In case anyone else has the same problem and finds this - in my case it was
fixed by increasing spark.sql.broadcastTimeout (I used 9000).
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yet, so changing how it executes the jobs on
the queue won't help.
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rk.redshift").save()}.execute()
> f1.get()
> f2.get()
>
> In the hope that the first write would no longer block the following steps,
> but instead it fails with a TimeoutException (see stack trace in previous
> message). Is there a way to
e). Is there a way to start the different writes concurrently, or is
that not possible in Spark?
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Redshift(RedshiftWriter.scala:374)
> at
>
> com.databricks.spark.redshift.DefaultSource.createRelation(DefaultSource.scala:106)
> at
>
> org.apache.spark.sql.execution.datasources.ResolvedDataSource$.a
aultSource.scala:106)
at
org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:222)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:148)
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map
over them in a high level way, but as this is not supported afaik).
Thanks!
Staffan
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