We’ve discovered a workaround for this; it’s described
here<https://issues.apache.org/jira/browse/HADOOP-18521>.
From: Eric Hanchrow
Date: Thursday, December 8, 2022 at 17:03
To: user@spark.apache.org
Subject: [Spark SQL]: unpredictable errors: java.io.IOException: can not read
My company runs java code that uses Spark to read from, and write to, Azure
Blob storage. This code runs more or less 24x7.
Recently we've noticed a few failures that leave stack traces in our logs; what
they have in common are exceptions that look variously like
Caused by: java.io.IOExcep
pends on Hadoop writing files. You can try to set the
> Hadoop property: mapreduce.output.basename
>
>
> https://spark.apache.org/docs/latest/api/java/org/apache/spark/SparkContext.html#hadoopConfiguration--
>
>
> Am 18.07.2021 um 01:15 schrieb Eric Beabes :
>
>
>
We’ve two datasets that look like this:
Dataset A: App specific data that contains (among other fields): ip_address
Dataset B: Location data that contains start_ip_address_int,
end_ip_address_int, latitude, longitude
We’re (left) joining these two datasets as: A.ip_address >=
B.start_ip_address
own risk. Any and all responsibility for any
> loss, damage or destruction of data or any other property which may arise
> from relying on this email's technical content is explicitly disclaimed.
> The author will in no case be liable for any monetary damages arising from
> such loss,
;
>
>
> *Disclaimer:* Use it at your own risk. Any and all responsibility for any
> loss, damage or destruction of data or any other property which may arise
> from relying on this email's technical content is explicitly disclaimed.
> The author will in no case be liable for any
yan guha wrote:
> IMHO - this is a bad idea esp in failure scenarios.
>
> How about creating a subfolder each for the jobs?
>
> On Sat, 17 Jul 2021 at 9:11 am, Eric Beabes
> wrote:
>
>> We've two (or more) jobs that write data into the same directory via a
>> Da
We've two (or more) jobs that write data into the same directory via a
Dataframe.save method. We need to be able to figure out which job wrote
which file. Maybe provide a 'prefix' to the file names. I was wondering if
there's any 'option' that allows us to do this. Googling didn't come up
with any
out.
Thanks,
Eric
On Mon, Jun 21, 2021 at 5:45 PM Eric Richardson
wrote:
> Ok, that sounds like a plan. I will gather what I found and either reach
> out on the security channel and/or try and upgrade with a pull request.
>
> Thanks for pointing me in the right direction.
>
&
Unsubscribe
On Sun, Jul 11, 2021 at 9:59 PM Rishi Raj Tandon
wrote:
> Unsubscribe
>
>> a valid vulnerability the best path forward is likely reaching out to
>> private@ to figure out how to do a security release.
>>
>> On Mon, Jun 21, 2021 at 4:42 PM Eric Richardson
>> wrote:
>>
>>> Thanks for the quick reply. Yes, since it is included
ly their own Jackson.
>
> If someone had a legit view that this is potentially more serious I think
> we could _probably backport that update, but Jackson can be a little bit
> tricky with compatibility IIRC so would just bear some testing.
>
>
> On Mon, Jun 21, 2021 at 5:27 PM
://github.com/FasterXML/jackson-databind/issues/2589 - but Spark
supplies 2.10.0.
Thanks,
Eric
ay be significant. But it seems like the
> simplest thing and will probably work fine.
>
> On Tue, May 25, 2021 at 4:34 PM Eric Beabes
> wrote:
>
>> Right... but the problem is still the same, no? Those N Jobs (aka Futures
>> or Threads) will all be running on the Driver.
arquet, for
> example. You would just have 10s or 100s of those jobs running at the same
> time. You have to write a bit of async code to do it, but it's pretty easy
> with Scala Futures.
