Hello,
I am new to Spark and I am evaluating its suitability for my machine
learning tasks. I am using Spark v. 1.2.1. I would really appreciate if
someone could provide any insight about the following two issues.
1. I'd like to try a leave one out approach for training my SVM,
meaning
Hi Sean,
Below is the sbt dependencies that I am using.
I gave another try by removing the provided keyword which failed with the
same error.
What confuses me is that the stack trace appears after few of the stages
have already run completely.
object V {
val spark = 1.2.0-cdh5.3.0
val
Hi,
I have built spark version 1.3 and tried to use this in my spark scala
application. When I tried to compile and build the application by SBT, I
got error
bad symbolic reference. A signature in SparkContext.class refers to term
conf in value org.apache.hadoop which is not available
It
Hm, but you already only have to define it in one place, rather than
on each transformation. I thought you wanted exception handling at
each transformation?
Or do you want it once for all actions? you can enclose all actions in
a try-catch block, I suppose, to write exception handling code once.
Jackknife / leave-one-out is just a special case of bootstrapping. In
general I don't think you'd ever do it this way with large data, as
you are creating N-1 models, which could be billions. It's suitable
when you have very small data.
So I suppose I'd say the general bootstrapping you see in
Thank you everyone!! I have started implementing the join using the geohash
and using the first 4 alphabets of the HASH as the key.
Can I assign a Confidence factor in terms of distance based on number of
characters matching in the HASH code?
I will also look at the other options listed here.
Hi all
Is it possible to read recursively folders to read parquet files?
Thanks.
--
Saludos.
Miguel Ángel
Hi, the Random Forest implementation (1.2.1) is repeatably crashing when I
increase the depth to 20. I generate random synthetic data (36 workers,
1,000,000 examples per worker, 30 features per example) as follows:
val data = sc.parallelize(1 to 36, 36).mapPartitionsWithIndex((i, _) =
{
Is there a good architecture doc that gives a sufficient overview of high
level and low level details of spark with some good diagrams?
Hi guys,
I am a newbie in running Spark SQL / Spark. My goal is to run some TPC-H
queries atop Spark SQL using Hive metastore.
It looks like spark 1.2.1 release has Spark SQL / Hive support. However, I am
not able to fully connect all the dots.
I did the following:
1. Copied hive-site.xml
Hello all,
I am wondering if spark already has support for optimizations on sorted
data and/or if such support could be added (I am comfortable dropping to a
lower level if necessary to implement this, but I'm not sure if it is
possible at all).
Context: we have a number of data sets which are
You might also want to see if TaskScheduler helps with that. I have not
used it with Windows 2008 R2 but it generally does allow you to schedule a
bat file to run on startup
On Wed, Mar 11, 2015 at 10:16 AM, Wang, Ningjun (LNG-NPV)
ningjun.w...@lexisnexis.com wrote:
Thanks for the suggestion.
In Performance and Scalability of Broadcast in Spark by Mosharaf Chowdhury
I read that Spark uses HDFS for its broadcast variables. This seems highly
inefficient. In the same paper alternatives are proposed, among which
Bittorent Broadcast (BTB). While studying Learning Spark, page 105,
second
Many times, when I'm setting up a cluster, I have to use an operating
system (as RedHat or CentOS 6.5) which has an old version of Python (Python
2.6). For example, when using a Hadoop distribution that only supports
those operating systems (as Hortonworks' HDP or Cloudera).
And that also makes
Does somebody used SVD from MLlib for very large (like 10^6 x 10^7) sparse
matrix?
What time did it take?
What implementation of SVD is used in MLLib?
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/SVD-transform-of-large-matrix-with-MLlib-tp22005.html
Hi Tom,
That's an outdated document from 4/5 years ago.
Spark currently uses a BitTorrent like mechanism that's been tuned for
datacenter environments.
Mosharaf
-Original Message-
From: Tom thubregt...@gmail.com
Sent: 3/11/2015 4:58 PM
To: user@spark.apache.org
I've looked at the Zaharia PhD thesis to get an idea of the underlying
system.
http://www.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-12.html
On 3/11/15 12:48 PM, Mohit Anchlia wrote:
Is there a good architecture doc that gives a sufficient overview of
high level and low level details of
Hi Experts,
I have a scenario, where in I want to write to a avro file from a streaming
job that reads data from kafka.
