diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
index 7efeab528c..2156806d21 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
@@ -350,28 +350,47 @@ class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest {
     withTempPath { dir =>
       withSQLConf(SQLConf.ORC_FILTER_PUSHDOWN_ENABLED.key -> "true") {
         import testImplicits._
-
         val path = dir.getCanonicalPath
-        sqlContext.range(10).coalesce(1).write.orc(path)
+
+        // For field "a", the first column has odds integers. This is to check the filtered count
+        // when `isNull` is performed. For Field "b", `isNotNull` of ORC file filters rows
+        // only when all the values are null (maybe this works differently when the data
+        // or query is complicated). So, simply here a column only having `null` is added.
+        val data = (0 until 10).map { i =>
+          val maybeInt = if (i % 2 == 0) None else Some(i)
+          val nullValue: Option[String] = None
+          (maybeInt, nullValue)
+        }
+        createDataFrame(data).toDF("a", "b").write.orc(path)
         val df = sqlContext.read.orc(path)
 
-        def checkPredicate(pred: Column, answer: Seq[Long]): Unit = {
-          checkAnswer(df.where(pred), answer.map(Row(_)))
+        def checkPredicate(pred: Column, answer: Seq[Row]): Unit = {
+          val sourceDf = stripSparkFilter(df.where(pred))
+          val data = sourceDf.collect().toSet
+          val expectedData = answer.toSet
+
+          // When a filter is pushed to ORC, ORC can apply it to rows. So, we can check
+          // the number of rows returned from the ORC to make sure our filter pushdown work.
+          // A tricky part is, ORC does not process filter rows fully but return some possible
+          // results. So, this checks if the number of result is less than the original count
+          // of data, and then checks if it contains the expected data.
+          val isOrcFiltered = sourceDf.count < 10 && expectedData.subsetOf(data)
+          assert(isOrcFiltered)
         }
 
-        checkPredicate('id === 5, Seq(5L))
-        checkPredicate('id <=> 5, Seq(5L))
-        checkPredicate('id < 5, 0L to 4L)
-        checkPredicate('id <= 5, 0L to 5L)
-        checkPredicate('id > 5, 6L to 9L)
-        checkPredicate('id >= 5, 5L to 9L)
-        checkPredicate('id.isNull, Seq.empty[Long])
-        checkPredicate('id.isNotNull, 0L to 9L)
-        checkPredicate('id.isin(1L, 3L, 5L), Seq(1L, 3L, 5L))
-        checkPredicate('id > 0 && 'id < 3, 1L to 2L)
-        checkPredicate('id < 1 || 'id > 8, Seq(0L, 9L))
-        checkPredicate(!('id > 3), 0L to 3L)
-        checkPredicate(!('id > 0 && 'id < 3), Seq(0L) ++ (3L to 9L))
+        checkPredicate('a === 5, List(5).map(Row(_, null)))
+        checkPredicate('a <=> 5, List(5).map(Row(_, null)))
+        checkPredicate('a < 5, List(1, 3).map(Row(_, null)))
+        checkPredicate('a <= 5, List(1, 3, 5).map(Row(_, null)))
+        checkPredicate('a > 5, List(7, 9).map(Row(_, null)))
+        checkPredicate('a >= 5, List(5, 7, 9).map(Row(_, null)))
+        checkPredicate('a.isNull, List(null).map(Row(_, null)))
+        checkPredicate('b.isNotNull, List())
+        checkPredicate('a.isin(3, 5, 7), List(3, 5, 7).map(Row(_, null)))
+        checkPredicate('a > 0 && 'a < 3, List(1).map(Row(_, null)))
+        checkPredicate('a < 1 || 'a > 8, List(9).map(Row(_, null)))
+        checkPredicate(!('a > 3), List(1, 3).map(Row(_, null)))
+        checkPredicate(!('a > 0 && 'a < 3), List(3, 5, 7, 9).map(Row(_, null)))
       }
     }
   }