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/*
* FILE: Adapter.scala
* Copyright (c) 2015 - 2018 GeoSpark Development Team
*
* MIT License
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
*/
package org.datasyslab.geosparksql.utils
import com.vividsolutions.jts.geom.Geometry
import org.apache.spark.api.java.{JavaPairRDD, JavaRDD}
import org.apache.spark.rdd.RDD
//import org.apache.spark.sql.geosparksql.GeometryWrapper
import org.apache.spark.sql.types._
import org.apache.spark.sql.{DataFrame, Row, SparkSession}
import org.datasyslab.geospark.spatialRDD.SpatialRDD
object Adapter {
def toJavaRdd(dataFrame: DataFrame): JavaRDD[Geometry] = {
return toRdd(dataFrame).toJavaRDD()
}
def toRdd(dataFrame: DataFrame): RDD[Geometry] = {
return dataFrame.rdd.map[Geometry](f => f.get(0).asInstanceOf[Geometry])
}
def toDf(spatialRDD: SpatialRDD[Geometry], sparkSession: SparkSession): DataFrame = {
val rowRdd = spatialRDD.rawSpatialRDD.rdd.map[Row](f => Row.fromSeq(f.toString.split("\t").toSeq))
var fieldArray = new Array[StructField](rowRdd.take(1)(0).size)
fieldArray(0) = StructField("rddshape", StringType)
for (i <- 1 to fieldArray.length - 1) fieldArray(i) = StructField("_c" + i, StringType)
val schema = StructType(fieldArray)
return sparkSession.createDataFrame(rowRdd, schema)
}
def toDf(spatialPairRDD: JavaPairRDD[Geometry, Geometry], sparkSession: SparkSession): DataFrame = {
val rowRdd = spatialPairRDD.rdd.map[Row](f => {
val seq1 = f._1.toString.split("\t").toSeq
val seq2 = f._2.toString.split("\t").toSeq
val result = seq1 ++ seq2
Row.fromSeq(result)
})
var fieldArray = new Array[StructField](rowRdd.take(1)(0).size)
fieldArray(0) = StructField("windowrddshape", StringType)
for (i <- 1 to fieldArray.length - 1) fieldArray(i) = StructField("_c" + i, StringType)
val schema = StructType(fieldArray)
return sparkSession.createDataFrame(rowRdd, schema)
}
/*
* Since UserDefinedType is hidden from users. We cannot directly return spatialRDD to spatialDf.
* Let's wait for Spark side's change
*/
/*
def toSpatialDf(spatialRDD: SpatialRDD[Geometry], sparkSession: SparkSession): DataFrame =
{
val rowRdd = spatialRDD.rawSpatialRDD.rdd.map[Row](f =>
{
var seq = Seq(new GeometryWrapper(f))
var otherFields = f.getUserData.asInstanceOf[String].split("\t").toSeq
seq :+ otherFields
Row.fromSeq(seq)
}
)
var fieldArray = new Array[StructField](rowRdd.take(1)(0).size)
fieldArray(0) = StructField("rddshape", ArrayType(ByteType, false))
for (i <- 1 to fieldArray.length-1) fieldArray(i) = StructField("_c"+i, StringType)
val schema = StructType(fieldArray)
return sparkSession.createDataFrame(rowRdd, schema)
}
*/
}
