スレーブで動作していないSpark：初期のジョブは何もリソースを受け入れていません

Question

私はSparkでSSHトンネリングを使って非常に簡単な設定をしようとしています。

私はこの設定で./sbin/start-master.sh -h localhost -p 7077と私のPC上で動作しているマスターを持っています。

他のドメインにある私のスレーブPC（IPは192.168.0.222）には、私はssh -N -L localhost:7078:localhost:7077 myMasterPCSSHaliasを作って./sbin/start-slave.sh spark://localhost:7078でスレーブを実行しました。私はこのスレーブをダッシュ​​ボードの​​に私のブラウザで見ることができます。 14GBの空きメモリがあることがわかります。

この例では：私は私が利用できる持っているより私はより多くのリソースを使用していないと確信して

WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources 

：

それは私がそれを殺すまで、このメッセージでハング

./bin/spark-submit --master spark://localhost:7077 examples/src/main/python/pi.py 10 

（あなたは以下の完全なログメッセージを見ることができます） 、私は--executor-memory 512mを使用していて、実行中の実行プログラムが単に実行状態を通知しているにもかかわらず、問題は依然として続きます。エラー・ログ内の唯一のものはこれです：

Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties 
16/05/09 22:45:44 INFO CoarseGrainedExecutorBackend: Registered signal handlers for [TERM, HUP, INT] 
16/05/09 22:45:44 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 
16/05/09 22:45:45 INFO SecurityManager: Changing view acls to: hnykdan1,dan 
16/05/09 22:45:45 INFO SecurityManager: Changing modify acls to: hnykdan1,dan 
16/05/09 22:45:45 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(hnykdan1, dan); users with modify permissions: Set(hnykdan1, dan) 

およびスレーブログに

はこれです：

16/05/09 22:48:56 INFO Worker: Asked to launch executor app-20160509224034-0013/0 for PythonPi 
16/05/09 22:48:56 INFO SecurityManager: Changing view acls to: hnykdan1 
16/05/09 22:48:56 INFO SecurityManager: Changing modify acls to: hnykdan1 
16/05/09 22:48:56 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(hnykdan1); users with modify permissions: Set(hnykdan1) 
16/05/09 22:48:56 INFO ExecutorRunner: Launch command: "/usr/lib/jvm/java-7-openjdk-amd64/jre/bin/java" "-cp" "/home/hnykdan1/spark/conf/:/home/hnykdan1/spark/lib/spark-assembly-1.6.1-hadoop2.6.0.jar:/home/hnykdan1/spark/lib/datanucleus-core-3.2.10.jar:/home/hnykdan1/spark/lib/datanucleus-api-jdo-3.2.6.jar:/home/hnykdan1/spark/lib/datanucleus-rdbms-3.2.9.jar" "-Xms1024M" "-Xmx1024M" "-Dspark.driver.port=37450" "-XX:MaxPermSize=256m" "org.apache.spark.executor.CoarseGrainedExecutorBackend" "--driver-url" "spark://CoarseGrainedScheduler@192.168.0.222:37450" "--executor-id" "0" "--hostname" "147.32.8.103" "--cores" "8" "--app-id" "app-20160509224034-0013" "--worker-url" "spark://Worker@147.32.8.103:54894" 

すべてが非常に正常に見えると私は問題がある可能性がありますどこか分かりません。私は他の方法でトンネルする必要がありますか？スレーブをまったく同じ方法でローカルに実行するとうまく動作します。おかげ