>
> On Tue, May 25, 2021 at 3:31 PM Eric Beabes
> wrote:
>
>> Here's
>
> val df = spark.read.option(“mergeSchema”, “true”).load(listOfPaths)
>
>
>
> *From: *Eric Beabes
> *Date: *Tuesday, May 25, 2021 at 1:24 PM
> *To: *spark-user
> *Subject: *Reading parquet files in parallel on the cluster
>
>
>
> I've a use case in which
I've a use case in which I need to read Parquet files in parallel from over
1000+ directories. I am doing something like this:
val df = list.toList.toDF()
df.foreach(c => {
val config = *getConfigs()*
doSomething(spark, config)
})
In the doSomething method, when I try to
I keep getting the following exception when I am trying to read a Parquet
file from a Path on S3 in Spark/Scala. Note: I am running this on EMR.
java.lang.NullPointerException
at
org.apache.spark.sql.SparkSession.sessionState$lzycompute(SparkSession.scala:144)
at
org.apache.spark
no case be liable for any monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
>>
>> On Mon, 3 May 2021 at 18:27, "Yuri Oleynikov (יורי אולייניקוב)" <
>> yur...@gmail.com> wrote:
>>
>>> You
wn risk. Any and all responsibility for any
> loss, damage or destruction of data or any other property which may arise
> from relying on this email's technical content is explicitly disclaimed.
> The author will in no case be liable for any monetary damages arising from
> such lo
I would like to develop a Spark Structured Streaming job that reads
messages in a Stream which needs to be “joined” with another Stream of
“Reference” data.
For example, let’s say I’m reading messages from Kafka coming in from (lots
of) IOT devices. This message has a ‘device_id’. We have a DEVICE
When I do the following, Spark( 2.4) doesn't put _SUCCESS file in the
partition directory:
val outputPath = s"s3://mybucket/$table"
df
.orderBy(time)
.coalesce(numFiles)
.write
.partitionBy("partitionDate")
.mode("overwrite")
.format("parquet")
.save(outputPath)
But when I remove 'partitionBy'
fter a long time. Some memory leak in your app
> putting GC/memory pressure on the JVM, etc too.
>
> On Thu, Jan 21, 2021 at 5:13 AM Eric Beabes
> wrote:
>
>> Hello,
>>
>> My Spark Structured Streaming application was performing well for quite
>> some ti
t;
>> https://about.me/JacekLaskowski
>> "The Internals Of" Online Books <https://books.japila.pl/>
>> Follow me on https://twitter.com/jaceklaskowski
>>
>> <https://twitter.com/jaceklaskowski>
>>
>>
>> On Thu, Jan 21, 2021
Will do, thanks!
On Tue, Jan 19, 2021 at 1:39 PM Gabor Somogyi
wrote:
> Thanks for double checking the version. Please report back with 3.1
> version whether it works or not.
>
> G
>
>
> On Tue, 19 Jan 2021, 07:41 Eric Beabes, wrote:
>
>> Confirmed. The cluster
Confirmed. The cluster Admin said his team installed the latest version
from Cloudera which comes with Spark 3.0.0-preview2. They are going to try
to upgrade it with the Community edition Spark 3.1.0.
Thanks Jungtaek for the tip. Greatly appreciate it.
On Tue, Jan 19, 2021 at 8:45 AM Eric Beabes
e.org/jira/projects/SPARK/summary> and
>>> regarding the repo, I believe just commit it to your personal repo and that
>>> should be it.
>>>
>>> Regards
>>>
>>> On Mon, 18 Jan 2021 at 15:46, Eric Beabes
>>> wrote:
>>>
>
e a jira and commit the
> code into github?
> It would speed things up a lot.
>
> G
>
>
> On Mon, Jan 18, 2021 at 2:14 PM Eric Beabes
> wrote:
>
>> Here's a very simple reproducer app. I've attached 3 files:
>> SparkTest.scala, QueryListener.
org.scalastyle
scalastyle-maven-plugin
1.0.0
false
true
true
false
${project.basedir}/src/main/scala
${project.basedir}/src/test/scala
lib/scalastyle_config
uild
>> script.
>>
>> Thanks in advance!
>>
>> On Wed, Jan 13, 2021 at 3:46 PM Eric Beabes
>> wrote:
>>
>>> I tried both. First tried 3.0.0. That didn't work so I tried 3.1.0.
>>>
>>> On Wed, Jan 13, 2021 at 11:35 AM Jungta
because you've said you've used Spark 3.0 but spark-sql-kafka
> dependency pointed to 3.1.0.)
>
> On Tue, Jan 12, 2021 at 11:04 PM Eric Beabes
> wrote:
>
>> org.apache.spark.sql.streaming.StreamingQueryException: Data source v2
>> streaming sinks does not support
gt;
>> For example look at the details per executor (the numbers you reported
>> are aggregated values), then also look at the “storage tab” for a list of
>> cached RDDs with details.
