Class

org.apache.spark.sql

DataFrameWithTime

Related Doc: package sql

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final class DataFrameWithTime extends DataFrame with Serializable

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Dataset[Row], Serializable, Serializable, AnyRef, Any
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Inherited
  1. DataFrameWithTime
  2. Dataset
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new DataFrameWithTime(_snappySession: SnappySession, _logicalPlan: LogicalPlan, time: Long)

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Value Members

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

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    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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    Dataset
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    @varargs()

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

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

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    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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    Dataset

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

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    Dataset

  10. def apply(colName: String): Column

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    Dataset

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

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    Dataset

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

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    Dataset

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

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    Dataset
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    @Experimental() @Evolving()

  14. final def asInstanceOf[T0]: T0

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

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    Definition Classes
    Dataset

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

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    Dataset
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    @Experimental() @Evolving()

  17. def checkpoint(): Dataset[Row]

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    Dataset
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    @Experimental() @Evolving()

  18. def clone(): AnyRef

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    @throws( ... )

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

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    Dataset

  20. def col(colName: String): Column

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    Dataset

  21. def collect(): Array[Row]

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    Dataset

  22. def collectAsList(): List[Row]

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  23. def columns: Array[String]

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  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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  27. def createTempView(viewName: String): Unit

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    @throws( ... )

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

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

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    @varargs()

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

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

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    @varargs()

  35. def drop(colName: String): DataFrame

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

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

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

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  42. def equals(arg0: Any): Boolean

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

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    Dataset

  44. def explain(): Unit

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    Dataset

  45. def explain(extended: Boolean): Unit

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

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    Dataset
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    @Experimental() @Evolving()

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

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    @Experimental() @Evolving()

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

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

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  50. def finalize(): Unit

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

  51. def first(): Row

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    Dataset

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

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

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    @Experimental() @Evolving()

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

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

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

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

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

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

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    @varargs()

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

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    @varargs()

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

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    Dataset
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    @Experimental() @Evolving()

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

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    @Experimental() @Evolving()

  63. def hashCode(): Int

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  64. def head(): Row

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

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  66. def inputFiles: Array[String]

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

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  68. final def isInstanceOf[T0]: Boolean

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  69. def isLocal: Boolean

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  70. def isStreaming: Boolean

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    @Experimental() @Evolving()

  71. def javaRDD: JavaRDD[Row]

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

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

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

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

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

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

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

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    @Experimental() @Evolving()

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

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

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

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    @Experimental() @Evolving()

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

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    @Experimental() @Evolving()

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

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    @Experimental() @Evolving()

  84. 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()

  85. def na: DataFrameNaFunctions

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

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  87. final def notify(): Unit

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  88. final def notifyAll(): Unit

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

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    @varargs()

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

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    @varargs()

  91. def persist(newLevel: StorageLevel): DataFrameWithTime.this.type

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  92. def persist(): DataFrameWithTime.this.type

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  93. def printSchema(): Unit

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  94. val queryExecution: QueryExecution

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

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

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

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  98. lazy val rdd: RDD[Row]

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

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    @Experimental() @Evolving()

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

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    @Experimental() @Evolving()

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

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    @varargs()

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

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

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

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    @varargs()

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

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    @varargs()

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

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

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  108. def schema: StructType

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  109. 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()

  110. 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()

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

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

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

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    @varargs()

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

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    @varargs()

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

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    @varargs()

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

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    Attributes
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  118. def show(numRows: Int, truncate: Int): Unit

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

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  120. def show(truncate: Boolean): Unit

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  121. def show(): Unit

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  122. def show(numRows: Int): Unit

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

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    @varargs()

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

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    @varargs()

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

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    @varargs()

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

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    @varargs()

  127. val sparkSession: SparkSession

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  128. lazy val sqlContext: SQLContext

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  129. def stat: DataFrameStatFunctions

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  130. def storageLevel: StorageLevel

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

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

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

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  134. val time: Long

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

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    @varargs()

  136. def toDF(): DataFrame

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  137. def toJSON: Dataset[String]

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  138. def toJavaRDD: JavaRDD[Row]

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  139. def toLocalIterator(): Iterator[Row]

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  140. def toString(): String

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

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

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  143. def unpersist(): DataFrameWithTime.this.type

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  144. def unpersist(blocking: Boolean): DataFrameWithTime.this.type

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  145. final def wait(): Unit

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

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

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

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

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

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

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

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    @Experimental() @Evolving()

  153. def write: DataFrameWriter[Row]

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  154. 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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    @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