I can’t exactly reproduce your environment and I’m also unable to reproduce
your error using the CLI. if you take that snippet of data from your program
and use the CLI to read it, does the error still occur because when I try
everything is fine.
But like I said, I can’t use a clustered yarn.
Here’s a snippet you can try, a trait that I attach to my Scala App to get some
Mahout/Spark setup. It will create a SparkContext inside of mahoutSparkContext
so do all your job config first.
/** Put things here that setup the context for typical execution with Spark.
This
* should be mixed in to the object executing rdd operations to provide
implicit config and context
* values.
*/
abstract trait SparkJobContext {
implicit protected var sparkConf = new SparkConf()
implicit protected var sc: SparkContext = _
implicit var mc: SparkDistributedContext = _
def setupSparkContext(master: String = "local", sem: String = "3G",
appName: String = this.getClass.getSimpleName, customJars: String = ""):
Unit = {
val closeables = new java.util.ArrayDeque[Closeable]()
sparkConf.set("spark.kryo.referenceTracking", "false")
.set("spark.kryoserializer.buffer.mb", "200")
val jars = customJars.split(",").toTraversable
mc = mahoutSparkContext(masterUrl = master, appName = appName, customJars =
jars, sparkConf = sparkConf)
sc = mc.sc
}
}
On Jul 13, 2015, at 10:37 AM, Hegner, Travis <[email protected]> wrote:
I've added the Kryo registrator configs to my SparkConf and get the same
results. I am very new to Mahout so I was not aware of the requirement for
those.
I did have some unused imports in there if that is what you meant by the
"distributed context" comment. I've pushed a couple of updates to add the
configs you mentioned, and remove the unused imports.
Keep in mind that the program I linked to has the sole purpose of reproducing
the exception I'm experiencing. I am not using int.max in my actual driver
program, I am using just the defaults of 50 and 500. I only put those into this
program to make it easier to reproduce the exception. The reproduction program
is as stripped down and simple as possible while still producting the exception
to attempt to aid in troubleshooting this thing. Is there some documentation
somewhere on the recommended minimum sizes for those parameters given the size
of a dataset? I'm sure that question is dataset specific, but some general
guidelines could be helpful if they exist somewhere so that I'm not burning CPU
for no reasonable difference in accuracy.
Given your suggestions, I am still getting the same exception. Everything for
the spark instance on my cloudera cluster is the default. Would it still be
helpful to see a dump of information from somewhere? The 'Environment' tab from
the job's web interface? I typically try to let everything run with defaults,
until I need to make/test something more specific. I guess it's how I learn to
use the software. I am running this command to submit the job:
spark-submit --class com.travishegner.RowSimTest.RowSimTest
RowSimTest-0.0.1-SNAPSHOT.jar
The only difference in the calling command for my real driver program is a
"--jars" option to distribute a dependency.
Thanks again for the help!
Travis
-----Original Message-----
From: Pat Ferrel [mailto:[email protected]]
Sent: Monday, July 13, 2015 12:36 PM
To: [email protected]
Cc: Dmitriy Lyubimov
Subject: Re: RowSimilarity API -- illegal argument exception from
org.apache.mahout.math.stats.LogLikelihood.logLikelihoodRatio()
You aren’t setting up the Mahout Kryo registrator. Without this I’m surprised
it runs at all. Make sure the Spark settings use these values:
"spark.serializer": "org.apache.spark.serializer.KryoSerializer",
"spark.kryo.registrator":
"org.apache.mahout.sparkbindings.io.MahoutKryoRegistrator",
"spark.kryo.referenceTracking": "false",
"spark.kryoserializer.buffer.mb": "300",
"spark.executor.memory": “4g” // or something larger than default
Not sure if the distributed context is needed too, maybe Dmitriy knows more.
BTW I wouldn’t use Int.max. The calculation will approach O(n^2) with virtually
no effective gain in accuracy and my even cause problems.
If none of this helps I can set up yarn on my dev machine, can you give me the
spark-submit CLI and all Spark settings?
On Jul 13, 2015, at 7:51 AM, Hegner, Travis <[email protected]> wrote:
So, I've yet to be able to reproduce this with "--master local" or "--master
local[8]", it has only occurred on my cloudera/spark/yarn cluster with both
"--master yarn-client" and "--master yarn-cluster". I don't have a spark
standalone cluster to test against.