>>
>> In case, Spark 3.0 has improved memory instrumentation and improved
>> instru
Trying to port my Spark 2.4 based (Structured) streaming application to
Spark 3.0. I compiled it using the dependency given below:
org.apache.spark
spark-sql-kafka-0-10_${scala.binary.version}
3.1.0
Every time I run it under Spark 3.0, I get this message: *Data source v2
streaming
the system is low
after 5pm PST.
I would expect the "Memory used" to be lower than 3.3Tb after 5pm PST.
Does Spark 3.0 do a better job of memory management? Wondering if upgrading
to Spark 3.0 would improve performance?
On Wed, Jan 6, 2021 at 2:29 PM Luca Canali wrote:
> Hi Eric,
unsubscribe
, Nov 20, 2020 at 7:30 AM Gabor Somogyi
wrote:
> Happy that saved some time for you :)
> We've invested quite an effort in the latest releases into streaming and
> hope there will be less and less headaches like this.
>
> On Thu, Nov 19, 2020 at 5:55 PM Eric Beabes
> wrote:
t;stateful" SS job,
> the blacklisting structure can be put into the user-defined state.
> To use a 3rd-party cache should also be a good choice.
>
> Eric Beabes 于2020年11月11日周三 上午6:54写道：
>
>> Currently we’ve a “Stateful” Spark Structured Streaming job that computes
>
ough time to migrate to
> Spark 3.
>
>
> On Wed, Nov 18, 2020 at 11:12 PM Eric Beabes
> wrote:
>
>> I must say.. *Spark has let me down in this case*. I am surprised an
>> important issue like this hasn't been fixed in Spark 2.4.
>>
>> I am fighting a batt
been asked to rewrite the
code in Flink*.
Moving to Spark 3.0 is not an easy option 'cause Cloudera 6.2 doesn't have
a Spark 3.0 parcel So we can't upgrade to 3.0.
So sad. Let me ask one more time. *Is there no way to fix this in Spark
2.4?*
On Tue, Nov 10, 2020 at 11:33 AM Eric Bea
Currently we’ve a “Stateful” Spark Structured Streaming job that computes
aggregates for each ID. I need to implement a new requirement which says
that if the no. of incoming messages for a particular ID exceeds a certain
value then add this ID to a blacklist & remove the state for it. Going
forwar
ov 10, 2020 at 11:17 AM Eric Beabes
wrote:
> Thanks for the reply. We are on Spark 2.4. Is there no way to get this
> fixed in Spark 2.4?
>
> On Mon, Nov 2, 2020 at 8:32 PM Jungtaek Lim
> wrote:
>
>> Which Spark version do you use? There's a known issue on Kafka produ
;d like to check
> whether your case is bound to the known issue or not.
>
> https://issues.apache.org/jira/browse/SPARK-21869
>
>
> On Tue, Nov 3, 2020 at 1:53 AM Eric Beabes
> wrote:
>
>> I know this is related to Kafka but it happens during the Spark
>> Structured
I know this is related to Kafka but it happens during the Spark Structured
Streaming job that's why I am asking on this mailing list.
How would you debug this or get around this in Spark Structured Streaming?
Any tips would be appreciated. Thanks.
java.lang.IllegalStateException: Cannot perform
We're using Spark 2.4. We recently pushed to production a product that's
using Spark Structured Streaming. It's working well most of the time but
occasionally, when the load is high, we've noticed that there are only 10+
'Active Tasks' even though we've provided 128 cores. Would like to debug
this
We're using Stateful Structured Streaming in Spark 2.4. We are noticing
that when the load on the system is heavy & LOTs of messages are coming in
some of the states disappear with no error message. Any suggestions on how
we can debug this? Any tips for fixing this?
Thanks in advance.
ay to upload the JAR file prior to running this? Get the
Id of this file & then submit the Spark job. Kinda like how Flink does
it.
I realize this is an Apache Livy question so I will also ask on their
mailing list. Thanks.
On Thu, Sep 3, 2020 at 11:47 AM Eric Beabes
wrote:
> Thank you all
Thank you all for your responses. Will try them out.
