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From "Reynold Xin (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-7869) Spark Data Frame Fails to Load Postgres Tables with JSONB DataType Columns
Date Thu, 08 Oct 2015 06:13:26 GMT

     [ https://issues.apache.org/jira/browse/SPARK-7869?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Reynold Xin resolved SPARK-7869.
--------------------------------
       Resolution: Fixed
    Fix Version/s: 1.6.0

> Spark Data Frame Fails to Load Postgres Tables with JSONB DataType Columns
> --------------------------------------------------------------------------
>
>                 Key: SPARK-7869
>                 URL: https://issues.apache.org/jira/browse/SPARK-7869
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark, SQL
>    Affects Versions: 1.3.0, 1.3.1
>         Environment: Spark 1.3.1
>            Reporter: Brad Willard
>            Assignee: Alexey Grishchenko
>            Priority: Minor
>             Fix For: 1.6.0
>
>
> Most of our tables load into dataframes just fine with postgres. However we have a number
of tables leveraging the JSONB datatype. Spark will error and refuse to load this table. While
asking for Spark to support JSONB might be a tall order in the short term, it would be great
if Spark would at least load the table ignoring the columns it can't load or have it be an
option.
> {code}
> pdf = sql_context.load(source="jdbc", url=url, dbtable="table_of_json")
> Py4JJavaError: An error occurred while calling o41.load.
> : java.sql.SQLException: Unsupported type 1111
>     at org.apache.spark.sql.jdbc.JDBCRDD$.getCatalystType(JDBCRDD.scala:78)
>     at org.apache.spark.sql.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:112)
>     at org.apache.spark.sql.jdbc.JDBCRelation.<init>(JDBCRelation.scala:133)
>     at org.apache.spark.sql.jdbc.DefaultSource.createRelation(JDBCRelation.scala:121)
>     at org.apache.spark.sql.sources.ResolvedDataSource$.apply(ddl.scala:219)
>     at org.apache.spark.sql.SQLContext.load(SQLContext.scala:697)
>     at org.apache.spark.sql.SQLContext.load(SQLContext.scala:685)
>     at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>     at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
>     at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
>     at java.lang.reflect.Method.invoke(Method.java:606)
>     at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
>     at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
>     at py4j.Gateway.invoke(Gateway.java:259)
>     at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
>     at py4j.commands.CallCommand.execute(CallCommand.java:79)
>     at py4j.GatewayConnection.run(GatewayConnection.java:207)
>     at java.lang.Thread.run(Thread.java:745)
> {code}



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