. var implementor: SampleDataFrameContract

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  69. def inputFiles: Array[String]

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  70. def intersect(other: Dataset[Row]): Dataset[Row]

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  71. final def isInstanceOf[T0]: Boolean

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  72. def isLocal: Boolean

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  73. def isStreaming: Boolean

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    @Experimental() @Evolving()

  74. def javaRDD: JavaRDD[Row]

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  75. def join(right: Dataset[_], joinExprs: Column, joinType: String): DataFrame

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  76. def join(right: Dataset[_], joinExprs: Column): DataFrame

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  77. def join(right: Dataset[_], usingColumns: Seq[String], joinType: String): DataFrame

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  78. def join(right: Dataset[_], usingColumns: Seq[String]): DataFrame

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  79. def join(right: Dataset[_], usingColumn: String): DataFrame

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  80. def join(right: Dataset[_]): DataFrame

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  81. def joinWith[U](other: Dataset[U], condition: Column): Dataset[(Row, U)]

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    @Experimental() @Evolving()

  82. def joinWith[U](other: Dataset[U], condition: Column, joinType: String): Dataset[(Row, U)]

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    @Experimental() @Evolving()

  83. def limit(n: Int): Dataset[Row]

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    Dataset

  84. val logicalPlan: LogicalPlan

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    SampleDataFrame → Dataset

  85. def map[U](func: MapFunction[Row, U], encoder: Encoder[U]): Dataset[U]

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    @Experimental() @Evolving()

  86. def map[U](func: (Row) ⇒ U)(implicit arg0: Encoder[U]): Dataset[U]

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    @Experimental() @Evolving()

  87. def mapPartitions[U](f: MapPartitionsFunction[Row, U], encoder: Encoder[U]): Dataset[U]

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    Definition Classes
    Dataset
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    @Experimental() @Evolving()

  88. def mapPartitions[U](func: (Iterator[Row]) ⇒ Iterator[U])(implicit arg0: Encoder[U]): Dataset[U]

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    Definition Classes
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    @Experimental() @Evolving()

  89. def na: DataFrameNaFunctions

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  90. final def ne(arg0: AnyRef): Boolean

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  91. final def notify(): Unit

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  92. final def notifyAll(): Unit

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  93. def orderBy(sortExprs: Column*): Dataset[Row]

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    @varargs()

  94. def orderBy(sortCol: String, sortCols: String*): Dataset[Row]

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    @varargs()

  95. def persist(newLevel: StorageLevel): SampleDataFrame.this.type

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    Dataset

  96. def persist(): SampleDataFrame.this.type

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    Dataset

  97. def printSchema(): Unit

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  98. val queryExecution: QueryExecution

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  99. def randomSplit(weights: Array[Double]): Array[Dataset[Row]]

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  100. def randomSplit(weights: Array[Double], seed: Long): Array[Dataset[Row]]

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  101. def randomSplitAsList(weights: Array[Double], seed: Long): List[Dataset[Row]]

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  102. lazy val rdd: RDD[Row]

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  103. def reduce(func: ReduceFunction[Row]): Row

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    @Experimental() @Evolving()

  104. def reduce(func: (Row, Row) ⇒ Row): Row

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    @Experimental() @Evolving()

  105. def registerSampleTable(tableName: String, baseTable: Option[String]): Unit

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  106. def repartition(partitionExprs: Column*): Dataset[Row]

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    @varargs()

  107. def repartition(numPartitions: Int, partitionExprs: Column*): Dataset[Row]

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    @varargs()

  108. def repartition(numPartitions: Int): Dataset[Row]

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    Dataset

  109. def rollup(col1: String, cols: String*): RelationalGroupedDataset

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    @varargs()

  110. def rollup(cols: Column*): RelationalGroupedDataset

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    @varargs()

  111. def sample(withReplacement: Boolean, fraction: Double): Dataset[Row]

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  112. def sample(withReplacement: Boolean, fraction: Double, seed: Long): Dataset[Row]

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  113. def schema: StructType

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  114. def select[U1, U2, U3, U4, U5](c1: TypedColumn[Row, U1], c2: TypedColumn[Row, U2], c3: TypedColumn[Row, U3], c4: TypedColumn[Row, U4], c5: TypedColumn[Row, U5]): Dataset[(U1, U2, U3, U4, U5)]

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    @Experimental() @Evolving()

