Idea里面远程提交spark任务到yarn集群
Idea里面远程提交spark任务到yarn集群
作者：JasonLee
1.本地idea远程提交到yarn集群
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf}
import spark.wordcount.kafkaStreams
object RemoteSubmitApp {
def main(args: Array[String]) {
// 设置提交任务的用户
System.setProperty("HADOOP_USER_NAME", "root")
val conf = new SparkConf()
.setAppName("WordCount")
// 设置yarn-client模式提交
.setMaster("yarn")
// 设置resourcemanager的ip
.set("yarn.resourcemanager.hostname","master")
// 设置executor的个数
.set("spark.executor.instance","2")
// 设置executor的内存大小
.set("spark.executor.memory", "1024M")
// 设置提交任务的yarn队列
.set("spark.yarn.queue","spark")
// 设置driver的ip地址
.set("spark.driver.host","192.168.17.1")
// 设置jar包的路径,如果有其他的依赖包,可以在这里添加,逗号隔开
.setJars(List("D:\\develop_soft\\idea_workspace_2018\\sparkdemo\\target\\sparkdemo-1.0-SNAPSHOT.jar"
))
conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
val scc = new StreamingContext(conf, Seconds(1))
scc.sparkContext.setLogLevel("WARN")
//scc.checkpoint("/spark/checkpoint")
val topic = "jason_flink"
val topicSet = Set(topic)
val kafkaParams = Map[String, Object](
"auto.offset.reset" -> "latest",
"value.deserializer" -> classOf[StringDeserializer]
, "key.deserializer" -> classOf[StringDeserializer]
, "bootstrap.servers" -> "master:9092,storm1:9092,storm2:9092"
, "group.id" -> "jason_"
, "enable.auto.commit" -> (true: java.lang.Boolean)
)
kafkaStreams = KafkaUtils.createDirectStream[String, String](
scc,
LocationStrategies.PreferConsistent,
ConsumerStrategies.Subscribe[String, String](topicSet, kafkaParams))
kafkaStreams.foreachRDD(rdd=> {
if (!rdd.isEmpty()) {
rdd.foreachPartition(fp=> {
fp.foreach(f=> {
println(f.value().toString)
})
})
}
})
scc.start()
scc.awaitTermination()
}
}
然后我们直接右键运行,看下打印的日志
19/08/16 23:17:24 INFO SparkContext: Running Spark version 2.2.0
19/08/16 23:17:25 INFO SparkContext: Submitted application: WordCount
19/08/16 23:17:25 INFO SecurityManager: Changing view acls to: JasonLee,root
19/08/16 23:17:25 INFO SecurityManager: Changing modify acls to: JasonLee,root
19/08/16 23:17:25 INFO SecurityManager: Changing view acls groups to:
19/08/16 23:17:25 INFO SecurityManager: Changing modify acls groups to:
19/08/16 23:17:25 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(JasonLee, root); groups with view permissions: Set(); users with modify permissions: Set(JasonLee, root); groups with modify permissions: Set()
19/08/16 23:17:26 INFO Utils: Successfully started service 'sparkDriver' on port 62534.
19/08/16 23:17:26 INFO SparkEnv: Registering MapOutputTracker
19/08/16 23:17:26 INFO SparkEnv: Registering BlockManagerMaster
19/08/16 23:17:26 INFO BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information
19/08/16 23:17:26 INFO BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up
19/08/16 23:17:26 INFO DiskBlockManager: Created local directory at C:\Users\jason\AppData\Local\Temp\blockmgr-6ec3ae57-661d-4974-8bc9-7357ab4a0c06
19/08/16 23:17:26 INFO MemoryStore: MemoryStore started with capacity 4.1 GB
19/08/16 23:17:26 INFO SparkEnv: Registering OutputCommitCoordinator
19/08/16 23:17:26 INFO log: Logging initialized @2170ms
19/08/16 23:17:26 INFO Server: jetty-9.3.z-SNAPSHOT
19/08/16 23:17:26 INFO Server: Started @2236ms
19/08/16 23:17:26 INFO AbstractConnector: Started ServerConnector@4a534663{HTTP/1.1,[http/1.1]}{0.0.0.0:4040}
19/08/16 23:17:26 INFO Utils: Successfully started service 'SparkUI' on port 4040.
