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https://issues.apache.org/jira/browse/SPARK-17634?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15516975#comment-15516975
]
Thomas Powell commented on SPARK-17634:
---------------------------------------
Several hours. I modified {{worker.R}} so that it logs the time it takes to
read each 10000 rows. I saw terrible slow down.
{code}
Starting to read the data
10000: 2.663s
20000: 4.223s
30000: 6.005s
40000: 7.799s
50000: 9.998s
...
540000: 116.293s
550000: 122.248s
560000: 122.798s
570000: 126.57s
580000: 135.371s
{code}
This isn't total time to read rows. It is the time to read each 10000 rows. I
modified the {{readMultipleObjects}} function to be:
{code}
readMultipleObjectsWithDebug <- function(inputCon) {
# readMultipleObjects will read multiple continuous objects from
# a DataOutputStream. There is no preceding field telling the count
# of the objects, so the number of objects varies, we try to read
# all objects in a loop until the end of the stream.
cat("Starting to read the data", file=debugFile, append=TRUE, sep="\n")
data <- list()
secs <- elapsedSecs()
while (TRUE) {
# If reaching the end of the stream, type returned should be "".
type <- SparkR:::readType(inputCon)
if (type == "") {
break
}
data[[length(data) + 1L]] <- SparkR:::readTypedObject(inputCon, type)
if (length(data) %% 10000 == 0) {
duration <- elapsedSecs() - secs
cat(paste(length(data), ": ", duration, "s", sep=""),
file=debugFile, append=TRUE, sep="\n")
secs <- elapsedSecs()
}
}
data # this is a list of named lists now
}
{code}
> Spark job hangs when using dapply
> ---------------------------------
>
> Key: SPARK-17634
> URL: https://issues.apache.org/jira/browse/SPARK-17634
> Project: Spark
> Issue Type: Bug
> Components: SparkR
> Affects Versions: 2.0.0
> Reporter: Thomas Powell
> Priority: Critical
>
> I'm running into an issue when using dapply on yarn. I have a data frame
> backed by files in parquet with around 200 files that is around 2GB. When I
> load this in with the new partition coalescing it ends up having around 20
> partitions so each one roughly 100MB. The data frame itself has 4 columns of
> integers and doubles. If I run a count over this things work fine.
> However, if I add a {{dapply}} in between the read and the {{count}} that
> just uses an identity function the tasks hang and make no progress. Both the
> R and Java processes are running on the Spark nodes and are listening on the
> {{SPARKR_WORKER_PORT}}.
> {{result <- dapply(df, function(x){x}, SparkR::schema(df))}}
> I took a jstack of the Java process and see that it is just listening on the
> socket but never seems to make any progress. The R process is harder to debug
> what it is doing.
> {code}
> Thread 112823: (state = IN_NATIVE)
> - java.net.SocketInputStream.socketRead0(java.io.FileDescriptor, byte[],
> int, int, int) @bci=0 (Interpreted frame)
> - java.net.SocketInputStream.socketRead(java.io.FileDescriptor, byte[], int,
> int, int) @bci=8, line=116 (Interpreted frame)
> - java.net.SocketInputStream.read(byte[], int, int, int) @bci=79, line=170
> (Interpreted frame)
> - java.net.SocketInputStream.read(byte[], int, int) @bci=11, line=141
> (Interpreted frame)
> - java.io.BufferedInputStream.fill() @bci=214, line=246 (Interpreted frame)
> - java.io.BufferedInputStream.read() @bci=12, line=265 (Compiled frame)
> - java.io.DataInputStream.readInt() @bci=4, line=387 (Compiled frame)
> - org.apache.spark.api.r.RRunner.org$apache$spark$api$r$RRunner$$read()
