PySparkアプリを提出してYARNをクラスタモードで起動する

Question

私が働いているチーム用に構築された大きなデータプラットフォームをテストしようとしています.YARN上でスパークしています。

PySparkアプリを作成してYARNクラスターに提出することはできますか？

サンプルSparkPi jarファイルを正常に送信できます。出力はYARN stdoutログに出力されます。

ここに私がテストしようとしているPySparkのコードがあります。

from pyspark import SparkConf 
from pyspark import SparkContext 

HDFS_MASTER = 'hadoop-master' 

conf = SparkConf() 
conf.setMaster('yarn') 
conf.setAppName('spark-test') 
sc = SparkContext(conf=conf) 

distFile = sc.textFile('hdfs://{0}:9000/tmp/test/test.csv'.format(HDFS_MASTER)) 

nonempty_lines = distFile.filter(lambda x: len(x) > 0) 
print ('Nonempty lines', nonempty_lines.count()) 

私はスパークディレクトリに私のCMDで試すコマンド：私のスクリプトは私のスパークディレクトリに私のexamplesディレクトリ内sparktest2.pyと呼ばれる

bin\spark-submit --master yarn --deploy-mode cluster --driver-memory 4g 
executor-memory 2g --executor-cores 1 examples\sparktest2.py 10 

。

ログ（標準エラー出力）：標準出力から

application from cluster with 3 NodeManagers 
17/03/22 15:18:39 INFO Client: Verifying our application has not requested more than the maximum memory capability of the cluster (8192 MB per container) 
17/03/22 15:18:39 INFO Client: Will allocate AM container, with 896 MB memory including 384 MB overhead 
17/03/22 15:18:39 INFO Client: Setting up container launch context for our AM 
17/03/22 15:18:39 ERROR SparkContext: Error initializing SparkContext. 
java.util.NoSuchElementException: key not found: SPARK_HOME 
at scala.collection.MapLike$class.default(MapLike.scala:228) 
at scala.collection.AbstractMap.default(Map.scala:59) 
at scala.collection.MapLike$class.apply(MapLike.scala:141) 
at scala.collection.AbstractMap.apply(Map.scala:59) 
at org.apache.spark.deploy.yarn.Client$$anonfun$findPySparkArchives$2.apply(Client.scala:1148) 
at org.apache.spark.deploy.yarn.Client$$anonfun$findPySparkArchives$2.apply(Client.scala:1147) 
at scala.Option.getOrElse(Option.scala:121) 
at org.apache.spark.deploy.yarn.Client.findPySparkArchives(Client.scala:1147) 
at org.apache.spark.deploy.yarn.Client.createContainerLaunchContext(Client.scala:829) 
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:167) 
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) 
at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:149) 
at org.apache.spark.SparkContext.<init>(SparkContext.scala:497) 
at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58) 
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method) 
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) 
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) 
at java.lang.reflect.Constructor.newInstance(Constructor.java:423) 
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) 
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) 
at py4j.Gateway.invoke(Gateway.java:236) 
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) 
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) 
at py4j.GatewayConnection.run(GatewayConnection.java:214) 
at java.lang.Thread.run(Thread.java:745) 
17/03/22 15:18:39 INFO SparkUI: Stopped Spark web UI at http://10.0.9.24:42155 
17/03/22 15:18:39 WARN YarnSchedulerBackend$YarnSchedulerEndpoint: Attempted to request executors before the AM has registered! 
17/03/22 15:18:39 INFO YarnClientSchedulerBackend: Stopped 
17/03/22 15:18:39 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped! 
17/03/22 15:18:39 INFO MemoryStore: MemoryStore cleared 
17/03/22 15:18:39 INFO BlockManager: BlockManager stopped 
17/03/22 15:18:39 INFO BlockManagerMaster: BlockManagerMaster stopped 
17/03/22 15:18:39 WARN MetricsSystem: Stopping a MetricsSystem that is not running 
17/03/22 15:18:39 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped! 
17/03/22 15:18:39 INFO SparkContext: Successfully stopped SparkContext 
17/03/22 15:18:39 ERROR ApplicationMaster: User application exited with status 1 
17/03/22 15:18:39 INFO ApplicationMaster: Final app status: FAILED, exitCode: 1, (reason: User application exited with status 1) 
17/03/22 15:18:47 ERROR ApplicationMaster: SparkContext did not initialize after waiting for 100000 ms. Please check earlier log output for errors. Failing the application. 
17/03/22 15:18:47 INFO ApplicationMaster: Unregistering ApplicationMaster with FAILED (diag message: User application exited with status 1) 
17/03/22 15:18:47 INFO ApplicationMaster: Deleting staging directory hdfs://hadoop-master.overlaynet:9000/user/ahmeds/.sparkStaging/application_1489001113497_0038 
17/03/22 15:18:47 INFO ShutdownHookManager: Shutdown hook called 
17/03/22 15:18:47 INFO ShutdownHookManager: Deleting directory /tmp/hadoop-root/nm-local-dir/usercache/ahmeds/appcache/application_1489001113497_0038/spark-1b4d971c-4448-4a5f-b917-3b6e2d31bb95 