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@427b5f92{/jobs,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@1991f767{/jobs/json,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@4c6daf0{/jobs/job,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@2488b073{/jobs/job/json,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@55787112{/stages,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@7db82169{/stages/json,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@f74e835{/stages/stage,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@19fe4644{/stages/stage/json,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@5be067de{/stages/pool,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@18245eb0{/stages/pool/json,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@24fb6a80{/storage,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@72a85671{/storage/json,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@18f20260{/storage/rdd,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@7a48e6e2{/storage/rdd/json,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@3a94964{/environment,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@6d0b5baf{/environment/json,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@2a3591c5{/executors,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@346a361{/executors/json,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@1643d68f{/executors/threadDump,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@2e029d61{/executors/threadDump/json,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@4052274f{/static,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@24faea88{/,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@64beebb7{/api,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@29ef6856{/jobs/job/kill,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@3faf2e7d{/stages/stage/kill,null,AVAILABLE,@Spark}
19/08/16 23:17:26 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://192.168.17.1:4040
19/08/16 23:17:26 INFO SparkContext: Added JAR D:\develop_soft\idea_workspace_2018\sparkdemo\target\sparkdemo-1.0-SNAPSHOT.jar at spark://192.168.17.1:62534/jars/sparkdemo-1.0-SNAPSHOT.jar with timestamp 1565968646369
19/08/16 23:17:27 INFO RMProxy: Connecting to ResourceManager at master/192.168.17.142:8032
19/08/16 23:17:27 INFO Client: Requesting a new application from cluster with 2 NodeManagers
19/08/16 23:17:27 INFO Client: Verifying our application has not requested more than the maximum memory capability of the cluster (8192 MB per container)
19/08/16 23:17:27 INFO Client: Will allocate AM container, with 896 MB memory including 384 MB overhead
19/08/16 23:17:27 INFO Client: Setting up container launch context for our AM
19/08/16 23:17:27 INFO Client: Setting up the launch environment for our AM container
19/08/16 23:17:27 INFO Client: Preparing resources for our AM container
19/08/16 23:17:28 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
19/08/16 23:17:31 INFO Client: Uploading resource file:/C:/Users/jason/AppData/Local/Temp/spark-7ed16f4e-0f99-44cf-8553-b4541337d0f0/__spark_libs__5037580728569655338.zip -> hdfs://master:9000/user/root/.sparkStaging/application_1565990507758_0020/__spark_libs__5037580728569655338.zip
19/08/16 23:17:34 INFO Client: Uploading resource file:/C:/Users/jason/AppData/Local/Temp/spark-7ed16f4e-0f99-44cf-8553-b4541337d0f0/__spark_conf__5359714098313821798.zip -> hdfs://master:9000/user/root/.sparkStaging/application_1565990507758_0020/__spark_conf__.zip
19/08/16 23:17:34 INFO SecurityManager: Changing view acls to: JasonLee,root
19/08/16 23:17:34 INFO SecurityManager: Changing modify acls to: JasonLee,root
19/08/16 23:17:34 INFO SecurityManager: Changing view acls groups to:
19/08/16 23:17:34 INFO SecurityManager: Changing modify acls groups to:
19/08/16 23:17:34 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(JasonLee, root); groups with view permissions: Set(); users with modify permissions: Set(JasonLee, root); groups with modify permissions: Set()
19/08/16 23:17:34 INFO Client: Submitting application application_1565990507758_0020 to ResourceManager
19/08/16 23:17:34 INFO YarnClientImpl: Submitted application application_1565990507758_0020
19/08/16 23:17:34 INFO SchedulerExtensionServices: Starting Yarn extension services with app application_1565990507758_0020 and attemptId None
19/08/16 23:17:35 INFO Client: Application report for application_1565990507758_0020 (state: ACCEPTED)
19/08/16 23:17:35 INFO Client:
client token: N/A
diagnostics: AM container is launched, waiting for AM container to Register with RM
ApplicationMaster host: N/A
ApplicationMaster RPC port: -1
queue: spark
start time: 1565997454105
final status: UNDEFINED
tracking URL: http://master:8088/proxy/application_1565990507758_0020/
user: root
19/08/16 23:17:36 INFO Client: Application report for application_1565990507758_0020 (state: ACCEPTED)
19/08/16 23:17:37 INFO Client: Application report for application_1565990507758_0020 (state: ACCEPTED)
19/08/16 23:17:38 INFO Client: Application report for application_1565990507758_0020 (state: ACCEPTED)
19/08/16 23:17:39 INFO Client: Application report for application_1565990507758_0020 (state: ACCEPTED)
19/08/16 23:17:40 INFO YarnSchedulerBackend$YarnSchedulerEndpoint: ApplicationMaster registered as NettyRpcEndpointRef(spark-client://YarnAM)
19/08/16 23:17:40 INFO YarnClientSchedulerBackend: Add WebUI Filter. org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter, Map(PROXY_HOSTS -> master, PROXY_URI_BASES -> http://master:8088/proxy/application_1565990507758_0020), /proxy/application_1565990507758_0020
19/08/16 23:17:40 INFO JettyUtils: Adding filter: org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter
19/08/16 23:17:40 INFO Client: Application report for application_1565990507758_0020 (state: ACCEPTED)
19/08/16 23:17:41 INFO Client: Application report for application_1565990507758_0020 (state: RUNNING)
19/08/16 23:17:41 INFO Client:
client token: N/A
diagnostics: N/A
ApplicationMaster host: 192.168.17.145
ApplicationMaster RPC port: 0
queue: spark
start time: 1565997454105
final status: UNDEFINED
tracking URL: http://master:8088/proxy/application_1565990507758_0020/
user: root
19/08/16 23:17:41 INFO YarnClientSchedulerBackend: Application application_1565990507758_0020 has started running.
