1. we have a data frame which we want to write to s3 as parquet format and in overwrite mode.
2. every time we write the dataframe it's always a new folder. The code to write the s3 location is as follows:

        df.write
          .option("dateFormat", "yyyy-MM-dd'T'HH:mm:ss.SSS'Z'")
          .option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss.SSS'Z'")
          .option("maxRecordsPerFile", maxRecordsPerFile)
          .mode("overwrite")
          .format(format)
          .save(output)

What we observe is, at times we get FilenotFoundException (full trace below). Can somebody help me understand

1. when i am writing to a new s3 location (meaning nobody is reading from the location); why does the writing program throw the below exception?
2. how to fix it? --i see couple of stackoverflows pointing to this exception. But they say that it happens when you try to read when write is happening. But my case is not like that. i dont read when write happens.
3. my spark is 2.3.2 ; EMR-5.18.1 ; the code is written in scala
4. I am using s3:// as output folder path. Should i change it to some s3n or s3a ? will that help?

Caused by: java.io.FileNotFoundException: No such file or directory 's3://BUCKET/snapshots/FOLDER/_bid_9223370368440344985/part-00020-693dfbcb-74e9-45b0-b892-0b19fa92365c-c000.snappy.parquet'
It is possible the underlying files have been updated. You can explicitly invalidate the cache in Spark by running 'REFRESH TABLE tableName' command in SQL or by recreating the Dataset/DataFrame involved.
    at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.org$apache$spark$sql$execution$datasources$FileScanRDD$$anon$$readCurrentFile(FileScanRDD.scala:131)
    at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:182)
    at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:109)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:461)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$doExecute$1$$anonfun$4.apply(HashAggregateExec.scala:104)
    at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$doExecute$1$$anonfun$4.apply(HashAggregateExec.scala:101)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$26.apply(RDD.scala:853)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$26.apply(RDD.scala:853)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:49)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
    at org.apache.spark.scheduler.Task.run(Task.scala:109)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:750)

CodePudding user response：

I finally was able to solve the problem

1. The df : DataFrame was formed on the same s3 folder to which the same is being written in overwrite mode.

2. So during the overwrite; the source folder is getting cleared --which was resulting into the error

Hope this helps somebody.