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From Andres M Jimenez T <ad...@hotmail.com>
Subject Re: how to increase threads per executor
Date Thu, 02 Jun 2016 22:15:54 GMT
Mich, thanks for your time,


i am launching spark-submit as follows:


bin/spark-submit --class com.example.SparkStreamingImpl --master spark://dev1.dev:7077 --verbose
--driver-memory 1g --executor-memory 1g --conf "spark.driver.extraJavaOptions=-Dcom.sun.management.jmxremote
-Dcom.sun.management.jmxremote.port=8090 -Dcom.sun.management.jmxremote.rmi.port=8091 -Dcom.sun.management.jmxremote.authenticate=false
-Dcom.sun.management.jmxremote.ssl=false" --conf "spark.executor.extraJavaOptions=-Dcom.sun.management.jmxremote
-Dcom.sun.management.jmxremote.port=8092 -Dcom.sun.management.jmxremote.rmi.port=8093 -Dcom.sun.management.jmxremote.authenticate=false
-Dcom.sun.management.jmxremote.ssl=false" --conf "spark.scheduler.mode=FAIR" --conf /home/Processing.jar


When i use --executor-cores=12 i get "Initial job has not accepted any resources; check your
cluster UI to ensure that workers are registered and have sufficient resources".


This, because my nodes are single core, but i want to use more than one thread per core, is
this possible?


root@dev1:/home/spark-1.6.1-bin-hadoop2.6# lscpu
Architecture:          x86_64
CPU op-mode(s):        32-bit, 64-bit
Byte Order:            Little Endian
CPU(s):                1
On-line CPU(s) list:   0
Thread(s) per core:    1
Core(s) per socket:    1
Socket(s):             1
NUMA node(s):          1
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 58
Model name:            Intel(R) Xeon(R) CPU E5-2690 v2 @ 3.00GHz
Stepping:              0
CPU MHz:               2999.999
BogoMIPS:              5999.99
Hypervisor vendor:     VMware
Virtualization type:   full
L1d cache:             32K
L1i cache:             32K
L2 cache:              256K
L3 cache:              25600K
NUMA node0 CPU(s):     0



Thanks



________________________________
From: Mich Talebzadeh <mich.talebzadeh@gmail.com>
Sent: Thursday, June 2, 2016 5:00 PM
To: Andres M Jimenez T
Cc: user@spark.apache.org
Subject: Re: how to increase threads per executor

What are passing as parameters to Spark-submit?


${SPARK_HOME}/bin/spark-submit \
                --executor-cores=12 \

Also check

http://spark.apache.org/docs/latest/configuration.html
Configuration - Spark 1.6.1 Documentation<http://spark.apache.org/docs/latest/configuration.html>
spark.apache.org
Spark Configuration. Spark Properties. Dynamically Loading Spark Properties; Viewing Spark
Properties; Available Properties. Application Properties; Runtime Environment



Execution Behavior/spark.executor.cores


HTH



Dr Mich Talebzadeh



LinkedIn  https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw



http://talebzadehmich.wordpress.com<http://talebzadehmich.wordpress.com/>



On 2 June 2016 at 17:29, Andres M Jimenez T <adrz1@hotmail.com<mailto:adrz1@hotmail.com>>
wrote:

Hi,


I am working with Spark 1.6.1, using kafka direct connect for streaming data.

Using spark scheduler and 3 slaves.

Kafka topic is partitioned with a value of 10.


The problem i have is, there is only one thread per executor running my function (logic implementation).


Can anybody tell me how can i increase threads per executor to get better use of CPUs?