19/08/16 23:17:41 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 62586.
19/08/16 23:17:41 INFO NettyBlockTransferService: Server created on 192.168.17.1:62586
19/08/16 23:17:41 INFO BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy
19/08/16 23:17:41 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, 192.168.17.1, 62586, None)
19/08/16 23:17:41 INFO BlockManagerMasterEndpoint: Registering block manager 192.168.17.1:62586 with 4.1 GB RAM, BlockManagerId(driver, 192.168.17.1, 62586, None)
19/08/16 23:17:41 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, 192.168.17.1, 62586, None)
19/08/16 23:17:41 INFO BlockManager: Initialized BlockManager: BlockManagerId(driver, 192.168.17.1, 62586, None)
19/08/16 23:17:41 INFO ContextHandler: Started o.s.j.s.ServletContextHandler@3bb9ca38{/metrics/json,null,AVAILABLE,@Spark}
19/08/16 23:17:44 INFO YarnSchedulerBackend$YarnDriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (192.168.17.145:40622) with ID 1
19/08/16 23:17:44 INFO BlockManagerMasterEndpoint: Registering block manager storm1:44607 with 366.3 MB RAM, BlockManagerId(1, storm1, 44607, None)
19/08/16 23:17:48 INFO YarnSchedulerBackend$YarnDriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (192.168.17.147:58232) with ID 2
19/08/16 23:17:48 INFO YarnClientSchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.8
19/08/16 23:17:48 INFO BlockManagerMasterEndpoint: Registering block manager storm2:34000 with 366.3 MB RAM, BlockManagerId(2, storm2, 34000, None)
19/08/16 23:17:49 WARN KafkaUtils: overriding enable.auto.commit to false for executor
19/08/16 23:17:49 WARN KafkaUtils: overriding auto.offset.reset to none for executor
19/08/16 23:17:49 WARN KafkaUtils: overriding executor group.id to spark-executor-jason_
19/08/16 23:17:49 WARN KafkaUtils: overriding receive.buffer.bytes to 65536 see KAFKA-3135
看到提交成功了,然后我们打开yarn的监控页面看下有没有job。
看到有一个spark程序在运行,然后我们点进去,看下具体的运行情况：
选择一下job,看下executor打印的日志
写到kafka的数据,没什么问题,停止的时候,只需要在idea里面点击停止程序就可以了,这样测试起来就会方便很多.
2.运行过程中可能会遇到的问题
2.1首先需要把yarn-site.xml,core-site.xml,hdfs-site.xml放到resource下面,因为程序运行的时候需要这些环境
2.2权限问题
Caused by: org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.security.AccessControlException): Permission denied: user=JasonLee, access=WRITE, inode="/user":root:supergroup:drwxr-xr-x
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.check(FSPermissionChecker.java:342)
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkPermission(FSPermissionChecker.java:251)
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkPermission(FSPermissionChecker.java:189)
at org.apache.hadoop.hdfs.server.namenode.FSDirectory.checkPermission(FSDirectory.java:1744)
at org.apache.hadoop.hdfs.server.namenode.FSDirectory.checkPermission(FSDirectory.java:1728)
at org.apache.hadoop.hdfs.server.namenode.FSDirectory.checkAncestorAccess(FSDirectory.java:1687)
at org.apache.hadoop.hdfs.server.namenode.FSDirMkdirOp.mkdirs(FSDirMkdirOp.java:60)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.mkdirs(FSNamesystem.java:2980)
at org.apache.hadoop.hdfs.server.namenode.NameNodeRpcServer.mkdirs(NameNodeRpcServer.java:1096)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolServerSideTranslatorPB.mkdirs(ClientNamenodeProtocolServerSideTranslatorPB.java:652)
at org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$ClientNamenodeProtocol$2.callBlockingMethod(ClientNamenodeProtocolProtos.java)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:503)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:989)
at org.apache.hadoop.ipc.Server$RpcCall.run(Server.java:868)
at org.apache.hadoop.ipc.Server$RpcCall.run(Server.java:814)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1886)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2603)
这个是因为在本地提交的所以用户名是JasonLee,它没有访问hdfs的权限,最简单的解决方法就是在代码里面设置用户是root。
System.setProperty("HADOOP_USER_NAME", "root")
2.3缺失环境变量
Exception in thread "main" java.lang.IllegalStateException: Library directory 'D:\develop_soft\idea_workspace_2018\sparkdemo\assembly\target\scala-2.11\jars' does not exist; make sure Spark is built.