完全なログコンソール

Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties 
16/05/09 22:28:21 INFO SparkContext: Running Spark version 1.6.1 
16/05/09 22:28:21 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 
16/05/09 22:28:22 INFO SecurityManager: Changing view acls to: dan 
16/05/09 22:28:22 INFO SecurityManager: Changing modify acls to: dan 
16/05/09 22:28:22 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(dan); users with modify permissions: Set(dan) 
16/05/09 22:28:22 INFO Utils: Successfully started service 'sparkDriver' on port 34508. 
16/05/09 22:28:23 INFO Slf4jLogger: Slf4jLogger started 
16/05/09 22:28:23 INFO Remoting: Starting remoting 
16/05/09 22:28:23 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriverActorSystem@192.168.0.222:44359] 
16/05/09 22:28:23 INFO Utils: Successfully started service 'sparkDriverActorSystem' on port 44359. 
16/05/09 22:28:23 INFO SparkEnv: Registering MapOutputTracker 
16/05/09 22:28:23 INFO SparkEnv: Registering BlockManagerMaster 
16/05/09 22:28:23 INFO DiskBlockManager: Created local directory at /tmp/blockmgr-db4c3293-423f-4966-a479-b69a90439da9 
16/05/09 22:28:23 INFO MemoryStore: MemoryStore started with capacity 511.1 MB 
16/05/09 22:28:23 INFO SparkEnv: Registering OutputCommitCoordinator 
16/05/09 22:28:24 INFO Utils: Successfully started service 'SparkUI' on port 4040. 
16/05/09 22:28:24 INFO SparkUI: Started SparkUI at http://192.168.0.222:4040 
16/05/09 22:28:24 INFO HttpFileServer: HTTP File server directory is /tmp/spark-d532a9c1-0455-4937-ad27-b47abb2a65e8/httpd-aa031b8c-f605-41c3-aabe-fc4fe01bdcf8 
16/05/09 22:28:24 INFO HttpServer: Starting HTTP Server 
16/05/09 22:28:24 INFO Utils: Successfully started service 'HTTP file server' on port 41770. 
16/05/09 22:28:24 INFO Utils: Copying /home/hnykdan1/spark/examples/src/main/python/pi.py to /tmp/spark-d532a9c1-0455-4937-ad27-b47abb2a65e8/userFiles-14720bed-cd41-4b15-9bd3-38dbf4f268ff/pi.py 
16/05/09 22:28:24 INFO SparkContext: Added file file:/home/hnykdan1/spark/examples/src/main/python/pi.py at http://192.168.0.222:41770/files/pi.py with timestamp 1462825704629 
16/05/09 22:28:24 INFO AppClient$ClientEndpoint: Connecting to master spark://localhost:7077... 
16/05/09 22:28:24 INFO SparkDeploySchedulerBackend: Connected to Spark cluster with app ID app-20160509222824-0011 
16/05/09 22:28:24 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 44617. 
16/05/09 22:28:24 INFO NettyBlockTransferService: Server created on 44617 
16/05/09 22:28:24 INFO AppClient$ClientEndpoint: Executor added: app-20160509222824-0011/0 on worker-20160509214654-147.32.8.103-54894 (147.32.8.103:54894) with 8 cores 
16/05/09 22:28:24 INFO BlockManagerMaster: Trying to register BlockManager 
16/05/09 22:28:24 INFO SparkDeploySchedulerBackend: Granted executor ID app-20160509222824-0011/0 on hostPort 147.32.8.103:54894 with 8 cores, 1024.0 MB RAM 
16/05/09 22:28:24 INFO BlockManagerMasterEndpoint: Registering block manager 192.168.0.222:44617 with 511.1 MB RAM, BlockManagerId(driver, 192.168.0.222, 44617) 
16/05/09 22:28:24 INFO BlockManagerMaster: Registered BlockManager 
16/05/09 22:28:25 INFO AppClient$ClientEndpoint: Executor updated: app-20160509222824-0011/0 is now RUNNING 
16/05/09 22:28:25 INFO SparkDeploySchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.0 
16/05/09 22:28:25 INFO SparkContext: Starting job: reduce at /home/hnykdan1/spark/examples/src/main/python/pi.py:39 
16/05/09 22:28:25 INFO DAGScheduler: Got job 0 (reduce at /home/hnykdan1/spark/examples/src/main/python/pi.py:39) with 10 output partitions 
16/05/09 22:28:25 INFO DAGScheduler: Final stage: ResultStage 0 (reduce at /home/hnykdan1/spark/examples/src/main/python/pi.py:39) 
16/05/09 22:28:25 INFO DAGScheduler: Parents of final stage: List() 
16/05/09 22:28:25 INFO DAGScheduler: Missing parents: List() 
16/05/09 22:28:25 INFO DAGScheduler: Submitting ResultStage 0 (PythonRDD[1] at reduce at /home/hnykdan1/spark/examples/src/main/python/pi.py:39), which has no missing parents 
16/05/09 22:28:26 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 4.0 KB, free 4.0 KB) 
16/05/09 22:28:26 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 2.7 KB, free 6.7 KB) 
16/05/09 22:28:26 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.0.222:44617 (size: 2.7 KB, free: 511.1 MB) 
16/05/09 22:28:26 INFO SparkContext: Created broadcast 0 from broadcast at DAGScheduler.scala:1006 
16/05/09 22:28:26 INFO DAGScheduler: Submitting 10 missing tasks from ResultStage 0 (PythonRDD[1] at reduce at /home/hnykdan1/spark/examples/src/main/python/pi.py:39) 
16/05/09 22:28:26 INFO TaskSchedulerImpl: Adding task set 0.0 with 10 tasks 
16/05/09 22:28:41 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources 
16/05/09 22:28:56 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources 
16/05/09 22:29:11 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources 
16/05/09 22:29:26 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources 
16/05/09 22:29:41 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources 
16/05/09 22:29:56 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources 
16/05/09 22:30:11 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources 
16/05/09 22:30:26 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources 

Answer 1

から、あなたはリソースを持っていることを確認しているので、次の最も可能性の高い問題は、executorが戻ってドライバーに接続できないことです。ジョブをサブミットするとき、ドライバーは、jarをダウンロードするためにエグゼキュータが接続するサーバーを開始します。

はい、エラーメッセージ（Initial job has not accepted any resources...）はネットワークの問題に関連していません。これは、ここで例として説明している既知の問題です。 https://github.com/databricks/spark-knowledgebase/issues/9