Spark入门，概述，部署，以及学习（Spark是一种快速、通用、可扩展的大数据分析引擎）

1：Spark的官方网址：http://spark.apache.org/

1：Spark生态系统已经发展成为一个包含多个子项目的集合，其中包含SparkSQL、Spark Streaming、GraphX、MLlib等子项目，Spark是基于内存计算的大数据并行计算框架。Spark基于内存计算，提高了在大数据环境下数据处理的实时性，同时保证了高容错性和高可伸缩性，允许用户将Spark部署在大量廉价硬件之上，形成集群。
2：Spark是MapReduce的替代方案，而且兼容HDFS、Hive，可融入Hadoop的生态系统，以弥补MapReduce的不足。
3：Spark是一种通用的大数据计算框架，一种通用的大数据快速处理引擎，正如传统大数据技术，hadoop的mapreduce，hive引擎，以及Storm流式实时计算引擎等等。
4：Spark包含了大数据领域常见的各种计算框架，比如Spark core用于离线计算，Spark SQL用于交互式查询，Spark Streaming用于实时流式计算，Spark MLlib用于机器学习，Spark GraphX用于图计算。
5：Spark主要用户大数据的计算，而Hadoop以后主要用于大数据的存储(比如，hdfs,hive,hbase)，以及资源调度(yarn)。
6：Spark的核心，其实就是一种新型的大数据框架，而不是对Hadoop的替代，可以基于Hadoop上存储的大数据进行计算(比如：Hdfs,Hive)。Spark只是替代Hadoop的一部分，也就是Hadoop的计算框架Mapreduce，Hive查询引擎。但是Spark本身是不提供大数据的存储的。
7：对比:Spark Core(Spark SQL,Spark Streaming,Spark ML,Spark Graphx,Spark R);和Hadoop(Hive,Storm,Mahout,Griph)；

2：Spark特点：

 ：特点一:快
     与Hadoop的MapReduce相比，Spark基于内存的运算要快100倍以上，基于硬盘的运算也要快10倍以上。Spark实现了高效的DAG执行引擎，可以通过基于内存来高效处理数据流。
 ：特点二:易用
     Spark支持Java、Python和Scala的API，还支持超过80种高级算法，使用户可以快速构建不同的应用。而且Spark支持交互式的Python和Scala的shell，可以非常方便地在这些shell中使用Spark集群来验证解决问题的方法。
 ：特点三:通用
     Spark提供了统一的解决方案。Spark可以用于批处理、交互式查询（Spark SQL）、实时流处理（Spark Streaming）、机器学习（Spark MLlib）和图计算（GraphX）。这些不同类型的处理都可以在同一个应用中无缝使用。Spark统一的解决方案非常具有吸引力，毕竟任何公司都想用统一的平台去处理遇到的问题，减少开发和维护的人力成本和部署平台的物力成本。
 ：特点四:兼容性
     Spark可以非常方便地与其他的开源产品进行融合。比如，Spark可以使用Hadoop的YARN和Apache Mesos作为它的资源管理和调度器，器，并且可以处理所有Hadoop支持的数据，包括HDFS、HBase和Cassandra等。这对于已经部署Hadoop集群的用户特别重要，因为不需要做任何数据迁移就可以使用Spark的强大处理能力。Spark也可以不依赖于第三方的资源管理和调度器，它实现了Standalone作为其内置的资源管理和调度框架，这样进一步降低了Spark的使用门槛，使得所有人都可以非常容易地部署和使用Spark。此外，Spark还提供了在EC2上部署Standalone的Spark集群的工具。

Spark的算子分为两类，一类叫做Transformation转换，一类叫做Action动作。Transformation延迟执行，当计算任务触发Action时候才会真正开始计算。

3：Spark的部署安装（上传jar，过程省略，记得安装好jdk。）：

下载网址：http://www.apache.org/dyn/closer.lua/spark/或者 http://spark.apache.org/downloads.html

Spark的解压缩操作，如下所示：

哈哈哈，犯了一个低级错误，千万记得加-C，解压安装包到指定位置。是大写的哦；

然后呢，进入到Spark安装目录，进入conf目录并重命名并修改spark-env.sh.template文件，操作如下所示：

将spark-env.sh.template 名称修改为spark-env.sh，然后在该配置文件中添加如下配置，之后保存退出：

 [root@localhost conf]# mv spark-env.sh.template spark-env.sh

具体操作如下所示：

也可以将scala和hadoop的目录以及自定义内存大小进行定义，如下所示：

注意：可以去spark的sbin目录里面的start-master.sh使用more start-master.sh命令来查找spark-env.sh里面对应的端口号，或者找其他的.sh文件找对应的值；

