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）
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）
别先生
发布于 2018-04-02 14:39:22
发布于 2018-04-02 14:39:22
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：
1 1：特点一:快
2 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。
3 2：特点二:易用
4 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。
5 3：特点三:通用
6 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。
7 4：特点四:兼容性
8 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。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：
1 [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：
然后呢，重命名并修改slaves.template文件，如下所示：
1 [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：
1 [root@localhost hadoop]# scp -r spark-1.6.1-bin-hadoop2.6/ slaver1:/home/hadoop/
2 [root@localhost hadoop]# scp -r spark-1.6.1-bin-hadoop2.6/ 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：
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。
1 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：
2 Spark集群规划：node1，node2是Master；node3，node4，node5是Worker
3 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,
4 在该配置文件中删掉SPARK_MASTER_IP并添加如下配置:
5 export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=zk1,zk2,zk3 -Dspark.deploy.zookeeper.dir=/spark"
6 1.在node1节点上修改slaves配置文件内容指定worker节点
7 2.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Master4：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：
1 [root@master bin]# ./spark-submit \
2 > --class org.apache.spark.examples.SparkPi \
3 > --master spark://master:7077 \
4 > --executor-memory 1G \
5 > --total-executor-cores 2 \
6 > /home/hadoop/spark-1.6.1-bin-hadoop2.6/l
7 lib/ licenses/ logs/
8 > /home/hadoop/spark-1.6.1-bin-hadoop2.6/lib/spark-examples-1.6.1-hadoop2.6.0.jar \
9 > 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：
1 [root@master hadoop]# cd spark-1.6.1-bin-hadoop2.6/
2 [root@master spark-1.6.1-bin-hadoop2.6]# ls
3 bin conf ec2 lib licenses NOTICE R RELEASE
4 CHANGES.txt data examples LICENSE logs python README.md sbin
5 [root@master spark-1.6.1-bin-hadoop2.6]# bi
6 bind biosdecode biosdevname
7 [root@master spark-1.6.1-bin-hadoop2.6]# cd bin/
8 [root@master bin]# ls
9 beeline pyspark run-example2.cmd spark-class.cmd spark-shell spark-submit
10 beeline.cmd pyspark2.cmd run-example.cmd sparkR spark-shell2.cmd spark-submit2.cmd
11 load-spark-env.cmd pyspark.cmd spark-class sparkR2.cmd spark-shell.cmd spark-submit.cmd
12 load-spark-env.sh run-example spark-class2.cmd sparkR.cmd spark-sql
13 [root@master bin]# ./spark-submit \
14 > --class org.apache.spark.examples.SparkPi \
15 > --master spark://master:7077 \
16 > --executor-memory 1G \
17 > --total-executor-cores 2 \
18 > /home/hadoop/spark-1.6.1-bin-hadoop2.6/l
19 lib/ licenses/ logs/
20 > /home/hadoop/spark-1.6.1-bin-hadoop2.6/lib/spark-examples-1.6.1-hadoop2.6.0.jar \
21 > 100
22 Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
23 18/01/02 19:44:01 INFO SparkContext: Running Spark version 1.6.1
24 18/01/02 19:44:05 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
25 18/01/02 19:44:06 INFO SecurityManager: Changing view acls to: root
26 18/01/02 19:44:06 INFO SecurityManager: Changing modify acls to: root
27 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)
28 18/01/02 19:44:09 INFO Utils: Successfully started service 'sparkDriver' on port 41731.
29 18/01/02 19:44:11 INFO Slf4jLogger: Slf4jLogger started
30 18/01/02 19:44:11 INFO Remoting: Starting remoting
31 18/01/02 19:44:12 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriverActorSystem@192.168.3.129:49630]
32 18/01/02 19:44:12 INFO Utils: Successfully started service 'sparkDriverActorSystem' on port 49630.
