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| -Djava.awt.headless=true | ||
| -J-Xmx2g | ||
| -J-XX:MaxMetaspaceSize=1g |
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bench/src/main/scala/astraea/spark/rasterframes/bench/HistogramEncodeBench.scala
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core/src/main/scala/astraea/spark/rasterframes/PairRDDConverter.scala
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| package astraea.spark.rasterframes | ||
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|
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| import astraea.spark.rasterframes.util._ | ||
| import geotrellis.raster.{MultibandTile, Tile, TileFeature} | ||
| import geotrellis.spark.{SpaceTimeKey, SpatialKey, TemporalKey} | ||
| import org.apache.spark.rdd.RDD | ||
| import org.apache.spark.sql._ | ||
| import org.apache.spark.sql.gt.types.TileUDT | ||
| import org.apache.spark.sql.types._ | ||
|
|
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| import scala.annotation.implicitNotFound | ||
|
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| /** | ||
| * Typeclass for converting a Pair RDD into a dataframe. | ||
| * | ||
| * @since 4/8/18 | ||
| */ | ||
| @implicitNotFound("An RDD converter is required create a RasterFrame. " + | ||
| "Please provide an implementation of PairRDDConverter[${K}, ${V}].") | ||
| trait PairRDDConverter[K, V] extends Serializable { | ||
| val schema: StructType | ||
| def toDataFrame(rdd: RDD[(K, V)])(implicit spark: SparkSession): DataFrame | ||
| } | ||
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|
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| object PairRDDConverter { | ||
| /** Enrichment over a pair RDD for converting it to a DataFrame given a converter. */ | ||
| implicit class RDDCanBeDataFrame[K, V](rdd: RDD[(K, V)])(implicit spark: SparkSession, converter: PairRDDConverter[K, V]) { | ||
| def toDataFrame: DataFrame = converter.toDataFrame(rdd) | ||
| } | ||
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| // Hack around Spark bug when singletons are used in schemas | ||
| private val serializableTileUDT = new TileUDT() | ||
|
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| /** Fetch converter from implicit scope. */ | ||
| def apply[K, V](implicit sp: PairRDDConverter[K, V]) = sp | ||
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| /** Enables conversion of `RDD[(SpatialKey, Tile)]` to DataFrame. */ | ||
| implicit val spatialTileConverter = new PairRDDConverter[SpatialKey, Tile] { | ||
| val schema: StructType = { | ||
| StructType(Seq( | ||
| StructField(SPATIAL_KEY_COLUMN.columnName, spatialKeyEncoder.schema, nullable = false), | ||
| StructField(TILE_COLUMN.columnName, serializableTileUDT, nullable = false) | ||
| )) | ||
| } | ||
|
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| def toDataFrame(rdd: RDD[(SpatialKey, Tile)])(implicit spark: SparkSession): DataFrame = { | ||
| import spark.implicits._ | ||
| rdd.toDF(schema.fields.map(_.name): _*) | ||
| } | ||
| } | ||
|
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| /** Enables conversion of `RDD[(SpaceTimeKey, Tile)]` to DataFrame. */ | ||
| implicit val spaceTimeTileConverter = new PairRDDConverter[SpaceTimeKey, Tile] { | ||
| val schema: StructType = { | ||
| val base = spatialTileConverter.schema | ||
| val addedFields = Seq(StructField(TEMPORAL_KEY_COLUMN.columnName, temporalKeyEncoder.schema, nullable = false)) | ||
| StructType(base.fields.patch(1, addedFields, 0)) | ||
| } | ||
|
|
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| def toDataFrame(rdd: RDD[(SpaceTimeKey, Tile)])(implicit spark: SparkSession): DataFrame = { | ||
| import spark.implicits._ | ||
| rdd.map{ case (k, v) ⇒ (k.spatialKey, k.temporalKey, v)}.toDF(schema.fields.map(_.name): _*) | ||
| } | ||
| } | ||
|
|
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| /** Enables conversion of `RDD[(SpatialKey, TileFeature[Tile, D])]` to DataFrame. */ | ||
| implicit def spatialTileFeatureConverter[D: Encoder] = new PairRDDConverter[SpatialKey, TileFeature[Tile, D]] { | ||
