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From chenghao-intel <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-7322] [SQL] [WIP] Support Window Functi...
Date Tue, 19 May 2015 02:19:58 GMT
Github user chenghao-intel commented on a diff in the pull request:

    https://github.com/apache/spark/pull/6104#discussion_r30564645
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/WindowFunctionDefinition.scala
---
    @@ -0,0 +1,341 @@
    +/*
    + * 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 scala.language.implicitConversions
    +
    +import org.apache.spark.annotation.Experimental
    +import org.apache.spark.sql.catalyst.expressions._
    +
    +/**
    + * :: Experimental ::
    + * A set of methods for window function definition for aggregate expressions.
    + * For example:
    + * {{{
    + *   // predefine a window
    + *   val w = partitionBy("name").orderBy("id")
    + *
    + *   df.select(
    + *     first("value")
    + *       over(w).as("first_value"),
    + *     last("value")
    + *       over(w).as("last_value"),
    + *     avg("value")
    + *       over(
    + *       partitionBy("k1")
    + *       .orderBy("k2", "k3")
    + *       .rows
    + *       .following(1)).as("avg_value"),
    + *     max("value")
    + *       .over(
    + *       partitionBy("k2")
    + *       .orderBy("k3")
    + *       .range
    + *       .between
    + *       .preceding(4)
    + *       .and
    + *       .following(3)).as("max_value"))
    + *
    + * }}}
    + *
    + * @param column The bounded the aggregate/window function
    + * @param partitionSpec The partition of the window
    + * @param orderSpec The ordering of the window
    + * @param frame The Window Frame type
    + * @param bindLower A hint of when call the methods `.preceding(n)` `.currentRow()` `.following()`
    + *                  if bindLower == true, then we will set the lower bound, otherwise,
we should
    + *                  set the upper bound for the Row/Range Frame.
    + */
    +@Experimental
    +class WindowFunctionDefinition protected[sql](
    +    column: Column = null,
    +    partitionSpec: Seq[Expression] = Nil,
    +    orderSpec: Seq[SortOrder] = Nil,
    +    frame: WindowFrame = UnspecifiedFrame,
    +    bindLower: Boolean = true) {
    --- End diff --
    
    No, it's not always `true`, will be set `false` once the lower bound of the window specified.


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