  115. def select[U1, U2, U3, U4](c1: TypedColumn[Row, U1], c2: TypedColumn[Row, U2], c3: TypedColumn[Row, U3], c4: TypedColumn[Row, U4]): Dataset[(U1, U2, U3, U4)]

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    @Experimental() @Evolving()

  116. def select[U1, U2, U3](c1: TypedColumn[Row, U1], c2: TypedColumn[Row, U2], c3: TypedColumn[Row, U3]): Dataset[(U1, U2, U3)]

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  117. def select[U1, U2](c1: TypedColumn[Row, U1], c2: TypedColumn[Row, U2]): Dataset[(U1, U2)]

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    @Experimental() @Evolving()

  118. def select[U1](c1: TypedColumn[Row, U1]): Dataset[U1]

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    @Experimental() @Evolving()

  119. def select(col: String, cols: String*): DataFrame

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    @varargs()

  120. def select(cols: Column*): DataFrame

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    @varargs()

  121. def selectExpr(exprs: String*): DataFrame

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    @varargs()

  122. def selectUntyped(columns: TypedColumn[_, _]*): Dataset[_]

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    Attributes
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    Dataset

  123. def show(numRows: Int, truncate: Int): Unit

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  124. def show(numRows: Int, truncate: Boolean): Unit

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  125. def show(truncate: Boolean): Unit

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  126. def show(): Unit

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  127. def show(numRows: Int): Unit

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  128. val snappySession: SnappySession

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  129. def sort(sortExprs: Column*): Dataset[Row]

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    @varargs()

  130. def sort(sortCol: String, sortCols: String*): Dataset[Row]

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    @varargs()

  131. def sortWithinPartitions(sortExprs: Column*): Dataset[Row]

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    @varargs()

  132. def sortWithinPartitions(sortCol: String, sortCols: String*): Dataset[Row]

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    @varargs()

  133. val sparkSession: SparkSession

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  134. lazy val sqlContext: SQLContext

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  135. def stat: DataFrameStatFunctions

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  136. def storageLevel: StorageLevel

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

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  138. def take(n: Int): Array[Row]

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  139. def takeAsList(n: Int): List[Row]

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  140. def toDF(colNames: String*): DataFrame

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    @varargs()

  141. def toDF(): DataFrame

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  142. def toJSON: Dataset[String]

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  143. def toJavaRDD: JavaRDD[Row]

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  144. def toLocalIterator(): Iterator[Row]

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  145. def toString(): String

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    Dataset → AnyRef → Any

  146. def transform[U](t: (Dataset[Row]) ⇒ Dataset[U]): Dataset[U]

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  147. def union(other: Dataset[Row]): Dataset[Row]

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  148. def unpersist(): SampleDataFrame.this.type

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  149. def unpersist(blocking: Boolean): SampleDataFrame.this.type

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  150. final def wait(): Unit

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

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  152. final def wait(arg0: Long): Unit

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  153. def where(conditionExpr: String): Dataset[Row]

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  154. def where(condition: Column): Dataset[Row]

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  155. def withColumn(colName: String, col: Column): DataFrame

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  156. def withColumnRenamed(existingName: String, newName: String): DataFrame

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  157. def withWatermark(eventTime: String, delayThreshold: String): Dataset[Row]

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    @Experimental() @Evolving()

  158. def write: DataFrameWriter[Row]

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  159. def writeStream: DataStreamWriter[Row]

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    @Experimental() @Evolving()

Deprecated Value Members

  1. def explode[A, B](inputColumn: String, outputColumn: String)(f: (A) ⇒ TraversableOnce[B])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[B]): DataFrame

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    Definition Classes
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    @deprecated
    Deprecated

    (Since version 2.0.0) use flatMap() or select() with functions.explode() instead

  2. def explode[A <: Product](input: Column*)(f: (Row) ⇒ TraversableOnce[A])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[A]): DataFrame

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    Definition Classes
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    Annotations
    @deprecated
    Deprecated

    (Since version 2.0.0) use flatMap() or select() with functions.explode() instead

  3. def registerTempTable(tableName: String): Unit

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    Definition Classes
    Dataset
    Annotations
    @deprecated
    Deprecated

    (Since version 2.0.0) Use createOrReplaceTempView(viewName) instead.

  4. def unionAll(other: Dataset[Row]): Dataset[Row]

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    Definition Classes
    Dataset
    Annotations
    @deprecated
    Deprecated

    (Since version 2.0.0) use union()

Inherited from Dataset[Row]

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped