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https://issues.apache.org/jira/browse/SPARK-15822?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15324475#comment-15324475
]
Adam Roberts commented on SPARK-15822:
--------------------------------------
Herman, here's the application, note my HashedRelation comment is only a theory
at this stage.
{code}
import org.apache.spark.SparkConf
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.functions._
object SQLFlights {
def displayTop(title: String, df: DataFrame) {
println(title);
df.sort(desc("rank")).take(10).foreach(println)
}
def main(args: Array[String]) {
val inputfile = args(0)
val airport = args(1)
val conf = new SparkConf().setAppName("SQL Flights")
val sqlContext =
org.apache.spark.sql.SparkSession.builder.config(conf).getOrCreate()
val df = sqlContext.read.format("com.databricks.spark.csv")
.option("header", "true")
.option("inferSchema", "true")
.load(inputfile)
.cache()
val arrivals = df.filter(s"Dest = '$airport'").cache();
val departures = df.filter(s"Origin = '$airport'").cache();
val departuresByCarrier = departures.groupBy("Dest",
"UniqueCarrier").count().withColumnRenamed("count", "total")
val a = departures.filter("Cancelled != 0 and CancellationCode = 'A'")
println("done a")
val b = a.groupBy("Dest", "UniqueCarrier").count()
println("done b")
val c = b.join(departuresByCarrier, Seq("Dest", "UniqueCarrier"))
println("done c")
val d = c.selectExpr("Dest", "UniqueCarrier", "round(count * 100 / total,
2) as rank")
println("done d")
displayTop("Top Departure Carrier Cancellations:", d)
}
}
{code}
in conf/spark-env.sh:
{code}
export SPARK_WORKER_CORES=2
export SPARK_WORKER_INSTANCES=2
{code}
in conf/spark-defaults.conf:
{code}
spark.sql.warehouse.dir /home/aroberts/sql-flights
{code}
Submit including --packages com.databricks:spark-csv_2.11:1.4.0
The job will complete but if you look in the $SPARK_HOME/work dir you'll see
that after our queries are done, executors will die due to the segv and by
looking in the stderr files we can see the problem.
> segmentation violation in o.a.s.unsafe.types.UTF8String
> --------------------------------------------------------
>
> Key: SPARK-15822
> URL: https://issues.apache.org/jira/browse/SPARK-15822
> Project: Spark
> Issue Type: Bug
> Affects Versions: 2.0.0
> Environment: linux amd64
> openjdk version "1.8.0_91"
> OpenJDK Runtime Environment (build 1.8.0_91-b14)
> OpenJDK 64-Bit Server VM (build 25.91-b14, mixed mode)
> Reporter: Pete Robbins
> Assignee: Herman van Hovell
> Priority: Blocker
>
> Executors fail with segmentation violation while running application with
> spark.memory.offHeap.enabled true
> spark.memory.offHeap.size 512m
> Also now reproduced with
> spark.memory.offHeap.enabled false
> {noformat}
> #
> # A fatal error has been detected by the Java Runtime Environment:
> #
> # SIGSEGV (0xb) at pc=0x00007f4559b4d4bd, pid=14182, tid=139935319750400
> #
> # JRE version: OpenJDK Runtime Environment (8.0_91-b14) (build 1.8.0_91-b14)
> # Java VM: OpenJDK 64-Bit Server VM (25.91-b14 mixed mode linux-amd64
> compressed oops)
> # Problematic frame:
> # J 4816 C2
> org.apache.spark.unsafe.types.UTF8String.compareTo(Lorg/apache/spark/unsafe/types/UTF8String;)I
> (64 bytes) @ 0x00007f4559b4d4bd [0x00007f4559b4d460+0x5d]
> {noformat}
> We initially saw this on IBM java on PowerPC box but is recreatable on linux
> with OpenJDK. On linux with IBM Java 8 we see a null pointer exception at the
> same code point:
> {noformat}
> 16/06/08 11:14:58 ERROR Executor: Exception in task 1.0 in stage 5.0 (TID 48)
> java.lang.NullPointerException
> at
> org.apache.spark.unsafe.types.UTF8String.compareTo(UTF8String.java:831)
> at org.apache.spark.unsafe.types.UTF8String.compare(UTF8String.java:844)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.findNextInnerJoinRows$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$doExecute$2$$anon$2.hasNext(WholeStageCodegenExec.scala:377)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at
> scala.collection.convert.Wrappers$IteratorWrapper.hasNext(Wrappers.scala:30)
> at org.spark_project.guava.collect.Ordering.leastOf(Ordering.java:664)
> at org.apache.spark.util.collection.Utils$.takeOrdered(Utils.scala:37)
> at
> org.apache.spark.rdd.RDD$$anonfun$takeOrdered$1$$anonfun$30.apply(RDD.scala:1365)
> at
> org.apache.spark.rdd.RDD$$anonfun$takeOrdered$1$$anonfun$30.apply(RDD.scala:1362)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:757)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:757)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:318)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:282)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
> at org.apache.spark.scheduler.Task.run(Task.scala:85)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1153)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
> at java.lang.Thread.run(Thread.java:785)
> {noformat}
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