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From Alonso Isidoro Roman <alons...@gmail.com>
Subject Re: About a problem running a spark job in a cdh-5.7.0 vmware image.
Date Mon, 06 Jun 2016 09:28:27 GMT
Hi guys, i finally understand that i cannot use sbt-pack to use
programmatically  the spark-streaming job as unix commands, i have to use
yarn or mesos  in order to run the jobs.

I have some doubts, if i run the spark streaming jogs as yarn client mode,
i am receiving this exception:

[cloudera@quickstart ~]$ spark-submit --class
example.spark.AmazonKafkaConnectorWithMongo --master yarn --deploy-mode
client --driver-memory 4g --executor-memory 2g --executor-cores 3
/home/cloudera/awesome-recommendation-engine/target/scala-2.10/my-recommendation-spark-engine_2.10-1.0-SNAPSHOT.jar
192.168.1.35:9092 amazonRatingsTopic
java.lang.ClassNotFoundException:
example.spark.AmazonKafkaConnectorWithMongo
at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:270)
at org.apache.spark.util.Utils$.classForName(Utils.scala:175)
at
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:689)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)


But, if i use cluster mode, i have that is job is accepted.

[cloudera@quickstart ~]$ spark-submit --class
example.spark.AmazonKafkaConnectorWithMongo --master yarn --deploy-mode
cluster --driver-memory 4g --executor-memory 2g --executor-cores 2
/home/cloudera/awesome-recommendation-engine/target/scala-2.10/my-recommendation-spark-engine_2.10-1.0-SNAPSHOT.jar
192.168.1.35:9092 amazonRatingsTopic
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in
[jar:file:/usr/lib/zookeeper/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in
[jar:file:/usr/lib/flume-ng/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an
explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
16/06/06 11:16:46 WARN util.NativeCodeLoader: Unable to load native-hadoop
library for your platform... using builtin-java classes where applicable
16/06/06 11:16:46 INFO client.RMProxy: Connecting to ResourceManager at /
0.0.0.0:8032
16/06/06 11:16:46 INFO yarn.Client: Requesting a new application from
cluster with 1 NodeManagers
16/06/06 11:16:46 INFO yarn.Client: Verifying our application has not
requested more than the maximum memory capability of the cluster (8192 MB
per container)
16/06/06 11:16:46 INFO yarn.Client: Will allocate AM container, with 4505
MB memory including 409 MB overhead
16/06/06 11:16:46 INFO yarn.Client: Setting up container launch context for
our AM
16/06/06 11:16:46 INFO yarn.Client: Setting up the launch environment for
our AM container
16/06/06 11:16:46 INFO yarn.Client: Preparing resources for our AM container
16/06/06 11:16:47 WARN shortcircuit.DomainSocketFactory: The short-circuit
local reads feature cannot be used because libhadoop cannot be loaded.
16/06/06 11:16:47 INFO yarn.Client: Uploading resource
file:/usr/lib/spark/lib/spark-assembly-1.6.0-cdh5.7.0-hadoop2.6.0-cdh5.7.0.jar
->
hdfs://quickstart.cloudera:8020/user/cloudera/.sparkStaging/application_1465201086091_0006/spark-assembly-1.6.0-cdh5.7.0-hadoop2.6.0-cdh5.7.0.jar
16/06/06 11:16:47 INFO yarn.Client: Uploading resource
file:/home/cloudera/awesome-recommendation-engine/target/scala-2.10/my-recommendation-spark-engine_2.10-1.0-SNAPSHOT.jar
->
hdfs://quickstart.cloudera:8020/user/cloudera/.sparkStaging/application_1465201086091_0006/my-recommendation-spark-engine_2.10-1.0-SNAPSHOT.jar
16/06/06 11:16:47 INFO yarn.Client: Uploading resource
file:/tmp/spark-8e5fe800-bed2-4173-bb11-d47b3ab3b621/__spark_conf__5840282197389631291.zip
->
hdfs://quickstart.cloudera:8020/user/cloudera/.sparkStaging/application_1465201086091_0006/__spark_conf__5840282197389631291.zip
16/06/06 11:16:47 INFO spark.SecurityManager: Changing view acls to:
cloudera
16/06/06 11:16:47 INFO spark.SecurityManager: Changing modify acls to:
cloudera
16/06/06 11:16:47 INFO spark.SecurityManager: SecurityManager:
authentication disabled; ui acls disabled; users with view permissions:
Set(cloudera); users with modify permissions: Set(cloudera)
16/06/06 11:16:47 INFO yarn.Client: Submitting application 6 to
ResourceManager
16/06/06 11:16:48 INFO impl.YarnClientImpl: Submitted application
application_1465201086091_0006
16/06/06 11:16:49 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:16:49 INFO yarn.Client:
client token: N/A
diagnostics: N/A
ApplicationMaster host: N/A
ApplicationMaster RPC port: -1
queue: root.cloudera
start time: 1465204607993
final status: UNDEFINED
tracking URL:
http://quickstart.cloudera:8088/proxy/application_1465201086091_0006/
user: cloudera
16/06/06 11:16:50 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:16:51 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:16:52 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:16:53 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:16:54 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:16:55 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:16:56 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:16:57 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:16:58 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:16:59 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:00 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:01 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:02 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:03 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:04 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:05 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:06 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:07 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:08 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:09 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:10 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:11 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:12 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:13 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:14 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:15 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:16 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:17 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:18 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
16/06/06 11:17:19 INFO yarn.Client: Application report for
application_1465201086091_0006 (state: ACCEPTED)
...

