diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ColumnarBatchScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ColumnarBatchScan.scala
index 088681628cc3d..05c861e353dad 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ColumnarBatchScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ColumnarBatchScan.scala
@@ -1,4 +1,3 @@
-<<<<<<< fc1f6aa66c67e7d97ded9a9816ad0fc3c7439ca2
 /*
  * Licensed to the Apache Software Foundation (ASF) under one or more
  * contributor license agreements.  See the NOTICE file distributed with
@@ -150,166 +149,3 @@ private[sql] trait ColumnarBatchScan extends CodegenSupport {
   }
 
 }
-=======
-/*
- * 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.SparkConf
-import org.apache.spark.sql.catalyst.expressions.UnsafeRow
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenContext, ExprCode}
-import org.apache.spark.sql.execution.columnar.InMemoryTableScanExec
-import org.apache.spark.sql.execution.metric.SQLMetrics
-import org.apache.spark.sql.execution.vectorized.{ColumnarBatch, ColumnVector, OnHeapUnsafeColumnVector}
-import org.apache.spark.sql.types.DataType
-
-
-/**
- * Helper trait for abstracting scan functionality using
- * [[org.apache.spark.sql.execution.vectorized.ColumnarBatch]]es.
- */
-private[sql] trait ColumnarBatchScan extends CodegenSupport {
-
-  val columnIndexes: Array[Int] = null
-
-  override lazy val metrics = Map(
-    "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows"),
-    "scanTime" -> SQLMetrics.createTimingMetric(sparkContext, "scan time"))
-
-  val inMemoryTableScan: InMemoryTableScanExec = null
-
-  lazy val enableScanStatistics: Boolean =
-    sqlContext.getConf("spark.sql.inMemoryTableScanStatistics.enable", "false").toBoolean
-
-  /**
-   * Generate [[ColumnVector]] expressions for our parent to consume as rows.
-   * This is called once per [[ColumnarBatch]].
-   */
-  private def genCodeColumnVector(
-      ctx: CodegenContext,
-      columnVar: String,
-      ordinal: String,
-      dataType: DataType,
-      nullable: Boolean): ExprCode = {
-    val javaType = ctx.javaType(dataType)
-    val value = ctx.getValue(columnVar, dataType, ordinal)
-    val isNullVar = if (nullable) { ctx.freshName("isNull") } else { "false" }
-    val valueVar = ctx.freshName("value")
-    val str = s"columnVector[$columnVar, $ordinal, ${dataType.simpleString}]"
-    val code = s"${ctx.registerComment(str)}\n" + (if (nullable) {
-      s"""
-        boolean $isNullVar = $columnVar.isNullAt($ordinal);
-        $javaType $valueVar = $isNullVar ? ${ctx.defaultValue(dataType)} : ($value);
-      """
-    } else {
-      s"$javaType $valueVar = $value;"
-    }).trim
-    ExprCode(code, isNullVar, valueVar)
-  }
-
-  /**
-   * Produce code to process the input iterator as [[ColumnarBatch]]es.
-   * This produces an [[UnsafeRow]] for each row in each batch.
-   */
-  // TODO: return ColumnarBatch.Rows instead
-  override protected def doProduce(ctx: CodegenContext): String = {
-    val input = ctx.freshName("input")
-    // PhysicalRDD always just has one input
-    ctx.addMutableState("scala.collection.Iterator", input, s"$input = inputs[0];")
-
-    // metrics
-    val numOutputRows = metricTerm(ctx, "numOutputRows")
-    val scanTimeMetric = metricTerm(ctx, "scanTime")
-    val scanTimeTotalNs = ctx.freshName("scanTime")
-    ctx.addMutableState("long", scanTimeTotalNs, s"$scanTimeTotalNs = 0;")
-    val incReadBatches = if (!enableScanStatistics) "" else {
-      val readPartitions = ctx.addReferenceObj("readPartitions", inMemoryTableScan.readPartitions)
-      val readBatches = ctx.addReferenceObj("readBatches", inMemoryTableScan.readBatches)
-      ctx.addMutableState("int", "initializeInMemoryTableScanStatistics",
-        s"""
-         |$readPartitions.setValue(0);
-         |$readBatches.setValue(0);
-         |if ($input.hasNext()) { $readPartitions.add(1); }
-       """.stripMargin)
-      s"$readBatches.add(1);"
-    }
-
-    val columnarBatchClz = "org.apache.spark.sql.execution.vectorized.ColumnarBatch"
-    val batch = ctx.freshName("batch")
-    ctx.addMutableState(columnarBatchClz, batch, s"$batch = null;")
-
-    val generateDecompress = if (inMemoryTableScan != null) true else false
-    val confVar = if (!generateDecompress) null else {
-      val conf = inMemoryTableScan.sparkContext.conf
-      ctx.addReferenceObj("conf", conf, classOf[SparkConf].getName)
-    }
-    val onHeapUnsafeColumnVectorCls = classOf[OnHeapUnsafeColumnVector].getName
-
-    val columnVectorClz = "org.apache.spark.sql.execution.vectorized.ColumnVector"
-    val idx = ctx.freshName("batchIdx")
-    ctx.addMutableState("int", idx, s"$idx = 0;")
-    val colVars = output.indices.map(i => ctx.freshName("colInstance" + i))
-    val columnAssigns = colVars.zipWithIndex.map { case (name, i) =>
-      ctx.addMutableState(columnVectorClz, name, s"$name = null;")
-      val index = if (columnIndexes == null) i else columnIndexes(i)
-      val decompress = if (!generateDecompress) ""
-        else s" (($onHeapUnsafeColumnVectorCls)$name).decompress($confVar);"
-      s"$name = $batch.column($index);$decompress"
-    }
-
-    val nextBatch = ctx.freshName("nextBatch")
-    ctx.addNewFunction(nextBatch,
-      s"""
-         |private void $nextBatch() throws java.io.IOException {
-         |  long getBatchStart = System.nanoTime();
-         |  if ($input.hasNext()) {
-         |    $batch = ($columnarBatchClz)$input.next();
-         |    $incReadBatches
-         |    $numOutputRows.add($batch.numRows());
-         |    $idx = 0;
-         |    ${columnAssigns.mkString("", "\n", "\n")}
-         |  }
-         |  $scanTimeTotalNs += System.nanoTime() - getBatchStart;
-         |}""".stripMargin)
-
-    ctx.currentVars = null
-    val rowidx = ctx.freshName("rowIdx")
-    val columnsBatchInput = (output zip colVars).map { case (attr, colVar) =>
-      genCodeColumnVector(ctx, colVar, rowidx, attr.dataType, attr.nullable)
-    }
-    s"""
-       |if ($batch == null) {
-       |  $nextBatch();
-       |}
-       |while ($batch != null) {
-       |  int numRows = $batch.numRows();
-       |  while ($idx < numRows) {
-       |    int $rowidx = $idx++;
-       |    ${consume(ctx, columnsBatchInput).trim}
-       |    if (shouldStop()) return;
-       |  }
-       |  $batch = null;
-       |  $nextBatch();
-       |}
-       |$scanTimeMetric.add($scanTimeTotalNs / (1000 * 1000));
-       |$scanTimeTotalNs = 0;
-     """.stripMargin
-  }
-
-}
->>>>>>> support UDT and column pruning