On Thu, Sep 3, 2020 at 12:06 AM tianlangstudio
wrote:
> Hello, Eric
> Maybe you can use Spark JobServer 0.10.0
> https://github.com/spark-jobserver/spark-jobserverl
> We used this with Spark 1.6, and it is awesome. You know
>
Under Spark 2.4 is it possible to submit a Spark job thru REST API - just
like the Flink job?
Here's the use case: We need to submit a Spark Job to the EMR cluster but
our security team is not allowing us to submit a job from the Master node
or thru UI. They want us to create a "Docker Container"
In my structured streaming job I've noticed that a LOT of data keeps going
to one executor whereas other executors don't process that much data. As a
result, tasks on that executor take a lot of time to complete. In other
words, the distribution is skewed.
I believe in Structured streaming the Par
Currently my job fails even on a single failure. In other words, even if
one incoming message is malformed the job fails. I believe there's a
property that allows us to set an acceptable number of failures. I Googled
but couldn't find the answer. Can someone please help? Thanks.
While running my Spark (Stateful) Structured Streaming job I am setting
'maxOffsetsPerTrigger' value to 10 Million. I've noticed that messages are
processed faster if I use a large value for this property.
What I am also noticing is that until the batch is completely processed, no
messages are get
My apologies... After I set the 'maxOffsetsPerTrigger' to a value such as
'20' it started working. Hopefully this will help someone. Thanks.
On Fri, Jun 26, 2020 at 2:12 PM Something Something <
mailinglist...@gmail.com> wrote:
> My Spark Structured Streaming job works fine when I set "start
Unsubscribe
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contener información privilegiada o confidencial y es para uso exclusivo de la
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notificado de
Hi,
I’m using Spark 2.4.3 on K8s and would like to to what’s solved in
[Spark-23153], that is, be able to download dependencies through —packages and
that the driver could access them. Right now, in Spark 2.4.3, after the
spark-submit and download of dependencies the driver cannot access them.
/cloud.google.com/sol
>> utions/spark-on-kubernetes-engine which could be relevant.
>>
>> On Mon, Apr 30, 2018 at 7:51 PM, Eric Wang
>> wrote:
>>
>>> Hello all,
>>>
>>> I've been trying to spark-submit a job to the Google Kubernetes E
ttps://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html
Thanks,
Eric
I need to write a Spark Structured Streaming pipeline that involves
multiple aggregations, splitting data into multiple sub-pipes and union
them. Also it need to have stateful aggregation with timeout.
Spark Structured Streaming support all of the required functionality but
not as one stream. I di
ferred and performant way to do that using Apache Spark ?
Best,
Eric
r
specific truststore in my Spark config ? Do I just give -D flags via
JAVA_OPTS ?
Thx
--
-eric ho
I'm trying to pass a trustStore pathname into pyspark.
What env variable and/or config file or script I need to change to do this ?
I've tried setting JAVA_OPTS env var but to no avail...
any pointer much appreciated... thx
--
-eric ho
I'm interested in what I should put into the trustStore file, not just for
Spark but also for Kafka and Cassandra sides..
The way I generated self-signed certs for Kafka and Cassandra sides are
slightly different...
On Thu, Sep 1, 2016 at 1:09 AM, Eric Ho wrote:
> A working example
A working example would be great...
Thx
--
-eric ho
)*
*at org.apache.spark.deploy.master.Master.main(Master.scala)*
=====
--
-eric ho
I can't find in Spark 1.6.2's docs in how to turn encryption on for Spark
to Kafka communication ... I think that the Spark docs only tells you how
to turn on encryption for inter Spark node communications .. Am I wrong ?
Thanks.
--
-eric ho
I heard that Kryo will get phased out at some point but not sure which
Spark release.
I'm using PySpark, does anyone has any docs on how to call / use Kryo
Serializer in PySpark ?
Thanks.
--
-eric ho
u're asking about.
>
> I would personally use something like CoGroup or Join between the two
> RDDs. if index matters, you can use ZipWithIndex on both before you join
> and then see which indexes match up.
>
> On Mon, Aug 15, 2016 at 1:15 PM Eric Ho wrote:
>
>>
this RRD would have contain
elements in array B as well as array A.
Same argument for RRD(B).
Any pointers much appreciated.
Thanks.