或者添加更多的配置，这样初始化不会使用默认的配置，更多配置自己可以看注释进行添加：

export JAVA_HOME=/home/hadoop/soft/jdk1..0_65
export SCALA_HOME=/home/hadoop/soft/scala-2.10.
export HADOOP_HOME=/home/hadoop/soft/hadoop-2.6.4
export HADOOP_CONF_DIR=/home/hadoop/soft/hadoop-2.6.4/etc/hadoop
export SPARK_MASTER_IP=slaver1
export SPARK_MASTER_PORT=
export SPARK_MASTER_WEBUI_PORT=
export SPARK_WORKER_PORT=
export SPARK_WORKER_WEBUI_PORT=
export SPARK_WORKER_CORES=
export SPARK_WORKER_MEMORY=800M
export SPARK_WORKER_INSTANCES=

具体操作如下所示:

下面这个图片的hadoop_conf_dir目录出现错误，注意修改：

然后呢，重命名并修改slaves.template文件，如下所示：

 [root@localhost conf]# mv slaves.template slaves

在该文件中添加子节点所在的位置（Worker节点），操作如下所示，然后保存退出：

如果想记录日志，可以将log4j.properties.template修改为log4j.properties，用于记录日志，查看自己的错误信息：

[root@master conf]# cp log4j.properties.template log4j.properties

将配置好的Spark拷贝到其他节点上：

 [root@localhost hadoop]# scp -r spark-1.6.-bin-hadoop2./ slaver1:/home/hadoop/
 [root@localhost hadoop]# scp -r spark-1.6.-bin-hadoop2./ slaver2:/home/hadoop/

Spark集群配置完毕，目前是1个Master，2个Work(可以是多个Work)，在master节点上启动Spark集群：

注意:启动的过程中，如果进入到spark的sbin目录直接输入start-all.sh是不行的，为什么呢，因为之前配置hadoop是配置的全局的，所以呢，这里不能直接输入start-all.sh命令来启动spark；可以输入sbin/start-all.sh启动spark;

启动后执行jps命令，主节点上有Master进程，其他子节点上有Work进行，登录Spark管理界面查看集群状态（主节点）：http://master:8080/：

可以查看一下是否启动起来，如下所示：

然后在页面可以查看信息，如下所示，如果浏览器一直加载不出来，可能是防火墙没关闭（service iptables stop暂时关闭，chkconfig iptables off永久关闭）：

到此为止，Spark集群安装完毕。

 但是有一个很大的问题，那就是Master节点存在单点故障，要解决此问题，就要借助zookeeper，并且启动至少两个Master节点来实现高可靠，配置方式比较简单，如下所示：
 Spark集群规划：node1，node2是Master；node3，node4，node5是Worker
 安装配置zk集群，并启动zk集群，然后呢，停止spark所有服务，修改配置文件spark-env.sh,
 在该配置文件中删掉SPARK_MASTER_IP并添加如下配置:
 export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=zk1,zk2,zk3 -Dspark.deploy.zookeeper.dir=/spark"
 .在node1节点上修改slaves配置文件内容指定worker节点
 .在node1上执行sbin/start-all.sh脚本，然后在node2上执行sbin/start-master.sh启动第二个Master

4：执行Spark程序（执行第一个spark程序，如下所示）：

执行如下所示，然后就报了一大推错误，由于错误过多就隐藏了，方便以后脑补：

 [root@master bin]# ./spark-submit \
 > --class org.apache.spark.examples.SparkPi \
 > --master spark://master:7077 \
 > --executor-memory 1G \
 > --total-executor-cores 2 \
 > /home/hadoop/spark-1.6.1-bin-hadoop2.6/l
 lib/      licenses/ logs/
 > /home/hadoop/spark-1.6.1-bin-hadoop2.6/lib/spark-examples-1.6.1-hadoop2.6.0.jar \
 > 100

或者如下所示也可：
[root@master spark-1.6.1-bin-hadoop2.6]# bin/spark-submit --class org.apache.spark.examples.SparkPi --master spark://master:7077 --executor-memory 512M --total-executor-cores 2 /home/hadoop/spark-1.6.1-bin-hadoop2.6/lib/spark-examples-1.6.1-hadoop2.6.0.jar 10

