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Cheng Lian commented on SPARK-7755: ----------------------------------- Thanks for reporting, would you mind to elaborate on some more details? # Could you provide more details about the failure (e.g. full stack trace)? # What version of Spark SQL were you using? (So that we can fill the "Affects Version/s" field of this ticket.) # What {{OutputCommitter}} were you using? For normal {{ParquetOutputCommitter}}, if files are partially written, they won't be committed. But the newly introduced {{DirectParquetOutputCommitter}} may confront this problem. Taking {{_SUCCESS}} into account should make sense for most cases. But the {{_SUCCESS}} marker is also optional, and can be turned off by setting {{mapreduce.fileoutputcommitter.marksuccessfuljobs}} to false. Spark SQL uses this property internally when writing Hive dynamic partitions to workaround a Hadoop compatibility issue ([PR #2663|https://github.com/apache/spark/pull/2663]). Not sure whether there are other scenarios that disable {{_SUCCESS}}. > MetadataCache.refresh does not take into account _SUCCESS > --------------------------------------------------------- > > Key: SPARK-7755 > URL: https://issues.apache.org/jira/browse/SPARK-7755 > Project: Spark > Issue Type: Improvement > Components: SQL > Reporter: Rowan Chattaway > Priority: Minor > Original Estimate: 1h > Remaining Estimate: 1h > > When you make a call to sqlc.parquetFile(path) where that path contains > partially written files, then refresh will fail in strange ways when it > attempts to read footer files. > I would like to adjust the file discovery to take into account the presence > of _SUCCESS and therefore only attempt to ready is we have the success marker. > I have made the changes locally and it doesn't appear to have any side > effects. > What are peoples thoughts about this? -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org