If i try to push a product to the kafka topic (amazonRatingsTopic), the
kafka broker is living in my host machine (192.168.1.35:9092), i cannot see
nothing in the logs. I can see in
http://quickstart.cloudera:8888/jobbrowser/ that the job is accepted, when
i click on the application_id, i can see this:

The application might not be running yet or there is no Node Manager or
Container available. This page will be automatically refreshed.

even if i push data into the kafka topic. Another think i have noticed is
that spark-worker is dead after a few minutes that the job is accepted, i
have to restart it manually doing sudo service spark-worker restart.

If i run jus command, i see this:

[cloudera@quickstart ~]$ jps
11904 SparkSubmit
12890 Jps
7271 sbt-launch.jar
[cloudera@quickstart ~]$

I know that sbt-launch is the sbt command running in another terminal, but,
 ┬┐Are NameNode processes and DataNode should not appear?

Thank you very much for reading until here.


Alonso Isidoro Roman
[image: https://]about.me/alonso.isidoro.roman
<https://about.me/alonso.isidoro.roman?promo=email_sig&utm_source=email_sig&utm_medium=email_sig&utm_campaign=external_links>

2016-06-04 18:23 GMT+02:00 Mich Talebzadeh <mich.talebzadeh@gmail.com>:

> Hi,
>
> Spark works in local, standalone and yarn-client mode. Start as master =
> local. That is the simplest model.You DO not need to start
> $SPAK_HOME/sbin/start-master.sh and $SPAK_HOME/sbin/start-slaves.sh
>
>
> Also you do not need to specify all that in spark-submit. In the Scala
> code you can do
>
> val sparkConf = new SparkConf().
>              setAppName("CEP_streaming_with_JDBC").
>              set("spark.driver.allowMultipleContexts", "true").
>              set("spark.hadoop.validateOutputSpecs", "false")
>
> And specify all that in spark-submit itself with minimum resources
>
> ${SPARK_HOME}/bin/spark-submit \
>                 --packages com.databricks:spark-csv_2.11:1.3.0 \
>                 --driver-memory 2G \
>                 --num-executors 1 \
>                 --executor-memory 2G \
>                 --master local \
>                 --executor-cores 2 \
>                 --conf
> "spark.executor.extraJavaOptions=-XX:+PrintGCDetails
> -XX:+PrintGCTimeStamps" \
>                 --jars
> /home/hduser/jars/spark-streaming-kafka-assembly_2.10-1.6.1.jar \
>                 --class "${FILE_NAME}" \
>                 --class ${FILE_NAME} \
>                 --conf "spark.ui.port=4040" \
>                 ${JAR_FILE}
>
> The spark GUI UI port is 4040 (the default). Just track the progress of
> the job. You can specify your own port by replacing 4040 by a nom used port
> value
>
> Try it anyway.
>
> HTH
>
>
> Dr Mich Talebzadeh
>
>
>
> LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
>
>
>
> http://talebzadehmich.wordpress.com
>
>
>
> On 3 June 2016 at 11:39, Alonso <alonsoir@gmail.com> wrote:
>
>> Hi, i am developing a project that needs to use kafka, spark-streaming
>> and spark-mllib, this is the github project
>> <https://github.com/alonsoir/awesome-recommendation-engine/tree/develop>
>> .
>>
>> I am using a vmware cdh-5.7-0 image, with 4 cores and 8 GB of ram, the