错误如下所示，由于太长了就折叠起来了：

 [root@master hadoop]# cd spark-1.6.1-bin-hadoop2.6/
 [root@master spark-1.6.1-bin-hadoop2.6]# ls
 bin          conf  ec2       lib      licenses  NOTICE  R          RELEASE
 CHANGES.txt  data  examples  LICENSE  logs      python  README.md  sbin
 [root@master spark-1.6.1-bin-hadoop2.6]# bi
 bind         biosdecode   biosdevname
 [root@master spark-1.6.1-bin-hadoop2.6]# cd bin/
 [root@master bin]# ls
 beeline             pyspark       run-example2.cmd  spark-class.cmd  spark-shell       spark-submit
 beeline.cmd         pyspark2.cmd  run-example.cmd   sparkR           spark-shell2.cmd  spark-submit2.cmd
 load-spark-env.cmd  pyspark.cmd   spark-class       sparkR2.cmd      spark-shell.cmd   spark-submit.cmd
 load-spark-env.sh   run-example   spark-class2.cmd  sparkR.cmd       spark-sql
 [root@master bin]# ./spark-submit \
 > --class org.apache.spark.examples.SparkPi \
 > --master spark://master:7077 \
 > --executor-memory 1G \
 > --total-executor-cores 2 \
 > /home/hadoop/spark-1.6.1-bin-hadoop2.6/l
 lib/      licenses/ logs/
 > /home/hadoop/spark-1.6.1-bin-hadoop2.6/lib/spark-examples-1.6.1-hadoop2.6.0.jar \
 > 100
 Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
 18/01/02 19:44:01 INFO SparkContext: Running Spark version 1.6.1
 18/01/02 19:44:05 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
 18/01/02 19:44:06 INFO SecurityManager: Changing view acls to: root
 18/01/02 19:44:06 INFO SecurityManager: Changing modify acls to: root
 18/01/02 19:44:06 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with modify permissions: Set(root)
 18/01/02 19:44:09 INFO Utils: Successfully started service 'sparkDriver' on port 41731.
 18/01/02 19:44:11 INFO Slf4jLogger: Slf4jLogger started
 18/01/02 19:44:11 INFO Remoting: Starting remoting
 18/01/02 19:44:12 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriverActorSystem@192.168.3.129:49630]
 18/01/02 19:44:12 INFO Utils: Successfully started service 'sparkDriverActorSystem' on port 49630.
 18/01/02 19:44:13 INFO SparkEnv: Registering MapOutputTracker
 18/01/02 19:44:13 INFO SparkEnv: Registering BlockManagerMaster
 18/01/02 19:44:13 INFO DiskBlockManager: Created local directory at /tmp/blockmgr-c154fc3f-8552-49d4-9a9a-1ce79dba74d7
 18/01/02 19:44:13 INFO MemoryStore: MemoryStore started with capacity 517.4 MB
 18/01/02 19:44:14 INFO SparkEnv: Registering OutputCommitCoordinator
 18/01/02 19:44:15 INFO Utils: Successfully started service 'SparkUI' on port 4040.
 18/01/02 19:44:15 INFO SparkUI: Started SparkUI at http://192.168.3.129:4040
 18/01/02 19:44:15 INFO HttpFileServer: HTTP File server directory is /tmp/spark-2b7d6514-96ad-4999-a7d0-5797b4a53652/httpd-fda58f3c-9d2e-49df-bfe7-2a72fd6dab39
 18/01/02 19:44:15 INFO HttpServer: Starting HTTP Server
 18/01/02 19:44:15 INFO Utils: Successfully started service 'HTTP file server' on port 42161.
 18/01/02 19:44:18 INFO SparkContext: Added JAR file:/home/hadoop/spark-1.6.1-bin-hadoop2.6/lib/spark-examples-1.6.1-hadoop2.6.0.jar at http://192.168.3.129:42161/jars/spark-examples-1.6.1-hadoop2.6.0.jar with timestamp 1514951058742
 18/01/02 19:44:19 INFO AppClient$ClientEndpoint: Connecting to master spark://master:7077...
 18/01/02 19:44:28 INFO SparkDeploySchedulerBackend: Connected to Spark cluster with app ID app-20180102194427-0000
 18/01/02 19:44:30 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 58259.
 18/01/02 19:44:30 INFO NettyBlockTransferService: Server created on 58259
 18/01/02 19:44:30 INFO BlockManagerMaster: Trying to register BlockManager
 18/01/02 19:44:30 INFO BlockManagerMasterEndpoint: Registering block manager 192.168.3.129:58259 with 517.4 MB RAM, BlockManagerId(driver, 192.168.3.129, 58259)
 18/01/02 19:44:30 INFO BlockManagerMaster: Registered BlockManager
 18/01/02 19:44:31 INFO AppClient$ClientEndpoint: Executor added: app-20180102194427-0000/0 on worker-20180103095039-192.168.3.131-39684 (192.168.3.131:39684) with 1 cores
 18/01/02 19:44:31 INFO SparkDeploySchedulerBackend: Granted executor ID app-20180102194427-0000/0 on hostPort 192.168.3.131:39684 with 1 cores, 1024.0 MB RAM
 18/01/02 19:44:31 INFO AppClient$ClientEndpoint: Executor added: app-20180102194427-0000/1 on worker-20180103095039-192.168.3.130-46477 (192.168.3.130:46477) with 1 cores
 18/01/02 19:44:31 INFO SparkDeploySchedulerBackend: Granted executor ID app-20180102194427-0000/1 on hostPort 192.168.3.130:46477 with 1 cores, 1024.0 MB RAM
 18/01/02 19:44:33 INFO SparkDeploySchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.0
 18/01/02 19:44:37 INFO SparkContext: Starting job: reduce at SparkPi.scala:36
 18/01/02 19:44:38 INFO DAGScheduler: Got job 0 (reduce at SparkPi.scala:36) with 100 output partitions
 18/01/02 19:44:38 INFO DAGScheduler: Final stage: ResultStage 0 (reduce at SparkPi.scala:36)
 18/01/02 19:44:38 INFO DAGScheduler: Parents of final stage: List()
 18/01/02 19:44:38 INFO DAGScheduler: Missing parents: List()
 18/01/02 19:44:38 INFO DAGScheduler: Submitting ResultStage 0 (MapPartitionsRDD[1] at map at SparkPi.scala:32), which has no missing parents
 18/01/02 19:44:41 INFO AppClient$ClientEndpoint: Executor updated: app-20180102194427-0000/0 is now RUNNING
 18/01/02 19:44:41 INFO AppClient$ClientEndpoint: Executor updated: app-20180102194427-0000/1 is now RUNNING
 18/01/02 19:44:44 WARN SizeEstimator: Failed to check whether UseCompressedOops is set; assuming yes
 18/01/02 19:44:45 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 1904.0 B, free 1904.0 B)
 18/01/02 19:44:46 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 1216.0 B, free 3.0 KB)
 18/01/02 19:44:46 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.3.129:58259 (size: 1216.0 B, free: 517.4 MB)
 18/01/02 19:44:46 INFO SparkContext: Created broadcast 0 from broadcast at DAGScheduler.scala:1006
 18/01/02 19:44:46 INFO DAGScheduler: Submitting 100 missing tasks from ResultStage 0 (MapPartitionsRDD[1] at map at SparkPi.scala:32)
 18/01/02 19:44:46 INFO TaskSchedulerImpl: Adding task set 0.0 with 100 tasks
 18/01/02 19:45:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:45:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:45:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:45:46 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:46:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:46:07 INFO AppClient$ClientEndpoint: Executor updated: app-20180102194427-0000/0 is now EXITED (Command exited with code 1)
