spark streaming 优雅的停止方法失效,无法停止程序运行

问题是:
按照如下触发式停止方法,在运行一段时间,比如一天后,程序不能停止。但是在IDEA测试时可以实现停止,在运行时间不长比如一两个小时后也可以停止。这是为什么。

    def stopByMarkFile(streamContext: StreamingContext, log: Logger) = {
val intervalMills = 10 * 1000 // 每隔10秒扫描一次消息是否存在
var isStop = false
val hdfs_file_path = "hdfs://0.0.01:9000/lserver/stop" //判断消息文件是否存在,如果存在就停止


while (!isStop) {
  isStop = streamContext.awaitTerminationOrTimeout(intervalMills)
  if (!isStop && isExistsMarkFile(hdfs_file_path)) {
    log.warn("2秒后开始关闭sparstreaming程序.....")
    Thread.sleep(2000)
    streamContext.stop(true, true)
  }
}

}

// 指定目录是否存在文件
def isExistsMarkFile(hdfs_file_path:String):Boolean={
val conf = new Configuration()
val path = new Path(hdfs_file_path)
val fs = path.getFileSystem(conf)
return fs.exists(path)
}

1个回答

打开任务管理器,找到运行程序强行停止

Csdn user default icon
上传中...
上传图片
插入图片
抄袭、复制答案,以达到刷声望分或其他目的的行为,在CSDN问答是严格禁止的,一经发现立刻封号。是时候展现真正的技术了!
其他相关推荐
Spark Streaming作业如何停止
RT,我用submit提交作业加了& 现在 在spark history上面看到还在运行 我现在想关闭这个作业 提交的方式为LOCAL
spark streaming实时分析去重问题
spark streaming实时分析处理时,处理的数据可能会出现重复,需要根据唯一的key进行处理,谁知道怎么处理
spark streaming如何更好的计算关系型数据库中数据?
各位大虾过来围观一下。 spark streaming在计算日志时通常会使用kafka+spark的架构, 目前很少看到有大虾讲spark streaming计算关系型数据库中的数据。 希望有大虾过来围观讨论,如何更好的把关系型数据库中的数据同步至spark中, 进行实时计算。有什么更好的架构或者开源软件的解决方案
Spark Streaming 交互式查询问题
假设我的通过Spark Streaming 来分析用户实时提交过来的数据,数据包含<uid, time,等其他信息>, 假设.对应 spark代码为 ssc.socketTextStream("...",port) 然后,我想实现基于用户ID的查询 var qid = “u_123“//从控制台中读入待查询的uid ssc.filter(uid==qid) ssc.start() ssc.awaitTermination() 好问题来了,该如何实现交互式的根据用户输入的查询uid,返回流上的查询结果(如何在不需修改太多代码的情况下实现呢?)
spark streaming 报错
Caused by: java.lang.ClassNotFoundException: org.apache.spark.streaming.kafka.KafkaUtils
java 后台查询数据使用spark Streaming处理
哪位大神知道怎么使用spark Streaming处理从数据库查询出来的数据,然后传给前台吗
spark streaming监控hdfs的文件变化
spark streaming中有对hdfs中新增文件的监控,但是如何对具体的某个文件进行监控呢,比如文件a后面增加了一行,如何才能get到这个信息呢 ![图片说明](https://img-ask.csdn.net/upload/201610/08/1475916517_4375.jpg)
在spark streaming中实时更新mllib的ALS算法的模型遇到的问题!
![图片说明](https://img-ask.csdn.net/upload/201506/15/1434358402_24697.jpg) ![图片说明](https://img-ask.csdn.net/upload/201506/15/1434358368_454177.jpg) ![图片说明](https://img-ask.csdn.net/upload/201506/15/1434358416_667645.jpg) 在spark streaming中使用ALS算法,实现模型的实时更新有人了解吗? 总是出ERROR [dag-scheduler-event-loop] scheduler.DAGSchedulerEventProcessLoop (Logging.scala:logError(96)) - DAGSchedulerEventProcessLoop failed; shutting down SparkContext 这个异常是什么意思?网上找了好久都没解决。。快疯了 大概就是上面几张图描述的那样子,求教育!
spark streaming运行一段时间报以下异常,怎么解决
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 2 in stage 1568735.0 failed 4 times, most recent failure: Lost task 2.3 in stage 1568735.0 (TID 11808399, iZ94pshi327Z): java.lang.Exception: Could not compute split, block input-0-1438413230200 not found at org.apache.spark.rdd.BlockRDD.compute(BlockRDD.scala:51) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) at org.apache.spark.scheduler.Task.run(Task.scala:64) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:744) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1204) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1193) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1192) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1192) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:693) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:693) at scala.Option.foreach(Option.scala:236) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:693) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1393) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1354) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) 15/08/01 08:53:09 WARN AkkaUtils: Error sending message [message = Heartbeat(0,[Lscala.Tuple2;@544fc1ff,BlockManagerId(0, iZ94w2tczvjZ, 41595))] in 2 attempts java.util.concurrent.TimeoutException: Futures timed out after [30 seconds] at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219) at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223) at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107) at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53) at scala.concurrent.Await$.result(package.scala:107) at org.apache.spark.util.AkkaUtils$.askWithReply(AkkaUtils.scala:195) at org.apache.spark.executor.Executor$$anon$1.run(Executor.scala:427) 15/08/01 08:53:28 WARN AkkaUtils: Error sending message [message = UpdateBlockInfo(BlockManagerId(0, iZ94w2tczvjZ, 41595),input-0-1438385673800,StorageLevel(false, false, false, false, 1),0,0,0)] in 1 attempts java.util.concurrent.TimeoutException: Futures timed out after [30 seconds] at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219) at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223) at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107) at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53) at scala.concurrent.Await$.result(package.scala:107) at org.apache.spark.util.AkkaUtils$.askWithReply(AkkaUtils.scala:195) at org.apache.spark.storage.BlockManagerMaster.askDriverWithReply(BlockManagerMaster.scala:221) at org.apache.spark.storage.BlockManagerMaster.updateBlockInfo(BlockManagerMaster.scala:62) at org.apache.spark.storage.BlockManager.org$apache$spark$storage$BlockManager$$tryToReportBlockStatus(BlockManager.scala:384) at org.apache.spark.storage.BlockManager.reportBlockStatus(BlockManager.scala:360) at org.apache.spark.storage.BlockManager.dropOldBlocks(BlockManager.scala:1138) at org.apache.spark.storage.BlockManager.org$apache$spark$storage$BlockManager$$dropOldNonBroadcastBlocks(BlockManager.scala:1115) at org.apache.spark.storage.BlockManager$$anonfun$1.apply$mcVJ$sp(BlockManager.scala:149) at org.apache.spark.util.MetadataCleaner$$anon$1.run(MetadataCleaner.scala:43) at java.util.TimerThread.mainLoop(Timer.java:555) at java.util.TimerThread.run(Timer.java:505) 15/08/01 08:53:42 WARN AkkaUtils: Error sending message [message = Heartbeat(0,[Lscala.Tuple2;@544fc1ff,BlockManagerId(0, iZ94w2tczvjZ, 41595))] in 3 attempts java.util.concurrent.TimeoutException: Futures timed out after [30 seconds] at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219) at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223) at scala.concurrent.Await$$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.util.AkkaUtils$.askWithReply(AkkaUtils.scala:195) at org.apache.spark.executor.Executor$$anon$1.run(Executor.scala:427) 15/08/01 08:53:45 WARN Executor: Issue communicating with driver in heartbeater org.apache.spark.SparkException: Error sending message [message = Heartbeat(0,[Lscala.Tuple2;@544fc1ff,BlockManagerId(0, iZ94w2tczvjZ, 41595))] at org.apache.spark.util.AkkaUtils$.askWithReply(AkkaUtils.scala:209) at org.apache.spark.executor.Executor$$anon$1.run(Executor.scala:427) Caused by: java.util.concurrent.TimeoutException: Futures timed out after [30 seconds] at scala.concurrent.impl.Promise$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.util.AkkaUtils$.askWithReply(AkkaUtils.scala:195) ... 1 more