>> file that i want to use is only 16 MB, if i finding problems related with
>> resources because the process outputs this message:
>>
>>
>>  .set("spark.driver.allowMultipleContexts", "true")
>>
>> <https://about.me/alonso.isidoro.roman?promo=email_sig&utm_source=email_sig&utm_medium=email_sig&utm_campaign=external_links>
>> 16/06/03 11:58:09 WARN TaskSchedulerImpl: Initial job has not accepted
>> any resources; check your cluster UI to ensure that workers are registered
>> and have sufficient resources
>>
>>
>> when i go to spark-master page, i can see this:
>>
>>
>> *Spark Master at spark://192.168.30.137:7077*
>>
>> *    URL: spark://192.168.30.137:7077*
>> *    REST URL: spark://192.168.30.137:6066 (cluster mode)*
>> *    Alive Workers: 0*
>> *    Cores in use: 0 Total, 0 Used*
>> *    Memory in use: 0.0 B Total, 0.0 B Used*
>> *    Applications: 2 Running, 0 Completed*
>> *    Drivers: 0 Running, 0 Completed*
>> *    Status: ALIVE*
>>
>> *Workers*
>> *Worker Id Address State Cores Memory*
>> *Running Applications*
>> *Application ID Name Cores Memory per Node Submitted Time User State
>> Duration*
>> *app-20160603115752-0001*
>> *(kill)*
>> * AmazonKafkaConnector 0 1024.0 MB 2016/06/03 11:57:52 cloudera WAITING
>> 2.0 min*
>> *app-20160603115751-0000*
>> *(kill)*
>> * AmazonKafkaConnector 0 1024.0 MB 2016/06/03 11:57:51 cloudera WAITING
>> 2.0 min*
>>
>>
>> And this is the spark-worker output:
>>
>> *Spark Worker at 192.168.30.137:7078*
>>
>> *    ID: worker-20160603115937-192.168.30.137-7078*
>> *    Master URL:*
>> *    Cores: 4 (0 Used)*
>> *    Memory: 6.7 GB (0.0 B Used)*
>>
>> *Back to Master*
>> *Running Executors (0)*
>> *ExecutorID Cores State Memory Job Details Logs*
>>
>> It is weird isn't ? master url is not set up and there is not any
>> ExecutorID, Cores, so on so forth...
>>
>> If i do a ps xa | grep spark, this is the output:
>>
>> [cloudera@quickstart bin]$ ps xa | grep spark
>>  6330 ?        Sl     0:11 /usr/java/jdk1.7.0_67-cloudera/bin/java -cp
>> /usr/lib/spark/conf/:/usr/lib/spark/lib/spark-assembly-1.6.0-cdh5.7.0-hadoop2.6.0-cdh5.7.0.jar:/etc/hadoop/conf/:/usr/lib/spark/lib/spark-assembly.jar:/usr/lib/hadoop/lib/*:/usr/lib/hadoop/*:/usr/lib/hadoop-hdfs/lib/*:/usr/lib/hadoop-hdfs/*:/usr/lib/hadoop-mapreduce/lib/*:/usr/lib/hadoop-mapreduce/*:/usr/lib/hadoop-yarn/lib/*:/usr/lib/hadoop-yarn/*:/usr/lib/hive/lib/*:/usr/lib/flume-ng/lib/*:/usr/lib/paquet/lib/*:/usr/lib/avro/lib/*
>> -Dspark.deploy.defaultCores=4 -Xms1g -Xmx1g -XX:MaxPermSize=256m
>> org.apache.spark.deploy.master.Master
>>
>>  6674 ?        Sl     0:12 /usr/java/jdk1.7.0_67-cloudera/bin/java -cp
>> /etc/spark/conf/:/usr/lib/spark/lib/spark-assembly-1.6.0-cdh5.7.0-hadoop2.6.0-cdh5.7.0.jar:/etc/hadoop/conf/:/usr/lib/spark/lib/spark-assembly.jar:/usr/lib/hadoop/lib/*:/usr/lib/hadoop/*:/usr/lib/hadoop-hdfs/lib/*:/usr/lib/hadoop-hdfs/*:/usr/lib/hadoop-mapreduce/lib/*:/usr/lib/hadoop-mapreduce/*:/usr/lib/hadoop-yarn/lib/*:/usr/lib/hadoop-yarn/*:/usr/lib/hive/lib/*:/usr/lib/flume-ng/lib/*:/usr/lib/paquet/lib/*:/usr/lib/avro/lib/*
>> -Dspark.history.fs.logDirectory=hdfs:///user/spark/applicationHistory