Thanks


Here is the code i have implemented:


Driver:


JavaStreamingContext ssc = new JavaStreamingContext(conf, new Duration(10000));

//prepare streaming from kafka

Set<String> topicsSet = new HashSet<>(Arrays.asList("stage1-in,stage1-retry".split(",")));

Map<String, String> kafkaParams = new HashMap<>();

kafkaParams.put("metadata.broker.list", kafkaBrokers);

kafkaParams.put("group.id<http://group.id>", SparkStreamingImpl.class.getName());


JavaPairInputDStream<String, String> inputMessages = KafkaUtils.createDirectStream(

ssc,

String.class,

String.class,

StringDecoder.class,

StringDecoder.class,

kafkaParams,

topicsSet

);


inputMessages.foreachRDD(new ForeachRDDFunction());


ForeachFunction:


class ForeachFunction implements VoidFunction<Tuple2<String, String>> {

private static final Counter foreachConcurrent = ProcessingMetrics.metrics.counter( "foreach-concurrency"
);

public ForeachFunction() {

LOG.info("Creating a new ForeachFunction");

}


public void call(Tuple2<String, String> t) throws Exception {

foreachConcurrent.inc();

LOG.info("processing message [" + t._1() + "]");

try {

Thread.sleep(1000);

} catch (Exception e) { }

foreachConcurrent.dec();

}

}


ForeachRDDFunction:


class ForeachRDDFunction implements VoidFunction<JavaPairRDD<String, String>>
{

private static final Counter foreachRDDConcurrent = ProcessingMetrics.metrics.counter( "foreachRDD-concurrency"
);

private ForeachFunction foreachFunction = new ForeachFunction();

public ForeachRDDFunction() {

LOG.info("Creating a new ForeachRDDFunction");

}


public void call(JavaPairRDD<String, String> t) throws Exception {

foreachRDDConcurrent.inc();

LOG.info("call from inputMessages.foreachRDD with [" + t.partitions().size() + "] partitions");

for (Partition p : t.partitions()) {

if (p instanceof KafkaRDDPartition){

LOG.info("partition [" + p.index() + "] with count [" + ((KafkaRDDPartition) p).count() +
"]");

}

}

t.foreachAsync(foreachFunction);

foreachRDDConcurrent.dec();

}

}


The log from driver that tells me my RDD is partitioned to process in parallel:


[Stage 70:>  (3 + 3) / 20][Stage 71:>  (0 + 0) / 20][Stage 72:>  (0 + 0) / 20]16/06/02
08:32:10 INFO SparkStreamingImpl: call from inputMessages.foreachRDD with [20] partitions

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [0] with count [24]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [1] with count [0]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [2] with count [0]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [3] with count [19]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [4] with count [19]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [5] with count [20]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [6] with count [0]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [7] with count [23]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [8] with count [21]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [9] with count [0]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [10] with count [0]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [11] with count [0]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [12] with count [0]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [13] with count [26]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [14] with count [0]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [15] with count [27]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [16] with count [0]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [17] with count [16]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [18] with count [15]

16/06/02 08:32:10 INFO SparkStreamingImpl: partition [19] with count [0]


The log from one of executors showing exactly one message per second was processed (only by
one thread):


16/06/02 08:32:46 INFO SparkStreamingImpl: processing message [f2b22bb9-3bd8-4e5b-b9fb-afa7e8c4deb8]

16/06/02 08:32:47 INFO SparkStreamingImpl: processing message [e267cde2-ffea-4f7a-9934-f32a3b7218cc]

16/06/02 08:32:48 INFO SparkStreamingImpl: processing message [f055fe3c-0f72-4f41-9a31-df544f1e1cd3]

16/06/02 08:32:49 INFO SparkStreamingImpl: processing message [854faaa5-0abe-49a2-b13a-c290a3720b0e]

16/06/02 08:32:50 INFO SparkStreamingImpl: processing message [1bc0a141-b910-45fe-9881-e2066928fbc6]

16/06/02 08:32:51 INFO SparkStreamingImpl: processing message [67fb99c6-1ca1-4dfb-bffe-43b927fdec07]

16/06/02 08:32:52 INFO SparkStreamingImpl: processing message [de7d5934-bab2-4019-917e-c339d864ba18]

16/06/02 08:32:53 INFO SparkStreamingImpl: processing message [e63d7a7e-de32-4527-b8f1-641cfcc8869c]

16/06/02 08:32:54 INFO SparkStreamingImpl: processing message [1ce931ee-b8b1-4645-8a51-2c697bf1513b]

16/06/02 08:32:55 INFO SparkStreamingImpl: processing message [5367f3c1-d66c-4647-bb44-f5eab719031d]



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