33 18/01/02 19:44:13 INFO SparkEnv: Registering MapOutputTracker
34 18/01/02 19:44:13 INFO SparkEnv: Registering BlockManagerMaster
35 18/01/02 19:44:13 INFO DiskBlockManager: Created local directory at /tmp/blockmgr-c154fc3f-8552-49d4-9a9a-1ce79dba74d7
36 18/01/02 19:44:13 INFO MemoryStore: MemoryStore started with capacity 517.4 MB
37 18/01/02 19:44:14 INFO SparkEnv: Registering OutputCommitCoordinator
38 18/01/02 19:44:15 INFO Utils: Successfully started service 'SparkUI' on port 4040.
39 18/01/02 19:44:15 INFO SparkUI: Started SparkUI at http://192.168.3.129:4040
40 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
41 18/01/02 19:44:15 INFO HttpServer: Starting HTTP Server
42 18/01/02 19:44:15 INFO Utils: Successfully started service 'HTTP file server' on port 42161.
43 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
44 18/01/02 19:44:19 INFO AppClient$ClientEndpoint: Connecting to master spark://master:7077...
45 18/01/02 19:44:28 INFO SparkDeploySchedulerBackend: Connected to Spark cluster with app ID app-20180102194427-0000
46 18/01/02 19:44:30 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 58259.
47 18/01/02 19:44:30 INFO NettyBlockTransferService: Server created on 58259
48 18/01/02 19:44:30 INFO BlockManagerMaster: Trying to register BlockManager
49 18/01/02 19:44:30 INFO BlockManagerMasterEndpoint: Registering block manager 192.168.3.129:58259 with 517.4 MB RAM, BlockManagerId(driver, 192.168.3.129, 58259)
50 18/01/02 19:44:30 INFO BlockManagerMaster: Registered BlockManager
51 18/01/02 19:44:31 INFO AppClient$ClientEndpoint: Executor added: app-20180102194427-0000/0 on worker-20180103095039-192.168.3.131-39684 (192.168.3.131:39684) with 1 cores
52 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
53 18/01/02 19:44:31 INFO AppClient$ClientEndpoint: Executor added: app-20180102194427-0000/1 on worker-20180103095039-192.168.3.130-46477 (192.168.3.130:46477) with 1 cores
54 18/01/02 19:44:31 INFO SparkDeploySchedulerBackend: Granted executor ID app-20180102194427-0000/1 on hostPort 192.168.3.130:46477 with 1 cores, 1024.0 MB RAM
55 18/01/02 19:44:33 INFO SparkDeploySchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.0
56 18/01/02 19:44:37 INFO SparkContext: Starting job: reduce at SparkPi.scala:36
57 18/01/02 19:44:38 INFO DAGScheduler: Got job 0 (reduce at SparkPi.scala:36) with 100 output partitions
58 18/01/02 19:44:38 INFO DAGScheduler: Final stage: ResultStage 0 (reduce at SparkPi.scala:36)
59 18/01/02 19:44:38 INFO DAGScheduler: Parents of final stage: List()
60 18/01/02 19:44:38 INFO DAGScheduler: Missing parents: List()
61 18/01/02 19:44:38 INFO DAGScheduler: Submitting ResultStage 0 (MapPartitionsRDD[1] at map at SparkPi.scala:32), which has no missing parents
62 18/01/02 19:44:41 INFO AppClient$ClientEndpoint: Executor updated: app-20180102194427-0000/0 is now RUNNING
63 18/01/02 19:44:41 INFO AppClient$ClientEndpoint: Executor updated: app-20180102194427-0000/1 is now RUNNING
64 18/01/02 19:44:44 WARN SizeEstimator: Failed to check whether UseCompressedOops is set; assuming yes
65 18/01/02 19:44:45 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 1904.0 B, free 1904.0 B)
66 18/01/02 19:44:46 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 1216.0 B, free 3.0 KB)
67 18/01/02 19:44:46 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.3.129:58259 (size: 1216.0 B, free: 517.4 MB)
68 18/01/02 19:44:46 INFO SparkContext: Created broadcast 0 from broadcast at DAGScheduler.scala:1006
69 18/01/02 19:44:46 INFO DAGScheduler: Submitting 100 missing tasks from ResultStage 0 (MapPartitionsRDD[1] at map at SparkPi.scala:32)
70 18/01/02 19:44:46 INFO TaskSchedulerImpl: Adding task set 0.0 with 100 tasks
71 18/01/02 19:45:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
72 18/01/02 19:45:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
73 18/01/02 19:45:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
74 18/01/02 19:45:46 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
75 18/01/02 19:46:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
76 18/01/02 19:46:07 INFO AppClient$ClientEndpoint: Executor updated: app-20180102194427-0000/0 is now EXITED (Command exited with code 1)
77 18/01/02 19:46:07 INFO SparkDeploySchedulerBackend: Executor app-20180102194427-0000/0 removed: Command exited with code 1
78 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
79 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
80 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
81 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
82 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
83 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
84 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
85 ^C18/01/02 19:47:58 INFO SparkContext: Invoking stop() from shutdown hook
86 18/01/02 19:47:58 INFO SparkUI: Stopped Spark web UI at http://192.168.3.129:4040
87 18/01/02 19:47:58 INFO DAGScheduler: Job 0 failed: reduce at SparkPi.scala:36, took 201.147338 s
88 18/01/02 19:47:58 INFO DAGScheduler: ResultStage 0 (reduce at SparkPi.scala:36) failed in 191.823 s
89 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)
90 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))
91 18/01/02 19:47:58 INFO SparkDeploySchedulerBackend: Shutting down all executors
92 org.apache.spark.SparkException: Job 0 cancelled because SparkContext was shut down
93 at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:806)
94 at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:804)
95 at scala.collection.mutable.HashSet.foreach(HashSet.scala:79)
96 at org.apache.spark.scheduler.DAGScheduler.cleanUpAfterSchedulerStop(DAGScheduler.scala:804)
97 at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onStop(DAGScheduler.scala:1658)
98 at org.apache.spark.util.EventLoop.stop(EventLoop.scala:84)
99 at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1581)
100 at org.apache.spark.SparkContext$$anonfun$stop$9.apply$mcV$sp(SparkContext.scala:1740)
101 at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1229)
102 at org.apache.spark.SparkContext.stop(SparkContext.scala:1739)
103 at org.apache.spark.SparkContext$$anonfun$3.apply$mcV$sp(SparkContext.scala:596)
104 at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:267)
105 at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ShutdownHookManager.scala:239)
106 at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:239)
107 at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:239)
108 at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1765)
109 at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply$mcV$sp(ShutdownHookManager.scala:239)
110 at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:239)
111 at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:239)
112 at scala.util.Try$.apply(Try.scala:161)
113 at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:239)
114 at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:218)
115 at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:54)
116 at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
117 at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
118 at org.apache.spark.SparkContext.runJob(SparkContext.scala:1952)
119 at org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1025)
120 at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
121 at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
122 at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
123 at org.apache.spark.rdd.RDD.reduce(RDD.scala:1007)
124 at org.apache.spark.examples.SparkPi$.main(SparkPi.scala:36)
125 at org.apache.spark.examples.SparkPi.main(SparkPi.scala)