But the issue is, as there are multiple executors and when all try to write
to a given file I get a concurrent exception.
I way to mitigate the issue is to repartition have a
Hi,
I’ve written a Spark Streaming Job that inserts into a Parquet, using
stream.foreachRDD(_insertInto(“table”, overwrite = true). Now I’ve added
checkpointing; everything works fine when starting from scratch. When starting
from a checkpoint however, the job doesn’t work and produces the
That val is not really your problem. In general, there is a lot of global
state throughout the hive codebase that make it unsafe to try and connect
to more than one hive installation from the same JVM.
On Tue, Mar 10, 2015 at 11:36 PM, Haopu Wang hw...@qilinsoft.com wrote:
Hao, thanks for the
You can add resolvers on SBT using
resolvers +=
Sonatype OSS Snapshots at
https://oss.sonatype.org/content/repositories/snapshots;
On Thu, Feb 26, 2015 at 4:09 PM, MEETHU MATHEW meethu2...@yahoo.co.in
wrote:
Hi,
I am not able to read from HDFS(Intel distribution hadoop,Hadoop version
is
Forgot to mention, it works when using .foreachRDD(_.saveAsTextFile(“”)).
On 11.03.2015, at 18:35, Marius Soutier mps@gmail.com wrote:
Hi,
I’ve written a Spark Streaming Job that inserts into a Parquet, using
stream.foreachRDD(_insertInto(“table”, overwrite = true). Now I’ve added
Why do you have to write a single file?
On Wed, Mar 11, 2015 at 1:00 PM, SamyaMaiti samya.maiti2...@gmail.com
wrote:
Hi Experts,
I have a scenario, where in I want to write to a avro file from a streaming
job that reads data from kafka.
But the issue is, as there are multiple executors
is your data skewed? Could it be that there are a few keys with a huge
number of records? You might consider outputting
(recordA, count)
(recordB, count)
instead of
recordA
recordA
recordA
...
you could do this with:
input = sc.textFile
pairsCounts = input.map{x = (x,1)}.reduceByKey{_ + _}
Hi,
On Wed, Mar 11, 2015 at 11:05 PM, Cesar Flores ces...@gmail.com wrote:
Thanks for both answers. One final question. *This registerTempTable is
not an extra process that the SQL queries need to do that may decrease
performance over the language integrated method calls? *
As far as I
I am not entirely sure I understand your question -- are you saying:
* scoring a sample of 50k events is fast
* taking the top N scores of 77M events is slow, no matter what N is
?
if so, this shouldn't come as a huge surprise. You can't find the top
scoring elements (no matter how small N is)
I'm not sure what you mean. Are you asking how you can recompile all of
spark and deploy it, instead of using one of the pre-built versions?
https://spark.apache.org/docs/latest/building-spark.html
Or are you seeing compile problems specifically w/
HighlyCompressedMapStatus? The code
RangePartitioner?
At least for join, you can implement your own partitioner, to utilize the
sorted data.
Just my 2 cents.
Date: Wed, 11 Mar 2015 17:38:04 -0400
Subject: can spark take advantage of ordered data?
From: jcove...@gmail.com
To: User@spark.apache.org
Hello all,
I am wondering if spark
Sean,
On Wed, Mar 11, 2015 at 7:43 PM, Tobias Pfeiffer t...@preferred.jp wrote:
it seems like I am unable to shut down my StreamingContext properly, both
in local[n] and yarn-cluster mode. In addition, (only) in yarn-cluster
mode, subsequent use of a new StreamingContext will raise
an
You need to include the Hadoop native library in your spark-shell/spark-sql,
assuming your hadoop native library including native snappy library.
spark-sql --driver-library-path point_to_your_hadoop_native_library
In spark-sql, you can just use any command as you are in Hive CLI.
Yong
Date: Wed,
this is a very interesting use case. First of all, its worth pointing out
that if you really need to process the data sequentially, fundamentally you
are limiting the parallelism you can get. Eg., if you need to process the
entire data set sequentially, then you can't get any parallelism. If
at least 1.4 I think
now using YARN or allowing multiple worker instances are just fine
Best,
--
Nan Zhu
http://codingcat.me
On Wednesday, March 11, 2015 at 8:42 PM, Du Li wrote:
Is it being merged in the next release? It's indeed a critical patch!
