Repository: spark Updated Branches: refs/heads/master 4ee79c71a -> 5b9760de8
http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/core/src/main/scala/org/apache/spark/sql/dsl/package.scala ---------------------------------------------------------------------- diff --git a/sql/core/src/main/scala/org/apache/spark/sql/dsl/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/dsl/package.scala deleted file mode 100644 index 4c44e17..0000000 --- a/sql/core/src/main/scala/org/apache/spark/sql/dsl/package.scala +++ /dev/null @@ -1,496 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql - -import java.sql.{Timestamp, Date} - -import scala.language.implicitConversions -import scala.reflect.runtime.universe.{TypeTag, typeTag} - -import org.apache.spark.sql.catalyst.ScalaReflection -import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.types.DataType - - -package object dsl { - - implicit def symbolToColumn(s: Symbol): ColumnName = new ColumnName(s.name) - - /** Converts $"col name" into an [[Column]]. */ - implicit class StringToColumn(val sc: StringContext) extends AnyVal { - def $(args: Any*): ColumnName = { - new ColumnName(sc.s(args :_*)) - } - } - - private[this] implicit def toColumn(expr: Expression): Column = new Column(expr) - - def sum(e: Column): Column = Sum(e.expr) - def sumDistinct(e: Column): Column = SumDistinct(e.expr) - def count(e: Column): Column = Count(e.expr) - - @scala.annotation.varargs - def countDistinct(expr: Column, exprs: Column*): Column = - CountDistinct((expr +: exprs).map(_.expr)) - - def avg(e: Column): Column = Average(e.expr) - def first(e: Column): Column = First(e.expr) - def last(e: Column): Column = Last(e.expr) - def min(e: Column): Column = Min(e.expr) - def max(e: Column): Column = Max(e.expr) - - def upper(e: Column): Column = Upper(e.expr) - def lower(e: Column): Column = Lower(e.expr) - def sqrt(e: Column): Column = Sqrt(e.expr) - def abs(e: Column): Column = Abs(e.expr) - - // scalastyle:off - - object literals { - - implicit def booleanToLiteral(b: Boolean): Column = Literal(b) - - implicit def byteToLiteral(b: Byte): Column = Literal(b) - - implicit def shortToLiteral(s: Short): Column = Literal(s) - - implicit def intToLiteral(i: Int): Column = Literal(i) - - implicit def longToLiteral(l: Long): Column = Literal(l) - - implicit def floatToLiteral(f: Float): Column = Literal(f) - - implicit def doubleToLiteral(d: Double): Column = Literal(d) - - implicit def stringToLiteral(s: String): Column = Literal(s) - - implicit def dateToLiteral(d: Date): Column = Literal(d) - - implicit def bigDecimalToLiteral(d: BigDecimal): Column = Literal(d.underlying()) - - implicit def bigDecimalToLiteral(d: java.math.BigDecimal): Column = Literal(d) - - implicit def timestampToLiteral(t: Timestamp): Column = Literal(t) - - implicit def binaryToLiteral(a: Array[Byte]): Column = Literal(a) - } - - - /* Use the following code to generate: - (0 to 22).map { x => - val types = (1 to x).foldRight("RT")((i, s) => {s"A$i, $s"}) - val typeTags = (1 to x).map(i => s"A$i: TypeTag").foldLeft("RT: TypeTag")(_ + ", " + _) - val args = (1 to x).map(i => s"arg$i: Column").mkString(", ") - val argsInUdf = (1 to x).map(i => s"arg$i.expr").mkString(", ") - println(s""" - /** - * Call a Scala function of ${x} arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[$typeTags](f: Function$x[$types]${if (args.length > 0) ", " + args else ""}): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq($argsInUdf)) - }""") - } - - (0 to 22).map { x => - val args = (1 to x).map(i => s"arg$i: Column").mkString(", ") - val fTypes = Seq.fill(x + 1)("_").mkString(", ") - val argsInUdf = (1 to x).map(i => s"arg$i.expr").mkString(", ") - println(s""" - /** - * Call a Scala function of ${x} arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function$x[$fTypes], returnType: DataType${if (args.length > 0) ", " + args else ""}): Column = { - ScalaUdf(f, returnType, Seq($argsInUdf)) - }""") - } - } - */ - /** - * Call a Scala function of 0 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag](f: Function0[RT]): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq()) - } - - /** - * Call a Scala function of 1 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag](f: Function1[A1, RT], arg1: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr)) - } - - /** - * Call a Scala function of 2 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag](f: Function2[A1, A2, RT], arg1: Column, arg2: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr)) - } - - /** - * Call a Scala function of 3 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag](f: Function3[A1, A2, A3, RT], arg1: Column, arg2: Column, arg3: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr)) - } - - /** - * Call a Scala function of 4 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag](f: Function4[A1, A2, A3, A4, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr)) - } - - /** - * Call