| implicit val featureEncoder = implicitly[Encoder[D]] | ||
| implicit val rowEncoder = Encoders.tuple(spatialKeyEncoder, singlebandTileEncoder, featureEncoder) | ||
|
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| val schema: StructType = { | ||
| val base = spatialTileConverter.schema | ||
| StructType(base.fields :+ StructField(TILE_FEATURE_DATA_COLUMN.columnName, featureEncoder.schema, nullable = true)) | ||
| } | ||
|
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| def toDataFrame(rdd: RDD[(SpatialKey, TileFeature[Tile, D])])(implicit spark: SparkSession): DataFrame = { | ||
| import spark.implicits._ | ||
| rdd.map{ case (k, v) ⇒ (k, v.tile, v.data)}.toDF(schema.fields.map(_.name): _*) | ||
| } | ||
| } | ||
|
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| /** Enables conversion of `RDD[(SpaceTimeKey, TileFeature[Tile, D])]` to DataFrame. */ | ||
| implicit def spaceTimeTileFeatureConverter[D: Encoder] = new PairRDDConverter[SpaceTimeKey, TileFeature[Tile, D]] { | ||
| implicit val featureEncoder = implicitly[Encoder[D]] | ||
| implicit val rowEncoder = Encoders.tuple(spatialKeyEncoder, temporalKeyEncoder, singlebandTileEncoder, featureEncoder) | ||
|
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| val schema: StructType = { | ||
| val base = spaceTimeTileConverter.schema | ||
| StructType(base.fields :+ StructField(TILE_FEATURE_DATA_COLUMN.columnName, featureEncoder.schema, nullable = true)) | ||
| } | ||
|
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| def toDataFrame(rdd: RDD[(SpaceTimeKey, TileFeature[Tile, D])])(implicit spark: SparkSession): DataFrame = { | ||
| import spark.implicits._ | ||
| val tupRDD = rdd.map { case (k, v) ⇒ (k.spatialKey, k.temporalKey, v.tile, v.data) } | ||
|
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| rddToDatasetHolder(tupRDD) | ||
| tupRDD.toDF(schema.fields.map(_.name): _*) | ||
| } | ||
| } | ||
|
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| /** Enables conversion of `RDD[(SpatialKey, MultibandTile)]` to DataFrame. */ | ||
| def forSpatialMultiband(bands: Int) = new PairRDDConverter[SpatialKey, MultibandTile] { | ||
| val schema: StructType = { | ||
| val base = spatialTileConverter.schema | ||
|
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| val basename = TILE_COLUMN.columnName | ||
|
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| val tiles = for(i ← 1 to bands) yield { | ||
| StructField(s"${basename}_$i" , serializableTileUDT, nullable = false) | ||
| } | ||
|
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| StructType(base.fields.patch(1, tiles, 1)) | ||
| } | ||
|
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| def toDataFrame(rdd: RDD[(SpatialKey, MultibandTile)])(implicit spark: SparkSession): DataFrame = { | ||
| spark.createDataFrame( | ||
| rdd.map { case (k, v) ⇒ Row(Row(k.col, k.row) +: v.bands: _*) }, | ||
| schema | ||
| ) | ||
| } | ||
| } | ||
|
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| /** Enables conversion of `RDD[(SpaceTimeKey, MultibandTile)]` to DataFrame. */ | ||
| def forSpaceTimeMultiband(bands: Int) = new PairRDDConverter[SpaceTimeKey, MultibandTile] { | ||
| val schema: StructType = { | ||
| val base = spaceTimeTileConverter.schema | ||
|
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| val basename = TILE_COLUMN.columnName | ||
|
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| val tiles = for(i ← 1 to bands) yield { | ||
| StructField(s"${basename}_$i" , serializableTileUDT, nullable = false) | ||
| } | ||
|
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| StructType(base.fields.patch(2, tiles, 1)) | ||
| } | ||
|
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| def toDataFrame(rdd: RDD[(SpaceTimeKey, MultibandTile)])(implicit spark: SparkSession): DataFrame = { | ||
| spark.createDataFrame( | ||
| rdd.map { case (k, v) ⇒ Row(Seq(Row(k.spatialKey.col, k.spatialKey.row), Row(k.temporalKey)) ++ v.bands: _*) }, | ||
| schema | ||
| ) | ||
| } | ||
| } | ||
| } |
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