Du
On Wednesday,
Hi Jonathan,
you might be interested in https://issues.apache.org/jira/browse/SPARK-3655
(not yet available) and https://github.com/tresata/spark-sorted (not part
of spark, but it is available right now). Hopefully thats what you are
looking for. To the best of my knowledge that covers what is
Hi,
On Thu, Mar 12, 2015 at 12:08 AM, Huang, Jie jie.hu...@intel.com wrote:
According to my understanding, your approach is to register a series of
tables by using transformWith, right? And then, you can get a new Dstream
(i.e., SchemaDstream), which consists of lots of SchemaRDDs.
Yep,
-- Forwarded message --
From: Steve Lewis lordjoe2...@gmail.com
Date: Wed, Mar 11, 2015 at 9:13 AM
Subject: Re: Numbering RDD members Sequentially
To: Daniel, Ronald (ELS-SDG) r.dan...@elsevier.com
perfect - exactly what I was looking for, not quite sure why it is
called
Is it possible to extend this PR further (or create another PR) to allow for
per-node configuration of workers?
There are many discussions about heterogeneous spark cluster. Currently
configuration on master will override those on the workers. Many spark users
have the need for having machines
not quite sure why it is called zipWithIndex since zipping is not involved
It isn't?
http://stackoverflow.com/questions/1115563/what-is-zip-functional-programming
On Wed, Mar 11, 2015 at 5:18 PM, Steve Lewis lordjoe2...@gmail.com wrote:
-- Forwarded message --
From: Steve
did you forget to specify the main class w/ --class Main? though if that
was it, you should at least see *some* error message, so I'm confused
myself ...
On Wed, Mar 11, 2015 at 6:53 AM, Aung Kyaw Htet akh...@gmail.com wrote:
Hi Everyone,
I am developing a scala app, in which the main object
Hi I am trying to run my spark service against cluster. As it turns out I
have to do setJars and set my applicaiton jar in there. If I do it using
physical path like following it works
`conf.setJars(new String[]{/path/to/jar/Sample.jar});`
but If i try to use JavaSparkContext (or SparkContext)
Hi, Robert
Spark SQL currently only support Hive 0.12.0(need to re-compile the package)
and 0.13.1(by default), I am not so sure if it supports the Hive 0.14 metastore
service as backend. Another way you can try is configure the
$SPARK_HOME/conf/hive-site.xml to access the remote metastore
Hi I am currently using spark 1.3.0-snapshot to run the fpg algorithm from
the mllib library. When I am trying to run the algorithm over a large
basket(over 1000 items) the program seems to never finish. Did anyone find
a workaround for this problem?
After setting SPARK_LOCAL_DIRS/SPARK_WORKER_DIR you need to restart your
spark instances (stop-all.sh and start-all.sh), You can also try setting
java.io.tmpdir while creating the SparkContext.
Thanks
Best Regards
On Wed, Mar 11, 2015 at 1:47 AM, Justin Yip yipjus...@prediction.io wrote:
We don't support expressions or wildcards in that configuration. For
each application, the local directories need to be constant. If you
have users submitting different Spark applications, those can each set
spark.local.dirs.
- Patrick
On Wed, Mar 11, 2015 at 12:14 AM, Jianshi Huang
Unfortunately /tmp mount is really small in our environment. I need to
provide a per-user setting as the default value.
I hacked bin/spark-class for the similar effect. And spark-defaults.conf
can override it. :)
Jianshi
On Wed, Mar 11, 2015 at 3:28 PM, Patrick Wendell pwend...@gmail.com wrote:
I don't think there is enough information here. Where is the program
spending its time? where does it stop? how many partitions are
there?
On Wed, Mar 11, 2015 at 7:10 AM, Akhil Das ak...@sigmoidanalytics.com wrote:
You need to set spark.cores.max to a number say 16, so that on all 4
machines
Thanks Sean. I'll ask our Hadoop admin.
Actually I didn't find hadoop.tmp.dir in the Hadoop settings...using user
home is suggested by other users.
