Thanks for the feedback, I filed a couple of issues: https://github.com/databricks/spark-avro/issues
On Fri, Nov 21, 2014 at 5:04 AM, thomas j <beanb...@googlemail.com> wrote: > I've been able to load a different avro file based on GenericRecord with: > > val person = sqlContext.avroFile("/tmp/person.avro") > > When I try to call `first()` on it, I get "NotSerializableException" > exceptions again: > > person.first() > > ... > 14/11/21 12:59:17 ERROR Executor: Exception in task 0.0 in stage 14.0 (TID > 20) > java.io.NotSerializableException: > org.apache.avro.generic.GenericData$Record > at > java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1183) > at java.io.ObjectOutputStream.writeArray(ObjectOutputStream.java:1377) > at > java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1173) > at > java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547) > ... > > Apart from this I want to transform the records into pairs of (user_id, > record). I can do this by specifying the offset of the user_id column with > something like this: > > person.map(r => (r.getInt(2), r)).take(4).collect() > > Is there any way to be able to specify the column name ("user_id") instead > of needing to know/calculate the offset somehow? > > Thanks again > > > On Fri, Nov 21, 2014 at 11:48 AM, thomas j <beanb...@googlemail.com> > wrote: > >> Thanks for the pointer Michael. >> >> I've downloaded spark 1.2.0 from >> https://people.apache.org/~pwendell/spark-1.2.0-snapshot1/ and clone and >> built the spark-avro repo you linked to. >> >> When I run it against the example avro file linked to in the >> documentation it works. However, when I try to load my avro file (linked to >> in my original question) I receive the following error: >> >> java.lang.RuntimeException: Unsupported type LONG >> at scala.sys.package$.error(package.scala:27) >> at com.databricks.spark.avro.AvroRelation.com >> $databricks$spark$avro$AvroRelation$$toSqlType(AvroRelation.scala:116) >> at >> com.databricks.spark.avro.AvroRelation$$anonfun$5.apply(AvroRelation.scala:97) >> at >> com.databricks.spark.avro.AvroRelation$$anonfun$5.apply(AvroRelation.scala:96) >> at >> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) >> at >> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) >> ... >> >> If this is useful I'm happy to try loading the various different avro >> files I have to try to battle-test spark-avro. >> >> Thanks >> >> On Thu, Nov 20, 2014 at 6:30 PM, Michael Armbrust <mich...@databricks.com >> > wrote: >> >>> One option (starting with Spark 1.2, which is currently in preview) is >>> to use the Avro library for Spark SQL. This is very new, but we would love >>> to get feedback: https://github.com/databricks/spark-avro >>> >>> On Thu, Nov 20, 2014 at 10:19 AM, al b <beanb...@googlemail.com> wrote: >>> >>>> I've read several posts of people struggling to read avro in spark. The >>>> examples I've tried don't work. When I try this solution ( >>>> https://stackoverflow.com/questions/23944615/how-can-i-load-avros-in-spark-using-the-schema-on-board-the-avro-files) >>>> I get errors: >>>> >>>> spark java.io.NotSerializableException: >>>> org.apache.avro.mapred.AvroWrapper >>>> >>>> How can I read the following sample file in spark using scala? >>>> >>>> http://www.4shared.com/file/SxnYcdgJce/sample.html >>>> >>>> Thomas >>>> >>> >>> >> >