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Cheng Lian commented on SPARK-11153: ------------------------------------ Yes, it's the statistics information that is corrupted. And yes, Parquet does write version in the metadata. Parquet-mr 1.8 handles this issue in exactly the way you suggested, namely ignoring binary statistics when necessary according to version information written in the metadata. However, Spark SQL performs filter push-down on driver side. This means we need to gather Parquet versions from all Parquet files using a distributed Spark job. We can probably merge this one into the job used to merge Spark schemata. But I think this is too risky for 1.5.2 at this stage. So I'd propose we simply disables filter push-down for strings and binaries in all cases until a parquet-mr upgrade. > Turns off Parquet filter push-down for string and binary columns > ---------------------------------------------------------------- > > Key: SPARK-11153 > URL: https://issues.apache.org/jira/browse/SPARK-11153 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 1.5.0, 1.5.1 > Reporter: Cheng Lian > Assignee: Cheng Lian > Priority: Blocker > > Due to PARQUET-251, {{BINARY}} columns in existing Parquet files may be > written with corrupted statistics information. This information is used by > filter push-down optimization. Since Spark 1.5 turns on Parquet filter > push-down by default, we may end up with wrong query results. PARQUET-251 has > been fixed in parquet-mr 1.8.1, but Spark 1.5 is still using 1.7.0. > Note that this kind of corrupted Parquet files could be produced by any > Parquet data models. > This affects all Spark SQL data types that can be mapped to Parquet > {{BINARY}}, namely: > - {{StringType}} > - {{BinaryType}} > - {{DecimalType}} (but Spark SQL doesn't support pushing down {{DecimalType}} > columns for now.) > To avoid wrong query results, we should disable filter push-down for columns > of {{StringType}} and {{BinaryType}} until we upgrade to parquet-mr 1.8. -- 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