Move to companion

Author: JoshRosen

core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala

@@ -88,50 +88,6 @@ class KryoSerializer(conf: SparkConf)
   private val useUnsafe = conf.get(KRYO_USE_UNSAFE)
   private val usePool = conf.get(KRYO_USE_POOL)
 
-  // classForName() is expensive in case the class is not found, so we filter the list of
-  // SQL / ML / MLlib classes once and then re-use that filtered list in newInstance() calls.
-  private lazy val loadableClasses: Seq[Class[_]] = {
-    Seq(
-      "org.apache.spark.sql.catalyst.expressions.UnsafeRow",
-      "org.apache.spark.sql.catalyst.expressions.UnsafeArrayData",
-      "org.apache.spark.sql.catalyst.expressions.UnsafeMapData",
-
-      "org.apache.spark.ml.attribute.Attribute",
-      "org.apache.spark.ml.attribute.AttributeGroup",
-      "org.apache.spark.ml.attribute.BinaryAttribute",
-      "org.apache.spark.ml.attribute.NominalAttribute",
-      "org.apache.spark.ml.attribute.NumericAttribute",
-
-      "org.apache.spark.ml.feature.Instance",
-      "org.apache.spark.ml.feature.LabeledPoint",
-      "org.apache.spark.ml.feature.OffsetInstance",
-      "org.apache.spark.ml.linalg.DenseMatrix",
-      "org.apache.spark.ml.linalg.DenseVector",
-      "org.apache.spark.ml.linalg.Matrix",
-      "org.apache.spark.ml.linalg.SparseMatrix",
-      "org.apache.spark.ml.linalg.SparseVector",
-      "org.apache.spark.ml.linalg.Vector",
-      "org.apache.spark.ml.stat.distribution.MultivariateGaussian",
-      "org.apache.spark.ml.tree.impl.TreePoint",
-      "org.apache.spark.mllib.clustering.VectorWithNorm",
-      "org.apache.spark.mllib.linalg.DenseMatrix",
-      "org.apache.spark.mllib.linalg.DenseVector",
-      "org.apache.spark.mllib.linalg.Matrix",
-      "org.apache.spark.mllib.linalg.SparseMatrix",
-      "org.apache.spark.mllib.linalg.SparseVector",
-      "org.apache.spark.mllib.linalg.Vector",
-      "org.apache.spark.mllib.regression.LabeledPoint",
-      "org.apache.spark.mllib.stat.distribution.MultivariateGaussian"
-    ).flatMap { name =>
-      try {
-        Some[Class[_]](Utils.classForName(name))
-      } catch {
-        case NonFatal(_) => None // do nothing
-        case _: NoClassDefFoundError if Utils.isTesting => None // See SPARK-23422.
-      }
-    }
-  }
-
   def newKryoOutput(): KryoOutput =
     if (useUnsafe) {
       new KryoUnsafeOutput(bufferSize, math.max(bufferSize, maxBufferSize))
@@ -256,7 +212,7 @@ class KryoSerializer(conf: SparkConf)
 
     // We can't load those class directly in order to avoid unnecessary jar dependencies.
     // We load them safely, ignore it if the class not found.
-    loadableClasses.foreach { clazz =>
+    KryoSerializer.loadableSparkClasses.foreach { clazz =>
       try {
         kryo.register(clazz)
       } catch {
@@ -528,6 +484,50 @@ private[serializer] object KryoSerializer {
       }
     }
   )
+
+  // classForName() is expensive in case the class is not found, so we filter the list of
+  // SQL / ML / MLlib classes once and then re-use that filtered list in newInstance() calls.
+  private lazy val loadableSparkClasses: Seq[Class[_]] = {
+    Seq(
+      "org.apache.spark.sql.catalyst.expressions.UnsafeRow",
+      "org.apache.spark.sql.catalyst.expressions.UnsafeArrayData",
+      "org.apache.spark.sql.catalyst.expressions.UnsafeMapData",
+
+      "org.apache.spark.ml.attribute.Attribute",
+      "org.apache.spark.ml.attribute.AttributeGroup",
+      "org.apache.spark.ml.attribute.BinaryAttribute",
+      "org.apache.spark.ml.attribute.NominalAttribute",
+      "org.apache.spark.ml.attribute.NumericAttribute",
+
+      "org.apache.spark.ml.feature.Instance",
+      "org.apache.spark.ml.feature.LabeledPoint",
+      "org.apache.spark.ml.feature.OffsetInstance",
+      "org.apache.spark.ml.linalg.DenseMatrix",
+      "org.apache.spark.ml.linalg.DenseVector",
+      "org.apache.spark.ml.linalg.Matrix",
+      "org.apache.spark.ml.linalg.SparseMatrix",
+      "org.apache.spark.ml.linalg.SparseVector",
+      "org.apache.spark.ml.linalg.Vector",
+      "org.apache.spark.ml.stat.distribution.MultivariateGaussian",
+      "org.apache.spark.ml.tree.impl.TreePoint",
+      "org.apache.spark.mllib.clustering.VectorWithNorm",
+      "org.apache.spark.mllib.linalg.DenseMatrix",
+      "org.apache.spark.mllib.linalg.DenseVector",
+      "org.apache.spark.mllib.linalg.Matrix",
+      "org.apache.spark.mllib.linalg.SparseMatrix",
+      "org.apache.spark.mllib.linalg.SparseVector",
+      "org.apache.spark.mllib.linalg.Vector",
+      "org.apache.spark.mllib.regression.LabeledPoint",
+      "org.apache.spark.mllib.stat.distribution.MultivariateGaussian"
+    ).flatMap { name =>
+      try {
+        Some[Class[_]](Utils.classForName(name))
+      } catch {
+        case NonFatal(_) => None // do nothing
+        case _: NoClassDefFoundError if Utils.isTesting => None // See SPARK-23422.
+      }
+    }
+  }
 }
 
 /**