--
-eric ho
I couldn't find any RDD functions that would do this for me efficiently. I
don't really want elements of RDD(A) and RDD(B) flying all over the network
piecemeal...
THanks.
--
-eric ho
; Jenkins jobs have been running against Scala 2.11:
>
> [INFO] --- scala-maven-plugin:3.2.2:testCompile (scala-test-compile-first) @
> java8-tests_2.11 ---
>
>
> FYI
>
>
> On Mon, May 16, 2016 at 2:45 PM, Eric Richardson
> wrote:
>
>> On Thu, May 12, 2016 at
On Thu, May 12, 2016 at 9:23 PM, Luciano Resende
wrote:
> Spark has moved to build using Scala 2.11 by default in master/trunk.
>
Does this mean that the pre-built binaries for download will also move to
2.11 as well?
>
>
> As for the 2.0.0-SNAPSHOT, it is actually the version of master/trunk
the types you're passing in don't
> match. For instance, you're passing in a message handler that returns
> a tuple, but the rdd return type you're specifying (the 5th type
> argument) is just String.
>
> On Fri, May 6, 2016 at 9:49 AM, Eric Friedman
>
.1'
compile 'org.apache.kafka:kafka_2.10:0.8.2.1'
compile 'com.yammer.metrics:metrics-core:2.2.0'
On Fri, May 6, 2016 at 7:47 AM, Eric Friedman
wrote:
> Hello,
>
> I've been using createDirectStream with Kafka and now need to switch to
> the version of tha
Hello,
I've been using createDirectStream with Kafka and now need to switch to the
version of that API that lets me supply offsets for my topics. I'm unable
to get this to compile for some reason, even if I lift the very same usage
from the Spark test suite.
I'm calling it like this:
val to
Hello,
Where in the Spark APIs can I get access to the Hadoop Context instance? I
am trying to implement the Spark equivalent of this
public void reduce(Text key, Iterable values, Context
context)
throws IOException, InterruptedException {
if (record == null) {
throw
ase so the bug can be more
easily investigated?
Best,
Eric Martin
I don't think it is a deliberate design.
So you may need do action on the RDD before the action of
RDD, if you want to explicitly checkpoint RDD.
2015-11-26 13:23 GMT+08:00 wyphao.2007 :
> Spark 1.5.2.
>
> 在 2015-11-26 13:19:39，"张志强(旺轩)" 写道：
>
> What’s your spark version?
>
> *发件人:* wyphao.
ot;C2"),
Row("A1", "B1", "C1")
))
val schema = StructType(Seq("a", "b", "c").map(c => StructField(c, StringType)))
val df = sqlContext.createDataFrame(rdd, schema)
df.registerTempTable("rows")
sqlContext.sql("select a,
y and spark.shuffle.memoryFraction had
no observable effect. It is possible that the ignoring of
the spark.shuffle.spill setting was just a manifestation of a larger issue
going back to a misconfiguration.
Eric
On Wed, Sep 9, 2015 at 4:48 PM, Richard Marscher
wrote:
> Hi Eric,
>
> I just wanted to do a sanity
plenty of space (perhaps after the
fact, when temporary files have been cleaned up).
Has anyone run into something like this before? I would be happy to see
OOM errors, because that would be consistent with one understanding of what
might be going on, but I haven't yet.
Eric
[1] https://www
ondered whether there had been some kind of shifting in the data.)
Eric
On Tue, Sep 1, 2015 at 9:54 PM, Jeff Zhang wrote:
> Hi Eric,
>
> If the 2 jobs share the same parent stages. these stages can be skipped
> for the second job.
>
> Here's one simple example:
>
>
ng in
order to get a better sense of the worst-case scenario?
(It's also possible that I've simply changed something that made things
faster.)
Eric
nd
from its response to changes I subsequently made that the actual code that
was running was the code doing the HBase lookups. I suspect the actual
shuffle, once it occurred, required on the same order of network IO as the
upload to Elasticsearch that followed.
Eric
On Mon, Aug 31, 2015 at
Does anyone know what might be going on here, and what I might be able to
do to get rid of the last `repartition` call before the upload to ES?
Eric
not visible to the
> Maven process. Or maybe you have JRE 7 installed but not JDK 7 and
> it's somehow still finding the Java 6 javac.