六台机器集群,40M数据就报错,spark streaming运行例子程序wordcount
请大神帮忙解决一下:六台机器,SparkStreaming的例子程序,运行在yarn上四个计算节点(nodemanager),每台8G内存,i7处理器,想测测性能。 自己写了socket一直向一个端口发送数据,spark 接收并处理 运行十几分钟汇报错:WARN scheduler TaskSetManagerost task 0.1 in stage 265.0 :java.lang.Exception:Could not compute split ,block input-0-145887651600 not found![图片说明](https://img-ask.csdn.net/upload/201603/29/1459223107_940575.png)
Spark Streaming读取kafka数据解析后写入ES,处理效率太低太慢
环境: * Kafka 0.10+(不影响) * Spark 2.4.0 + Yarn * ES 6.5.4 问题: 从Kafka读取获取消息,然后进行简单过滤清晰操作后,将消息写入到ES中,发现处理效率很低, Kafka有三个partition maxRatePerPartition=2000 batchInterval=1s //这种情况下刚刚好,就是处理延迟在1s左右浮动,不会出现任务堆积的情况 //此时处理配置 //num_executor=3 //executor_core=8 然后将读数据的maxRatePerPartition增大到10000乃至20000,发现处理速度始终没有变化 期间将num_executor设置为8,executor_core设置为8,还是没啥用 还增加了设置: ```java conf.set("spark.streaming,concurrentJobs","20") conf.set("spark.local.wait","100ms") ``` 还是没啥变化,大佬们,到底要咋调啊
spark structured streaming实现每30秒计算前30分钟的用户增长率
spark structured streaming实现每30秒计算前30分钟的用户增长率,spark structured stream是否可以实现?如何实现
在使用Spark Streaming向HDFS中保存数据时,文件内容会被覆盖掉,怎么解决?
我的Spark Streaming代码如下所示: ``` val lines=FlumeUtils.createStream(ssc,"hdp2.domain",22222,StorageLevel.MEMORY_AND_DISK_SER_2) val words = lines.filter(examtep(_)) words.foreachRDD(exam(_)) //some other code def exam(rdd:RDD[SparkFlumeEvent]):Unit={ if(rdd.count()>0) { println("****Something*****") val newrdd=rdd.map(sfe=>{ val tmp=new String(sfe.event.getBody.array()) tmp }) newrdd.saveAsTextFile("/user/spark/appoutput/Temperaturetest") } } ``` 当words.foreachRDD(exam(_))中每次执行exam()方法的时候,都会执行newrdd.saveAsTextFile("/user/''''''"),但是HDFS上Temperaturetest文件夹里的内容每次都会被覆盖掉,只保存着最后一次saveAsTextFIle的内容,怎样才能让所有数据都存储到Temperaturetest中呢??
能否用spark streaming和flume或kafka对实时网络数据进行检测
目前已经有一个训练好的机器学习分类模型,存在于HDFS上,可以对LibSVMFile格式的数据进行检测。它是对很多的一段时间内的流量数据(比如1s,很多个1s)提取特征训练之后得到的。 我们知道streaming是将输入流分成微切片,微切片能否可以是从pcap文件读取呢?因为提取特征包括训练模型的时候是需要对pcap文件操作的。 flume和kafka都是可以传输txt的,能不能传输pcap文件呢?要将输入的网络数据流像tcpdump一样可以存为pcap文件,又有像kafka一样的缓存功能可以用哪些技术呢? 最后就是能否用spark streaming利用分类模型对网络数据流进行提特征并预测,而且与防火墙联动,这在技术上是否可行?
spark streaming直连kafka,手动更新offset,offset前面出现一段乱码
如题,我是通过 offsetsRanges.foreach(offsetRange => { val path = s"${zkPath}/${offsetRange.partition}" val offset = offsetRange.fromOffset.toString // 将该 partition 的 offset 保存到 zookeeper logger.warn(s"向zookeeper中topic:${offsetRange.topic}的${offsetRange.partition}分区写入offset:${offset}") ZkUtils.updatePersistentPath(zkClient, path, offset) } ) 手动更新offset的,但是我用zkClient查看的时候发现,在offset前面多了一段乱码,导致用kafka监控程序的时候,无法监控
sparkStreaming 接收ServerSocket程序写过来的字符串
spark Streaming 接收ServerSocket程序写过来的字符串 ServerSocket 怎么写呢?!!!!!本人新手请指教
socket producer scala
spark streaming 中如何使用socket做producer,求这个程序的scala版本
spark 读取不到hive metastore 获取不到数据库
直接上异常 ``` Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties SLF4J: Class path contains multiple SLF4J bindings. SLF4J: Found binding in [jar:file:/data01/hadoop/yarn/local/filecache/355/spark2-hdp-yarn-archive.tar.gz/slf4j-log4j12-1.7.16.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: Found binding in [jar:file:/usr/hdp/2.6.5.0-292/hadoop/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation. SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory] 19/08/13 19:53:17 INFO SignalUtils: Registered signal handler for TERM 19/08/13 19:53:17 INFO SignalUtils: Registered signal handler for HUP 19/08/13 19:53:17 INFO SignalUtils: Registered signal handler for INT 19/08/13 19:53:17 INFO SecurityManager: Changing view acls to: yarn,hdfs 19/08/13 19:53:17 INFO SecurityManager: Changing modify acls to: yarn,hdfs 19/08/13 19:53:17 INFO SecurityManager: Changing view acls groups to: 19/08/13 19:53:17 INFO SecurityManager: Changing modify acls groups to: 19/08/13 19:53:17 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(yarn, hdfs); groups with view permissions: Set(); users with modify permissions: Set(yarn, hdfs); groups with modify permissions: Set() 19/08/13 19:53:18 INFO ApplicationMaster: Preparing Local resources 19/08/13 19:53:19 INFO ApplicationMaster: ApplicationAttemptId: appattempt_1565610088533_0087_000001 19/08/13 19:53:19 INFO ApplicationMaster: Starting the user application in a separate Thread 19/08/13 19:53:19 INFO ApplicationMaster: Waiting for spark context initialization... 19/08/13 19:53:19 INFO SparkContext: Running Spark version 2.3.0.2.6.5.0-292 19/08/13 19:53:19 INFO SparkContext: Submitted application: voice_stream 19/08/13 19:53:19 INFO SecurityManager: Changing view acls to: yarn,hdfs 19/08/13 19:53:19 INFO SecurityManager: Changing modify acls to: yarn,hdfs 19/08/13 19:53:19 INFO SecurityManager: Changing view acls groups to: 19/08/13 19:53:19 INFO SecurityManager: Changing modify acls groups to: 19/08/13 19:53:19 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(yarn, hdfs); groups with view permissions: Set(); users with modify permissions: Set(yarn, hdfs); groups with modify permissions: Set() 19/08/13 19:53:19 INFO Utils: Successfully started service 'sparkDriver' on port 20410. 19/08/13 19:53:19 INFO SparkEnv: Registering MapOutputTracker 19/08/13 19:53:19 INFO SparkEnv: Registering BlockManagerMaster 19/08/13 19:53:19 INFO BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information 19/08/13 19:53:19 INFO BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up 19/08/13 19:53:19 INFO DiskBlockManager: Created local directory at /data01/hadoop/yarn/local/usercache/hdfs/appcache/application_1565610088533_0087/blockmgr-94d35b97-43b2-496e-a4cb-73ecd3ed186c 19/08/13 19:53:19 INFO MemoryStore: MemoryStore started with capacity 366.3 MB 19/08/13 19:53:19 INFO SparkEnv: Registering OutputCommitCoordinator 19/08/13 19:53:19 INFO JettyUtils: Adding filter: org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter 19/08/13 19:53:19 INFO Utils: Successfully started service 'SparkUI' on port 28852. 19/08/13 19:53:19 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://datanode02:28852 19/08/13 19:53:19 INFO YarnClusterScheduler: Created YarnClusterScheduler 19/08/13 19:53:20 INFO SchedulerExtensionServices: Starting Yarn extension services with app application_1565610088533_0087 and attemptId Some(appattempt_1565610088533_0087_000001) 19/08/13 19:53:20 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 31984. 