 18/01/02 19:46:07 INFO SparkDeploySchedulerBackend: Executor app-20180102194427-0000/0 removed: Command exited with code 1
 18/01/02 19:46:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:46:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:46:46 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:47:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:47:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:47:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:47:46 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 ^C18/01/02 19:47:58 INFO SparkContext: Invoking stop() from shutdown hook
 18/01/02 19:47:58 INFO SparkUI: Stopped Spark web UI at http://192.168.3.129:4040
 18/01/02 19:47:58 INFO DAGScheduler: Job 0 failed: reduce at SparkPi.scala:36, took 201.147338 s
 18/01/02 19:47:58 INFO DAGScheduler: ResultStage 0 (reduce at SparkPi.scala:36) failed in 191.823 s
 Exception in thread "main" 18/01/02 19:47:58 ERROR LiveListenerBus: SparkListenerBus has already stopped! Dropping event SparkListenerStageCompleted(org.apache.spark.scheduler.StageInfo@10d7390)
 18/01/02 19:47:58 ERROR LiveListenerBus: SparkListenerBus has already stopped! Dropping event SparkListenerJobEnd(0,1514951278747,JobFailed(org.apache.spark.SparkException: Job 0 cancelled because SparkContext was shut down))
 18/01/02 19:47:58 INFO SparkDeploySchedulerBackend: Shutting down all executors
 org.apache.spark.SparkException: Job 0 cancelled because SparkContext was shut down
     at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:806)
     at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:804)
     at scala.collection.mutable.HashSet.foreach(HashSet.scala:79)
     at org.apache.spark.scheduler.DAGScheduler.cleanUpAfterSchedulerStop(DAGScheduler.scala:804)
     at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onStop(DAGScheduler.scala:1658)
     at org.apache.spark.util.EventLoop.stop(EventLoop.scala:84)
     at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1581)
     at org.apache.spark.SparkContext$$anonfun$stop$9.apply$mcV$sp(SparkContext.scala:1740)
     at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1229)
     at org.apache.spark.SparkContext.stop(SparkContext.scala:1739)
     at org.apache.spark.SparkContext$$anonfun$3.apply$mcV$sp(SparkContext.scala:596)
     at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:267)
     at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ShutdownHookManager.scala:239)
     at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:239)
     at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:239)
     at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1765)
     at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply$mcV$sp(ShutdownHookManager.scala:239)
     at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:239)
     at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:239)
     at scala.util.Try$.apply(Try.scala:161)
     at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:239)
     at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:218)
     at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:54)
     at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
     at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
     at org.apache.spark.SparkContext.runJob(SparkContext.scala:1952)
     at org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1025)
     at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
     at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
     at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
     at org.apache.spark.rdd.RDD.reduce(RDD.scala:1007)
     at org.apache.spark.examples.SparkPi$.main(SparkPi.scala:36)
     at org.apache.spark.examples.SparkPi.main(SparkPi.scala)
     at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
     at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
     at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
     at java.lang.reflect.Method.invoke(Method.java:606)
     at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
     at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
     at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
     at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
     at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
 ^C18/01/02 19:48:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 ^C^C^C^C^C
 18/01/02 19:48:07 WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(0,Command exited with code 1)] in 1 attempts
 org.apache.spark.rpc.RpcTimeoutException: Futures timed out after [120 seconds]. This timeout is controlled by spark.rpc.askTimeout
     at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
     at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
     at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:76)
     at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:101)
     at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:77)
     at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.removeExecutor(CoarseGrainedSchedulerBackend.scala:359)
     at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.executorRemoved(SparkDeploySchedulerBackend.scala:144)
     at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anonfun$receive$1.applyOrElse(AppClient.scala:186)
     at org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:116)
     at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:204)
     at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100)
     at org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)
     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
     at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
     at java.lang.Thread.run(Thread.java:745)
 Caused by: java.util.concurrent.TimeoutException: Futures timed out after [120 seconds]
     at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
     at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
     at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
     at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
     at scala.concurrent.Await$.result(package.scala:107)
     at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
     ... 12 more
 ^C^C^C^C^C^C^C^C^C

