sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.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.sources

import org.apache.hadoop.fs.Path

import org.apache.spark.deploy.SparkHadoopUtil
import org.apache.spark.sql.catalyst.expressions.PredicateHelper
import org.apache.spark.sql.types._

class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest with PredicateHelper {
  override val dataSourceName: String = classOf[SimpleTextSource].getCanonicalName

  // We have a very limited number of supported types at here since it is just for a
  // test relation and we do very basic testing at here.
  override protected def supportsDataType(dataType: DataType): Boolean = dataType match {
    case _: BinaryType => false
    // We are using random data generator and the generated strings are not really valid string.
    case _: StringType => false
    case _: BooleanType => false // see https://issues.apache.org/jira/browse/SPARK-10442
    case _: CalendarIntervalType => false
    case _: DateType => false
    case _: TimestampType => false
    case _: ArrayType => false
    case _: MapType => false
    case _: StructType => false
    case _: UserDefinedType[_] => false
    case _ => true
  }

  test("save()/load() - partitioned table - simple queries - partition columns in data") {
    withTempDir { file =>
      val basePath = new Path(file.getCanonicalPath)
      val fs = basePath.getFileSystem(SparkHadoopUtil.get.conf)
      val qualifiedBasePath = fs.makeQualified(basePath)

      for (p1 <- 1 to 2; p2 <- Seq("foo", "bar")) {
        val partitionDir = new Path(qualifiedBasePath, s"p1=$p1/p2=$p2")
        sparkContext
          .parallelize(for (i <- 1 to 3) yield s"$i,val_$i,$p1")
          .saveAsTextFile(partitionDir.toString)
      }

      val dataSchemaWithPartition =
        StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))

      checkQueries(
        hiveContext.read.format(dataSourceName)
          .option("dataSchema", dataSchemaWithPartition.json)
          .load(file.getCanonicalPath))
    }
  }
}