I have put up a test program on my github account which contains hard coded
test data: https://github.com/travishegner/RowSimTest. My pom.xml is including
the mahout libraries into my final jar via shade in order to test against my
own version of mahout (actually your's right now Pat!), rather than the one
built into the cluster.
With this dataset the exception is sporadic (50% maybe) with the default params
for "maxInterestingSimilaritiesPerRow" and "maxObservationsPerRow", but when I
pass Int.MaxValue for each of those it seems to occur more regularly, but still
succeeds at times. Sometimes, my driver program will throw the exception, but
retry the failed task and continue on to complete the program successfully,
other times it will completely fail after too many retries. I can literally run
the same jar back-to-back without recompiling and get different results. I also
ruled out a hardware issue by decommisioning the Yarn NodeManager service on
all but one of my nodes to isolate it to a single node. I did that again on a
separate node with similar results. The frequency of the exception is directly
related to the size of the dataset. The smaller I make the dataset, the more
often it succeeds, and I have yet to get a successful execution with a large
enough subset of my full dataset.
Interestingly enough, if I map the IDS into flipped values (<tag>, <doc_id>)
and run it through the cooccurrencesIDSs() method, it never fails (see the
commented code block). If I run the reverse mapping through
rowSimilarityIDS(), it still fails in the same way.
Can you recommend any other troubleshooting steps to try? Is there any more
information that I can provide?
Thanks,
Travis
-----Original Message-----
From: Pat Ferrel [mailto:[email protected]]
Sent: Sunday, July 12, 2015 8:18 PM
To: [email protected]
Subject: Re: RowSimilarity API -- illegal argument exception from
org.apache.mahout.math.stats.LogLikelihood.logLikelihoodRatio()
I tried a couple datasets this weekend and could not get the error to
reproduce. Could you share some data or the code that creates the
IndexedDataset?
I wonder whether the IndexedDataset is created correctly, it will construct
from rdd and two BiDictionaries but that doesn’t mean they have correctly
formatted values. It needs a Mahout DRM in the rdd, which means int keys and
vector values with two BiDictionaries for key <-> string mappings for column
and row. Also the int keys need to be contiguous ints 0..n
On Jul 10, 2015, at 11:40 AM, Pat Ferrel <[email protected]> wrote:
The IndexDataset creates two BiDictionaries (Bi-directional dictionaries) of
Int <-> String so if it can be a String the element ids have no other
restrictions.
May indeed be a bug I’ll look at is asap, since it passes the scala tests, any
data you can spare might help but if you are doing a lot of prep, maybe that’s
not so easy?
On Jul 10, 2015, at 11:16 AM, Hegner, Travis <[email protected]> wrote:
I am actually not using the CLI, I am using the API directly. Also, I am
transforming the data into an RDD of (BigDecimal, String), mapping that to
(String,String) and creating an IndexedDatasetSpark which I feed into
rowSimilarityIDS(). This same process works flawlessly when calling
cooccurrencesIDSs(Array(IDS)) on an IDS that was generated from an RDD of
(<tag>, <doc_id>).
My string tags do have some special characters, so I have been simply hashing
them into an md5 string as a precaution since it shouldn't change the final
result. I will try and scan the data for any nulls or other oddities. If I
can't find anything obvious, then I'll try to pair it down to a small enough
sample that is still affected in order to share.
Are there any normalizing rules that I should be aware of? For example, all the
doc_id's must be the same length string?
Thanks,
Travis
-----Original Message-----
From: Pat Ferrel [mailto:[email protected]]
Sent: Friday, July 10, 2015 1:34 PM
To: [email protected]
Subject: Re: RowSimilarity API -- illegal argument exception from
org.apache.mahout.math.stats.LogLikelihood.logLikelihoodRatio()
Ok. Don’t suppose you could share your data or at least a snippet? Some odd
errors can creep in if there is invalid data, like a null doc id or tag. Very
little data validation is done, which is something I need to address. I’ll it
try on some sample data I have.
BTW you understand that rowSimilarity input is a doc-id, list-of-tags where by
default tab separates doc-id from the list and a space separates items in the
list. Separators can be changed in the code but not the CLI.