 ^C^C^C^C^C^C^C^C^C^C^C18/01/02 19:48:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 ^C^C^C^C^C^C^C^C^C^C18/01/02 19:48:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:48:46 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:49:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:49:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:49:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:49:46 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:49:58 WARN NettyRpcEndpointRef: Error sending message [message = StopExecutors] in 1 attempts
 org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
     at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
     at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
     at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
     at scala.util.Try$.apply(Try.scala:161)
     at scala.util.Failure.recover(Try.scala:185)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
     at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.complete(Promise.scala:55)
     at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
     at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
     at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
     at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
     at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
     at java.util.concurrent.FutureTask.run(FutureTask.java:262)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
     at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
     at java.lang.Thread.run(Thread.java:745)
 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
     ... 7 more
 18/01/02 19:50:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:50:10 WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(0,Command exited with code 1)] in 2 attempts
 org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
     at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
     at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
     at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
     at scala.util.Try$.apply(Try.scala:161)
     at scala.util.Failure.recover(Try.scala:185)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
     at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.complete(Promise.scala:55)
     at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
     at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
     at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
     at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
     at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
     at java.util.concurrent.FutureTask.run(FutureTask.java:262)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
     at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
     at java.lang.Thread.run(Thread.java:745)
 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
     ... 7 more
 18/01/02 19:50:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:50:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:50:46 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:51:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:51:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:51:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:51:46 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:52:01 WARN NettyRpcEndpointRef: Error sending message [message = StopExecutors] in 2 attempts
 org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
     at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
     at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
     at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
     at scala.util.Try$.apply(Try.scala:161)
     at scala.util.Failure.recover(Try.scala:185)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
     at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.complete(Promise.scala:55)
     at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
     at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
     at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
     at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
     at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
     at java.util.concurrent.FutureTask.run(FutureTask.java:262)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
     at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
     at java.lang.Thread.run(Thread.java:745)
 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
     ... 7 more
 18/01/02 19:52:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:52:13 WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(0,Command exited with code 1)] in 3 attempts
 org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
     at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
     at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
     at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
     at scala.util.Try$.apply(Try.scala:161)
     at scala.util.Failure.recover(Try.scala:185)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
     at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.complete(Promise.scala:55)
     at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
     at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
     at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
     at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
     at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
     at java.util.concurrent.FutureTask.run(FutureTask.java:262)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
     at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
     at java.lang.Thread.run(Thread.java:745)
 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
     ... 7 more
 18/01/02 19:52:13 ERROR Inbox: Ignoring error
 org.apache.spark.SparkException: Error notifying standalone scheduler's driver endpoint
     at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.removeExecutor(CoarseGrainedSchedulerBackend.scala:362)
     at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.executorRemoved(SparkDeploySchedulerBackend.scala:144)
     at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anonfun$receive$1.applyOrElse(AppClient.scala:186)
     at org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:116)
     at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:204)
     at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100)
     at org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)
     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
     at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
     at java.lang.Thread.run(Thread.java:745)
 Caused by: org.apache.spark.SparkException: Error sending message [message = RemoveExecutor(0,Command exited with code 1)]
     at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:118)
     at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:77)
     at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.removeExecutor(CoarseGrainedSchedulerBackend.scala:359)
     ... 9 more
 Caused by: org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
     at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
     at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
     at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
     at scala.util.Try$.apply(Try.scala:161)
     at scala.util.Failure.recover(Try.scala:185)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
     at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.complete(Promise.scala:55)
     at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
     at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
     at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
     at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
     at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
     at java.util.concurrent.FutureTask.run(FutureTask.java:262)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
     ... 3 more
 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
     ... 7 more
 18/01/02 19:52:13 INFO AppClient$ClientEndpoint: Executor added: app-20180102194427-0000/2 on worker-20180103095039-192.168.3.131-39684 (192.168.3.131:39684) with 1 cores
 18/01/02 19:52:13 INFO SparkDeploySchedulerBackend: Granted executor ID app-20180102194427-0000/2 on hostPort 192.168.3.131:39684 with 1 cores, 1024.0 MB RAM
 18/01/02 19:52:13 INFO AppClient$ClientEndpoint: Executor updated: app-20180102194427-0000/1 is now EXITED (Command exited with code 1)
 18/01/02 19:52:13 INFO SparkDeploySchedulerBackend: Executor app-20180102194427-0000/1 removed: Command exited with code 1
 18/01/02 19:52:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:52:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:52:46 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:53:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:53:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:53:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:53:46 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:54:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 18/01/02 19:54:04 WARN NettyRpcEndpointRef: Error sending message [message = StopExecutors] in 3 attempts
 org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
     at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
     at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
     at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
     at scala.util.Try$.apply(Try.scala:161)
     at scala.util.Failure.recover(Try.scala:185)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
     at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.complete(Promise.scala:55)
     at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
     at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
     at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
     at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
     at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
     at java.util.concurrent.FutureTask.run(FutureTask.java:262)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
     at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
     at java.lang.Thread.run(Thread.java:745)
 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
     ... 7 more
 18/01/02 19:54:04 ERROR Utils: Uncaught exception in thread Thread-3
 org.apache.spark.SparkException: Error asking standalone scheduler to shut down executors
     at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stopExecutors(CoarseGrainedSchedulerBackend.scala:328)
     at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stop(CoarseGrainedSchedulerBackend.scala:333)
     at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.org$apache$spark$scheduler$cluster$SparkDeploySchedulerBackend$$stop(SparkDeploySchedulerBackend.scala:197)
     at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.stop(SparkDeploySchedulerBackend.scala:101)
     at org.apache.spark.scheduler.TaskSchedulerImpl.stop(TaskSchedulerImpl.scala:446)
     at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1582)
     at org.apache.spark.SparkContext$$anonfun$stop$9.apply$mcV$sp(SparkContext.scala:1740)
     at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1229)
     at org.apache.spark.SparkContext.stop(SparkContext.scala:1739)
     at org.apache.spark.SparkContext$$anonfun$3.apply$mcV$sp(SparkContext.scala:596)
     at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:267)
     at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ShutdownHookManager.scala:239)
     at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:239)
     at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:239)
     at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1765)
     at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply$mcV$sp(ShutdownHookManager.scala:239)
     at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:239)
     at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:239)
     at scala.util.Try$.apply(Try.scala:161)
     at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:239)
     at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:218)
     at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:54)
 Caused by: org.apache.spark.SparkException: Error sending message [message = StopExecutors]
     at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:118)
     at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:77)
     at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stopExecutors(CoarseGrainedSchedulerBackend.scala:324)
     ... 21 more
 Caused by: org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
     at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
     at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
     at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
     at scala.util.Try$.apply(Try.scala:161)
     at scala.util.Failure.recover(Try.scala:185)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
     at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.complete(Promise.scala:55)
     at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
     at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
     at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
     at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
     at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
     at java.util.concurrent.FutureTask.run(FutureTask.java:262)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
     at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
     at java.lang.Thread.run(Thread.java:745)
 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
     ... 7 more
 18/01/02 19:54:13 WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(1,Command exited with code 1)] in 1 attempts
 org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
     at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
     at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
     at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
     at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
     at scala.util.Try$.apply(Try.scala:161)
     at scala.util.Failure.recover(Try.scala:185)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
     at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.complete(Promise.scala:55)
     at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
     at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
     at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
     at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
     at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
     at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
     at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
     at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
     at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
     at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
     at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
     at java.util.concurrent.FutureTask.run(FutureTask.java:262)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
     at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
     at java.lang.Thread.run(Thread.java:745)
 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
     at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
     ... 7 more
 ^C^C^C^C^C^C^C
 18/01/02 19:54:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 ^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C^C

