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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 03:59:40 UTC
[jira] [Updated] (SPARK-13209) transitive closure on a dataframe
[ https://issues.apache.org/jira/browse/SPARK-13209?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-13209:
---------------------------------
Labels: bulk-closed (was: )
> transitive closure on a dataframe
> ---------------------------------
>
> Key: SPARK-13209
> URL: https://issues.apache.org/jira/browse/SPARK-13209
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.0
> Reporter: Placek
> Priority: Major
> Labels: bulk-closed
>
> When I run the following loop the join gets slower and slower regardless of caching. If I change the data frame to rdd and then back again (uncomment the last commented line) it seems that there is no slow down but I get an error after around 30 iterations. The code assumes that Edge is a simple case class with start and end.
> {code}
> val e = Seq((1, 2), (1, 3), (2, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 10), (10, 11), (11, 12), (12, 13), (13, 14), (14, 15), (15, 16), (16, 17), (17, 18), (18, 19), (19, 20), (20, 21), (21, 22), (22, 23), (23, 24), (24, 25), (25, 26), (26, 27), (27, 28), (23, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 10), (10, 11), (11, 12), (12, 13))
> //val e = Seq((1, 2), (1, 3), (2, 4), (4, 5), (5, 6), (6, 7))
> var edges = sc
> .parallelize(e, N)
> .map(p => Edge(p._1, p._2))
> .toDF()
> .cache()
> //var edges = e.map(p => Edge(p._1, p._2)).toDF().cache()
> var filtered = edges
> .filter("start = 1")
> .distinct()
> .withColumnRenamed("start", "fStart")
> .withColumnRenamed("end", "fEnd")
> .cache()
> var i = 0
> while (i < 300) {
> i = i + 1
> println("\n i = " + i)
> filtered = filtered
> .join(edges, filtered("fEnd") === edges("start"))
> .select(filtered("fStart"), edges("end"))
> .withColumnRenamed("start", "fStart")
> .withColumnRenamed("end", "fEnd")
> .distinct
> .cache()
> //filtered.explain()
> //filtered.explain(true)
>
> //filtered = sqlContext.createDataFrame(filtered.rdd, filtered.schema)
> filtered.show
> }
> {code}
> The error I get is and it causes (TaskSchedulerImpl: Lost executor driver on localhost: Executor heartbeat timed out after 121001 ms):
> {code}
> 16/02/07 01:55:59 ERROR Utils: Uncaught exception in thread driver-heartbeater
> java.io.IOException: java.lang.ClassCastException: cannot assign instance of scala.collection.immutable.HashMap$SerializationProxy to field org.apache.spark.executor.TaskMetrics._accumulatorUpdates of type scala.collection.immutable.Map in instance of org.apache.spark.executor.TaskMetrics
> at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1207)
> at org.apache.spark.executor.TaskMetrics.readObject(TaskMetrics.scala:219)
> at sun.reflect.GeneratedMethodAccessor18.invoke(Unknown Source)
> at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1058)
> at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1900)
> at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801)
> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:371)
> at org.apache.spark.util.Utils$.deserialize(Utils.scala:92)
> at org.apache.spark.executor.Executor$$anonfun$org$apache$spark$executor$Executor$$reportHeartBeat$1$$anonfun$apply$6.apply(Executor.scala:436)
> at org.apache.spark.executor.Executor$$anonfun$org$apache$spark$executor$Executor$$reportHeartBeat$1$$anonfun$apply$6.apply(Executor.scala:426)
> at scala.Option.foreach(Option.scala:236)
> at org.apache.spark.executor.Executor$$anonfun$org$apache$spark$executor$Executor$$reportHeartBeat$1.apply(Executor.scala:426)
> at org.apache.spark.executor.Executor$$anonfun$org$apache$spark$executor$Executor$$reportHeartBeat$1.apply(Executor.scala:424)
> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
> at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
> at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
> at org.apache.spark.executor.Executor.org$apache$spark$executor$Executor$$reportHeartBeat(Executor.scala:424)
> at org.apache.spark.executor.Executor$$anon$1$$anonfun$run$1.apply$mcV$sp(Executor.scala:468)
> at org.apache.spark.executor.Executor$$anon$1$$anonfun$run$1.apply(Executor.scala:468)
> at org.apache.spark.executor.Executor$$anon$1$$anonfun$run$1.apply(Executor.scala:468)
> at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1741)
> at org.apache.spark.executor.Executor$$anon$1.run(Executor.scala:468)
> at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
> at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:308)
> at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:180)
> at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:294)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: java.lang.ClassCastException: cannot assign instance of scala.collection.immutable.HashMap$SerializationProxy to field org.apache.spark.executor.TaskMetrics._accumulatorUpdates of type scala.collection.immutable.Map in instance of org.apache.spark.executor.TaskMetrics
> at java.io.ObjectStreamClass$FieldReflector.setObjFieldValues(ObjectStreamClass.java:2133)
> at java.io.ObjectStreamClass.setObjFieldValues(ObjectStreamClass.java:1305)
> at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2006)
> at java.io.ObjectInputStream.defaultReadObject(ObjectInputStream.java:501)
> at org.apache.spark.executor.TaskMetrics$$anonfun$readObject$1.apply$mcV$sp(TaskMetrics.scala:220)
> at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1204)
> ... 32 more
> {code}
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