Interesting
For some stuff I create an uber jar file and use that against spark-submit.
I have not attempted to start the cluster from through application.
I tend to use a shell program (actually a k-shell) to compile it via maven
or sbt and then run it accordingly. In general you can parameterise
everything for runtime parameters say --driver-memory ${DRIVER_MEMORY} to
practically any other parameter . That way I find it more flexible because
I can submit the jar file and the class in any environment and adjust those
runtime parameters accordingly. There are certain advantages to using
spark-submit, for example, since driver-memory setting encapsulates the
JVM, you will need to set the amount of driver memory for any non-default
value before starting JVM by providing the value in spark-submit.
I would be keen in hearing the pros and cons of the above approach. I am
sure you programmers (Scala/Java) know much more than me :)
Cheers
Dr Mich Talebzadeh
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On 15 July 2016 at 16:42, Jean Georges Perrin <j...@jgp.net> wrote:
> lol - young padawan I am and path to knowledge seeking I am...
>
> And on this path I also tried (without luck)...
>
> if (restId == 0) {
> conf = conf.setExecutorEnv("spark.executor.cores", "22");
> } else {
> conf = conf.setExecutorEnv("spark.executor.cores", "2");
> }
>
> and
>
> if (restId == 0) {
> conf.setExecutorEnv("spark.executor.cores", "22");
> } else {
> conf.setExecutorEnv("spark.executor.cores", "2");
> }
>
> the only annoying thing I see is we designed some of the work to be
> handled by the driver/client app and we will have to rethink a bit the
> design of the app for that...
>
>
> On Jul 15, 2016, at 11:34 AM, Daniel Darabos <
> daniel.dara...@lynxanalytics.com> wrote:
>
> Mich's invocation is for starting a Spark application against an already
> running Spark standalone cluster. It will not start the cluster for you.
>
> We used to not use "spark-submit", but we started using it when it solved
> some problem for us. Perhaps that day has also come for you? :)
>
> On Fri, Jul 15, 2016 at 5:14 PM, Jean Georges Perrin <j...@jgp.net> wrote:
>
>> I don't use submit: I start my standalone cluster and connect to it
>> remotely. Is that a bad practice?
>>
>> I'd like to be able to it dynamically as the system knows whether it
>> needs more or less resources based on its own context
>>
>> On Jul 15, 2016, at 10:55 AM, Mich Talebzadeh <mich.talebza...@gmail.com>
>> wrote:
>>
>> Hi,
>>
>> You can also do all this at env or submit time with spark-submit which I
>> believe makes it more flexible than coding in.
>>
>> Example
>>
>> ${SPARK_HOME}/bin/spark-submit \
>> --packages com.databricks:spark-csv_2.11:1.3.0 \
>> --driver-memory 2G \
>> --num-executors 2 \
>> --executor-cores 3 \
>> --executor-memory 2G \
>> --master spark://50.140.197.217:7077 \
>> --conf "spark.scheduler.mode=FAIR" \
>> --conf
>> "spark.executor.extraJavaOptions=-XX:+PrintGCDetails
>> -XX:+PrintGCTimeStamps" \
>> --jars
>> /home/hduser/jars/spark-streaming-kafka-assembly_2.10-1.6.1.jar \
>> --class "${FILE_NAME}" \
>> --conf "spark.ui.port=${SP}" \
>>
>> HTH
>>
>> Dr Mich Talebzadeh
>>
>>
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>>
>>
>> http://talebzadehmich.wordpress.com
>>
>> *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 monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
>> On 15 July 2016 at 13:48, Jean Georges Perrin <j...@jgp.net> wrote:
>>
>>> Merci Nihed, this is one of the tests I did :( still not working
>>>
>>>
>>>
>>> On Jul 15, 2016, at 8:41 AM, nihed mbarek <nihe...@gmail.com> wrote:
>>>
>>> can you try with :
>>> SparkConf conf = new SparkConf().setAppName("NC Eatery app").set(
>>> "spark.executor.memory", "4g")
>>> .setMaster("spark://10.0.100.120:7077");
>>> if (restId == 0) {
>>> conf = conf.set("spark.executor.cores", "22");
>>> } else {
>>> conf = conf.set("spark.executor.cores", "2");
>>> }
>>> JavaSparkContext javaSparkContext = new JavaSparkContext(conf);
>>>
>>> On Fri, Jul 15, 2016 at 2:31 PM, Jean Georges Perrin <j...@jgp.net>
>>> wrote:
>>>
>>>> Hi,
>>>>
>>>> Configuration: standalone cluster, Java, Spark 1.6.2, 24 cores
>>>>
>>>> My process uses all the cores of my server (good), but I am trying to
>>>> limit it so I can actually submit a second job.
>>>>
>>>> I tried
>>>>
>>>> SparkConf conf = new SparkConf().setAppName("NC Eatery app").set(
>>>> "spark.executor.memory", "4g")
>>>> .setMaster("spark://10.0.100.120:7077");
>>>> if (restId == 0) {
>>>> conf = conf.set("spark.executor.cores", "22");
>>>> } else {
>>>> conf = conf.set("spark.executor.cores", "2");
>>>> }
>>>> JavaSparkContext javaSparkContext = new JavaSparkContext(conf);
>>>>
>>>> and
>>>>
>>>> SparkConf conf = new SparkConf().setAppName("NC Eatery app").set(
>>>> "spark.executor.memory", "4g")
>>>> .setMaster("spark://10.0.100.120:7077");
>>>> if (restId == 0) {
>>>> conf.set("spark.executor.cores", "22");
>>>> } else {
>>>> conf.set("spark.executor.cores", "2");
>>>> }
>>>> JavaSparkContext javaSparkContext = new JavaSparkContext(conf);
>>>>
>>>> but it does not seem to take it. Any hint?
>>>>
>>>> jg
>>>>
>>>>
>>>>
>>>
>>>
>>> --
>>>
>>> M'BAREK Med Nihed,
>>> Fedora Ambassador, TUNISIA, Northern Africa
>>> http://www.nihed.com
>>>
>>> <http://tn.linkedin.com/in/nihed>
>>>
>>>
>>>
>>
>>
>
>