 ^X^X^X^X^C^C^C^C^C^C^C^C^C^C^C18/01/02 19:54:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
 ^C^C^C

由于之前学习hadoop，虚拟机内存才设置512M了，Spark是在内存中进行运算的，所以学习Spark一定要设置好内存啊，关闭虚拟机，将内存设置为1G，给Spark设置800M的内存，所以spark-env.sh配置，多添加了：

export SPARK_WORKER_MEMORY=800M

如下所示：

然后执行，如下所示命令：

 [root@master spark-1.6.1-bin-hadoop2.6]# bin/spark-submit \
 > --class org.apache.spark.examples.SparkPi \
 > --master spark://master:7077 \
 > --executor-memory 512M \
 > --total-executor-cores 2 \
 > /home/hadoop/spark-1.6.1-bin-hadoop2.6/lib/spark-examples-1.6.1-hadoop2.6.0.jar \
 > 100

5：启动Spark Shell：

　　spark-shell是Spark自带的交互式Shell程序，方便用户进行交互式编程，用户可以在该命令行下用scala编写spark程序。

启动spark shell，如下所示：

注意：如果配置文件spark-env.sh配置内存，核数信息这里直接使用bin/spark-shell命令启动即可：

 [root@master spark-1.6.1-bin-hadoop2.6]# bin/spark-shell \
 > --master spark://master:7077 \
 > --executor-memory 512M \
 > --total-executor-cores 2

 参数说明：
 --master spark://master:7077 指定Master的地址
 --executor-memory 512M 指定每个worker可用内存为512M
 --total-executor-cores 2 指定整个集群使用的cup核数为2个