at org.apache.spark.launcher.CommandBuilderUtils.checkState(CommandBuilderUtils.java:248)
at org.apache.spark.launcher.CommandBuilderUtils.findJarsDir(CommandBuilderUtils.java:347)
at org.apache.spark.launcher.YarnCommandBuilderUtils$.findJarsDir(YarnCommandBuilderUtils.scala:38)
at org.apache.spark.deploy.yarn.Client.prepareLocalResources(Client.scala:526)
at org.apache.spark.deploy.yarn.Client.createContainerLaunchContext(Client.scala:814)
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:169)
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56)
at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:173)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:509)
at org.apache.spark.streaming.StreamingContext$.createNewSparkContext(StreamingContext.scala:839)
at org.apache.spark.streaming.StreamingContext.<init>(StreamingContext.scala:85)
at spark.RemoteSubmitApp$.main(RemoteSubmitApp.scala:31)
at spark.RemoteSubmitApp.main(RemoteSubmitApp.scala)
这个报错是因为我们没有配置SPARK_HOME的环境变量,直接在idea里面的configurations里面的environment variables里面设置SPARK_HOME就可以了,如下图所示:
2.4 没有设置driver的ip
9/08/17 07:52:45 ERROR ApplicationMaster: Failed to connect to driver at 169.254.42.204:64010, retrying ...
19/08/17 07:52:48 ERROR ApplicationMaster: Failed to connect to driver at 169.254.42.204:64010, retrying ...
19/08/17 07:52:48 ERROR ApplicationMaster: Uncaught exception:
org.apache.spark.SparkException: Failed to connect to driver!
at org.apache.spark.deploy.yarn.ApplicationMaster.waitForSparkDriver(ApplicationMaster.scala:577)
at org.apache.spark.deploy.yarn.ApplicationMaster.runExecutorLauncher(ApplicationMaster.scala:433)
at org.apache.spark.deploy.yarn.ApplicationMaster.run(ApplicationMaster.scala:256)
at org.apache.spark.deploy.yarn.ApplicationMaster$$anonfun$main$1.apply$mcV$sp(ApplicationMaster.scala:764)
at org.apache.spark.deploy.SparkHadoopUtil$$anon$2.run(SparkHadoopUtil.scala:67)
at org.apache.spark.deploy.SparkHadoopUtil$$anon$2.run(SparkHadoopUtil.scala:66)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1692)
at org.apache.spark.deploy.SparkHadoopUtil.runAsSparkUser(SparkHadoopUtil.scala:66)
at org.apache.spark.deploy.yarn.ApplicationMaster$.main(ApplicationMaster.scala:762)
at org.apache.spark.deploy.yarn.ExecutorLauncher$.main(ApplicationMaster.scala:785)
at org.apache.spark.deploy.yarn.ExecutorLauncher.main(ApplicationMaster.scala)
这个报错是因为没有设置driver host,因为我们运行的是yarn-client模式,driver就是我们的本机,所以要设置本地的ip,不然找不到driver.
.set("spark.driver.host","192.168.17.1")
2.5保证自己的电脑和虚拟机在同一个网段内,而且要关闭自己电脑的防火墙,不然可能会出现连接不上的情况.
Idea里面远程提交spark任务到yarn集群的更多相关文章
- idea或者java远程提交spark任务到yarn,Exception1
spark通過idea远程提交job到yarn: Caused by: java.lang.ClassCastException: cannot assign instance of scala.co ...
