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Steve Loughran commented on SPARK-23308: ---------------------------------------- bq. Other option would be creating a special exception (CorruptedFileException?) that could be thrown by FS implementations and let them decide what is a corrupted file or just a transient error. Its pretty hard to get consistent semantics on "working" FS behaviour, let alone failure modes; it's why the Hadoop FS specs and compliance tests have the notion of strict failure "does what HDFS does" and "lax", "raises an IOE". AFAIK HDFS raises {{ChecksumException}} on checksum errors, I don't know what it does on. say. decryption failure or erasure coding problems. and don't really want to look. You could try to add a parent class here, "Unrecoverable IOE" & see about getting it in to everything over time. Common prefixes and the classic year=2018/month=12 partitioning is pretty pathological for S3. But like you say, 503 is the standard response, though it may be caught in the AWS SDK. Talk to the AWS people > ignoreCorruptFiles should not ignore retryable IOException > ---------------------------------------------------------- > > Key: SPARK-23308 > URL: https://issues.apache.org/jira/browse/SPARK-23308 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 2.2.1 > Reporter: Márcio Furlani Carmona > Priority: Minor > > When `spark.sql.files.ignoreCorruptFiles` is set it totally ignores any kind > of RuntimeException or IOException, but some possible IOExceptions may happen > even if the file is not corrupted. > One example is the SocketTimeoutException which can be retried to possibly > fetch the data without meaning the data is corrupted. > > See: > https://github.com/apache/spark/blob/e30e2698a2193f0bbdcd4edb884710819ab6397c/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileScanRDD.scala#L163 -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org