126 at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
127 at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
128 at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
129 at java.lang.reflect.Method.invoke(Method.java:606)
130 at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
131 at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
132 at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
133 at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
134 at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
135 ^C18/01/02 19:48:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
136 ^C^C^C^C^C
137 18/01/02 19:48:07 WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(0,Command exited with code 1)] in 1 attempts
138 org.apache.spark.rpc.RpcTimeoutException: Futures timed out after [120 seconds]. This timeout is controlled by spark.rpc.askTimeout
139 at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
140 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
141 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
142 at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
143 at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:76)
144 at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:101)
145 at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:77)
146 at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.removeExecutor(CoarseGrainedSchedulerBackend.scala:359)
147 at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.executorRemoved(SparkDeploySchedulerBackend.scala:144)
148 at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anonfun$receive$1.applyOrElse(AppClient.scala:186)
149 at org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:116)
150 at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:204)
151 at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100)
152 at org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)
153 at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
154 at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
155 at java.lang.Thread.run(Thread.java:745)
156 Caused by: java.util.concurrent.TimeoutException: Futures timed out after [120 seconds]
157 at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
158 at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
159 at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
160 at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
161 at scala.concurrent.Await$.result(package.scala:107)
162 at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
163 ... 12 more
164 ^C^C^C^C^C^C^C^C^C
165
166
167 ^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
168 ^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
169 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
170 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
171 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
172 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
173 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
174 18/01/02 19:49:58 WARN NettyRpcEndpointRef: Error sending message [message = StopExecutors] in 1 attempts
175 org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
176 at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
177 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
178 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
179 at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
180 at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
181 at scala.util.Try$.apply(Try.scala:161)
182 at scala.util.Failure.recover(Try.scala:185)
183 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
184 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
185 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
186 at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
187 at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
188 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
189 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
190 at scala.concurrent.Promise$class.complete(Promise.scala:55)
191 at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
192 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
193 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
194 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
195 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
196 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
197 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
198 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
199 at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
200 at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
201 at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
202 at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
203 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
204 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
205 at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
206 at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
207 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
208 at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
209 at java.util.concurrent.FutureTask.run(FutureTask.java:262)
210 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
211 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
212 at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
213 at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
214 at java.lang.Thread.run(Thread.java:745)
215 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
216 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
217 ... 7 more
218 18/01/02 19:50:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
219 18/01/02 19:50:10 WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(0,Command exited with code 1)] in 2 attempts
220 org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
221 at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
222 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
223 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
224 at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
225 at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
226 at scala.util.Try$.apply(Try.scala:161)
227 at scala.util.Failure.recover(Try.scala:185)
228 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
229 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
230 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
231 at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
232 at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
233 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
234 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
235 at scala.concurrent.Promise$class.complete(Promise.scala:55)
236 at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
237 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
238 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
239 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
240 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
241 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
242 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
243 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
244 at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
245 at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
246 at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
247 at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
248 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
249 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
250 at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
251 at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
252 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