Jianshi
On Wed, Mar 11, 2015 at 3:51 PM, Sean Owen so...@cloudera.com wrote:
You shouldn't use /tmp, but it doesn't mean you should use user home
The program spends its time when I am writing the output to a text file and
I am using 70 partitions
On Wed, 11 Mar 2015 9:55 am Sean Owen so...@cloudera.com wrote:
I don't think there is enough information here. Where is the program
spending its time? where does it stop? how many partitions
Can you paste your complete spark-submit command? Also did you try
specifying *--worker-cores*?
Thanks
Best Regards
On Tue, Mar 10, 2015 at 9:00 PM, htailor hemant.tai...@live.co.uk wrote:
Hi All,
I need some help with a problem in pyspark which is causing a major issue.
Recently I've
You need to set spark.cores.max to a number say 16, so that on all 4
machines the tasks will get distributed evenly, Another thing would be to
set spark.default.parallelism if you haven't tried already.
Thanks
Best Regards
On Wed, Mar 11, 2015 at 12:27 PM, Sean Barzilay sesnbarzi...@gmail.com
Dear all,
I tried the socketTextStream to receive message from a port, similar to
WordCount example, using
socketTextStream(host, ip.toInt, StorageLevel.MEMORY_AND_DISK_SER)…
The problem is that I can only receive messages typed from “nc –lk [port no.]
from my localhost, while the messages
Hey folks,
I am trying to split a data set into two parts. Since I am using Spark
1.0.0 I cannot use the randomSplit method. I found this SO question :
http://stackoverflow.com/questions/24864828/spark-scala-shuffle-rdd-split-rdd-into-two-random-parts-randomly
which contains this implementation
I don't have a complete example for your usecase, but you can see a lot of
codes showing how to use new APIHadoopFile from here
https://github.com/search?q=sc.newAPIHadoopFiletype=Codeutf8=%E2%9C%93
Thanks
Best Regards
On Tue, Mar 10, 2015 at 7:37 PM, Wang, Ningjun (LNG-NPV)
Hao, thanks for the response.
For Q1, in my case, I have a tool on SparkShell which serves multiple
users where they can use different Hive installation. I take a look at
the code of HiveContext. It looks like I cannot do that today because
catalog field cannot be changed after initialize.
Hi,
How can we simply cache hbase table and do sql query via java api in spark.
Thanks,
Udbhav Agarwal
Hi,
it seems like I am unable to shut down my StreamingContext properly, both
in local[n] and yarn-cluster mode. In addition, (only) in yarn-cluster
mode, subsequent use of a new StreamingContext will raise
an InvalidActorNameException.
I use
logger.info(stoppingStreamingContext)
I am finding that partitionBy is hanging - and it is not clear whether the
custom partitioner is even being invoked (i put an exception in there and
can not see it in the worker logs).
The structure is similar to the following:
inputPairedRdd = sc.parallelize([{0:Entry1,1,Entry2}])
def
Should the comma after 1 be colon ?
On Mar 11, 2015, at 1:41 AM, Stephen Boesch java...@gmail.com wrote:
I am finding that partitionBy is hanging - and it is not clear whether the
custom partitioner is even being invoked (i put an exception in there and can
not see it in the worker
Hi
I'm trying to do a join of two datasets: 800GB with ~50MB.
My code looks like this:
private def parseClickEventLine(line: String, jsonFormatBC:
Broadcast[LazyJsonFormat]): ClickEvent = {
val json = line.parseJson.asJsObject
val eventJson = if
Depending on your cluster setup (cores, memory), you need to specify the
parallelism/repartition the data.
Thanks
Best Regards
On Wed, Mar 11, 2015 at 12:18 PM, Sean Barzilay sesnbarzi...@gmail.com
wrote:
Hi I am currently using spark 1.3.0-snapshot to run the fpg algorithm from
the mllib
You want the s3n:// (native) protocol rather than s3://. s3:// is a block
filesystem based on S3 that doesn't respect paths.
More information on the Hadoop site: https://wiki.apache.org/hadoop/AmazonS3
Calum.
On Wed, 11 Mar 2015 04:47 cpalm3 cpa...@gmail.com wrote:
Hi All,
I am hoping
You shouldn't use /tmp, but it doesn't mean you should use user home
directories either. Typically, like in YARN, you would a number of
directories (on different disks) mounted and configured for local
storage for jobs.