- spark 选择不同yarn集群提交任务
修改环境变量中的HADOOP_CONF_DIR,可以配置多份配置文件.根据不同路径下yarn集群配置访问不同集群. 所使用的用户需要在yarn每个节点都存在且有对应的访问权限.
- linux平台使用spark-submit以cluster模式提交spark应用到standalone集群
shell脚本如下 sparkHome=/home/spark/spark-2.2.0-bin-hadoop2.7 $sparkHome/bin/spark-submit \ --class stre ...
- win下idea远程提交WordCount任务到HA集群
一,环境配置 1,修改win下的host文件:即C:\Windows\System32\drivers\etc\host中添加集群中机子的ip 2,win下hadoop,并为win的环境变量配置had ...
- windows平台使用spark-submit以client方式提交spark应用到standalone集群
1.spark应用打包,我喜欢打带依赖的,这样省事. 2.使用spark-submit.bat 提交应用,代码如下: for /f "tokens=1,2 delims==" %% ...
- 【Hadoop离线基础总结】Yarn集群的资源调度
Yarn集群的资源调度 介绍 概述 Yarn是 Hadoop 2.x 引入的新的资源管理系统模块,主要用于管理集群当中的资源(主要是服务器的各种硬件资源,比如内存.CPU等),它不光管理硬件资源,还管 ...
- Spark程序提交到Yarn集群时所遇异常
Exception 1:当我们将任务提交给Spark Yarn集群时,大多会出现以下异常,如下: 14/08/09 11:45:32 WARN component.AbstractLifeCycle: ...
- spark yarn 集群提交kafka代码
配置好hadoop的环境,具体根据http://blog.csdn.net/u010638969/article/details/51283216博客所写的进行配置. 运行start-dfs.sh启动 ...
- Spark on Yarn 集群运行要点
实验版本:spark-1.6.0-bin-hadoop2.6 本次实验主要是想在已有的Hadoop集群上使用Spark,无需过多配置 1.下载&解压到一台使用spark的机器上即可 2.修改配 ...
随机推荐
- 【Termux】使用指南
直入主题: 1.1 下载安装 Google下载(有条件的用!) F-droid下载(建议使用!) ps:不建议去酷安下载! 1.2 配置 apt update && apt upgra ...
- 第八章节 BJROBOT hector 算法构建地图【ROS全开源阿克曼转向智能网联无人驾驶车】
1.把小车平放在地板上,用资料里的虚拟机,打开一个终端 ssh 过去主控端启动roslaunch znjrobot bringup.launch. 2.在虚拟机端打开一个终端,ssh 过去主控端启动r ...
- Spring boot JPA读取数据库方法
方法1: 1 StringBuffer sb = new StringBuffer(300); 2 sb.append("SELECT v.id, v.container_number, v ...
- ES6 浅谈Reflect
Reflect 属于一个静态类,不能通过new的方法去创建实例,只能调用静态类中的一些静态方法,Reflect内部封装了一系列对对象的底层操作, Reflect 成员方法就是Proxy处理对象的默认实 ...
- Logstash学习之路（一）Logstash的安装
一.Logstash简介 Logstash 是一个实时数据收集引擎,可收集各类型数据并对其进行分析,过滤和归纳.按照自己条件分析过滤出符合数据导入到可视化界面.它可以实现多样化的数据源数据全量或增量传 ...
- Spark算子使用
一.spark的算子分类 转换算子和行动算子 转换算子:在使用的时候,spark是不会真正执行,直到需要行动算子之后才会执行.在spark中每一个算子在计算之后就会产生一个新的RDD. 二.在编写sp ...
- Debian9 升级至 Debian10
前言 目前国内云服务商提供的镜像最新只有 9 , 本文讲解升级至 10 的方法 正文 查看当前版本 lsb_release -a No LSB modules are available. Distr ...
- git的使用命令总结
git一般方法git init 本地目录生成仓库git clone +项目url地址 克隆远程仓库git status 查看状态git pull 拉取 拉取的代码在用户家目录下git push 上传g ...
- #2020征文-开发板# 用鸿蒙开发AI应用（五）HDF 驱动补光灯
目录: 前言 硬件准备 HDF 驱动开发 总结 前言上一篇,我们在鸿蒙上运行了第一个程序,这一篇我们来编写一个驱动开启摄像头的红外补光灯,顺便熟悉一下鸿蒙上的 HDF 驱动开发. 硬件准备先查一下原理 ...
- Linux find 命令的初步实现（C++）
Implement a myfind command following the find command in UNIX operating system. The myfind command s ...