253 at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
254 at java.util.concurrent.FutureTask.run(FutureTask.java:262)
255 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
256 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
257 at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
258 at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
259 at java.lang.Thread.run(Thread.java:745)
260 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
261 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
262 ... 7 more
263 18/01/02 19:50:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
264 18/01/02 19:50:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
265 18/01/02 19:50:46 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
266 18/01/02 19:51:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
267 18/01/02 19:51:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
268 18/01/02 19:51:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
269 18/01/02 19:51:46 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
270 18/01/02 19:52:01 WARN NettyRpcEndpointRef: Error sending message [message = StopExecutors] in 2 attempts
271 org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
272 at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
273 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
274 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
275 at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
276 at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
277 at scala.util.Try$.apply(Try.scala:161)
278 at scala.util.Failure.recover(Try.scala:185)
279 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
280 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
281 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
282 at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
283 at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
284 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
285 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
286 at scala.concurrent.Promise$class.complete(Promise.scala:55)
287 at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
288 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
289 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
290 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
291 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
292 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
293 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
294 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
295 at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
296 at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
297 at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
298 at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
299 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
300 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
301 at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
302 at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
303 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
304 at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
305 at java.util.concurrent.FutureTask.run(FutureTask.java:262)
306 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
307 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
308 at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
309 at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
310 at java.lang.Thread.run(Thread.java:745)
311 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
312 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
313 ... 7 more
314 18/01/02 19:52:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
315 18/01/02 19:52:13 WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(0,Command exited with code 1)] in 3 attempts
316 org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
317 at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
318 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
319 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
320 at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
321 at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
322 at scala.util.Try$.apply(Try.scala:161)
323 at scala.util.Failure.recover(Try.scala:185)
324 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
325 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
326 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
327 at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
328 at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
329 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
330 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
331 at scala.concurrent.Promise$class.complete(Promise.scala:55)
332 at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
333 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
334 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
335 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
336 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
337 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
338 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
339 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
340 at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
341 at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
342 at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
343 at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
344 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
345 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
346 at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
347 at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
348 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
349 at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
350 at java.util.concurrent.FutureTask.run(FutureTask.java:262)
351 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
352 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
353 at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
354 at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
355 at java.lang.Thread.run(Thread.java:745)
356 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
357 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
358 ... 7 more
359 18/01/02 19:52:13 ERROR Inbox: Ignoring error
360 org.apache.spark.SparkException: Error notifying standalone scheduler's driver endpoint
361 at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.removeExecutor(CoarseGrainedSchedulerBackend.scala:362)
362 at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.executorRemoved(SparkDeploySchedulerBackend.scala:144)
363 at org.apache.spark.deploy.client.AppClient$ClientEndpoint$$anonfun$receive$1.applyOrElse(AppClient.scala:186)
364 at org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:116)
365 at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:204)
366 at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100)
367 at org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)
368 at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
369 at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
370 at java.lang.Thread.run(Thread.java:745)
371 Caused by: org.apache.spark.SparkException: Error sending message [message = RemoveExecutor(0,Command exited with code 1)]
372 at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:118)
373 at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:77)
374 at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.removeExecutor(CoarseGrainedSchedulerBackend.scala:359)
375 ... 9 more
376 Caused by: org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
