[GitHub] [spark] beliefer commented on a change in pull request #25416: [SPARK-28330][SQL] Support ANSI SQL: result offset clause in query expression

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beliefer commented on a change in pull request #25416: [SPARK-28330][SQL] 
Support ANSI SQL: result offset clause in query expression
URL: https://github.com/apache/spark/pull/25416#discussion_r333402192
 
 

 ##########
 File path: sql/core/src/main/scala/org/apache/spark/sql/execution/offset.scala
 ##########
 @@ -0,0 +1,66 @@
+/*
+ * 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.execution
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.serializer.Serializer
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, SortOrder}
+import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, 
SinglePartition}
+
+
+/**
+ * Skip the first `offset` elements and collect them to a single partition.
+ * This operator will be used when a logical `Offset` operation is the final 
operator in an
+ * logical plan, which happens when the user is collecting results back to the 
driver.
+ */
+case class CollectOffsetExec(offset: Int, child: SparkPlan) extends 
UnaryExecNode {
+
+  override def output: Seq[Attribute] = child.output
+
+  override def outputPartitioning: Partitioning = SinglePartition
+
+  override def outputOrdering: Seq[SortOrder] = child.outputOrdering
+
+  override def executeCollect(): Array[InternalRow] = 
child.executeCollect.drop(offset)
+
+  private val serializer: Serializer = new 
UnsafeRowSerializer(child.output.size)
+
+  protected override def doExecute(): RDD[InternalRow] = {
+    sparkContext.parallelize(executeCollect(), 1)
+  }
+
+}
+
+/**
+ * Skip the first `offset` elements and collect them to a single partition.
+ */
+case class OffsetExec(offset: Int, child: SparkPlan) extends UnaryExecNode {
+
+  override def output: Seq[Attribute] = child.output
+
+  override def outputOrdering: Seq[SortOrder] = child.outputOrdering
+
+  protected override def doExecute(): RDD[InternalRow] = {
+    val rdd = child.execute()
+    val arr = rdd.take(offset)
+    rdd.filter(!arr.contains(_))
 
 Review comment:
   @cloud-fan There are exists a problem the index of partition and the order 
of data are inconsistent.
   I have a new implement but not works file as I can't assurance the order of 
output produced by child plans.
   ```
     protected override def doExecute(): RDD[InternalRow] = {
       val rdd = child.execute()
       val partIdxToCountItr = rdd.mapPartitionsWithIndex{(partIdx, iter) => {
         val partIdxToRowCount = scala.collection.mutable.Map[Int,Int]()
         var rowCount = 0
         while(iter.hasNext){
           rowCount += 1
           iter.next()
         }
         partIdxToRowCount.put(partIdx, rowCount)
         partIdxToRowCount.iterator
       }}.collect().iterator
       var remainder = offset
       val partIdxToSkipCount = scala.collection.mutable.Map[Int,Int]()
       while (partIdxToCountItr.hasNext && remainder > 0) {
         val kv = partIdxToCountItr.next()
        val partIdx = kv._1
         val count = kv._2
         if (count > remainder) {
           partIdxToSkipCount(partIdx) = remainder
           remainder = 0
         } else {
           partIdxToSkipCount(partIdx) = count
           remainder -= count
         }
       }
       val broadcastPartIdxToSkipCount = 
sparkContext.broadcast(partIdxToSkipCount)
       rdd.mapPartitionsWithIndex{(partIdx, iter) => {
         val skipCount = broadcastPartIdxToSkipCount.value.getOrElse(partIdx, 0)
         iter.drop(skipCount)
       }}
     }
   ```

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