On Jul 10, 2015, at 9:54 AM, Hegner, Travis <[email protected]> wrote:
Thanks Pat,
With a clean version of your spark-1.3 branch I continue to get the error. You
can find the stack trace at the end of the message. As I mentioned in my
original message, I've narrowed it down to (k21 < 0), however, I'm not entirely
certain it's based on the data condition I described, as I set up a test case
with a small amount of data exhibiting the same condition described, and it
works OK.
How is it possible that "numInteractionsWithB=0" while
"numInteractionsWithAandB=1"? I would think that the latter would always have
to be less than or equal the former.
Thanks!
Travis
java.lang.IllegalArgumentException
at com.google.common.base.Preconditions.checkArgument(Preconditions.java:72)
at
org.apache.mahout.math.stats.LogLikelihood.logLikelihoodRatio(LogLikelihood.java:101)
at
org.apache.mahout.math.cf.SimilarityAnalysis$.logLikelihoodRatio(SimilarityAnalysis.scala:201)
at
org.apache.mahout.math.cf.SimilarityAnalysis$$anonfun$4$$anonfun$apply$1$$anonfun$apply$mcVI$sp$1.apply(SimilarityAnalysis.scala:229)
at
org.apache.mahout.math.cf.SimilarityAnalysis$$anonfun$4$$anonfun$apply$1$$anonfun$apply$mcVI$sp$1.apply(SimilarityAnalysis.scala:222)
at scala.collection.Iterator$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.mahout.math.cf.SimilarityAnalysis$$anonfun$4$$anonfun$apply$1.apply$mcVI$sp(SimilarityAnalysis.scala:222)
at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141)
at
org.apache.mahout.math.cf.SimilarityAnalysis$$anonfun$4.apply(SimilarityAnalysis.scala:215)
at
org.apache.mahout.math.cf.SimilarityAnalysis$$anonfun$4.apply(SimilarityAnalysis.scala:208)
at
org.apache.mahout.sparkbindings.blas.MapBlock$$anonfun$1.apply(MapBlock.scala:33)
at
org.apache.mahout.sparkbindings.blas.MapBlock$$anonfun$1.apply(MapBlock.scala:32)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$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.rdd.PairRDDFunctions$$anonfun$13.apply(PairRDDFunctions.scala:1071)
at
org.apache.spark.rdd.PairRDDFunctions$$anonfun$13.apply(PairRDDFunctions.scala:1059)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
at org.apache.spark.scheduler.Task.run(Task.scala:64)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
-----Original Message-----
From: Pat Ferrel [mailto:[email protected]]
Sent: Thursday, July 09, 2015 10:09 PM
To: [email protected]
Subject: Re: RowSimilarity API -- illegal argument exception from
org.apache.mahout.math.stats.LogLikelihood.logLikelihoodRatio()
I am using Mahout every day on Spark 1.3.1.
Try https://github.com/pferrel/mahout/tree/spark-1.3, which is the one I’m
using. Let me know if you still have the problem and include the stack trace.
I’ve been using cooccurrence, which is closely related to rowSimilarity.
> Third, what would be the mathematical implications if I run
> SimilarityAnalysis.cooccurrencesIDSs() with a list of (<tag>,<document_id>)
> pairs. Would the results be sound, or does that make absolutely no sense?
> Would it be beneficial even as only a troubleshooting step?
cooccurrence calculates llr(A’A), and rowSimilarity is doing llr(AA’). The
input you are talking about is A’ so you would be doing llr((A’)’(A’)) and so
should produce the same results but let’s get it working. I’ll look at it
either tomorrow or this weekend. If you have any stack trace using the above
branch, let me know.
BTW what Dmitriy said is correct, IntelliJ is often not able to determine every
decoration function available.
On Jul 9, 2015, at 12:02 PM, Hegner, Travis <[email protected]> wrote:
FYI, I just tested against the latest spark-1.3 version I found at:
https://github.com/andrewpalumbo/mahout/tree/MAHOUT-1653-shell-master
I am getting the exact results described below.
Thanks again!