> @bci=4, line=212 (Interpreted frame)
> -
> org.apache.spark.api.r.RRunner$$anon$1.<init>(org.apache.spark.api.r.RRunner)
> @bci=25, line=96 (Interpreted frame)
> - org.apache.spark.api.r.RRunner.compute(scala.collection.Iterator, int)
> @bci=109, line=87 (Interpreted frame)
> -
> org.apache.spark.sql.execution.r.MapPartitionsRWrapper.apply(scala.collection.Iterator)
> @bci=322, line=59 (Interpreted frame)
> -
> org.apache.spark.sql.execution.r.MapPartitionsRWrapper.apply(java.lang.Object)
> @bci=5, line=29 (Interpreted frame)
> -
> org.apache.spark.sql.execution.MapPartitionsExec$$anonfun$6.apply(scala.collection.Iterator)
> @bci=59, line=178 (Interpreted frame)
> -
> org.apache.spark.sql.execution.MapPartitionsExec$$anonfun$6.apply(java.lang.Object)
> @bci=5, line=175 (Interpreted frame)
> -
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(org.apache.spark.TaskContext,
> int, scala.collection.Iterator) @bci=8, line=784 (Interpreted frame)
> -
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(java.lang.Object,
> java.lang.Object, java.lang.Object) @bci=13, line=784 (Interpreted frame)
> - org.apache.spark.rdd.MapPartitionsRDD.compute(org.apache.spark.Partition,
> org.apache.spark.TaskContext) @bci=27, line=38 (Interpreted frame)
> -
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(org.apache.spark.Partition,
> org.apache.spark.TaskContext) @bci=26, line=319 (Interpreted frame)
> - org.apache.spark.rdd.RDD.iterator(org.apache.spark.Partition,
> org.apache.spark.TaskContext) @bci=33, line=283 (Interpreted frame)
> - org.apache.spark.rdd.MapPartitionsRDD.compute(org.apache.spark.Partition,
> org.apache.spark.TaskContext) @bci=24, line=38 (Interpreted frame)
> -
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(org.apache.spark.Partition,
> org.apache.spark.TaskContext) @bci=26, line=319 (Interpreted frame)
> - org.apache.spark.rdd.RDD.iterator(org.apache.spark.Partition,
> org.apache.spark.TaskContext) @bci=33, line=283 (Interpreted frame)
> - org.apache.spark.rdd.MapPartitionsRDD.compute(org.apache.spark.Partition,
> org.apache.spark.TaskContext) @bci=24, line=38 (Interpreted frame)
> -
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(org.apache.spark.Partition,
> org.apache.spark.TaskContext) @bci=26, line=319 (Interpreted frame)
> - org.apache.spark.rdd.RDD.iterator(org.apache.spark.Partition,
> org.apache.spark.TaskContext) @bci=33, line=283 (Interpreted frame)
> -
> org.apache.spark.scheduler.ShuffleMapTask.runTask(org.apache.spark.TaskContext)
> @bci=168, line=79 (Interpreted frame)
> -
> org.apache.spark.scheduler.ShuffleMapTask.runTask(org.apache.spark.TaskContext)
> @bci=2, line=47 (Interpreted frame)
> - org.apache.spark.scheduler.Task.run(long, int,
> org.apache.spark.metrics.MetricsSystem) @bci=82, line=85 (Interpreted frame)
> - org.apache.spark.executor.Executor$TaskRunner.run() @bci=374, line=274
> (Interpreted frame)
> -
> java.util.concurrent.ThreadPoolExecutor.runWorker(java.util.concurrent.ThreadPoolExecutor$Worker)
> @bci=95, line=1142 (Interpreted frame)
> - java.util.concurrent.ThreadPoolExecutor$Worker.run() @bci=5, line=617
> (Interpreted frame)
> - java.lang.Thread.run() @bci=11, line=745 (Interpreted frame)
> {code}
> Any recommendations on how best to debug? Nothing appears in the logs since
> the processes don't actually fail. The executors themselves have 4GB of
> memory which should be more than enough.
> My feeling is this could be something around serialization?
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