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Posted to issues@spark.apache.org by "Davies Liu (JIRA)" <ji...@apache.org> on 2015/10/19 19:08:05 UTC
[jira] [Resolved] (SPARK-9643) Error serializing datetimes with
timezones using Dataframes and Parquet
[ https://issues.apache.org/jira/browse/SPARK-9643?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Davies Liu resolved SPARK-9643.
-------------------------------
Resolution: Fixed
Fix Version/s: 1.6.0
Issue resolved by pull request 7950
[https://github.com/apache/spark/pull/7950]
> Error serializing datetimes with timezones using Dataframes and Parquet
> -----------------------------------------------------------------------
>
> Key: SPARK-9643
> URL: https://issues.apache.org/jira/browse/SPARK-9643
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 1.4.1
> Reporter: Alex Angelini
> Labels: upgrade
> Fix For: 1.6.0
>
>
> Trying to serialize a DataFrame with a datetime column that includes a timezone fails with the following error.
> {code}
> net.razorvine.pickle.PickleException: invalid pickle data for datetime; expected 1 or 7 args, got 2
> at net.razorvine.pickle.objects.DateTimeConstructor.createDateTime(DateTimeConstructor.java:69)
> at net.razorvine.pickle.objects.DateTimeConstructor.construct(DateTimeConstructor.java:32)
> at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:701)
> at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:171)
> at net.razorvine.pickle.Unpickler.load(Unpickler.java:85)
> at net.razorvine.pickle.Unpickler.loads(Unpickler.java:98)
> at org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:151)
> at org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:150)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.org$apache$spark$sql$execution$datasources$InsertIntoHadoopFsRelation$$writeRows$1(commands.scala:185)
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$insert$1.apply(commands.scala:163)
> at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$insert$1.apply(commands.scala:163)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:64)
> at org.apache.spark.scheduler.Task.run(Task.scala:86)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> {code}
> According to [~davies] timezone serialization is done directly in Spark and not dependent on Pyrolite, but I was not able to prove that.
> Upgrading to Pyrolite 4.9 fixed this issue
> https://github.com/apache/spark/pull/7950
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