Travis
-----Original Message-----
From: Hegner, Travis [mailto:[email protected]]
Sent: Thursday, July 09, 2015 10:25 AM
To: '[email protected]'
Subject: RowSimilarity API -- illegal argument exception from
org.apache.mahout.math.stats.LogLikelihood.logLikelihoodRatio()
Hello list,
I am having some trouble getting a SimilarityAnalysis.rowSimilarityIDS() job to
run. First some info on my environment:
I'm running hadoop with cloudera 5.4.2 with their built in spark on yarn setup
it's pretty much an OOTB setup, but it has been upgraded many times since
probably CDH4.8 or so. It's running spark 1.3.0 (perhaps some 1.3.1 commits
merged in from what I've read about cloudera's versioning). I have my own fork
of mahout which is currently just a mirror of 'github.com:pferrel/spark-1.3'.
I'm very comfortable making changes, compiling, and using my version of the
library should your suggestions lead me in that direction. I am still pretty
new to scala, so I have a hard time wrapping my head around what some of the
syntactic sugars actually do, but I'm getting there.
I'm successfully getting my data transformed to an RDD that essentially looks
like (<document_id>, <tag>), creating an IndexedDataSet with that, and feeding
that into SimilarityAnalysis.rowSimilarityIDS(). I've been able to narrow the
issue down to a specific case:
Let's say I have the following records (among others) in my RDD:
...
(doc1, tag1)
(doc2, tag1)
...
doc1, and doc2 have no other tags, but tag1 may exist on many other documents.
The rest of my dataset has many other doc/tag combinations, but I've narrowed
down the issue to seemingly only occur in this case. I've been able to trace
down that the java.lang.IllegalArgumentException is occuring because k21 is < 0
(i.e. "numInteractionsWithB = 0" and "numInteractionsWithAandB = 1") when
calling LogLikelihood.logLikelihoodRatio() from
SimilarityAnalysis.logLikelihoodRatio().
Speculating a bit, I see that in SimilarityAnalysys.rowSimilarity() on the line
(163 in my branch):
val bcastInteractionsPerItemA = drmBroadcast(drmA.numNonZeroElementsPerRow)
...my IDE (intellij) complains that it cannot resolve
"drmA.numNonZeroElementsPerRow", however the library compiles successfully.
Tracing the codepath shows that if that value is not being correctly populated,
it would have a direct impact on the values used in logLikelihoodRatio(). That
said, it seems to only fail in this very particular case.
I should note that I can run SimilarityAnalysis.cooccurrencesIDSs()
successfully with a single list of (<user_id>, <item_id>) pairs of my own data.
I have 3 questions given this scenario:
First, am I using the proper branch of code for attempting to run on a spark
1.3 cluster? I've read about a "joint effort" for spark 1.3, and this was the
only branch I could find for it.
Second, Is anyone able to shed some light on the above error? Is drmA not a
correct type, or does that method no longer apply to that type?
Third, what would be the mathematical implications if I run
SimilarityAnalysis.cooccurrencesIDSs() with a list of (<tag>,<document_id>)
pairs. Would the results be sound, or does that make absolutely no sense? Would
it be beneficial even as only a troubleshooting step?
Thanks in advance for any help you may be able to provide!
Travis Hegner
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________________________________
The information contained in this communication is confidential and is intended
only for the use of the named recipient. Unauthorized use, disclosure, or
copying is strictly prohibited and may be unlawful. If you have received this
communication in error, you should know that you are bound to confidentiality,
and should please immediately notify the sender.
________________________________
The information contained in this communication is confidential and is intended
only for the use of the named recipient. Unauthorized use, disclosure, or
copying is strictly prohibited and may be unlawful. If you have received this
communication in error, you should know that you are bound to confidentiality,
and should please immediately notify the sender.
________________________________
The information contained in this communication is confidential and is intended
only for the use of the named recipient. Unauthorized use, disclosure, or
copying is strictly prohibited and may be unlawful. If you have received this
communication in error, you should know that you are bound to confidentiality,
and should please immediately notify the sender.
________________________________
The information contained in this communication is confidential and is intended
only for the use of the named recipient. Unauthorized use, disclosure, or
copying is strictly prohibited and may be unlawful. If you have received this
communication in error, you should know that you are bound to confidentiality,
and should please immediately notify the sender.
________________________________
The information contained in this communication is confidential and is intended
only for the use of the named recipient. Unauthorized use, disclosure, or
copying is strictly prohibited and may be unlawful. If you have received this
communication in error, you should know that you are bound to confidentiality,
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