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Date: Wed, 7 Jan 2015 16:42:34 +0000 (UTC)
From: "Taiji Okada (JIRA)" <jira@apache.org>
To: issues@spark.apache.org
Message-ID: <JIRA.12759831.1417810726000.29267.1420648954510@Atlassian.JIRA>
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 <JIRA.12759831.1417810726739@arcas>
Subject: [jira] [Issue Comment Deleted] (SPARK-4768) Add Support For Impala
 Encoded Timestamp (INT96)
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     [ https://issues.apache.org/jira/browse/SPARK-4768?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Taiji Okada updated SPARK-4768:
-------------------------------
    Comment: was deleted

(was: I am currently out of the office with limited access to email or voicemail. Please contact me again from Sun 29th Jan
)

> Add Support For Impala Encoded Timestamp (INT96)
> ------------------------------------------------
>
>                 Key: SPARK-4768
>                 URL: https://issues.apache.org/jira/browse/SPARK-4768
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>            Reporter: Pat McDonough
>            Priority: Critical
>
> Impala is using INT96 for timestamps. Spark SQL should be able to read this data despite the fact that it is not part of the spec.
> Perhaps adding a flag to act like impala when reading parquet (like we do for strings already) would be useful.
> Here's an example of the error you might see:
> {code}
> Caused by: java.lang.RuntimeException: Potential loss of precision: cannot convert INT96
>         at scala.sys.package$.error(package.scala:27)
>         at org.apache.spark.sql.parquet.ParquetTypesConverter$.toPrimitiveDataType(ParquetTypes.scala:61)
>         at org.apache.spark.sql.parquet.ParquetTypesConverter$.toDataType(ParquetTypes.scala:113)
>         at org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$convertToAttributes$1.apply(ParquetTypes.scala:314)
>         at org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$convertToAttributes$1.apply(ParquetTypes.scala:311)
>         at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>         at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>         at scala.collection.Iterator$class.foreach(Iterator.scala:727)
>         at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
>         at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
>         at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
>         at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>         at scala.collection.AbstractTraversable.map(Traversable.scala:105)
>         at org.apache.spark.sql.parquet.ParquetTypesConverter$.convertToAttributes(ParquetTypes.scala:310)
>         at org.apache.spark.sql.parquet.ParquetTypesConverter$.readSchemaFromFile(ParquetTypes.scala:441)
>         at org.apache.spark.sql.parquet.ParquetRelation.<init>(ParquetRelation.scala:66)
>         at org.apache.spark.sql.SQLContext.parquetFile(SQLContext.scala:141)
> {code}


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