On Wed, Mar 11, 2015 at 7:42 AM, Jianshi Huang jianshi.hu...@gmail.com wrote:
My min support is low and after filling out all my space I am applying a
filter on the results to only get item seta that interest me
On Wed, 11 Mar 2015 1:58 pm Sean Owen so...@cloudera.com wrote:
Have you looked at how big your output is? for example, if your min
support is very low, you
There are few techniques currently available.
Geomesa which uses GeoHash also can be proved useful.(
https://github.com/locationtech/geomesa)
Other potential candidate is
https://github.com/Esri/gis-tools-for-hadoop especially
https://github.com/Esri/geometry-api-java for inner customization.
If
On Wed, 11 Mar 2015 11:19:56 +0100
Marcin Cylke marcin.cy...@ext.allegro.pl wrote:
Hi
I'm trying to do a join of two datasets: 800GB with ~50MB.
The job finishes if I set spark.yarn.executor.memoryOverhead to 2048MB.
If it is around 1000MB it fails with executor lost errors.
My spark
Yes, a previous prototype is available
https://github.com/Intel-bigdata/spark-streamsql, and a talk is given at
last year's Spark Summit (
http://spark-summit.org/2014/talk/streamsql-on-spark-manipulating-streams-by-sql-using-spark
)
We are currently porting the prototype to use the latest
Have you looked at how big your output is? for example, if your min
support is very low, you will output a massive volume of frequent item
sets. If that's the case, then it may be expected that it's taking
ages to write terabytes of data.
On Wed, Mar 11, 2015 at 8:34 AM, Sean Barzilay
Hi,
I am stuck at this for 3 days now. I am using the spark-cassandra-connector
with spark and I am able to make RDDs with sc.cassandraTable function that
means spark is able to communicate with Cassandra properly.
But somehow the saveToCassandra is not working. Below are the steps I am doing.
Can you show us the code that you are using?
This might help. This is the updated streaming programming guide for 1.3,
soon to be up, this is a quick preview.
http://people.apache.org/~tdas/spark-1.3.0-temp-docs/streaming-programming-guide.html#dataframe-and-sql-operations
TD
On Wed, Mar 11,
The current broadcast algorithm in Spark approximates the one described in
the Section 5 of this paper
http://www.mosharaf.com/wp-content/uploads/orchestra-sigcomm11.pdf.
It is expected to scale sub-linearly; i.e., O(log N), where N is the number
of machines in your cluster.
We evaluated up to 100
Those results look very good for the larger workloads (100MB and 1GB). Were
you also able to run experiments for smaller amounts of data? For instance
broadcasting a single variable to the entire cluster? In the paper you
state that HDFS-based mechanisms performed well only for small amounts of
Thanks Mosharaf, for the quick response! Can you maybe give me some
pointers to an explanation of this strategy? Or elaborate a bit more on it?
Which parts are involved in which way? Where are the time penalties and how
scalable is this implementation?
Thanks again,
Tom
On 11 March 2015 at
It should be *possible* to do what you want ... but if I understand you
right, there isn't really any very easy way to do it. I think you would
need to write your own subclass of RDD, which has its own logic on how the
input files get put divided among partitions. You can probably subclass
Is it being merged in the next release? It's indeed a critical patch!
Du
On Wednesday, January 21, 2015 3:59 PM, Nan Zhu zhunanmcg...@gmail.com
wrote:
…not sure when will it be reviewed…
but for now you can work around by allowing multiple worker instances on a
single machine
I think this should go to another PR
can you create a JIRA on that?
Best,
--
Nan Zhu
http://codingcat.me
On Wednesday, March 11, 2015 at 8:50 PM, Du Li wrote:
Is it possible to extend this PR further (or create another PR) to allow for
per-node configuration of workers?
There
Let me clarify - taking 1 elements of 5 elements using top or
takeOrdered is taking about 25-50s which seems to be slow. I also try to use
sortByKey to sort the elements to get a time estimate and I get numbers in the
same range.