19/08/13 19:53:20 INFO NettyBlockTransferService: Server created on datanode02:31984 19/08/13 19:53:20 INFO BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy 19/08/13 19:53:20 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, datanode02, 31984, None) 19/08/13 19:53:20 INFO BlockManagerMasterEndpoint: Registering block manager datanode02:31984 with 366.3 MB RAM, BlockManagerId(driver, datanode02, 31984, None) 19/08/13 19:53:20 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, datanode02, 31984, None) 19/08/13 19:53:20 INFO BlockManager: Initialized BlockManager: BlockManagerId(driver, datanode02, 31984, None) 19/08/13 19:53:20 INFO EventLoggingListener: Logging events to hdfs:/spark2-history/application_1565610088533_0087_1 19/08/13 19:53:20 INFO ApplicationMaster: =============================================================================== YARN executor launch context: env: CLASSPATH -> {{PWD}}<CPS>{{PWD}}/__spark_conf__<CPS>{{PWD}}/__spark_libs__/*<CPS>/usr/hdp/2.6.5.0-292/hadoop/conf<CPS>/usr/hdp/2.6.5.0-292/hadoop/*<CPS>/usr/hdp/2.6.5.0-292/hadoop/lib/*<CPS>/usr/hdp/current/hadoop-hdfs-client/*<CPS>/usr/hdp/current/hadoop-hdfs-client/lib/*<CPS>/usr/hdp/current/hadoop-yarn-client/*<CPS>/usr/hdp/current/hadoop-yarn-client/lib/*<CPS>/usr/hdp/current/ext/hadoop/*<CPS>$PWD/mr-framework/hadoop/share/hadoop/mapreduce/*:$PWD/mr-framework/hadoop/share/hadoop/mapreduce/lib/*:$PWD/mr-framework/hadoop/share/hadoop/common/*:$PWD/mr-framework/hadoop/share/hadoop/common/lib/*:$PWD/mr-framework/hadoop/share/hadoop/yarn/*:$PWD/mr-framework/hadoop/share/hadoop/yarn/lib/*:$PWD/mr-framework/hadoop/share/hadoop/hdfs/*:$PWD/mr-framework/hadoop/share/hadoop/hdfs/lib/*:$PWD/mr-framework/hadoop/share/hadoop/tools/lib/*:/usr/hdp/2.6.5.0-292/hadoop/lib/hadoop-lzo-0.6.0.2.6.5.0-292.jar:/etc/hadoop/conf/secure:/usr/hdp/current/ext/hadoop/*<CPS>{{PWD}}/__spark_conf__/__hadoop_conf__ SPARK_YARN_STAGING_DIR -> *********(redacted) SPARK_USER -> *********(redacted) command: LD_LIBRARY_PATH="/usr/hdp/current/hadoop-client/lib/native:/usr/hdp/current/hadoop-client/lib/native/Linux-amd64-64:$LD_LIBRARY_PATH" \ {{JAVA_HOME}}/bin/java \ -server \ -Xmx5120m \ -Djava.io.tmpdir={{PWD}}/tmp \ '-Dspark.history.ui.port=18081' \ '-Dspark.rpc.message.maxSize=100' \ -Dspark.yarn.app.container.log.dir=<LOG_DIR> \ -XX:OnOutOfMemoryError='kill %p' \ org.apache.spark.executor.CoarseGrainedExecutorBackend \ --driver-url \ spark://CoarseGrainedScheduler@datanode02:20410 \ --executor-id \ <executorId> \ --hostname \ <hostname> \ --cores \ 2 \ --app-id \ application_1565610088533_0087 \ --user-class-path \ file:$PWD/__app__.jar \ --user-class-path \ file:$PWD/hadoop-common-2.7.3.jar \ --user-class-path \ file:$PWD/guava-12.0.1.jar \ --user-class-path \ file:$PWD/hbase-server-1.2.8.jar \ --user-class-path \ file:$PWD/hbase-protocol-1.2.8.jar \ --user-class-path \ file:$PWD/hbase-client-1.2.8.jar \ --user-class-path \ file:$PWD/hbase-common-1.2.8.jar \ --user-class-path \ file:$PWD/mysql-connector-java-5.1.44-bin.jar \ --user-class-path \ file:$PWD/spark-streaming-kafka-0-8-assembly_2.11-2.3.2.jar \ --user-class-path \ file:$PWD/spark-examples_2.11-1.6.0-typesafe-001.jar \ --user-class-path \ file:$PWD/fastjson-1.2.7.jar \ 1><LOG_DIR>/stdout \ 2><LOG_DIR>/stderr resources: spark-streaming-kafka-0-8-assembly_2.11-2.3.2.jar -> resource { scheme: "hdfs" host: "CID-042fb939-95b4-4b74-91b8-9f94b999bdf7" port: -1 file: "/user/hdfs/.sparkStaging/application_1565610088533_0087/spark-streaming-kafka-0-8-assembly_2.11-2.3.2.jar" } size: 12271027 timestamp: 1565697198603 type: FILE visibility: PRIVATE spark-examples_2.11-1.6.0-typesafe-001.jar -> resource { scheme: "hdfs" host: "CID-042fb939-95b4-4b74-91b8-9f94b999bdf7" port: -1 file: "/user/hdfs/.sparkStaging/application_1565610088533_0087/spark-examples_2.11-1.6.0-typesafe-001.jar" } size: 1867746 timestamp: 1565697198751 type: FILE visibility: PRIVATE hbase-server-1.2.8.jar -> resource { scheme: "hdfs" host: "CID-042fb939-95b4-4b74-91b8-9f94b999bdf7" port: -1 file: "/user/hdfs/.sparkStaging/application_1565610088533_0087/hbase-server-1.2.8.jar" } size: 4197896 timestamp: 1565697197770 type: FILE visibility: PRIVATE hbase-common-1.2.8.jar -> resource { scheme: "hdfs" host: "CID-042fb939-95b4-4b74-91b8-9f94b999bdf7" port: -1 file: "/user/hdfs/.sparkStaging/application_1565610088533_0087/hbase-common-1.2.8.jar" } size: 570163 timestamp: 1565697198318 type: FILE visibility: PRIVATE __app__.jar -> resource { scheme: "hdfs" host: "CID-042fb939-95b4-4b74-91b8-9f94b999bdf7" port: -1 file: "/user/hdfs/.sparkStaging/application_1565610088533_0087/spark_history_data2.jar" } size: 44924 timestamp: 1565697197260 type: FILE visibility: PRIVATE guava-12.0.1.jar -> resource { scheme: "hdfs" host: "CID-042fb939-95b4-4b74-91b8-9f94b999bdf7" port: -1 file: "/user/hdfs/.sparkStaging/application_1565610088533_0087/guava-12.0.1.jar" } size: 1795932 timestamp: 1565697197614 type: FILE visibility: PRIVATE hbase-client-1.2.8.jar -> resource { scheme: "hdfs" host: "CID-042fb939-95b4-4b74-91b8-9f94b999bdf7" port: -1 file: "/user/hdfs/.sparkStaging/application_1565610088533_0087/hbase-client-1.2.8.jar" } size: 1306401 timestamp: 1565697198180 type: FILE visibility: PRIVATE __spark_conf__ -> resource { scheme: "hdfs" host: "CID-042fb939-95b4-4b74-91b8-9f94b999bdf7" port: -1 file: "/user/hdfs/.sparkStaging/application_1565610088533_0087/__spark_conf__.zip" } size: 273513 timestamp: 1565697199131 type: ARCHIVE visibility: PRIVATE fastjson-1.2.7.jar -> resource { scheme: "hdfs" host: "CID-042fb939-95b4-4b74-91b8-9f94b999bdf7" port: -1 file: "/user/hdfs/.sparkStaging/application_1565610088533_0087/fastjson-1.2.7.jar" } size: 417221 timestamp: 1565697198865 type: FILE visibility: PRIVATE hbase-protocol-1.2.8.jar -> resource { scheme: "hdfs" host: "CID-042fb939-95b4-4b74-91b8-9f94b999bdf7" port: -1 file: "/user/hdfs/.sparkStaging/application_1565610088533_0087/hbase-protocol-1.2.8.jar" } size: 4366252 timestamp: 1565697198023 type: FILE visibility: PRIVATE __spark_libs__ -> resource { scheme: "hdfs" host: "CID-042fb939-95b4-4b74-91b8-9f94b999bdf7" port: -1 file: "/hdp/apps/2.6.5.0-292/spark2/spark2-hdp-yarn-archive.tar.gz" } size: 227600110 timestamp: 1549953820247 type: ARCHIVE visibility: PUBLIC mysql-connector-java-5.1.44-bin.jar -> resource { scheme: "hdfs" host: "CID-042fb939-95b4-4b74-91b8-9f94b999bdf7" port: -1 file: "/user/hdfs/.sparkStaging/application_1565610088533_0087/mysql-connector-java-5.1.44-bin.jar" } size: 999635 timestamp: 1565697198445 type: FILE visibility: PRIVATE hadoop-common-2.7.3.jar -> resource { scheme: "hdfs" host: "CID-042fb939-95b4-4b74-91b8-9f94b999bdf7" port: -1 file: "/user/hdfs/.sparkStaging/application_1565610088533_0087/hadoop-common-2.7.3.jar" } size: 3479293 timestamp: 1565697197476 type: FILE visibility: PRIVATE =============================================================================== 19/08/13 19:53:20 INFO RMProxy: Connecting to ResourceManager at namenode02/10.1.38.38:8030 19/08/13 19:53:20 INFO YarnRMClient: Registering the ApplicationMaster 19/08/13 19:53:20 INFO YarnAllocator: Will request 3 executor container(s), each with 2 core(s) and 5632 MB memory (including 512 MB of overhead) 19/08/13 19:53:20 INFO YarnSchedulerBackend$YarnSchedulerEndpoint: ApplicationMaster registered as NettyRpcEndpointRef(spark://YarnAM@datanode02:20410) 19/08/13 19:53:20 INFO YarnAllocator: Submitted 3 unlocalized container requests. 19/08/13 19:53:20 INFO ApplicationMaster: Started progress reporter thread with (heartbeat : 3000, initial allocation : 200) intervals 19/08/13 19:53:20 INFO AMRMClientImpl: Received new token for : datanode03:45454 19/08/13 19:53:21 INFO YarnAllocator: Launching container container_e20_1565610088533_0087_01_000002 on host datanode03 for executor with ID 1 19/08/13 19:53:21 INFO YarnAllocator: Received 1 containers from YARN, launching executors on 1 of them. 19/08/13 19:53:21 INFO ContainerManagementProtocolProxy: yarn.client.max-cached-nodemanagers-proxies : 0 19/08/13 19:53:21 INFO ContainerManagementProtocolProxy: Opening proxy : datanode03:45454 19/08/13 19:53:21 INFO AMRMClientImpl: Received new token for : datanode01:45454 19/08/13 19:53:21 INFO YarnAllocator: Launching container container_e20_1565610088533_0087_01_000003 on host datanode01 for executor with ID 2 19/08/13 19:53:21 INFO YarnAllocator: Received 1 containers from YARN, launching executors on 1 of them. 19/08/13 19:53:21 INFO ContainerManagementProtocolProxy: yarn.client.max-cached-nodemanagers-proxies : 0 19/08/13 19:53:21 INFO ContainerManagementProtocolProxy: Opening proxy : datanode01:45454 19/08/13 19:53:22 INFO AMRMClientImpl: Received new token for : datanode02:45454 19/08/13 19:53:22 INFO YarnAllocator: Launching container container_e20_1565610088533_0087_01_000004 on host datanode02 for executor with ID 3 19/08/13 19:53:22 INFO YarnAllocator: Received 1 containers from YARN, launching executors on 1 of them. 19/08/13 19:53:22 INFO ContainerManagementProtocolProxy: yarn.client.max-cached-nodemanagers-proxies : 0 19/08/13 19:53:22 INFO ContainerManagementProtocolProxy: Opening proxy : datanode02:45454 19/08/13 19:53:24 INFO YarnSchedulerBackend$YarnDriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (10.1.198.144:41122) with ID 1 19/08/13 19:53:25 INFO YarnSchedulerBackend$YarnDriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (10.1.229.163:24656) with ID 3 19/08/13 19:53:25 INFO BlockManagerMasterEndpoint: Registering block manager datanode03:3328 with 2.5 GB RAM, BlockManagerId(1, datanode03, 3328, None) 19/08/13 19:53:25 INFO BlockManagerMasterEndpoint: Registering block manager datanode02:28863 with 2.5 GB RAM, BlockManagerId(3, datanode02, 28863, None) 19/08/13 19:53:25 INFO YarnSchedulerBackend$YarnDriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (10.1.229.158:64276) with ID 2 19/08/13 19:53:25 INFO YarnClusterSchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.8 19/08/13 19:53:25 INFO YarnClusterScheduler: YarnClusterScheduler.postStartHook done 19/08/13 19:53:25 INFO BlockManagerMasterEndpoint: Registering block manager datanode01:20487 with 2.5 GB RAM, BlockManagerId(2, datanode01, 20487, None) 19/08/13 19:53:25 WARN SparkContext: Using an existing SparkContext; some configuration may not take effect. 19/08/13 19:53:25 INFO SparkContext: Starting job: start at VoiceApplication2.java:128 19/08/13 19:53:25 INFO DAGScheduler: Registering RDD 1 (start at VoiceApplication2.java:128) 19/08/13 19:53:25 INFO DAGScheduler: Got job 0 (start at VoiceApplication2.java:128) with 20 output partitions 19/08/13 19:53:25 INFO DAGScheduler: Final stage: ResultStage 1 (start at VoiceApplication2.java:128) 19/08/13 19:53:25 INFO DAGScheduler: Parents of final stage: List(ShuffleMapStage 0) 19/08/13 19:53:25 INFO DAGScheduler: Missing parents: List(ShuffleMapStage 0) 19/08/13 19:53:26 INFO DAGScheduler: Submitting ShuffleMapStage 0 (MapPartitionsRDD[1] at start at VoiceApplication2.java:128), which has no missing parents 19/08/13 19:53:26 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 3.1 KB, free 366.3 MB) 19/08/13 19:53:26 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 2011.0 B, free 366.3 MB) 19/08/13 19:53:26 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on datanode02:31984 (size: 2011.0 B, free: 366.3 MB) 19/08/13 19:53:26 INFO SparkContext: Created broadcast 0 from broadcast at DAGScheduler.scala:1039 19/08/13 19:53:26 INFO DAGScheduler: Submitting 50 missing tasks from ShuffleMapStage 0 (MapPartitionsRDD[1] at start at VoiceApplication2.java:128) (first 15 tasks are for partitions Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) 19/08/13 19:53:26 INFO YarnClusterScheduler: Adding task set 0.0 with 50 tasks 19/08/13 19:53:26 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, datanode02, executor 3, partition 0, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1, datanode03, executor 1, partition 1, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 2.0 in stage 0.0 (TID 2, datanode01, executor 2, partition 2, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 3.0 in stage 0.0 (TID 3, datanode02, executor 3, partition 3, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 4.0 in stage 0.0 (TID 4, datanode03, executor 1, partition 4, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 5.0 in stage 0.0 (TID 5, datanode01, executor 2, partition 5, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on datanode02:28863 (size: 2011.0 B, free: 2.5 GB) 19/08/13 19:53:26 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on datanode03:3328 (size: 2011.0 B, free: 2.5 GB) 19/08/13 19:53:26 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on datanode01:20487 (size: 2011.0 B, free: 2.5 GB) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 6.0 in stage 0.0 (TID 6, datanode02, executor 3, partition 6, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 7.0 in stage 0.0 (TID 7, datanode02, executor 3, partition 7, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Finished task 3.0 in stage 0.0 (TID 3) in 693 ms on datanode02 (executor 3) (1/50) 19/08/13 19:53:26 INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 712 ms on datanode02 (executor 3) (2/50) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 8.0 in stage 0.0 (TID 8, datanode02, executor 3, partition 8, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Finished task 7.0 in stage 0.0 (TID 7) in 21 ms on datanode02 (executor 3) (3/50) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 9.0 in stage 0.0 (TID 9, datanode02, executor 3, partition 9, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Finished task 6.0 in stage 0.0 (TID 6) in 26 ms on datanode02 (executor 