a Scala function of 5 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag](f: Function5[A1, A2, A3, A4, A5, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr)) - } - - /** - * Call a Scala function of 6 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag](f: Function6[A1, A2, A3, A4, A5, A6, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr)) - } - - /** - * Call a Scala function of 7 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag](f: Function7[A1, A2, A3, A4, A5, A6, A7, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr)) - } - - /** - * Call a Scala function of 8 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag](f: Function8[A1, A2, A3, A4, A5, A6, A7, A8, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr)) - } - - /** - * Call a Scala function of 9 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag](f: Function9[A1, A2, A3, A4, A5, A6, A7, A8, A9, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr)) - } - - /** - * Call a Scala function of 10 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag](f: Function10[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr)) - } - - /** - * Call a Scala function of 11 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag](f: Function11[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr)) - } - - /** - * Call a Scala function of 12 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag](f: Function12[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr)) - } - - /** - * Call a Scala function of 13 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag](f: Function13[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr)) - } - - /** - * Call a Scala function of 14 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag](f: Function14[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr)) - } - - /** - * Call a Scala function of 15 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag](f: Function15[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr)) - } - - /** - * Call a Scala function of 16 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag](f: Function16[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column, arg16: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr, arg16.expr)) - } - - /** - * Call a Scala function of 17 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag](f: Function17[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column, arg16: Column, arg17: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr, arg16.expr, arg17.expr)) - } - - /** - * Call a Scala function of 18 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag](f: Function18[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column, arg16: Column, arg17: Column, arg18: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr, arg16.expr, arg17.expr, arg18.expr)) - } - - /** - * Call a Scala function of 19 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag](f: Function19[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column, arg16: Column, arg17: Column, arg18: Column, arg19: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr, arg16.expr, arg17.expr, arg18.expr, arg19.expr)) - } - - /** - * Call a Scala function of 20 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag, A20: TypeTag](f: Function20[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column, arg16: Column, arg17: Column, arg18: Column, arg19: Column, arg20: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr, arg16.expr, arg17.expr, arg18.expr, arg19.expr, arg20.expr)) - } - - /** - * Call a Scala function of 21 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag, A20: TypeTag, A21: TypeTag](f: Function21[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, A21, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column, arg16: Column, arg17: Column, arg18: Column, arg19: Column, arg20: Column, arg21: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr, arg16.expr, arg17.expr, arg18.expr, arg19.expr, arg20.expr, arg21.expr)) - } - - /** - * Call a Scala function of 22 arguments as user-defined function (UDF), and automatically - * infer the data types based on the function's signature. - */ - def callUDF[RT: TypeTag, A1: TypeTag, A2: TypeTag, A3: TypeTag, A4: TypeTag, A5: TypeTag, A6: TypeTag, A7: TypeTag, A8: TypeTag, A9: TypeTag, A10: TypeTag, A11: TypeTag, A12: TypeTag, A13: TypeTag, A14: TypeTag, A15: TypeTag, A16: TypeTag, A17: TypeTag, A18: TypeTag, A19: TypeTag, A20: TypeTag, A21: TypeTag, A22: TypeTag](f: Function22[A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20, A21, A22, RT], arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column, arg16: Column, arg17: Column, arg18: Column, arg19: Column, arg20: Column, arg21: Column, arg22: Column): Column = { - ScalaUdf(f, ScalaReflection.schemaFor(typeTag[RT]).dataType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr, arg16.expr, arg17.expr, arg18.expr, arg19.expr, arg20.expr, arg21.expr, arg22.expr)) - } - - ////////////////////////////////////////////////////////////////////////////////////////////////// - - /** - * Call