I'm running this application on a cluster with 5 nodes
Is there a way to have the exception handling go lazily along with the
definition?
e.g... we define it on the RDD but then our exception handling code gets
triggered on that first action... without us having to define it on the
first action? (e.g. that RDD code is boilerplate and we want to just
Hi,
Thanks for the response! That answered some questions I had, but the last
one I was wondering is what happens if you run a partition strategy and one
of the partitions ends up being too large? For example, let's say
partitions can hold 64MB (actually knowing the maximum possible size of a
Sorry typo; should be https://github.com/intel-spark/stream-sql
Thanks,
-Jason
On Wed, Mar 11, 2015 at 10:19 PM, Irfan Ahmad ir...@cloudphysics.com
wrote:
Got a 404 on that link: https://github.com/Intel-bigdata/spark-streamsql
*Irfan Ahmad*
CTO | Co-Founder | *CloudPhysics*
Well I'm thinking that this RDD would fail to build in a specific way...
different from the subsequent code (e.g. s3 access denied or timeout on
connecting to a database)
So for example, define the RDD failure handling on the RDD, define the
action failure handling on the action? Does this make
May be you can use this code for your purpose
https://gist.github.com/akhld/4286df9ab0677a555087 It basically sends the
content of the given file through Socket (both IO/NIO), i used it for a
benchmark between IO and NIO.
Thanks
Best Regards
On Wed, Mar 11, 2015 at 11:36 AM, Cui Lin
Does it write anything in BUCKET/SUB_FOLDER/output?
Thanks
Best Regards
On Wed, Mar 11, 2015 at 10:15 AM, cpalm3 cpa...@gmail.com wrote:
Hi All,
I am hoping someone has seen this issue before with S3, as I haven't been
able to find a solution for this problem.
When I try to save as Text
Hi,
I need to set per-user spark.local.dir, how can I do that?
I tried both
/x/home/${user.name}/spark/tmp
and
/x/home/${USER}/spark/tmp
And neither worked. Looks like it has to be a constant setting in
spark-defaults.conf. Right?
Any ideas how to do that?
Thanks,
--
Jianshi Huang
This usually means you are mixing different versions of code. Here it
is complaining about a Spark class. Are you sure you built vs the
exact same Spark binaries, and are not including them in your app?
On Wed, Mar 11, 2015 at 1:40 PM, manasdebashiskar
manasdebashis...@gmail.com wrote:
(This is
Even I am keen to learn an answer for this but as an alternate you can use
hive to create a table stored as parquet and then use it in spark.
On Wed, Mar 11, 2015 at 1:44 AM kpeng1 kpe...@gmail.com wrote:
Hi All,
I am currently trying to write a very wide file into parquet using spark
sql.
Hi Spark Community,
We would like to define exception handling behavior on RDD instantiation /
build. Since the RDD is lazily evaluated, it seems like we are forced to
put all exception handling in the first action call?
This is an example of something that would be nice:
def myRDD = {
Try {
Hi:
Thanks for both answers. One final question. *This registerTempTable is not
an extra process that the SQL queries need to do that may decrease
performance over the language integrated method calls? *The thing is that I
am planning to use them in the current version of the ML Pipeline
Thanks for the suggestion. I will try that.
Ningjun
From: Silvio Fiorito [mailto:silvio.fior...@granturing.com]
Sent: Wednesday, March 11, 2015 12:40 AM
To: Wang, Ningjun (LNG-NPV); user@spark.apache.org
Subject: Re: Is it possible to use windows service to start and stop spark
standalone
Got a 404 on that link: https://github.com/Intel-bigdata/spark-streamsql
*Irfan Ahmad*
CTO | Co-Founder | *CloudPhysics* http://www.cloudphysics.com
Best of VMworld Finalist
Best Cloud Management Award
NetworkWorld 10 Startups to Watch
EMA Most Notable Vendor
On Wed, Mar 11, 2015 at 6:41 AM,
Hi,
I discovered what caused my issue when running on YARN and was able to work
around it.
On Wed, Mar 11, 2015 at 7:43 PM, Tobias Pfeiffer t...@preferred.jp wrote:
The processing itself is complete, i.e., the batch currently processed at
the time of stop() is finished and no further batches
Answers:
databricks.com/blog/2014/07/21/distributing-the-singular-value-decomposition-with-spark.html
Reza
On Wed, Mar 11, 2015 at 2:33 PM, sergunok ser...@gmail.com wrote:
Does somebody used SVD from MLlib for very large (like 10^6 x 10^7) sparse
matrix?
What time did it take?
What
Given the following scenario:
dstream.map(...).filter(...).window(...).foreachrdd()
When would the onBatchCompleted fire?
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