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 61570339b1a99..948d16a6e7020 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
@@ -80,6 +80,16 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]](
 
   private val serializableConfOpt = conf.map(new SerializableWritable(_))
 
+  /**
+   * Minimum duration of remembering the information of selected files. Defaults to 1 minute.
+   *
+   * 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 minRememberDurationMin = Minutes(ssc.sparkContext.getConf
+                                               .get("spark.streaming.minRememberDurationMin", "1")
+                                               .toLong)
+
   // This is a def so that it works during checkpoint recovery:
   private def clock = ssc.scheduler.clock
 
@@ -95,7 +105,8 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]](
    * This would allow us to filter away not-too-old files which have already been recently
    * selected and processed.
    */
-  private val numBatchesToRemember = FileInputDStream.calculateNumBatchesToRemember(slideDuration)
+  private val numBatchesToRemember = FileInputDStream
+    .calculateNumBatchesToRemember(slideDuration, minRememberDurationMin)
   private val durationToRemember = slideDuration * numBatchesToRemember
   remember(durationToRemember)
 
@@ -330,23 +341,14 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]](
 private[streaming]
 object FileInputDStream {
 
-  /**
-   * Minimum duration of remembering the information of selected files. Defaults to 1 minute.
-   *
-   * 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 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 minRememberDurationMin duration can be remembered.
    */
-  def calculateNumBatchesToRemember(batchDuration: Duration): Int = {
+  def calculateNumBatchesToRemember(batchDuration: Duration,
+                                    minRememberDurationMin: Duration): Int = {
     math.ceil(minRememberDurationMin.milliseconds.toDouble / batchDuration.milliseconds).toInt
   }
 }