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https://issues.apache.org/jira/browse/SPARK-18011?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15827032#comment-15827032
]
Miao Wang edited comment on SPARK-18011 at 1/17/17 11:27 PM:
-------------------------------------------------------------
[~felixcheung] I did intensive debug in both R side and scala side.
On R side, I debugged `createDataFrame.default` and `parallelize`, which
converts the data.frame into RDD and DataFrame. The code of turning the data
into RDD is done in `parallelize`:
sliceLen <- ceiling(length(coll) / numSlices)
slices <- split(coll, rep(1: (numSlices + 1), each = sliceLen)[1:length(coll)])
serializedSlices <- lapply(slices, serialize, connection = NULL)
I add debug message after the `serialize`:
lapply(serializedSlices, function(`x`) {message(paste("unserialized ",
unserialize(x)))})
The data `NA` is unserialized successfully.
Then, the serialized data is transferred to Scala side by jrdd <-
callJStatic("org.apache.spark.api.r.RRDD", "createRDDFromArray", sc,
serializedSlices) and returns a handle of the RDD in `jrdd`, which is later
used by `createDataFrame.default`.
I did not find anything wrong here.
On the Scala side, the problem happens in
def readString(in: DataInputStream): String = {
val len = in.readInt() <=== it encounters the problem when reading `NA` as
a string.
readStringBytes(in, len)
}
Then, I changed the logic as follows:
def readString(in: DataInputStream): String = {
var len = in.readInt()
if (len < 0) {
len = 3<===== I enforce reading 3 bytes in this case, because I believe
that it is the case of `NA`
}
readStringBytes(in, len)
}
Then I run the following commands in sparkR:
> a <- as.Date(NA)
> b <- as.data.frame(a)
> c <- collect(select(createDataFrame(b), "*"))
> c
a
1 NA
It executes correctly without hitting the exception handling (I add debug
information in the handling logic. If it is hit, error message will be print on
the console and I verified that it is print out without the above logic).
So, we can conclude that the problem is caused by `serialize` function with my
local R installation, which serialize `NA` as string without packing its length
before the actual value. Since `unserialize` can decode the seralized data,
this protocol should be by R design when handling `NA` as `Date` type. I don't
find the source code of `serialize` in R source code, which calls
Internal(serialize(object, connection, type, version, refhook))
For the fix, we can either leave it as it is by an exception handling or
explicitly add a handling in readString when index is negative.
What do you think? Thanks!
was (Author: wm624):
[~felixcheung] I did intensive debug in both R side and scala side.
On R side, I debugged `createDataFrame.default` and `parallelize`, which
converts the data.frame into RDD and DataFrame. The code of turning the data
into RDD is done in `parallelize`:
sliceLen <- ceiling(length(coll) / numSlices)
slices <- split(coll, rep(1: (numSlices + 1), each = sliceLen)[1:length(coll)])
serializedSlices <- lapply(slices, serialize, connection = NULL)
I add debug message after the `serialize`:
lapply(serializedSlices, function(x) {message(paste("unserialized ",
unserialize(x)))})
The data `NA` is unserialized successfully.
Then, the serialized data is transferred to Scala side by jrdd <-
callJStatic("org.apache.spark.api.r.RRDD", "createRDDFromArray", sc,
serializedSlices) and returns a handle of the RDD in `jrdd`, which is later
used by `createDataFrame.default`.
I did not find anything wrong here.
On the Scala side, the problem happens in
def readString(in: DataInputStream): String = {
val len = in.readInt() <=== it encounters the problem when reading `NA` as
a string.
readStringBytes(in, len)
}
Then, I changed the logic as follows:
def readString(in: DataInputStream): String = {
var len = in.readInt()
if (len < 0) {
len = 3<===== I enforce reading 3 bytes in this case, because I believe
that it is the case of `NA`
}
readStringBytes(in, len)
}
Then I run the following commands in sparkR:
> a <- as.Date(NA)
> b <- as.data.frame(a)
> c <- collect(select(createDataFrame(b), "*"))
> c
a
1 NA
It executes correctly without hitting the exception handling (I add debug
information in the handling logic. If it is hit, error message will be print on
the console and I verified that it is print out without the above logic).
So, we can conclude that the problem is caused by `serialize` function with my
local R installation, which serialize `NA` as string without packing its length
before the actual value. Since `unserialize` can decode the seralized data,
this protocol should be by R design when handling `NA` as `Date` type. I don't
find the source code of `serialize` in R source code, which calls
Internal(serialize(object, connection, type, version, refhook))
For the fix, we can either leave it as it is by an exception handling or
explicitly add a handling in readString when index is negative.
What do you think? Thanks!
> SparkR serialize "NA" throws exception
> --------------------------------------
>
> Key: SPARK-18011
> URL: https://issues.apache.org/jira/browse/SPARK-18011
> Project: Spark
> Issue Type: Bug
> Components: SparkR
> Reporter: Miao Wang
>
> For some versions of R, if Date has "NA" field, backend will throw negative
> index exception.
> To reproduce the problem:
> {code}
> > a <- as.Date(c("2016-11-11", "NA"))
> > b <- as.data.frame(a)
> > c <- createDataFrame(b)
> > dim(c)
> 16/10/19 10:31:24 ERROR Executor: Exception in task 0.0 in stage 1.0 (TID 1)
> java.lang.NegativeArraySizeException
> at org.apache.spark.api.r.SerDe$.readStringBytes(SerDe.scala:110)
> at org.apache.spark.api.r.SerDe$.readString(SerDe.scala:119)
> at org.apache.spark.api.r.SerDe$.readDate(SerDe.scala:128)
> at org.apache.spark.api.r.SerDe$.readTypedObject(SerDe.scala:77)
> at org.apache.spark.api.r.SerDe$.readObject(SerDe.scala:61)
> at
> org.apache.spark.sql.api.r.SQLUtils$$anonfun$bytesToRow$1.apply(SQLUtils.scala:161)
> at
> org.apache.spark.sql.api.r.SQLUtils$$anonfun$bytesToRow$1.apply(SQLUtils.scala:160)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.immutable.Range.foreach(Range.scala:160)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.AbstractTraversable.map(Traversable.scala:104)
> at org.apache.spark.sql.api.r.SQLUtils$.bytesToRow(SQLUtils.scala:160)
> at
> org.apache.spark.sql.api.r.SQLUtils$$anonfun$5.apply(SQLUtils.scala:138)
> at
> org.apache.spark.sql.api.r.SQLUtils$$anonfun$5.apply(SQLUtils.scala:138)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithoutKey$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:372)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at
> org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:126)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
> 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}
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