a Scala function of 0 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function0[_], returnType: DataType): Column = { - ScalaUdf(f, returnType, Seq()) - } - - /** - * Call a Scala function of 1 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function1[_, _], returnType: DataType, arg1: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr)) - } - - /** - * Call a Scala function of 2 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function2[_, _, _], returnType: DataType, arg1: Column, arg2: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr)) - } - - /** - * Call a Scala function of 3 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function3[_, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr)) - } - - /** - * Call a Scala function of 4 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function4[_, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr)) - } - - /** - * Call a Scala function of 5 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function5[_, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr)) - } - - /** - * Call a Scala function of 6 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function6[_, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr)) - } - - /** - * Call a Scala function of 7 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function7[_, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr)) - } - - /** - * Call a Scala function of 8 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function8[_, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr)) - } - - /** - * Call a Scala function of 9 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function9[_, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr)) - } - - /** - * Call a Scala function of 10 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function10[_, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr)) - } - - /** - * Call a Scala function of 11 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function11[_, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr)) - } - - /** - * Call a Scala function of 12 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function12[_, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr)) - } - - /** - * Call a Scala function of 13 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function13[_, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr)) - } - - /** - * Call a Scala function of 14 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function14[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr)) - } - - /** - * Call a Scala function of 15 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function15[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr)) - } - - /** - * Call a Scala function of 16 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function16[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column, arg16: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr, arg16.expr)) - } - - /** - * Call a Scala function of 17 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function17[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column, arg16: Column, arg17: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr, arg16.expr, arg17.expr)) - } - - /** - * Call a Scala function of 18 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function18[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column, arg16: Column, arg17: Column, arg18: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr, arg16.expr, arg17.expr, arg18.expr)) - } - - /** - * Call a Scala function of 19 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function19[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column, arg16: Column, arg17: Column, arg18: Column, arg19: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr, arg16.expr, arg17.expr, arg18.expr, arg19.expr)) - } - - /** - * Call a Scala function of 20 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function20[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column, arg16: Column, arg17: Column, arg18: Column, arg19: Column, arg20: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr, arg16.expr, arg17.expr, arg18.expr, arg19.expr, arg20.expr)) - } - - /** - * Call a Scala function of 21 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function21[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column, arg16: Column, arg17: Column, arg18: Column, arg19: Column, arg20: Column, arg21: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr, arg16.expr, arg17.expr, arg18.expr, arg19.expr, arg20.expr, arg21.expr)) - } - - /** - * Call a Scala function of 22 arguments as user-defined function (UDF). This requires - * you to specify the return data type. - */ - def callUDF(f: Function22[_, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _], returnType: DataType, arg1: Column, arg2: Column, arg3: Column, arg4: Column, arg5: Column, arg6: Column, arg7: Column, arg8: Column, arg9: Column, arg10: Column, arg11: Column, arg12: Column, arg13: Column, arg14: Column, arg15: Column, arg16: Column, arg17: Column, arg18: Column, arg19: Column, arg20: Column, arg21: Column, arg22: Column): Column = { - ScalaUdf(f, returnType, Seq(arg1.expr, arg2.expr, arg3.expr, arg4.expr, arg5.expr, arg6.expr, arg7.expr, arg8.expr, arg9.expr, arg10.expr, arg11.expr, arg12.expr, arg13.expr, arg14.expr, arg15.expr, arg16.expr, arg17.expr, arg18.expr, arg19.expr, arg20.expr, arg21.expr, arg22.expr)) - } - - // scalastyle:on -} http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala ---------------------------------------------------------------------- diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala index e1e9692..cccc547 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql import org.apache.spark.sql.TestData._ import org.apache.spark.sql.columnar._ -import org.apache.spark.sql.dsl._ +import org.apache.spark.sql.api.scala.dsl._ import org.apache.spark.sql.test.TestSQLContext._ import org.apache.spark.storage.{StorageLevel, RDDBlockId} http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala ---------------------------------------------------------------------- diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala index 701950f..8202931 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql -import org.apache.spark.sql.dsl._ +import org.apache.spark.sql.api.scala.dsl._ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.types.{BooleanType, IntegerType, StructField, StructType} @@ -244,7 +244,7 @@ class ColumnExpressionSuite extends QueryTest { ) checkAnswer( - testData.select(sqrt(Literal(null))), + testData.select(sqrt(lit(null))), (1 to 100).map(_ => Row(null)) ) } @@ -261,7 +261,7 @@ class ColumnExpressionSuite extends QueryTest { ) checkAnswer( - testData.select(abs(Literal(null))), + testData.select(abs(lit(null))), (1 to 100).map(_ => Row(null)) ) } @@ -278,7 +278,7 @@ class ColumnExpressionSuite extends QueryTest { ) checkAnswer( - testData.select(upper(Literal(null))), + testData.select(upper(lit(null))), (1 to 100).map(n => Row(null)) ) } @@ -295,7 +295,7 @@ class ColumnExpressionSuite extends QueryTest { ) checkAnswer( - testData.select(lower(Literal(null))), + testData.select(lower(lit(null))), (1 to 100).map(n => Row(null)) ) } http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala ---------------------------------------------------------------------- diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index ec3770b..b1fb1bd 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql -import org.apache.spark.sql.dsl._ +import org.apache.spark.sql.api.scala.dsl._ import org.apache.spark.sql.types._ /* Implicits */ @@ -57,13 +57,13 @@ class DataFrameSuite extends QueryTest { test("convert $\"attribute name\" into unresolved attribute") { checkAnswer( - testData.where($"key" === Literal(1)).select($"value"), + testData.where($"key" === lit(1)).select($"value"), Row("1")) } test("convert Scala Symbol 'attrname into unresolved attribute") { checkAnswer( - testData.where('key === Literal(1)).select('value), + testData.where('key === lit(1)).select('value), Row("1")) } @@ -75,13 +75,13 @@ class DataFrameSuite extends QueryTest { test("simple select") { checkAnswer( - testData.where('key === Literal(1)).select('value), + testData.where('key === lit(1)).select('value), Row("1")) } test("select with functions") { checkAnswer( - testData.select(sum('value), avg('value), count(Literal(1))), + testData.select(sum('value), avg('value), count(lit(1))), Row(5050.0, 50.5, 100)) checkAnswer( @@ -215,7 +215,7 @@ class DataFrameSuite extends QueryTest { ) checkAnswer( - testData3.agg(count('a), count('b), count(Literal(1)), countDistinct('a), countDistinct('b)), + testData3.agg(count('a), count('b), count(lit(1)), countDistinct('a), countDistinct('b)), Row(2, 1, 2, 2, 1) ) http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala ---------------------------------------------------------------------- diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala index 561db59..bb95248 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql import org.scalatest.BeforeAndAfterEach import org.apache.spark.sql.TestData._ -import org.apache.spark.sql.dsl._ +import org.apache.spark.sql.api.scala.dsl._ import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation import org.apache.spark.sql.execution.joins._ import org.apache.spark.sql.test.TestSQLContext._ @@ -136,8 +136,8 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { } test("inner join, where, multiple matches") { - val x = testData2.where($"a" === Literal(1)).as("x") - val y = testData2.where($"a" === Literal(1)).as("y") + val x = testData2.where($"a" === 1).as("x") + val y = testData2.where($"a" === 