Supposons que je lance un shell pyspark sur un cluster Mesos. Je voudrais occuper seulement 12 cœurs de processeur. Alors je lance comme ça:Spark sur mesos: les tâches sont planifiées sur un seul noeud
[email protected]:~$ pyspark --master mesos://e3.test:5050 --total-executor-cores 12
Et ENSUITE les trucs habituels:
Python 2.7.13 |Anaconda 2.5.0 (64-bit)| (default, Dec 20 2016, 23:09:15)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
17/01/31 08:16:31 INFO SparkContext: Running Spark version 1.6.2
17/01/31 08:16:31 INFO SecurityManager: Changing view acls to: uu
17/01/31 08:16:31 INFO SecurityManager: Changing modify acls to: uu
17/01/31 08:16:31 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(uu); users with modify permissions: Set(uu)
17/01/31 08:16:31 INFO Utils: Successfully started service 'sparkDriver' on port 53336.
17/01/31 08:16:31 INFO Slf4jLogger: Slf4jLogger started
17/01/31 08:16:32 INFO Remoting: Starting remoting
17/01/31 08:16:32 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://[email protected]:59860]
17/01/31 08:16:32 INFO Utils: Successfully started service 'sparkDriverActorSystem' on port 59860.
17/01/31 08:16:32 INFO SparkEnv: Registering MapOutputTracker
17/01/31 08:16:32 INFO SparkEnv: Registering BlockManagerMaster
17/01/31 08:16:32 INFO DiskBlockManager: Created local directory at /var/tmp/spark/blockmgr-6b16ff11-b0bc-4a71-82f5-c69a363c8c1a
17/01/31 08:16:32 INFO MemoryStore: MemoryStore started with capacity 511.1 MB
17/01/31 08:16:32 INFO SparkEnv: Registering OutputCommitCoordinator
17/01/31 08:16:32 INFO Utils: Successfully started service 'SparkUI' on port 4040.
17/01/31 08:16:32 INFO SparkUI: Started SparkUI at http://r4.test:4040
I0131 08:16:32.582038 24965 sched.cpp:226] Version: 1.1.0
I0131 08:16:32.586931 24958 sched.cpp:330] New master detected at [email protected]:5050
I0131 08:16:32.587162 24958 sched.cpp:341] No credentials provided. Attempting to register without authentication
I0131 08:16:32.596922 24956 sched.cpp:743] Framework registered with 075ef8d0-de21-472d-8198-80805006b93d-0051
17/01/31 08:16:32 INFO CoarseMesosSchedulerBackend: Registered as framework ID 075ef8d0-de21-472d-8198-80805006b93d-0051
17/01/31 08:16:32 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 51135.
17/01/31 08:16:32 INFO NettyBlockTransferService: Server created on 51135
17/01/31 08:16:32 INFO BlockManagerMaster: Trying to register BlockManager
17/01/31 08:16:32 INFO BlockManagerMasterEndpoint: Registering block manager r4.test:51135 with 511.1 MB RAM, BlockManagerId(driver, r4.test, 51135)
17/01/31 08:16:32 INFO BlockManagerMaster: Registered BlockManager
17/01/31 08:16:32 INFO CoarseMesosSchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.0
17/01/31 08:16:32 INFO CoarseMesosSchedulerBackend: Mesos task 0 is now TASK_RUNNING
Welcome to
____ __
/__/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__/.__/\_,_/_/ /_/\_\ version 1.6.2
/_/
Using Python version 2.7.13 (default, Dec 20 2016 23:09:15)
SparkContext available as sc, HiveContext available as sqlContext.
mais il finit avec un seul exécuteur testamentaire enregistré:
>>> 17/01/31 08:16:35 INFO CoarseMesosSchedulerBackend: Registered executor NettyRpcEndpointRef(null) (r5.test:42965) with ID 023af0f2-fc60-4d9d-a3db-301ab34764c9-S3
17/01/31 08:16:35 INFO BlockManagerMasterEndpoint: Registering block manager r5.test:33239 with 511.1 MB RAM, BlockManagerId(023af0f2-fc60-4d9d-a3db-301ab34764c9-S3, r5.test, 33239)
ce qui signifie que toute la Spark L'application est sur le point de fonctionner sur le nœud unique. Et ce n'est pas la planification que je veux (principalement en raison de considérations de localisation des données). Ce à quoi je m'attendais s'apparentait davantage à la manière de configuration autonome de Spark: --total-executor-cores se répartit plus ou moins uniformément sur le cluster.
Un moyen d'y parvenir? Les options restantes mentionnant les numéros des exécutants/cœurs ne semblent pas avoir d'effet (et sont uniquement liées aux configurations autonome et de fil).
Pourquoi exactement Spark avec Mesos utilise-t-il cette stratégie de placement consistant à remplir les nœuds un par un plutôt qu'à distribuer du travail?
UPD: les entrées Conf mentionnées dans le docs ne fonctionnent pas non plus:
pyspark --master mesos://e3.test:5050 --conf spark.executor.cores=2 --conf spark.cores.max=12