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Posted to issues@spark.apache.org by "Praveen Tallapudi (JIRA)" <ji...@apache.org> on 2017/02/22 15:29:44 UTC
[jira] [Comment Edited] (SPARK-7869) Spark Data Frame Fails to Load
Postgres Tables with JSONB DataType Columns
[ https://issues.apache.org/jira/browse/SPARK-7869?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15878433#comment-15878433 ]
Praveen Tallapudi edited comment on SPARK-7869 at 2/22/17 3:28 PM:
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Hi Nipun, I am using Spark. Is there a way to insert the Jsonb data into postgres. We have a new project in design phase. We are thinking of using Apache Spark + Postgres DB. But we are facing issues while inserting JSONB data type.
Is there a support for Postgres-JSONB from spark? Can you please help us ? I have posted this question in the issues but no response. We really need help, can you please let us know if there is a way of inserting??
was (Author: praveen.tallapudi):
Hi Nipun, I am using Spark. Is there a way to insert the Jsonb data into postgres. We have a new project in design phase. We are thinking of using Apache Spark + Postgres DB. But we are facing issues while inserting JSONB data type.
Is there a support for Postgres-JSONB from spark? Can you please help us ? I have posted this question in the issues but no response. Can you please help?? We really need help, can you please help??
> 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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