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  1. Spark
  2. SPARK-27676

InMemoryFileIndex should hard-fail on missing files instead of logging and continuing

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    • Type: Bug
    • Status: Resolved
    • Priority: Major
    • Resolution: Fixed
    • Affects Version/s: 2.4.0
    • Fix Version/s: 3.0.0
    • Component/s: SQL
    • Labels:


      Spark's InMemoryFileIndex contains two places where FileNotFound exceptions are caught and logged as warnings (during directory listing and block location lookup). I think that this is a dangerous default behavior and would prefer that Spark hard-fails by default (with the ignore-and-continue behavior guarded by a SQL session configuration).

      In SPARK-17599 and SPARK-24364, logic was added to ignore missing files. Quoting from the PR for SPARK-17599:

      The ListingFileCatalog lists files given a set of resolved paths. If a folder is deleted at any time between the paths were resolved and the file catalog can check for the folder, the Spark job fails. This may abruptly stop long running StructuredStreaming jobs for example.

      Folders may be deleted by users or automatically by retention policies. These cases should not prevent jobs from successfully completing.

      Let's say that I'm not expecting to ever delete input files for my job. In that case, this behavior can mask bugs.

      One straightforward masked bug class is accidental file deletion: if I'm never expecting to delete files then I'd prefer to fail my job if Spark sees deleted files.

      A more subtle bug can occur when using a S3 filesystem. Say I'm running a Spark job against a partitioned Parquet dataset which is laid out like this:


      If I do spark.read.parquet("/data/date=1/") then Spark needs to perform multiple rounds of file listing, first listing /data/date=1 to discover the partitions for that date, then listing within each partition to discover the leaf files. Due to the eventual consistency of S3 ListObjects, it's possible that the first listing will show the region=west and region=east partitions existing and then the next-level listing fails to return any for some of the directories (e.g. /data/date=1/ returns files but /data/date=1/region=west/ throws a FileNotFoundException in S3A due to ListObjects inconsistency).

      If Spark propagated the FileNotFoundException and hard-failed in this case then I'd be able to fail the job in this case where we definitely know that the S3 listing is inconsistent (failing here doesn't guard against all potential S3 list inconsistency issues (e.g. back-to-back listings which both return a subset of the true set of objects), but I think it's still an improvement to fail for the subset of cases that we can detect even if that's not a surefire failsafe against the more general problem).

      Finally, I'm unsure if the original patch will have the desired effect: if a file is deleted once a Spark job expects to read it then that can cause problems at multiple layers, both in the driver (multiple rounds of file listing) and in executors (if the deletion occurs after the construction of the catalog but before the scheduling of the read tasks); I think the original patch only resolved the problem for the driver (unless I'm missing similar executor-side code specific to the original streaming use-case).

      Given all of these reasons, I think that the "ignore potentially deleted files during file index listing" behavior should be guarded behind a feature flag which defaults to false, consistent with the existing spark.files.ignoreMissingFiles and spark.sql.files.ignoreMissingFiles flags (which both default to false).


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            • Assignee:
              joshrosen Josh Rosen
              joshrosen Josh Rosen


              • Created:

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