>
> On Tue, Aug 25, 2015 at 3:45 AM, Eric Friedman
> wrote:
> > I'm trying to build Spark 1.4 with Java 7 and despite having tha
I'm trying to build Spark 1.4 with Java 7 and despite having that as my
JAVA_HOME, I get
[INFO] --- scala-maven-plugin:3.2.2:compile (scala-compile-first) @
spark-launcher_2.10 ---
[INFO] Using zinc server for incremental compilation
[info] Compiling 8 Java sources to
/Users/eric/spark/
ecial case logic.
Eric
is private. This
suggests to me that I'm doing something wrong, although I got it to work
with sufficient hackery.
What do people recommend for a general approach in getting PySpark RDDs
from HBase prefix scans? I hope I have not missed something obvious.
Eric
Previously I was getting a failure which included the message Container
killed by YARN for exceeding memory limits. 2.1 GB of 2 GB physical memory
used. Consider boosting spark.yarn.executor.memoryOverhead.
So I attempted the following - spark-submit --jars examples.jar
latest_msmtdt_by
row3'})
Just to be clear, you refer to "Spark update these two scripts recently.". What
two scripts were you referencing?
On Friday, August 7, 2015 7:59 PM, gen tang wrote:
Hi,
In fact, Pyspark use
org.apache.spark.examples.pythonconverters(./examples/sr
I’m having some difficulty getting the desired results fromthe Spark Python
example hbase_inputformat.py. I’m running with CDH5.4, hbaseVersion 1.0.0,
Spark v 1.3.0 Using Python version 2.6.6
I followed the example to create a test HBase table. Here’sthe data from the
table I created – hbase(m
If I have a Hive table with six columns and create a DataFrame (Spark
1.4.1) using a sqlContext.sql("select * from ...") query, the resulting
physical plan shown by explain reflects the goal of returning all six
columns.
If I then call select("one_column") on that first DataFrame, the resulting
Da
ices=login,sshd,sudo.
Thanks,
-- Eric
On Wed, Jul 8, 2015 at 2:27 PM, Eric Pederson wrote:
> All:
>
> I recently ran into a scenario where spark-shell could communicate with
> Hive but another application of mine (Spark Notebook) could not. When I
> tried to get a reference t
ml it does.
How does the communication between the driver and Hive work? And is
spark-shell somehow special in this regard?
Thanks,
-- Eric
Hi Ratio -
You need more than just hive-jdbc jar.
Here are all of the jars that I found were needed. I got this list from
https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Clients#HiveServer2Clients-RunningtheJDBCSampleCode
plus trial and error.
[image: Inline image 1]
-- Eric
On
In preparing a DataFrame (spark 1.4) to use with MLlib's kmeans.train
method, is there a cleaner way to create the Vectors than this?
data.map{r => Vectors.dense(r.getDouble(0), r.getDouble(3), r.getDouble(4),
r.getDouble(5), r.getDouble(6))}
Second, once I train the model and call predict on my
'm really comparing apples and oranges right now.
But it's an interesting experiment nonetheless.
-- Eric
On Wed, Jul 1, 2015 at 12:47 PM, Debasish Das
wrote:
> If you take bitmap indices out of sybase then I am guessing spark sql will
> be at par with sybase ?
>
> On that
interested to see how far
it can be pushed.
Thanks for your help!
-- Eric
On Tue, Jun 30, 2015 at 5:28 PM, Debasish Das
wrote:
> I got good runtime improvement from hive partitioninp, caching the dataset
> and increasing the cores through repartition...I think for your case
> gen
e impact...documentation
> says Spark SQL should read partitioned table...
>
> Could you please share your results with partitioned tables ?
>
> On Tue, Jun 30, 2015 at 5:24 AM, Eric Pederson wrote:
>
>> Hi Deb -
>>
>> One other consideration is that the filter
In a case that memory cannot hold all the cached RDD, then BlockManager
will evict some older block for storage of new RDD block.
Hope that will helpful.
2015-06-24 13:22 GMT+08:00 bit1...@163.com :
> I am kind of consused about when cached RDD will unpersist its data. I
> know we can explicitl
I logged this Jira this morning:
https://issues.apache.org/jira/browse/SPARK-8566
I'm curious if any of the cognoscenti can advise as to a likely cause of
the problem?
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