[CORE] Prior to apache#4893, add vanilla Spark's original scan source…

… code to keep git history

shims/spark32/src/main/scala/org/apache/spark/sql/execution/datasources/v2/AbstractBatchScanExec.scala

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+/*
+ * 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.datasources.v2
+
+import com.google.common.base.Objects
+import org.apache.spark.SparkException
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.QueryPlan
+import org.apache.spark.sql.catalyst.plans.physical.SinglePartition
+import org.apache.spark.sql.catalyst.util.truncatedString
+import org.apache.spark.sql.connector.read.{InputPartition, PartitionReaderFactory, Scan, SupportsRuntimeFiltering}
+import org.apache.spark.sql.execution.datasources.DataSourceStrategy
+
+/**
+ * Physical plan node for scanning a batch of data from a data source v2.
+ */
+case class AbstractBatchScanExec(
+    output: Seq[AttributeReference],
+    @transient scan: Scan,
+    runtimeFilters: Seq[Expression]) extends DataSourceV2ScanExecBase {
+
+  @transient lazy val batch = scan.toBatch
+
+  // TODO: unify the equal/hashCode implementation for all data source v2 query plans.
+  override def equals(other: Any): Boolean = other match {
+    case other: AbstractBatchScanExec =>
+      this.batch == other.batch && this.runtimeFilters == other.runtimeFilters
+    case _ =>
+      false
+  }
+
+  override def hashCode(): Int = Objects.hashCode(batch, runtimeFilters)
+
+  @transient override lazy val partitions: Seq[InputPartition] = batch.planInputPartitions()
+
+  @transient private lazy val filteredPartitions: Seq[InputPartition] = {
+    val dataSourceFilters = runtimeFilters.flatMap {
+      case DynamicPruningExpression(e) => DataSourceStrategy.translateRuntimeFilter(e)
+      case _ => None
+    }
+
+    if (dataSourceFilters.nonEmpty) {
+      val originalPartitioning = outputPartitioning
+
+      // the cast is safe as runtime filters are only assigned if the scan can be filtered
+      val filterableScan = scan.asInstanceOf[SupportsRuntimeFiltering]
+      filterableScan.filter(dataSourceFilters.toArray)
+
+      // call toBatch again to get filtered partitions
+      val newPartitions = scan.toBatch.planInputPartitions()
+
+      originalPartitioning match {
+        case p: DataSourcePartitioning if p.numPartitions != newPartitions.size =>
+          throw new SparkException(
+            "Data source must have preserved the original partitioning during runtime filtering; " +
+            s"reported num partitions: ${p.numPartitions}, " +
+            s"num partitions after runtime filtering: ${newPartitions.size}")
+        case _ =>
+          // no validation is needed as the data source did not report any specific partitioning
+      }
+
+      newPartitions
+    } else {
+      partitions
+    }
+  }
+
+  override lazy val readerFactory: PartitionReaderFactory = batch.createReaderFactory()
+
+  override lazy val inputRDD: RDD[InternalRow] = {
+    if (filteredPartitions.isEmpty && outputPartitioning == SinglePartition) {
+      // return an empty RDD with 1 partition if dynamic filtering removed the only split
+      sparkContext.parallelize(Array.empty[InternalRow], 1)
+    } else {
+      new DataSourceRDD(
+        sparkContext, filteredPartitions, readerFactory, supportsColumnar, customMetrics)
+    }
+  }
+
+  override def doCanonicalize(): AbstractBatchScanExec = {
+    this.copy(
+      output = output.map(QueryPlan.normalizeExpressions(_, output)),
+      runtimeFilters = QueryPlan.normalizePredicates(
+        runtimeFilters.filterNot(_ == DynamicPruningExpression(Literal.TrueLiteral)),
+        output))
+  }
+
+  override def simpleString(maxFields: Int): String = {
+    val truncatedOutputString = truncatedString(output, "[", ", ", "]", maxFields)
+    val runtimeFiltersString = s"RuntimeFilters: ${runtimeFilters.mkString("[", ",", "]")}"
+    val result = s"$nodeName$truncatedOutputString ${scan.description()} $runtimeFiltersString"
+    redact(result)
+  }
+}