如果启动spark-shell命令的时候，指定了--master的位置，那么运行的application就可以显示出来了，而不用去指定spark-default.sh文件；

注意：
    如果启动spark shell时没有指定master地址，但是也可以正常启动spark shell和执行spark shell中的程序，其实是启动了spark的local模式，该模式仅在本机启动一个进程，没有与集群建立联系。
    Spark Shell中已经默认将SparkContext类初始化为对象sc。用户代码如果需要用到，则直接应用sc即可；

操作如下所示：

退出使用命令exit即可；

贴一下日了狗了的报错，没有接受指令超过一定时间就报错了，如下所示，按Enter又回到scala> 等待命令键入：

scala> // :: WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(,Command exited with code )] in  attempts
org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in  seconds. This timeout is controlled by spark.rpc.askTimeout
    at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:)
    at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$.applyOrElse(RpcTimeout.scala:)
    at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$.applyOrElse(RpcTimeout.scala:)
    at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:)
    at scala.util.Failure$$anonfun$recover$.apply(Try.scala:)
    at scala.util.Try$.apply(Try.scala:)
    at scala.util.Failure.recover(Try.scala:)
    at scala.concurrent.Future$$anonfun$recover$.apply(Future.scala:)
    at scala.concurrent.Future$$anonfun$recover$.apply(Future.scala:)
    at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:)
    at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:)
    at scala.concurrent.impl.ExecutionContextImpl$$anon$.execute(ExecutionContextImpl.scala:)
    at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:)
    at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:)
    at scala.concurrent.Promise$class.complete(Promise.scala:)
    at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:)
    at scala.concurrent.Future$$anonfun$map$.apply(Future.scala:)
    at scala.concurrent.Future$$anonfun$map$.apply(Future.scala:)
    at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$.processBatch$(Future.scala:)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$.apply$mcV$sp(Future.scala:)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$.apply(Future.scala:)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$.apply(Future.scala:)
    at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:)
    at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:)
    at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:)
    at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:)
    at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:)
    at scala.concurrent.Promise$class.tryFailure(Promise.scala:)
    at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:)
    at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$.run(NettyRpcEnv.scala:)
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:)
    at java.util.concurrent.FutureTask.run(FutureTask.java:)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$(ScheduledThreadPoolExecutor.java:)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:)
    at java.lang.Thread.run(Thread.java:)
Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in  seconds
    at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$.run(NettyRpcEnv.scala:)
    ...  more
// :: WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(,Command exited with code )] in  attempts
org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in  seconds. This timeout is controlled by spark.rpc.askTimeout
    at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:)
    at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$.applyOrElse(RpcTimeout.scala:)
    at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$.applyOrElse(RpcTimeout.scala:)
    at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:)
    at scala.util.Failure$$anonfun$recover$.apply(Try.scala:)
    at scala.util.Try$.apply(Try.scala:)
    at scala.util.Failure.recover(Try.scala:)
    at scala.concurrent.Future$$anonfun$recover$.apply(Future.scala:)
    at scala.concurrent.Future$$anonfun$recover$.apply(Future.scala:)
    at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:)
    at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:)
    at scala.concurrent.impl.ExecutionContextImpl$$anon$.execute(ExecutionContextImpl.scala:)
    at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:)
    at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:)
    at scala.concurrent.Promise$class.complete(Promise.scala:)
    at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:)
    at scala.concurrent.Future$$anonfun$map$.apply(Future.scala:)
    at scala.concurrent.Future$$anonfun$map$.apply(Future.scala:)
    at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$.processBatch$(Future.scala:)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$.apply$mcV$sp(Future.scala:)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$.apply(Future.scala:)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$.apply(Future.scala:)
    at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:)
    at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:)
    at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:)
    at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:)
    at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:)
    at scala.concurrent.Promise$class.tryFailure(Promise.scala:)
    at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:)
    at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$.run(NettyRpcEnv.scala:)
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:)
    at java.util.concurrent.FutureTask.run(FutureTask.java:)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$(ScheduledThreadPoolExecutor.java:)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:)
    at java.lang.Thread.run(Thread.java:)
Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in  seconds
    at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$.run(NettyRpcEnv.scala:)
    ...  more

---

6：Spark 官网源码编译查看：

7：Linux安装Scala编译器：

下载地址：下载Scala地址http://downloads.typesafe.com/scala/2.10.6/scala-2.10.6.tgz然后解压Scala到指定目录

然后将下载的软件上传到虚拟机上面，过程省略。然后进行解压缩操作：

[root@master package]# tar -zxvf scala-2.10.6.tgz -C /home/hadoop/

然后，配置环境变量，将scala加入到PATH中：

[root@master package]# vim /etc/profile

配置内容如下所示：

然后刷新配置，最后进行验证即可：

退出按exit即可；

8：如果spark-defaults.conf文件（spark-defaults.conf是spark-defaults.conf.template文件cp过来的）不修改，默认的话是在本地运行的，如我的spark://master:7077，如果需要修改，就将这个默认值修改即可：

