Support java iterable types in POJOs

Author: Punya Biswal

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala

@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst
 
+import java.lang.{Iterable => JavaIterable}
 import java.util.{Map => JavaMap}
 
 import scala.collection.mutable.HashMap
@@ -49,6 +50,16 @@ object CatalystTypeConverters {
     case (s: Seq[_], arrayType: ArrayType) =>
       s.map(convertToCatalyst(_, arrayType.elementType))
 
+    case (jit: JavaIterable[_], arrayType: ArrayType) => {
+      val iter = jit.iterator
+      var listOfItems: List[Any] = List()
+      while (iter.hasNext) {
+        val item = iter.next()
+        listOfItems :+= convertToCatalyst(item, arrayType.elementType)
+      }
+      listOfItems
+    }
+
     case (s: Array[_], arrayType: ArrayType) =>
       s.toSeq.map(convertToCatalyst(_, arrayType.elementType))
 
@@ -124,6 +135,15 @@ object CatalystTypeConverters {
           extractOption(item) match {
             case a: Array[_] => a.toSeq.map(elementConverter)
             case s: Seq[_] => s.map(elementConverter)
+            case i: JavaIterable[_] => {
+              val iter = i.iterator
+              var convertedIterable: List[Any] = List()
+              while (iter.hasNext) {
+                val item = iter.next()
+                convertedIterable :+= elementConverter(item)
+              }
+              convertedIterable
+            }
             case null => null
           }
         }

sql/core/src/main/scala/org/apache/spark/sql/JavaTypeInference.scala

@@ -75,6 +75,10 @@ private [sql] object JavaTypeInference {
         val (dataType, nullable) = inferDataType(typeToken.getComponentType)
         (ArrayType(dataType, nullable), true)
 
+      case _ if iterableType.isAssignableFrom(typeToken) =>
+        val (dataType, nullable) = inferDataType(elementType(typeToken))
+        (ArrayType(dataType, nullable), true)
+
       case _ if mapType.isAssignableFrom(typeToken) =>
         val typeToken2 = typeToken.asInstanceOf[TypeToken[_ <: JMap[_, _]]]
         val mapSupertype = typeToken2.getSupertype(classOf[JMap[_, _]])

sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java

@@ -19,6 +19,7 @@
 
 import java.io.Serializable;
 import java.util.Arrays;
+import java.util.List;
 import java.util.Map;
 
 import com.google.common.collect.ImmutableMap;
@@ -112,6 +113,7 @@ public static class Bean implements Serializable {
     private double a = 0.0;
     private Integer[] b = new Integer[]{0, 1};
     private Map<String, int[]> c = ImmutableMap.of("hello", new int[] { 1, 2 });
+    private List<String> d = Arrays.asList("floppy", "disk");
 
     public double getA() {
       return a;
@@ -124,6 +126,10 @@ public Integer[] getB() {
     public Map<String, int[]> getC() {
       return c;
     }
+
+    public List<String> getD() {
+      return d;
+    }
   }
 
   @Test
@@ -142,7 +148,10 @@ public void testCreateDataFrameFromJavaBeans() {
     Assert.assertEquals(
       new StructField("c", mapType, true, Metadata.empty()),
       schema.apply("c"));
-    Row first = df.select("a", "b", "c").first();
+    Assert.assertEquals(
+      new StructField("d", new ArrayType(DataTypes.StringType, true), true, Metadata.empty()),
+      schema.apply("d"));
+    Row first = df.select("a", "b", "c", "d").first();
     Assert.assertEquals(bean.getA(), first.getDouble(0), 0.0);
     // Now Java lists and maps are converetd to Scala Seq's and Map's. Once we get a Seq below,
     // verify that it has the expected length, and contains expected elements.
@@ -155,6 +164,11 @@ public void testCreateDataFrameFromJavaBeans() {
     Assert.assertArrayEquals(
       bean.getC().get("hello"),
       Ints.toArray(JavaConversions.asJavaList(outputBuffer)));
+    Seq<String> d = first.getAs(3);
+    Assert.assertEquals(bean.getD().size(), d.length());
+    for (int i = 0; i < d.length(); i++) {
+      Assert.assertEquals(bean.getD().get(i), d.apply(i));
+    }
   }
 
 }