SPARK-3276 Added a new configuration spark.streaming.minRememberDuration

streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala

@@ -26,7 +26,7 @@ import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileSystem, Path, PathFilter}
 import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat}
 
-import org.apache.spark.SerializableWritable
+import org.apache.spark.{SparkConf, SerializableWritable}
 import org.apache.spark.rdd.{RDD, UnionRDD}
 import org.apache.spark.streaming._
 import org.apache.spark.util.{TimeStampedHashMap, Utils}
@@ -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.minRememberDuration`). 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.
@@ -80,6 +80,15 @@ 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 60 seconds.
+   *
+   * 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 minRememberDurationS =
+    Seconds(ssc.conf.getTimeAsSeconds("spark.streaming.minRememberDuration", "60s"))
+
   // This is a def so that it works during checkpoint recovery:
   private def clock = ssc.scheduler.clock
 
@@ -95,7 +104,8 @@ 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, minRememberDurationS)
   private val durationToRemember = slideDuration * numBatchesToRemember
   remember(durationToRemember)
 
@@ -330,20 +340,14 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]](
 private[streaming]
 object FileInputDStream {
 
-  /**
-   * Minimum duration of remembering the information of selected files. 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 MIN_REMEMBER_DURATION = Minutes(1)
-
   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 minRememberDurationS duration can be remembered.
    */
-  def calculateNumBatchesToRemember(batchDuration: Duration): Int = {
-    math.ceil(MIN_REMEMBER_DURATION.milliseconds.toDouble / batchDuration.milliseconds).toInt
+  def calculateNumBatchesToRemember(batchDuration: Duration,
+                                    minRememberDurationS: Duration): Int = {
+    math.ceil(minRememberDurationS.milliseconds.toDouble / batchDuration.milliseconds).toInt
   }
 }