注意：如果master节点的主机hostname名称不是master，而是其他，比如我的slaver1是hostname，所以这里需要在spark-default.conf文件修改，不然默认是本地模式。那么浏览器的ui界面显示不出来运行的Running Applications；

如下所示，再启动你的bin/spark-shell就可以显示出来运行的applications了，那么就可以看详细信息了：

修改如下所示：

9：读取hdfs上面的文件内容，案例如下操作所示：

首先启动hadoop集群，然后将文件上传到hdfs上面，然后启动spark集群，打开spark shell。

结果如下所示：

标准退出，sc.stop

10：可以使用帮助命令进行查看可以带的参数：

11：Spark的wordcount功能（类比hadoop的map，reduce操作，感觉spark瞬间简单了许多许多）：

然后查看结果如下所示：

简写如下所示：

注意：spark shell仅在测试和验证我们的程序时使用的较多，在生产环境中，通常会在IDE中编制程序，然后打成jar包，然后提交到集群，最常用的是创建一个Maven项目，利用Maven来管理jar包的依赖。

scala> sc.textFile("hdfs://master:9000/wordcount.txt").flatMap(_.split(" ")).map((_,)).reduceByKey(_ + _).collect 
解释说明：
sc是SparkContext对象,该对象时提交spark程序的入口。
textFile("hdfs://master:9000/wordcount.txt")是hdfs中读取数据。
flatMap(_.split(" "))先map再压平。
map((_,1))将单词和1构成元组。
reduceByKey(_+_)按照key进行reduce，并将value累加

12：Spark Running Architecture：

:构建Spark Application运行环境:
    在Driver Program中新建SparkContext(包含sparkcontext的程序称为Driver Program);Spark Application运行的表现方式为:
在集群上运行着一组独立的executor进程,这些进程由sparkcontext来协调;
:SparkContext向资源管理器申请运行Executor资源,并启动StandaloneExecutorBackend,executor向SparkContext申请task;集群通过SparkContext连接到不同的cluster manager(standalone,yarn,mesos),cluster mangaer为运行应用的Executor分配资源;一旦连接建立以后，Spark每个Application就会获得各个节点上的Executor(进程);每个Application都有自己独立的executor进程;Executor才是真正运行在WorkNode上的工作进程,它们为应用来计算或者存储数据;
:SparkContext获取到executor以后，Application的应用代码将会被发送到各个executor;
:SparkContext构建RDD DAG图，将RDD DAG图分解成Stage DAG图，将Stage提交给TaskScheduler,最后由TaskScheduler将Task发送给Executor运行。
:Task在Executor上运行,运行完毕后释放所有资源。

13、Spark JobHistoryServer：

、应用运行完成以后，如何监控呢？？？
　　对于MapReduce应用来说，监控已经运行完成的应用，尤其当应用运行失败的时候，去查看错误异常，非常的关键。
2、Spark Application,运行的时候，使用4040端口进行监控，应用运行所在的机器。

在Spark-env.sh文件里面添加：

export SPARK_HISTORY_OPTS=-Dspark.history.fs.logDirectory=hdfs://slaver1:9000//spark/history

然后配置一下Spark-default.sh文件（不然启动无法查看已经执行结束的应用的日志信息）：

启动如下所示命令：

[hadoop@slaver1 spark-1.5.1-bin-hadoop2.4]$ ./sbin/start-history-server.sh

然后可以查看到停止的应用也可以查看日志信息，访问地址:http://192.168.19.131:18080/：

直接访问18080端口号是没有问题了，但是点击Application Detail UI的时候会报无法找到文件路径的错误，解决方法还未找到，先记录一下：

错误如下所示：

14、Spark Application运行的两种方式Client和Cluster区别：

[hadoop@slaver1 spark-1.5.1-bin-hadoop2.4]$ spark-shell --help

可以看到Spark Application运行的两种方式Client（本地模式）和Cluster（运行在集群上面），默认是client模式的。可以在http://192.168.19.131:8080/页面查看到它们之间的区别，执行的命令也有区别：

spark-submit \
--master spark://slaver1:7077 \
--executor-memory 512M \
--deploy-mode client \
/home/hadoop/soft/spark-1.5.-bin-hadoop2./jars/helloScala.jar

spark-submit \
--master spark://slaver1:7077 \
--executor-memory 512M \
--deploy-mode cluster \
/home/hadoop/soft/spark-1.5.-bin-hadoop2./jars/helloScala.jar

15、 Spark 如何运行在YARN上（两种模式的区别）：

首先停止你的Spark集群哦：[hadoop@slaver1 spark-1.5.1-bin-hadoop2.4]$ sbin/stop-all.sh

可以启动history节点：[hadoop@slaver1 spark-1.5.1-bin-hadoop2.4]$ ./sbin/start-history-server.sh

然后启动Spark 运行在Yarn上面的命令：[hadoop@slaver1 spark-1.5.1-bin-hadoop2.4]$ spark-shell --master yarn-client

出错以及解决链接：执行Spark运行在yarn上的命令报错 spark-shell --master yarn-client

待续......