377 at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
378 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
379 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
380 at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
381 at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
382 at scala.util.Try$.apply(Try.scala:161)
383 at scala.util.Failure.recover(Try.scala:185)
384 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
385 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
386 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
387 at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
388 at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
389 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
390 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
391 at scala.concurrent.Promise$class.complete(Promise.scala:55)
392 at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
393 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
394 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
395 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
396 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
397 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
398 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
399 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
400 at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
401 at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
402 at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
403 at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
404 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
405 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
406 at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
407 at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
408 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
409 at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
410 at java.util.concurrent.FutureTask.run(FutureTask.java:262)
411 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
412 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
413 ... 3 more
414 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
415 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
416 ... 7 more
417 18/01/02 19:52:13 INFO AppClient$ClientEndpoint: Executor added: app-20180102194427-0000/2 on worker-20180103095039-192.168.3.131-39684 (192.168.3.131:39684) with 1 cores
418 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
419 18/01/02 19:52:13 INFO AppClient$ClientEndpoint: Executor updated: app-20180102194427-0000/1 is now EXITED (Command exited with code 1)
420 18/01/02 19:52:13 INFO SparkDeploySchedulerBackend: Executor app-20180102194427-0000/1 removed: Command exited with code 1
421 18/01/02 19:52:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
422 18/01/02 19:52:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
423 18/01/02 19:52:46 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
424 18/01/02 19:53:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
425 18/01/02 19:53:16 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
426 18/01/02 19:53:31 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
427 18/01/02 19:53:46 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
428 18/01/02 19:54:01 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
429 18/01/02 19:54:04 WARN NettyRpcEndpointRef: Error sending message [message = StopExecutors] in 3 attempts
430 org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
431 at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
432 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
433 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
434 at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
435 at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
436 at scala.util.Try$.apply(Try.scala:161)
437 at scala.util.Failure.recover(Try.scala:185)
438 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
439 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
440 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
441 at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
442 at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
443 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
444 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
445 at scala.concurrent.Promise$class.complete(Promise.scala:55)
446 at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
447 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
448 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
449 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
450 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
451 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
452 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
453 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
454 at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
455 at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
456 at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
457 at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
458 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
459 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
460 at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
461 at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
462 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
463 at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
464 at java.util.concurrent.FutureTask.run(FutureTask.java:262)
465 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
466 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
467 at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
468 at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
469 at java.lang.Thread.run(Thread.java:745)
470 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
471 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
472 ... 7 more
473 18/01/02 19:54:04 ERROR Utils: Uncaught exception in thread Thread-3
474 org.apache.spark.SparkException: Error asking standalone scheduler to shut down executors
475 at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stopExecutors(CoarseGrainedSchedulerBackend.scala:328)
476 at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stop(CoarseGrainedSchedulerBackend.scala:333)
477 at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.org$apache$spark$scheduler$cluster$SparkDeploySchedulerBackend$$stop(SparkDeploySchedulerBackend.scala:197)
478 at org.apache.spark.scheduler.cluster.SparkDeploySchedulerBackend.stop(SparkDeploySchedulerBackend.scala:101)
479 at org.apache.spark.scheduler.TaskSchedulerImpl.stop(TaskSchedulerImpl.scala:446)
480 at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1582)
481 at org.apache.spark.SparkContext$$anonfun$stop$9.apply$mcV$sp(SparkContext.scala:1740)
482 at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1229)
483 at org.apache.spark.SparkContext.stop(SparkContext.scala:1739)
484 at org.apache.spark.SparkContext$$anonfun$3.apply$mcV$sp(SparkContext.scala:596)
485 at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:267)
486 at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ShutdownHookManager.scala:239)
487 at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:239)
488 at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:239)
489 at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1765)
490 at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply$mcV$sp(ShutdownHookManager.scala:239)
491 at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:239)
492 at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:239)