>> -Dspark.history.ui.port=18088 -Xms1g -Xmx1g -XX:MaxPermSize=256m
>> org.apache.spark.deploy.history.HistoryServer
>>
>>  8153 pts/1    Sl+    0:14 /usr/java/jdk1.7.0_67-cloudera/bin/java -cp
>> /home/cloudera/awesome-recommendation-engine/target/pack/lib/*
>> -Dprog.home=/home/cloudera/awesome-recommendation-engine/target/pack
>> -Dprog.version=1.0-SNAPSHOT example.spark.AmazonKafkaConnector
>> 192.168.1.35:9092 amazonRatingsTopic
>>
>>  8413 ?        Sl     0:04 /usr/java/jdk1.7.0_67-cloudera/bin/java -cp
>> /usr/lib/spark/conf/:/usr/lib/spark/lib/spark-assembly-1.6.0-cdh5.7.0-hadoop2.6.0-cdh5.7.0.jar:/etc/hadoop/conf/:/usr/lib/spark/lib/spark-assembly.jar:/usr/lib/hadoop/lib/*:/usr/lib/hadoop/*:/usr/lib/hadoop-hdfs/lib/*:/usr/lib/hadoop-hdfs/*:/usr/lib/hadoop-mapreduce/lib/*:/usr/lib/hadoop-mapreduce/*:/usr/lib/hadoop-yarn/lib/*:/usr/lib/hadoop-yarn/*:/usr/lib/hive/lib/*:/usr/lib/flume-ng/lib/*:/usr/lib/paquet/lib/*:/usr/lib/avro/lib/*
>> -Xms1g -Xmx1g -XX:MaxPermSize=256m org.apache.spark.deploy.worker.Worker
>> spark://quickstart.cloudera:7077
>>
>>  8619 pts/3    S+     0:00 grep spark
>>
>> master is set up with four cores and 1 GB and worker has not any
>> dedicated core and it is using 1GB, that is weird isn't ? I have configured
>> the vmware image with 4 cores (from eight) and 8 GB (from 16).
>>
>> This is how it looks my build.sbt:
>>
>> libraryDependencies ++= Seq(
>>   "org.apache.kafka" % "kafka_2.10" % "0.8.1"
>>       exclude("javax.jms", "jms")
>>       exclude("com.sun.jdmk", "jmxtools")
>>       exclude("com.sun.jmx", "jmxri"),
>>    //not working play module!! check
>>    //jdbc,
>>    //anorm,
>>    //cache,
>>    // HTTP client
>>    "net.databinder.dispatch" %% "dispatch-core" % "0.11.1",
>>    // HTML parser
>>    "org.jodd" % "jodd-lagarto" % "3.5.2",
>>    "com.typesafe" % "config" % "1.2.1",
>>    "com.typesafe.play" % "play-json_2.10" % "2.4.0-M2",
>>    "org.scalatest" % "scalatest_2.10" % "2.2.1" % "test",
>>    "org.twitter4j" % "twitter4j-core" % "4.0.2",
>>    "org.twitter4j" % "twitter4j-stream" % "4.0.2",
>>    "org.codehaus.jackson" % "jackson-core-asl" % "1.6.1",
>>    "org.scala-tools.testing" % "specs_2.8.0" % "1.6.5" % "test",
>>    "org.apache.spark" % "spark-streaming-kafka_2.10" % "1.6.0-cdh5.7.0",
>>    "org.apache.spark" % "spark-core_2.10" % "1.6.0-cdh5.7.0",
>>    "org.apache.spark" % "spark-streaming_2.10" % "1.6.0-cdh5.7.0",
>>    "org.apache.spark" % "spark-sql_2.10" % "1.6.0-cdh5.7.0",
>>    "org.apache.spark" % "spark-mllib_2.10" % "1.6.0-cdh5.7.0",
>>    "com.google.code.gson" % "gson" % "2.6.2",
>>    "commons-cli" % "commons-cli" % "1.3.1",
>>    "com.stratio.datasource" % "spark-mongodb_2.10" % "0.11.1",
>>    // Akka
>>    "com.typesafe.akka" %% "akka-actor" % akkaVersion,
>>    "com.typesafe.akka" %% "akka-slf4j" % akkaVersion,
>>    // MongoDB
>>    "org.reactivemongo" %% "reactivemongo" % "0.10.0"
>> )
>>
>> packAutoSettings
>>
>> As you can see, i am using the exact version of spark modules for the
>> pseudo cluster and i want to use sbt-pack in order to create
