diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
index eceae2ed60ee1..61570339b1a99 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
@@ -63,7 +63,7 @@ import org.apache.spark.util.{TimeStampedHashMap, Utils}
  *   the streaming app.
  * - If a file is to be visible in the directory listings, it must be visible within a certain
  *   duration of the mod time of the file. This duration is the "remember window", which is set to
- *   1 minute (see `FileInputDStream.MIN_REMEMBER_DURATION`). Otherwise, the file will never be
+ *   1 minute (see `FileInputDStream.minRememberDurationMin`). Otherwise, the file will never be
  *   selected as the mod time will be less than the ignore threshold when it becomes visible.
  * - Once a file is visible, the mod time cannot change. If it does due to appends, then the
  *   processing semantics are undefined.
@@ -336,16 +336,17 @@ object FileInputDStream {
    * Files with mod times older than this "window" of remembering will be ignored. So if new
    * files are visible within this window, then the file will get selected in the next batch.
    */
-  private val minRememberDuration = new SparkConf().get("spark.streaming.minRememberDuration", "1")
-  private val MIN_REMEMBER_DURATION = Minutes(minRememberDuration.toLong)
+  private val minRememberDurationMin = Minutes(new SparkConf()
+                                               .get("spark.streaming.minRememberDurationMin", "1")
+                                               .toLong)
 
   def defaultFilter(path: Path): Boolean = !path.getName().startsWith(".")
 
   /**
    * Calculate the number of last batches to remember, such that all the files selected in
-   * at least last MIN_REMEMBER_DURATION duration can be remembered.
+   * at least last minRememberDurationMin duration can be remembered.
    */
   def calculateNumBatchesToRemember(batchDuration: Duration): Int = {
-    math.ceil(MIN_REMEMBER_DURATION.milliseconds.toDouble / batchDuration.milliseconds).toInt
+    math.ceil(minRememberDurationMin.milliseconds.toDouble / batchDuration.milliseconds).toInt
   }
 }