1).as("y") checkAnswer( x.join(y).where($"x.a" === $"y.a"), Row(1,1,1,1) :: @@ -148,8 +148,8 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { } test("inner join, no matches") { - val x = testData2.where($"a" === Literal(1)).as("x") - val y = testData2.where($"a" === Literal(2)).as("y") + val x = testData2.where($"a" === 1).as("x") + val y = testData2.where($"a" === 2).as("y") checkAnswer( x.join(y).where($"x.a" === $"y.a"), Nil) @@ -185,7 +185,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { Row(6, "F", null, null) :: Nil) checkAnswer( - upperCaseData.join(lowerCaseData, $"n" === $"N" && $"n" > Literal(1), "left"), + upperCaseData.join(lowerCaseData, $"n" === $"N" && $"n" > 1, "left"), Row(1, "A", null, null) :: Row(2, "B", 2, "b") :: Row(3, "C", 3, "c") :: @@ -194,7 +194,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { Row(6, "F", null, null) :: Nil) checkAnswer( - upperCaseData.join(lowerCaseData, $"n" === $"N" && $"N" > Literal(1), "left"), + upperCaseData.join(lowerCaseData, $"n" === $"N" && $"N" > 1, "left"), Row(1, "A", null, null) :: Row(2, "B", 2, "b") :: Row(3, "C", 3, "c") :: @@ -247,7 +247,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { Row(null, null, 5, "E") :: Row(null, null, 6, "F") :: Nil) checkAnswer( - lowerCaseData.join(upperCaseData, $"n" === $"N" && $"n" > Literal(1), "right"), + lowerCaseData.join(upperCaseData, $"n" === $"N" && $"n" > 1, "right"), Row(null, null, 1, "A") :: Row(2, "b", 2, "B") :: Row(3, "c", 3, "C") :: @@ -255,7 +255,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { Row(null, null, 5, "E") :: Row(null, null, 6, "F") :: Nil) checkAnswer( - lowerCaseData.join(upperCaseData, $"n" === $"N" && $"N" > Literal(1), "right"), + lowerCaseData.join(upperCaseData, $"n" === $"N" && $"N" > 1, "right"), Row(null, null, 1, "A") :: Row(2, "b", 2, "B") :: Row(3, "c", 3, "C") :: @@ -298,8 +298,8 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { } test("full outer join") { - upperCaseData.where('N <= Literal(4)).registerTempTable("left") - upperCaseData.where('N >= Literal(3)).registerTempTable("right") + upperCaseData.where('N <= 4).registerTempTable("left") + upperCaseData.where('N >= 3).registerTempTable("right") val left = UnresolvedRelation(Seq("left"), None) val right = UnresolvedRelation(Seq("right"), None) @@ -314,7 +314,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { Row(null, null, 6, "F") :: Nil) checkAnswer( - left.join(right, ($"left.N" === $"right.N") && ($"left.N" !== Literal(3)), "full"), + left.join(right, ($"left.N" === $"right.N") && ($"left.N" !== 3), "full"), Row(1, "A", null, null) :: Row(2, "B", null, null) :: Row(3, "C", null, null) :: @@ -324,7 +324,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { Row(null, null, 6, "F") :: Nil) checkAnswer( - left.join(right, ($"left.N" === $"right.N") && ($"right.N" !== Literal(3)), "full"), + left.join(right, ($"left.N" === $"right.N") && ($"right.N" !== 3), "full"), Row(1, "A", null, null) :: Row(2, "B", null, null) :: Row(3, "C", null, null) :: http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala ---------------------------------------------------------------------- diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index c00ae0a..9bb6403 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -21,7 +21,7 @@ import java.util.TimeZone import org.scalatest.BeforeAndAfterAll -import org.apache.spark.sql.dsl._ +import org.apache.spark.sql.api.scala.dsl._ import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.types._ http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala ---------------------------------------------------------------------- diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala index 82dd669..eae6acf 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql import java.sql.Timestamp import org.apache.spark.sql.catalyst.plans.logical -import org.apache.spark.sql.dsl._ +import org.apache.spark.sql.api.scala.dsl._ import org.apache.spark.sql.test._ /* Implicits */ http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala ---------------------------------------------------------------------- diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala index 5abd7b9..b122d7d 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql -import org.apache.spark.sql.dsl.StringToColumn +import org.apache.spark.sql.api.scala.dsl.StringToColumn import org.apache.spark.sql.test._ /* Implicits */ http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala ---------------------------------------------------------------------- diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala index 62b2e89..59e6f00 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql import scala.beans.