hvanhovell commented on a change in pull request #26434: [SPARK-29544] [SQL] 
optimize skewed partition based on data size
URL: https://github.com/apache/spark/pull/26434#discussion_r366346221
 
 

 ##########
 File path: 
sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/SkewedShuffledRowRDD.scala
 ##########
 @@ -0,0 +1,78 @@
+/*
+ * 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.adaptive
+
+import org.apache.spark._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.metric.{SQLMetric, 
SQLShuffleReadMetricsReporter}
+
+/**
+ * The [[Partition]] used by [[SkewedShuffledRowRDD]].
+ */
+class SkewedShuffledRowRDDPartition(override val index: Int) extends Partition
+
+/**
+ * This is a specialized version of 
[[org.apache.spark.sql.execution.ShuffledRowRDD]]. This is used
+ * in Spark SQL adaptive execution to solve data skew issues. This RDD 
includes rearranged
+ * partitions from mappers.
+ *
+ * This RDD takes a [[ShuffleDependency]] (`dependency`), a partitionIndex
+ * and the range of startMapIndex to endMapIndex.
+ */
+class SkewedShuffledRowRDD(
 
 Review comment:
   Why do we need a separate implementation of the shuffled row RDD? I am 
wondering if we can combine them all, and have a couple of partition 
implementations depending on which (mapper/reducer) coordinate we need to read 
from.

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