>> an unix command, this is how i am declaring programmatically the spark
>> context :
>>
>>
>> val sparkConf = new SparkConf().setAppName("AmazonKafkaConnector")
>>                                    //.setMaster("local[4]")
>>
>>  .setMaster("spark://192.168.30.137:7077")
>>                                    .set("spark.cores.max", "2")
>>
>> ...
>>
>> val ratingFile= "hdfs://192.168.30.137:8020/user/cloudera/ratings.csv"
>>
>>
>> println("Using this ratingFile: " + ratingFile)
>>   // first create an RDD out of the rating file
>>   val rawTrainingRatings = sc.textFile(ratingFile).map {
>>     line =>
>>       val Array(userId, productId, scoreStr) = line.split(",")
>>       AmazonRating(userId, productId, scoreStr.toDouble)
>>   }
>>
>>   // only keep users that have rated between MinRecommendationsPerUser
>> and MaxRecommendationsPerUser products
>>
>>
>> //THIS IS THE LINE THAT PROVOKES the
>> *WARN TaskSchedulerImp*
>>
>> <https://about.me/alonso.isidoro.roman?promo=email_sig&utm_source=email_sig&utm_medium=email_sig&utm_campaign=external_links>
>>
>> <https://about.me/alonso.isidoro.roman?promo=email_sig&utm_source=email_sig&utm_medium=email_sig&utm_campaign=external_links>
>> *!*
>>
>>
>> <https://about.me/alonso.isidoro.roman?promo=email_sig&utm_source=email_sig&utm_medium=email_sig&utm_campaign=external_links>
>> val trainingRatings = rawTrainingRatings.groupBy(_.userId)
>>                                           .filter(r =>
>> MinRecommendationsPerUser <= r._2.size  && r._2.size <
>> MaxRecommendationsPerUser)
>>                                           .flatMap(_._2)
>>                                           .repartition(NumPartitions)
>>                                           .cache()
>>
>>   println(s"Parsed $ratingFile. Kept ${trainingRatings.count()} ratings
>> out of ${rawTrainingRatings.count()}")
>>
>> My question is, do you see anything wrong with the code? is there
>> anything terrible wrong that i have to change? and,
>> what can i do to have this up and running with my resources?
>>
>> What most annoys me is that the above code works perfectly in the console
>> spark of the virtual image but when I try to make it run
>> programmatically creating the unix with SBT-pack command does not work.
>>
>> If the dedicated resources are too few to develop this project, what else
>> can i do? i mean, do i need to hire a tiny cluster with AWS
>> or any another provider? if that is a correct answer, which are yours
>> recommendation?
>>
>> Thank you very much for reading until here.
>>
>> Regards,
>>
>> Alonso
>>
>>
>>
>> <https://about.me/alonso.isidoro.roman?promo=email_sig&utm_source=email_sig&utm_medium=email_sig&utm_campaign=external_links>
>>
>> ------------------------------
>> View this message in context: About a problem running a spark job in a
>> cdh-5.7.0 vmware image.
>> <http://apache-spark-user-list.1001560.n3.nabble.com/About-a-problem-running-a-spark-job-in-a-cdh-5-7-0-vmware-image-tp27082.html>
>> Sent from the Apache Spark User List mailing list archive
>> <http://apache-spark-user-list.1001560.n3.nabble.com/> at Nabble.com.
>>
>
>

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