{BeanInfo, BeanProperty} import org.apache.spark.rdd.RDD -import org.apache.spark.sql.dsl._ +import org.apache.spark.sql.api.scala.dsl._ import org.apache.spark.sql.test.TestSQLContext._ import org.apache.spark.sql.types._ http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala ---------------------------------------------------------------------- diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala index 6f051df..2698a59 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.columnar -import org.apache.spark.sql.dsl._ +import org.apache.spark.sql.api.scala.dsl._ import org.apache.spark.sql.TestData._ import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.test.TestSQLContext._ http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala ---------------------------------------------------------------------- diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala index be5e63c..1f701e2 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql.execution import org.scalatest.FunSuite import org.apache.spark.sql.{SQLConf, execution} -import org.apache.spark.sql.dsl._ +import org.apache.spark.sql.api.scala.dsl._ import org.apache.spark.sql.TestData._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans._ http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala ---------------------------------------------------------------------- diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala index 5a75326..634792c 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala @@ -21,15 +21,16 @@ import java.sql.{Date, Timestamp} import org.apache.spark.sql.TestData._ import org.apache.spark.sql.catalyst.util._ -import org.apache.spark.sql.dsl._ +import org.apache.spark.sql.api.scala.dsl._ import org.apache.spark.sql.json.JsonRDD.{compatibleType, enforceCorrectType} import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext._ import org.apache.spark.sql.types._ -import org.apache.spark.sql.{Literal, QueryTest, Row, SQLConf} +import org.apache.spark.sql.{QueryTest, Row, SQLConf} class JsonSuite extends QueryTest { import org.apache.spark.sql.json.TestJsonData._ + TestJsonData test("Type promotion") { @@ -464,8 +465,8 @@ class JsonSuite extends QueryTest { // in the Project. checkAnswer( jsonDF. - where('num_str > Literal(BigDecimal("92233720368547758060"))). - select(('num_str + Literal(1.2)).as("num")), + where('num_str > BigDecimal("92233720368547758060")). + select(('num_str + 1.2).as("num")), Row(new java.math.BigDecimal("92233720368547758061.2")) ) http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala ---------------------------------------------------------------------- diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala index f03b3a3..0e91834 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala @@ -33,7 +33,7 @@ import parquet.schema.{MessageType, MessageTypeParser} import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.spark.sql.{DataFrame, QueryTest, SQLConf} -import org.apache.spark.sql.dsl._ +import org.apache.spark.sql.api.scala.dsl._ import org.apache.spark.sql.catalyst.ScalaReflection import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.test.TestSQLContext http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/hive/src/main/scala/org/apache/spark/sql/hive/package-info.java ---------------------------------------------------------------------- diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/package-info.java b/sql/hive/src/main/scala/org/apache/spark/sql/hive/package-info.java index 8b29fa7..4b23fbf 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/package-info.java +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/package-info.java @@ -15,4 +15,4 @@ * limitations under the License. */ -package org.apache.spark.sql.hive; \ No newline at end of file +package org.apache.spark.sql.hive; http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala ---------------------------------------------------------------------- diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala index 0c8a113..a485158 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala @@ -29,7 +29,7 @@ import org.apache.hadoop.hive.conf.HiveConf.ConfVars import org.apache.spark.{SparkFiles, SparkException} import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.catalyst.plans.logical.Project -import org.apache.spark.sql.dsl._ +import org.apache.spark.sql.api.scala.dsl._ import org.apache.spark.sql.hive._ import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ http://git-wip-us.apache.org/repos/asf/spark/blob/5b9760de/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala ---------------------------------------------------------------------- diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala index a081227..efea3d8 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTableScanSuite.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql.hive.execution import org.apache.spark.sql.Row -import org.apache.spark.sql.dsl._ +import org.apache.spark.sql.api.scala.dsl._ import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ --------------------------------------------------------------------- To 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