如何从H3边界创建多边形RDD

Question

我正在将Apache Spark与Apache Sedona(以前称为GeoSpark(一起使用，并尝试执行以下操作：

1. 取每行中包含纬度和经度的DataFrame(它来自任意来源，既不是PointRDD，也不是特定的文件格式(，并将其转换为具有每个点的H3索引的DataFrame
2. 取该DataFrame并创建包含每个不同H3索引的H3小区边界的PolygonRDD

这就是我目前所拥有的：

import org.apache.spark.serializer.KryoSerializer
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
import org.apache.sedona.core.spatialRDD.PolygonRDD
import org.apache.sedona.sql.utils.SedonaSQLRegistrator
import org.apache.sedona.viz.core.Serde.SedonaVizKryoRegistrator
import org.apache.sedona.viz.sql.utils.SedonaVizRegistrator
import org.locationtech.jts.geom.{Polygon, GeometryFactory, Coordinate}
import com.uber.h3core.H3Core
import com.uber.h3core.util.GeoCoord
object Main {
def main(args: Array[String]) {
val sparkSession: SparkSession = SparkSession
.builder()
.config("spark.serializer", classOf[KryoSerializer].getName)
.config("spark.kryo.registrator", classOf[SedonaVizKryoRegistrator].getName)
.master("local[*]")
.appName("Sedona-Analysis")
.getOrCreate()
import sparkSession.implicits._
SedonaSQLRegistrator.registerAll(sparkSession)
SedonaVizRegistrator.registerAll(sparkSession)
val df = Seq(
(-8.01681, -34.92618),
(-25.59306, -49.39895),
(-7.17897, -34.86518),
(-20.24521, -42.14273),
(-20.24628, -42.14785),
(-27.01641, -50.94109),
(-19.72987, -47.94319)
).toDF("latitude", "longitude")
val core: H3Core = H3Core.newInstance()
val geoFactory = new GeometryFactory()
val geoToH3 = udf((lat: Double, lng: Double, res: Int) => core.geoToH3(lat, lng, res))
val trdd = df
.select(geoToH3($"latitude", $"longitude", lit(7)).as("h3index"))
.distinct()
.rdd
.map(row => {
val h3 = row.getAs[Long](0)
val lboundary = core.h3ToGeoBoundary(h3)
val aboundary = lboundary.toArray(Array.ofDim[GeoCoord](lboundary.size))
val poly = geoFactory.createPolygon(
aboundary.map((c: GeoCoord) => new Coordinate(c.lat, c.lng))
)
poly.setUserData(h3)
poly
})
val polyRDD = new PolygonRDD(trdd)
polyRDD.rawSpatialRDD.foreach(println)
sparkSession.stop()
}
}

然而，在运行sbt assembly并将输出jar提交给spark-submit之后，我得到了以下错误：

Exception in thread "main" org.apache.spark.SparkException: Task not serializable
at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:416)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:406)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:162)
at org.apache.spark.SparkContext.clean(SparkContext.scala:2362)
at org.apache.spark.rdd.RDD.$anonfun$map$1(RDD.scala:396)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:388)
at org.apache.spark.rdd.RDD.map(RDD.scala:395)
at Main$.main(Main.scala:44)
at Main.main(Main.scala)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:928)
at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:180)
at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:203)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:90)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:1007)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:1016)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.io.NotSerializableException: com.uber.h3core.H3Core
Serialization stack:
- object not serializable (class: com.uber.h3core.H3Core, value: com.uber.h3core.H3Core@3407ded1)
- element of array (index: 0)
- array (class [Ljava.lang.Object;, size 2)
- field (class: java.lang.invoke.SerializedLambda, name: capturedArgs, type: class [Ljava.lang.Object;)
- object (class java.lang.invoke.SerializedLambda, SerializedLambda[capturingClass=class Main$, functionalInterfaceMethod=scala/Function1.apply:(Ljava/lang/Object;)Ljava/lang/Object;, implementation=invokeStatic Main$.$anonfun$main$2:(Lcom/uber/h3core/H3Core;Lorg/locationtech/jts/geom/GeometryFactory;Lorg/apache/spark/sql/Row;)Lorg/locationtech/jts/geom/Polygon;, instantiatedMethodType=(Lorg/apache/spark/sql/Row;)Lorg/locationtech/jts/geom/Polygon;, numCaptured=2])
- writeReplace data (class: java.lang.invoke.SerializedLambda)
- object (class Main$$$Lambda$1710/0x0000000840d7f040, Main$$$Lambda$1710/0x0000000840d7f040@4853f592)
at org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:41)
at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:47)
at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:101)
at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:413)
... 22 more

实现我想要做的事情的正确方法是什么？

Answer 1

因此，基本上只需将Serializable特性添加到包含H3Core的对象中就足够了。此外，我还必须调整Coordinate数组，使其以同一点开始和结束。

import org.apache.spark.serializer.KryoSerializer
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
import org.apache.sedona.core.spatialRDD.PolygonRDD
import org.apache.sedona.sql.utils.SedonaSQLRegistrator
import org.apache.sedona.viz.core.Serde.SedonaVizKryoRegistrator
import org.apache.sedona.viz.sql.utils.SedonaVizRegistrator
import org.locationtech.jts.geom.{Polygon, GeometryFactory, Coordinate}
import com.uber.h3core.H3Core
import com.uber.h3core.util.GeoCoord
object H3 extends Serializable {
val core = H3Core.newInstance()
val geoFactory = new GeometryFactory()
}
object Main {
def main(args: Array[String]) {
val sparkSession: SparkSession = SparkSession
.builder()
.config("spark.serializer", classOf[KryoSerializer].getName)
.config("spark.kryo.registrator", classOf[SedonaVizKryoRegistrator].getName)
.master("local[*]")
.appName("Sedona-Analysis")
.getOrCreate()
import sparkSession.implicits._
SedonaSQLRegistrator.registerAll(sparkSession)
SedonaVizRegistrator.registerAll(sparkSession)
val df = Seq(
(-8.01681, -34.92618),
(-25.59306, -49.39895),
(-7.17897, -34.86518),
(-20.24521, -42.14273),
(-20.24628, -42.14785),
(-27.01641, -50.94109),
(-19.72987, -47.94319)
).toDF("latitude", "longitude")
val geoToH3 = udf((lat: Double, lng: Double, res: Int) => H3.core.geoToH3(lat, lng, res))
val trdd = df
.select(geoToH3($"latitude", $"longitude", lit(7)).as("h3index"))
.distinct()
.rdd
.map(row => {
val h3 = row.getAs[Long](0)
val lboundary = H3.core.h3ToGeoBoundary(h3)
val aboundary = lboundary.toArray(Array.ofDim[GeoCoord](lboundary.size))
val poly = H3.geoFactory.createPolygon({
val ps = aboundary.map((c: GeoCoord) => new Coordinate(c.lat, c.lng))
ps :+ ps(0)
})
poly.setUserData(h3)
poly
})

val polyRDD = new PolygonRDD(trdd)
polyRDD.rawSpatialRDD.foreach(println)
sparkSession.stop()
}
}