integration/spark2/src/main/spark2.1/org/apache/spark/sql/catalyst/catalog/HiveTableRelation.scala

/*
 * 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
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package org.apache.spark.sql.catalyst.catalog

import com.google.common.base.Objects
import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
import org.apache.spark.sql.catalyst.expressions.AttributeReference
import org.apache.spark.sql.catalyst.plans.logical.{LeafNode, LogicalPlan, Statistics}
import org.apache.spark.sql.internal.SQLConf

/**
  * A `LogicalPlan` that represents a hive table.
  *
  * TODO: remove this after we completely make hive as a data source.
  */
case class HiveTableRelation(
                              tableMeta: CatalogTable,
                              dataCols: Seq[AttributeReference],
                              partitionCols: Seq[AttributeReference]) extends LeafNode with MultiInstanceRelation {
  assert(tableMeta.identifier.database.isDefined)
  assert(tableMeta.partitionSchema.sameType(partitionCols.toStructType))
  assert(tableMeta.schema.sameType(dataCols.toStructType))

  // The partition column should always appear after data columns.
  override def output: Seq[AttributeReference] = dataCols ++ partitionCols

  def isPartitioned: Boolean = partitionCols.nonEmpty

  override def equals(relation: Any): Boolean = relation match {
    case other: HiveTableRelation => tableMeta == other.tableMeta && output == other.output
    case _ => false
  }

  override def hashCode(): Int = {
    Objects.hashCode(tableMeta.identifier, output)
  }

  override def newInstance(): HiveTableRelation = copy(
    dataCols = dataCols.map(_.newInstance()),
    partitionCols = partitionCols.map(_.newInstance()))
}