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  1. Spark
  2. SPARK-27589 Spark file source V2
  3. SPARK-27504

File source V2: support refreshing metadata cache

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Details

    • Sub-task
    • Status: Resolved
    • Major
    • Resolution: Fixed
    • 3.0.0
    • 3.0.0
    • SQL
    • None

    Description

      In file source V1, if some file is deleted manually, reading the DataFrame/Table will throws an exception with suggestion message "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.".
      After refreshing the table/DataFrame, the reads should return correct results.

      We should follow it in file source V2 as well.

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            Gengliang.Wang Gengliang Wang Assign to me
            Gengliang.Wang Gengliang Wang
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              Updated:
              Resolved:

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