エラー：

Traceback (most recent call last): 
File "sparktest2.py", line 16, in <module> 
sc = SparkContext(conf=conf) 
File "/tmp/hadoop-root/nm-local dir/usercache/ahmeds/appcache/application_1489001113497_0038/container_1489001113497_0038_02_000001/pyspark.zip/pyspark/context.py", line 115, in __init__ 
File "/tmp/hadoop-root/nm-local-dir/usercache/ahmeds/appcache/application_1489001113497_0038/container_1489001113497_0038_02_000001/pyspark.zip/pyspark/context.py", line 168, in _do_init 
File "/tmp/hadoop-root/nm-local-dir/usercache/ahmeds/appcache/application_1489001113497_0038/container_1489001113497_0038_02_000001/pyspark.zip/pyspark/context.py", line 233, in _initialize_context 
File "/tmp/hadoop-root/nm-local-dir/usercache/ahmeds/appcache/application_1489001113497_0038/container_1489001113497_0038_02_000001/py4j-0.10.3-src.zip/py4j/java_gateway.py", line 1401, in __call__ 
File "/tmp/hadoop-root/nm-local-dir/usercache/ahmeds/appcache/application_1489001113497_0038/container_1489001113497_0038_02_000001/py4j-0.10.3-src.zip/py4j/protocol.py", line 319, in get_return_value 
py4j.protocol.Py4JJavaError: An error occurred while calling None.org.apache.spark.api.java.JavaSparkContext. 
: java.util.NoSuchElementException: key not found: SPARK_HOME 
at scala.collection.MapLike$class.default(MapLike.scala:228) 
at scala.collection.AbstractMap.default(Map.scala:59) 
at scala.collection.MapLike$class.apply(MapLike.scala:141) 
at scala.collection.AbstractMap.apply(Map.scala:59) 
at org.apache.spark.deploy.yarn.Client$$anonfun$findPySparkArchives$2.apply(Client.scala:1148) 
at org.apache.spark.deploy.yarn.Client$$anonfun$findPySparkArchives$2.apply(Client.scala:1147) 
at scala.Option.getOrElse(Option.scala:121) 
at org.apache.spark.deploy.yarn.Client.findPySparkArchives(Client.scala:1147) 
at org.apache.spark.deploy.yarn.Client.createContainerLaunchContext(Client.scala:829) 
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:167) 
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) 
at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:149) 
at org.apache.spark.SparkContext.<init>(SparkContext.scala:497) 
at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58) 
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method) 
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) 
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) 
at java.lang.reflect.Constructor.newInstance(Constructor.java:423) 
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) 
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) 
at py4j.Gateway.invoke(Gateway.java:236) 
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) 
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) 
at py4j.GatewayConnection.run(GatewayConnection.java:214) 
at java.lang.Thread.run(Thread.java:745) 

それはおよそSPARK_HOME、私は私の環境変数に設定を持っていると文句を言っているように見えます。

すべてのヘルプは大幅

Pythonのバージョン3.5
スパークバージョン2.0.1
OS高く評価されます。Windows 7

Answer 1

私にとってはうまくいきましたが、私のcmdでは次のように追加されました。

--conf spark.yarn.appMasterEnv.SPARK_HOME=/dev/null 
--conf spark.executorEnv.SPARK_HOME=/dev/null 
--files pythonscript.py 

Answer 2

を私はまた、同様の問題を得ました。 hadoop-env.shで "SPARK_HOME"を設定し、ResourceManager、NameNode、DataNodeを再起動してください。それは修正する必要があります。

Answer 3

./spark-submit --master yarn-cluster --queue default \ 
--num-executors 20 --executor-memory 1G --executor-cores 3 \ 
--driver-memory 1G \ 
--conf spark.yarn.appMasterEnv.SPARK_HOME=/dev/null \ 
--conf spark.executorEnv.SPARK_HOME=/dev/null \ 
--files /home/user/script.py