搭建Spark環境后，調測Spark樣例時，出現下面的錯誤：
WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
[hadoop@gpmaster bin]$ ./run-example org.apache.spark.examples.SparkPi
15/10/01 08:59:33 INFO spark.SparkContext: Running Spark version 1.5.0
.......................
15/10/01 08:59:35 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriver@192.168.1.128:17514]
15/10/01 08:59:35 INFO util.Utils: Successfully started service 'sparkDriver' on port 17514.
.......................
15/10/01 08:59:36 INFO ui.SparkUI: Started SparkUI at http://192.168.1.128:4040
15/10/01 08:59:37 INFO spark.SparkContext: Added JAR file:/home/hadoop/spark/lib/spark-examples-1.5.0-hadoop2.6.0.jar at http://192.168.1.128:36471/jars/spark-examples-1.5.0-hadoop2.6.0.jar with timestamp 1443661177865
15/10/01 08:59:37 WARN metrics.MetricsSystem: Using default name DAGScheduler for source because spark.app.id is not set.
15/10/01 08:59:38 INFO client.AppClient$ClientEndpoint: Connecting to master spark://192.168.1.128:7077...
15/10/01 08:59:38 INFO cluster.SparkDeploySchedulerBackend: Connected to Spark cluster with app ID app-20151001085938-0000
.................................
15/10/01 08:59:40 INFO scheduler.TaskSchedulerImpl: Adding task set 0.0 with 2 tasks
15/10/01 08:59:55 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
15/10/01 09:00:10 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
15/10/01 09:00:25 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
15/10/01 09:00:40 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
15/10/01 09:00:55 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
15/10/01 09:01:10 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
15/10/01 09:01:25 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
15/10/01 09:01:40 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
15/10/01 09:01:55 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
15/10/01 09:02:10 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
15/10/01 09:02:25 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
從警告信息大致可以知道：
初始化job時沒有獲取到任何資源；提示檢查集群，確保workers可以被注冊並有足夠的內存資源。
可能的原因有幾點，可以逐個排查：
1. 主機主機名和ip是否配置正確
先查看/etc/hosts文件配置是否正確
同時可以通過spark-shell查看SparkContext獲取的上下文信息, 如下操作：
[hadoop@gpmaster bin]$ ./spark-shell
........
scala> sc.getConf.getAll.foreach(println)
(spark.fileserver.uri,http://192.168.1.128:34634)
(spark.app.name,Spark shell)
(spark.driver.port,25392)
(spark.app.id,app-20151001090322-0001)
(spark.repl.class.uri,http://192.168.1.128:24988)
(spark.externalBlockStore.folderName,spark-1254a794-fbfa-4b4c-9757-b5a94dc26ffc)
(spark.jars,)
(spark.executor.id,driver)
(spark.submit.deployMode,client)
(spark.driver.host,192.168.1.128)
(spark.master,spark://192.168.1.128:7077)
scala> sc.getConf.toDebugString
res8: String =
spark.app.id=app-20151001090322-0001
spark.app.name=Spark shell
spark.driver.host=192.168.1.128
spark.driver.port=25392
spark.executor.id=driver
spark.externalBlockStore.folderName=spark-1254a794-fbfa-4b4c-9757-b5a94dc26ffc
spark.fileserver.uri=http://192.168.1.128:34634
spark.jars=
spark.master=spark://192.168.1.128:7077
spark.repl.class.uri=http://192.168.1.128:24988
spark.submit.deployMode=client
2. 內存不足
我的環境就是因為內存的原因。
我集群環境中，spark-env.sh 文件配置如下：
export JAVA_HOME=/usr/java/jdk1.7.0_60
export SCALA_HOME=/usr/local/scala
export SPARK_MASTER_IP=192.168.1.128
export SPARK_WORKER_MEMORY=100m
export HADOOP_CONF_DIR=/home/hadoop/hadoop-2.6.0/etc/hadoop
export MASTER=spark://192.168.1.128:7077
因為我的集群環境，每個節點只剩下500MB內存了，由於我沒有配置SPARK_EXECUTOR_MEMORY參數，默認會使用1G內存，所以會出現內存不足，從而出現上面日志報的警告信息。
所以解決辦法是添加如下參數：
export SPARK_EXECUTOR_MEMORY=512m
3.端口號被占用，之前的程序已運行。