I have started the spark-2.0 thrift server in local environment and it's working fine, when I'll tried with the cluster environment the following exception thrown.
16/06/02 10:21:06 INFO spark.SparkContext: Successfully stopped SparkContext
Exception in thread "main" org.apache.spark.SparkException: Yarn application has already ended!
It might have been killed or unable to launch application master.
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.waitFor
Application(YarnClientSchedulerBackend.scala:85)
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start
(YarnClientSchedulerBackend.scala:62)
at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:502)
at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2246)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:749)
at org.apache.spark.sql.hive.thriftserver.SparkSQLEnv$.init(SparkSQLEnv.scala:57)
at org.apache.spark.sql.hive.thriftserver.HiveThriftServer2$.main(HiveThriftServer2.scala:81)
at org.apache.spark.sql.hive.thriftserver.HiveThriftServer2.main(HiveThriftServer2.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:724)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:119)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
16/06/02 10:21:06 INFO util.ShutdownHookManager: Shutdown hook called
When checking in application master logs
Error: Could not find or load main class org.apache.spark.deploy.yarn.ExecutorLauncher
Spark-defaults configuration :
spark.executor.memory 2g
spark.driver.memory 4g
spark.executor.cores 1