493 at scala.util.Try$.apply(Try.scala:161)
494 at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:239)
495 at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:218)
496 at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:54)
497 Caused by: org.apache.spark.SparkException: Error sending message [message = StopExecutors]
498 at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:118)
499 at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:77)
500 at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.stopExecutors(CoarseGrainedSchedulerBackend.scala:324)
501 ... 21 more
502 Caused by: org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
503 at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
504 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
505 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
506 at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
507 at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
508 at scala.util.Try$.apply(Try.scala:161)
509 at scala.util.Failure.recover(Try.scala:185)
510 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
511 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
512 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
513 at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
514 at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
515 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
516 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
517 at scala.concurrent.Promise$class.complete(Promise.scala:55)
518 at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
519 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
520 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
521 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
522 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
523 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
524 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
525 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
526 at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
527 at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
528 at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
529 at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
530 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
531 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
532 at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
533 at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
534 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
535 at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
536 at java.util.concurrent.FutureTask.run(FutureTask.java:262)
537 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
538 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
539 at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
540 at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
541 at java.lang.Thread.run(Thread.java:745)
542 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
543 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
544 ... 7 more
545 18/01/02 19:54:13 WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(1,Command exited with code 1)] in 1 attempts
546 org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
547 at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
548 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
549 at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
550 at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
551 at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
552 at scala.util.Try$.apply(Try.scala:161)
553 at scala.util.Failure.recover(Try.scala:185)
554 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
555 at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
556 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
557 at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
558 at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
559 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
560 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
561 at scala.concurrent.Promise$class.complete(Promise.scala:55)
562 at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
563 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
564 at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
565 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
566 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
567 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
568 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
569 at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
570 at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
571 at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
572 at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
573 at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
574 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
575 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
576 at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
577 at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
578 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
579 at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
580 at java.util.concurrent.FutureTask.run(FutureTask.java:262)
581 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
582 at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
583 at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
584 at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
585 at java.lang.Thread.run(Thread.java:745)
586 Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
587 at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
588 ... 7 more
589 ^C^C^C^C^C^C^C
590 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
591 ^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
592
593
594
595
596 ^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
597 ^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：
1 [root@master spark-1.6.1-bin-hadoop2.6]# bin/spark-submit \
2 > --class org.apache.spark.examples.SparkPi \
3 > --master spark://master:7077 \
4 > --executor-memory 512M \
5 > --total-executor-cores 2 \
6 > /home/hadoop/spark-1.6.1-bin-hadoop2.6/lib/spark-examples-1.6.1-hadoop2.6.0.jar \
7 > 1005：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：
1 [root@master spark-1.6.1-bin-hadoop2.6]# bin/spark-shell \
2 > --master spark://master:7077 \
3 > --executor-memory 512M \
4 > --total-executor-cores 2
5
6 参数说明：
7 --master spark://master:7077 指定Master的地址
8 --executor-memory 512M 指定每个worker可用内存为512M
9 --total-executor-cores 2 指定整个集群使用的cup核数为2个注意： 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> 18/01/03 02:37:36 WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(0,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$$anon$1.run(NettyRpcEnv.scala:242)
... 7 more
18/01/03 02:39:39 WARN NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(0,Command exited with code 1)] in 2 attempts
org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$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 more6：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：
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((_,1)).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：
1: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;
2: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;
3:SparkContext获取到executor以后，Application的应用代码将会被发送到各个executor;
4: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。
5:Task在Executor上运行,运行完毕后释放所有资源。待续......
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原始发表：2018-01-03 ，如有侵权请联系 cloudcommunity@tencent.com 删除
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