3) (4/50) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 10.0 in stage 0.0 (TID 10, datanode02, executor 3, partition 10, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Finished task 8.0 in stage 0.0 (TID 8) in 23 ms on datanode02 (executor 3) (5/50) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 11.0 in stage 0.0 (TID 11, datanode02, executor 3, partition 11, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Finished task 9.0 in stage 0.0 (TID 9) in 25 ms on datanode02 (executor 3) (6/50) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 12.0 in stage 0.0 (TID 12, datanode02, executor 3, partition 12, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Finished task 10.0 in stage 0.0 (TID 10) in 18 ms on datanode02 (executor 3) (7/50) 19/08/13 19:53:26 INFO TaskSetManager: Finished task 11.0 in stage 0.0 (TID 11) in 14 ms on datanode02 (executor 3) (8/50) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 13.0 in stage 0.0 (TID 13, datanode02, executor 3, partition 13, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 14.0 in stage 0.0 (TID 14, datanode02, executor 3, partition 14, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Finished task 12.0 in stage 0.0 (TID 12) in 16 ms on datanode02 (executor 3) (9/50) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 15.0 in stage 0.0 (TID 15, datanode02, executor 3, partition 15, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Finished task 13.0 in stage 0.0 (TID 13) in 22 ms on datanode02 (executor 3) (10/50) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 16.0 in stage 0.0 (TID 16, datanode02, executor 3, partition 16, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Finished task 14.0 in stage 0.0 (TID 14) in 16 ms on datanode02 (executor 3) (11/50) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 17.0 in stage 0.0 (TID 17, datanode02, executor 3, partition 17, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Finished task 15.0 in stage 0.0 (TID 15) in 13 ms on datanode02 (executor 3) (12/50) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 18.0 in stage 0.0 (TID 18, datanode01, executor 2, partition 18, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 19.0 in stage 0.0 (TID 19, datanode01, executor 2, partition 19, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Finished task 5.0 in stage 0.0 (TID 5) in 787 ms on datanode01 (executor 2) (13/50) 19/08/13 19:53:26 INFO TaskSetManager: Finished task 2.0 in stage 0.0 (TID 2) in 789 ms on datanode01 (executor 2) (14/50) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 20.0 in stage 0.0 (TID 20, datanode03, executor 1, partition 20, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:26 INFO TaskSetManager: Starting task 21.0 in stage 0.0 (TID 21, datanode03, executor 1, partition 21, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 4.0 in stage 0.0 (TID 4) in 905 ms on datanode03 (executor 1) (15/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 1.0 in stage 0.0 (TID 1) in 907 ms on datanode03 (executor 1) (16/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 22.0 in stage 0.0 (TID 22, datanode02, executor 3, partition 22, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 23.0 in stage 0.0 (TID 23, datanode02, executor 3, partition 23, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 24.0 in stage 0.0 (TID 24, datanode01, executor 2, partition 24, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 18.0 in stage 0.0 (TID 18) in 124 ms on datanode01 (executor 2) (17/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 16.0 in stage 0.0 (TID 16) in 134 ms on datanode02 (executor 3) (18/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 25.0 in stage 0.0 (TID 25, datanode01, executor 2, partition 25, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 26.0 in stage 0.0 (TID 26, datanode03, executor 1, partition 26, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 17.0 in stage 0.0 (TID 17) in 134 ms on datanode02 (executor 3) (19/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 20.0 in stage 0.0 (TID 20) in 122 ms on datanode03 (executor 1) (20/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 27.0 in stage 0.0 (TID 27, datanode03, executor 1, partition 27, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 19.0 in stage 0.0 (TID 19) in 127 ms on datanode01 (executor 2) (21/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 21.0 in stage 0.0 (TID 21) in 123 ms on datanode03 (executor 1) (22/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 28.0 in stage 0.0 (TID 28, datanode02, executor 3, partition 28, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 29.0 in stage 0.0 (TID 29, datanode02, executor 3, partition 29, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 22.0 in stage 0.0 (TID 22) in 19 ms on datanode02 (executor 3) (23/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 23.0 in stage 0.0 (TID 23) in 18 ms on datanode02 (executor 3) (24/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 30.0 in stage 0.0 (TID 30, datanode01, executor 2, partition 30, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 31.0 in stage 0.0 (TID 31, datanode01, executor 2, partition 31, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 25.0 in stage 0.0 (TID 25) in 27 ms on datanode01 (executor 2) (25/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 24.0 in stage 0.0 (TID 24) in 29 ms on datanode01 (executor 2) (26/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 32.0 in stage 0.0 (TID 32, datanode02, executor 3, partition 32, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 29.0 in stage 0.0 (TID 29) in 16 ms on datanode02 (executor 3) (27/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 33.0 in stage 0.0 (TID 33, datanode03, executor 1, partition 33, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 26.0 in stage 0.0 (TID 26) in 30 ms on datanode03 (executor 1) (28/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 34.0 in stage 0.0 (TID 34, datanode02, executor 3, partition 34, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 28.0 in stage 0.0 (TID 28) in 21 ms on datanode02 (executor 3) (29/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 35.0 in stage 0.0 (TID 35, datanode03, executor 1, partition 35, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 27.0 in stage 0.0 (TID 27) in 32 ms on datanode03 (executor 1) (30/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 36.0 in stage 0.0 (TID 36, datanode02, executor 3, partition 36, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 32.0 in stage 0.0 (TID 32) in 11 ms on datanode02 (executor 3) (31/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 37.0 in stage 0.0 (TID 37, datanode01, executor 2, partition 37, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 30.0 in stage 0.0 (TID 30) in 18 ms on datanode01 (executor 2) (32/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 38.0 in stage 0.0 (TID 38, datanode01, executor 2, partition 38, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 31.0 in stage 0.0 (TID 31) in 20 ms on datanode01 (executor 2) (33/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 39.0 in stage 0.0 (TID 39, datanode03, executor 1, partition 39, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 33.0 in stage 0.0 (TID 33) in 17 ms on datanode03 (executor 1) (34/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 34.0 in stage 0.0 (TID 34) in 17 ms on datanode02 (executor 3) (35/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 40.0 in stage 0.0 (TID 40, datanode02, executor 3, partition 40, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 41.0 in stage 0.0 (TID 41, datanode03, executor 1, partition 41, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 35.0 in stage 0.0 (TID 35) in 17 ms on datanode03 (executor 1) (36/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 42.0 in stage 0.0 (TID 42, datanode02, executor 3, partition 42, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 36.0 in stage 0.0 (TID 36) in 16 ms on datanode02 (executor 3) (37/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 43.0 in stage 0.0 (TID 43, datanode01, executor 2, partition 43, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 37.0 in stage 0.0 (TID 37) in 16 ms on datanode01 (executor 2) (38/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 44.0 in stage 0.0 (TID 44, datanode02, executor 3, partition 44, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 45.0 in stage 0.0 (TID 45, datanode02, executor 3, partition 45, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 40.0 in stage 0.0 (TID 40) in 14 ms on datanode02 (executor 3) (39/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 42.0 in stage 0.0 (TID 42) in 11 ms on datanode02 (executor 3) (40/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 46.0 in stage 0.0 (TID 46, datanode03, executor 1, partition 46, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 39.0 in stage 0.0 (TID 39) in 20 ms on datanode03 (executor 1) (41/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 47.0 in stage 0.0 (TID 47, datanode03, executor 1, partition 47, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 41.0 in stage 0.0 (TID 41) in 20 ms on datanode03 (executor 1) (42/50) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 48.0 in stage 0.0 (TID 48, datanode01, executor 2, partition 48, PROCESS_LOCAL, 7831 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 49.0 in stage 0.0 (TID 49, datanode01, executor 2, partition 49, PROCESS_LOCAL, 7888 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 43.0 in stage 0.0 (TID 43) in 18 ms on datanode01 (executor 2) (43/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 38.0 in stage 0.0 (TID 38) in 31 ms on datanode01 (executor 2) (44/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 45.0 in stage 0.0 (TID 45) in 11 ms on datanode02 (executor 3) (45/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 44.0 in stage 0.0 (TID 44) in 16 ms on datanode02 (executor 3) (46/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 46.0 in stage 0.0 (TID 46) in 18 ms on datanode03 (executor 1) (47/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 48.0 in stage 0.0 (TID 48) in 15 ms on datanode01 (executor 2) (48/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 47.0 in stage 0.0 (TID 47) in 15 ms on datanode03 (executor 1) (49/50) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 49.0 in stage 0.0 (TID 49) in 25 ms on datanode01 (executor 2) (50/50) 19/08/13 19:53:27 INFO YarnClusterScheduler: Removed TaskSet 0.0, whose tasks have all completed, from pool 19/08/13 19:53:27 INFO DAGScheduler: ShuffleMapStage 0 (start at VoiceApplication2.java:128) finished in 1.174 s 19/08/13 19:53:27 INFO DAGScheduler: looking for newly runnable stages 19/08/13 19:53:27 INFO DAGScheduler: running: Set() 19/08/13 19:53:27 INFO DAGScheduler: waiting: Set(ResultStage 1) 19/08/13 19:53:27 INFO DAGScheduler: failed: Set() 19/08/13 19:53:27 INFO DAGScheduler: Submitting ResultStage 1 (ShuffledRDD[2] at start at VoiceApplication2.java:128), which has no missing parents 19/08/13 19:53:27 INFO MemoryStore: Block broadcast_1 stored as values in memory (estimated size 3.2 KB, free 366.3 MB) 19/08/13 19:53:27 INFO MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 1979.0 B, free 366.3 MB) 19/08/13 19:53:27 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on datanode02:31984 (size: 1979.0 B, free: 366.3 MB) 19/08/13 19:53:27 INFO SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:1039 19/08/13 19:53:27 INFO DAGScheduler: Submitting 20 missing tasks from ResultStage 1 (ShuffledRDD[2] at start at VoiceApplication2.java:128) (first 15 tasks are for partitions Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) 19/08/13 19:53:27 INFO YarnClusterScheduler: Adding task set 1.0 with 20 tasks 19/08/13 19:53:27 INFO TaskSetManager: Starting task 0.0 in stage 1.0 (TID 50, datanode03, executor 1, partition 0, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 1.0 in stage 1.0 (TID 51, datanode02, executor 3, partition 1, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 3.0 in stage 1.0 (TID 52, datanode01, executor 2, partition 3, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 2.0 in stage 1.0 (TID 53, datanode03, executor 1, partition 2, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 4.0 in stage 1.0 (TID 54, datanode02, executor 3, partition 4, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 5.0 in stage 1.0 (TID 55, datanode01, executor 2, partition 5, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on datanode02:28863 (size: 1979.0 B, free: 2.5 GB) 19/08/13 19:53:27 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on datanode01:20487 (size: 1979.0 B, free: 2.5 GB) 19/08/13 19:53:27 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on datanode03:3328 (size: 1979.0 B, free: 2.5 GB) 19/08/13 19:53:27 INFO MapOutputTrackerMasterEndpoint: Asked to send map output locations for shuffle 0 to 10.1.229.163:24656 19/08/13 19:53:27 INFO MapOutputTrackerMasterEndpoint: Asked to send map output locations for shuffle 0 to 10.1.198.144:41122 19/08/13 19:53:27 INFO MapOutputTrackerMasterEndpoint: Asked to send map output locations for shuffle 0 to 10.1.229.158:64276 19/08/13 19:53:27 INFO TaskSetManager: Starting task 7.0 in stage 1.0 (TID 56, datanode03, executor 1, partition 7, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 2.0 in stage 1.0 (TID 53) in 192 ms on datanode03 (executor 1) (1/20) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 8.0 in stage 1.0 (TID 57, datanode03, executor 1, partition 8, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 7.0 in stage 1.0 (TID 56) in 25 ms on datanode03 (executor 1) (2/20) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 6.0 in stage 1.0 (TID 58, datanode02, executor 3, partition 6, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 1.0 in stage 1.0 (TID 51) in 220 ms on datanode02 (executor 3) (3/20) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 14.0 in stage 1.0 (TID 59, datanode03, executor 1, partition 14, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 8.0 in stage 1.0 (TID 57) in 17 ms on datanode03 (executor 1) (4/20) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 16.0 in stage 1.0 (TID 60, datanode03, executor 1, partition 16, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 14.0 in stage 1.0 (TID 59) in 15 ms on datanode03 (executor 1) (5/20) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 16.0 in stage 1.0 (TID 60) in 21 ms on datanode03 (executor 1) (6/20) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 9.0 in stage 1.0 (TID 61, datanode02, executor 3, partition 9, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 4.0 in stage 1.0 (TID 54) in 269 ms on datanode02 (executor 3) (7/20) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 0.0 in stage 1.0 (TID 50) in 339 ms on datanode03 (executor 1) (8/20) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 10.0 in stage 1.0 (TID 62, datanode02, executor 3, partition 10, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 6.0 in stage 1.0 (TID 58) in 56 ms on datanode02 (executor 3) (9/20) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 11.0 in stage 1.0 (TID 63, datanode01, executor 2, partition 11, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 5.0 in stage 1.0 (TID 55) in 284 ms on datanode01 (executor 2) (10/20) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 12.0 in stage 1.0 (TID 64, datanode01, executor 2, partition 12, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 3.0 in stage 1.0 (TID 52) in 287 ms on datanode01 (executor 2) (11/20) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 13.0 in stage 1.0 (TID 65, datanode02, executor 3, partition 13, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 15.0 in stage 1.0 (TID 66, datanode02, executor 3, partition 15, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 10.0 in stage 1.0 (TID 62) in 25 ms on datanode02 (executor 3) (12/20) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 9.0 in stage 1.0 (TID 61) in 29 ms on datanode02 (executor 3) (13/20) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 17.0 in stage 1.0 (TID 67, datanode02, executor 3, partition 17, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 15.0 in stage 1.0 (TID 66) in 13 ms on datanode02 (executor 3) (14/20) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 13.0 in stage 1.0 (TID 65) in 16 ms on datanode02 (executor 3) (15/20) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 18.0 in stage 1.0 (TID 68, datanode02, executor 3, partition 18, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Starting task 19.0 in stage 1.0 (TID 69, datanode01, executor 2, partition 19, NODE_LOCAL, 7638 bytes) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 11.0 in stage 1.0 (TID 63) in 30 ms on datanode01 (executor 2) (16/20) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 12.0 in stage 1.0 (TID 64) in 30 ms on datanode01 (executor 2) (17/20) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 17.0 in stage 1.0 (TID 67) in 17 ms on datanode02 (executor 3) (18/20) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 19.0 in stage 1.0 (TID 69) in 13 ms on datanode01 (executor 2) (19/20) 19/08/13 19:53:27 INFO TaskSetManager: Finished task 18.0 in stage 1.0 (TID 68) in 20 ms on datanode02 (executor 3) (20/20) 19/08/13 19:53:27 INFO YarnClusterScheduler: Removed TaskSet 1.0, whose tasks have all completed, from pool 19/08/13 19:53:27 INFO DAGScheduler: ResultStage 1 (start at VoiceApplication2.java:128) finished in 0.406 s 19/08/13 19:53:27 INFO DAGScheduler: Job 0 finished: start at VoiceApplication2.java:128, took 1.850883 s 19/08/13 19:53:27 INFO ReceiverTracker: Starting 1 receivers 19/08/13 19:53:27 INFO ReceiverTracker: ReceiverTracker started 19/08/13 19:53:27 INFO KafkaInputDStream: Slide time = 60000 ms 19/08/13 19:53:27 INFO KafkaInputDStream: Storage level = Serialized 1x Replicated 19/08/13 19:53:27 INFO KafkaInputDStream: Checkpoint interval = null 19/08/13 19:53:27 INFO KafkaInputDStream: Remember interval = 60000 ms 19/08/13 19:53:27 INFO KafkaInputDStream: Initialized and validated org.apache.spark.streaming.kafka.KafkaInputDStream@5fd3dc81 19/08/13 19:53:27 INFO ForEachDStream: Slide time = 60000 ms 19/08/13 19:53:27 INFO ForEachDStream: Storage level = Serialized 1x Replicated 19/08/13 19:53:27 INFO ForEachDStream: Checkpoint interval = null 19/08/13 19:53:27 INFO ForEachDStream: Remember interval = 60000 ms 19/08/13 19:53:27 INFO ForEachDStream: Initialized and validated org.apache.spark.streaming.dstream.ForEachDStream@4044ec97 19/08/13 19:53:27 INFO KafkaInputDStream: Slide time = 60000 ms 19/08/13 19:53:27 INFO KafkaInputDStream: Storage level = Serialized 1x Replicated 19/08/13 19:53:27 INFO KafkaInputDStream: Checkpoint interval = null 19/08/13 19:53:27 INFO KafkaInputDStream: Remember interval = 60000 ms 19/08/13 19:53:27 INFO KafkaInputDStream: Initialized and validated org.apache.spark.streaming.kafka.KafkaInputDStream@5fd3dc81 19/08/13 19:53:27 INFO MappedDStream: Slide time = 60000 ms 19/08/13 19:53:27 INFO MappedDStream: Storage level = Serialized 1x Replicated 19/08/13 19:53:27 INFO MappedDStream: Checkpoint interval = null 19/08/13 19:53:27 INFO MappedDStream: Remember interval = 60000 ms 19/08/13 19:53:27 INFO MappedDStream: Initialized and validated org.apache.spark.streaming.dstream.MappedDStream@5dd4b960 19/08/13 19:53:27 INFO ForEachDStream: Slide time = 60000 ms 19/08/13 19:53:27 INFO ForEachDStream: Storage level = Serialized 1x Replicated 19/08/13 19:53:27 INFO ForEachDStream: Checkpoint interval = null 19/08/13 19:53:27 INFO ForEachDStream: Remember interval = 60000 ms 19/08/13 19:53:27 INFO ForEachDStream: Initialized and validated org.apache.spark.streaming.dstream.ForEachDStream@132d0c3c 19/08/13 19:53:27 INFO KafkaInputDStream: Slide time = 60000 ms 19/08/13 19:53:27 INFO KafkaInputDStream: Storage level = Serialized 1x Replicated 19/08/13 19:53:27 INFO KafkaInputDStream: Checkpoint interval = null 19/08/13 19:53:27 INFO KafkaInputDStream: Remember interval = 60000 ms 19/08/13 19:53:27 INFO KafkaInputDStream: Initialized and validated org.apache.spark.streaming.kafka.KafkaInputDStream@5fd3dc81 19/08/13 19:53:27 INFO MappedDStream: Slide time = 60000 ms 19/08/13 19:53:27 INFO MappedDStream: Storage level = Serialized 1x Replicated 19/08/13 19:53:27 INFO MappedDStream: Checkpoint interval = null 19/08/13 19:53:27 INFO MappedDStream: Remember interval = 60000 ms 19/08/13 19:53:27 INFO MappedDStream: Initialized and validated org.apache.spark.streaming.dstream.MappedDStream@5dd4b960 19/08/13 19:53:27 INFO ForEachDStream: Slide time = 60000 ms 19/08/13 19:53:27 INFO ForEachDStream: Storage level = Serialized 1x Replicated 19/08/13 19:53:27 INFO ForEachDStream: Checkpoint interval = null 19/08/13 19:53:27 INFO ForEachDStream: Remember interval = 60000 ms 19/08/13 19:53:27 INFO ForEachDStream: Initialized and validated org.apache.spark.streaming.dstream.ForEachDStream@525bed0c 19/08/13 19:53:27 INFO DAGScheduler: Got job 1 (start at VoiceApplication2.java:128) with 1 output partitions 19/08/13 19:53:27 INFO DAGScheduler: Final stage: ResultStage 2 (start at VoiceApplication2.java:128) 19/08/13 19:53:27 INFO DAGScheduler: Parents of final stage: List() 19/08/13 19:53:27 INFO DAGScheduler: Missing parents: List() 19/08/13 19:53:27 INFO DAGScheduler: Submitting ResultStage 2 (Receiver 0 ParallelCollectionRDD[3] at makeRDD at ReceiverTracker.scala:613), which has no missing parents 19/08/13 19:53:27 INFO ReceiverTracker: Receiver 0 started 19/08/13 19:53:27 INFO MemoryStore: Block broadcast_2 stored as values in memory (estimated size 133.5 KB, free 366.2 MB) 19/08/13 19:53:27 INFO MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 36.3 KB, free 366.1 MB) 19/08/13 19:53:27 INFO BlockManagerInfo: Added broadcast_2_piece0 in memory on datanode02:31984 (size: 36.3 KB, free: 366.3 MB) 19/08/13 19:53:27 INFO SparkContext: Created broadcast 2 from broadcast at DAGScheduler.scala:1039 19/08/13 19:53:27 INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 2 (Receiver 0 ParallelCollectionRDD[3] at makeRDD at ReceiverTracker.scala:613) (first 15 tasks are for partitions Vector(0)) 19/08/13 19:53:27 INFO YarnClusterScheduler: Adding task set 2.0 with 1 tasks 19/08/13 19:53:27 INFO TaskSetManager: Starting task 0.0 in stage 2.0 (TID 70, datanode01, executor 2, partition 0, PROCESS_LOCAL, 8757 bytes) 19/08/13 19:53:27 INFO RecurringTimer: Started timer for JobGenerator at time 1565697240000 19/08/13 19:53:27 INFO JobGenerator: Started JobGenerator at 1565697240000 ms 19/08/13 19:53:27 INFO JobScheduler: Started JobScheduler 19/08/13 19:53:27 INFO StreamingContext: StreamingContext started 19/08/13 19:53:27 INFO BlockManagerInfo: Added broadcast_2_piece0 in memory on datanode01:20487 (size: 36.3 KB, free: 2.5 GB) 19/08/13 19:53:27 INFO ReceiverTracker: Registered receiver for stream 0 from 10.1.229.158:64276 19/08/13 19:54:00 INFO JobScheduler: Added jobs for time 1565697240000 ms 19/08/13 19:54:00 INFO JobScheduler: Starting job streaming job 1565697240000 ms.0 from job set of time 1565697240000 ms 19/08/13 19:54:00 INFO JobScheduler: Starting job streaming job 1565697240000 ms.1 from job set of time 1565697240000 ms 19/08/13 19:54:00 INFO JobScheduler: Finished job streaming job 1565697240000 ms.1 from job set of time 1565697240000 ms 19/08/13 19:54:00 INFO JobScheduler: Finished job streaming job 1565697240000 ms.0 from job set of time 1565697240000 ms 19/08/13 19:54:00 INFO JobScheduler: Starting job streaming job 1565697240000 ms.2 from job set of time 1565697240000 ms 19/08/13 19:54:00 INFO SharedState: loading hive config file: file:/data01/hadoop/yarn/local/usercache/hdfs/filecache/85431/__spark_conf__.zip/__hadoop_conf__/hive-site.xml 19/08/13 19:54:00 INFO SharedState: Setting hive.metastore.warehouse.dir ('null') to the value of spark.sql.warehouse.dir ('hdfs://CID-042fb939-95b4-4b74-91b8-9f94b999bdf7/apps/hive/warehouse'). 19/08/13 19:54:00 INFO SharedState: Warehouse path is 'hdfs://CID-042fb939-95b4-4b74-91b8-9f94b999bdf7/apps/hive/warehouse'. 19/08/13 19:54:00 INFO StateStoreCoordinatorRef: Registered StateStoreCoordinator endpoint 19/08/13 19:54:00 INFO BlockManagerInfo: Removed broadcast_1_piece0 on datanode02:31984 in memory (size: 1979.0 B, free: 366.3 MB) 19/08/13 19:54:00 INFO BlockManagerInfo: Removed broadcast_1_piece0 on datanode02:28863 in memory (size: 1979.0 B, free: 2.5 GB) 19/08/13 19:54:00 INFO BlockManagerInfo: Removed broadcast_1_piece0 on datanode01:20487 in memory (size: 1979.0 B, free: 2.5 GB) 19/08/13 19:54:00 INFO BlockManagerInfo: Removed broadcast_1_piece0 on datanode03:3328 in memory (size: 1979.0 B, free: 2.5 GB) 19/08/13 19:54:02 INFO CodeGenerator: Code generated in 175.416957 ms 19/08/13 19:54:02 INFO JobScheduler: Finished job streaming job 1565697240000 ms.2 from job set of time 1565697240000 ms 19/08/13 19:54:02 ERROR JobScheduler: Error running job streaming job 1565697240000 ms.2 org.apache.spark.sql.catalyst.analysis.NoSuchDatabaseException: Database 'meta_voice' not found; at org.apache.spark.sql.catalyst.catalog.ExternalCatalog.requireDbExists(ExternalCatalog.scala:40) at org.apache.spark.sql.catalyst.catalog.InMemoryCatalog.tableExists(InMemoryCatalog.scala:331) at org.apache.spark.sql.catalyst.catalog.SessionCatalog.tableExists(SessionCatalog.scala:388) at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:398) at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:393) at com.stream.VoiceApplication2$2.call(VoiceApplication2.java:122) at com.stream.VoiceApplication2$2.call(VoiceApplication2.java:115) at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$2.apply(JavaDStreamLike.scala:280) at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$2.apply(JavaDStreamLike.scala:280) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51) at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:416) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:50) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50) at scala.util.Try$.apply(Try.scala:192) at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:257) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257) at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:256) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) 19/08/13 19:54:02 ERROR ApplicationMaster: User class threw exception: org.apache.spark.sql.catalyst.analysis.NoSuchDatabaseException: Database 'meta_voice' not found; org.apache.spark.sql.catalyst.analysis.NoSuchDatabaseException: Database 'meta_voice' not found; at org.apache.spark.sql.catalyst.catalog.ExternalCatalog.requireDbExists(ExternalCatalog.scala:40) at org.apache.spark.sql.catalyst.catalog.InMemoryCatalog.tableExists(InMemoryCatalog.scala:331) at org.apache.spark.sql.catalyst.catalog.SessionCatalog.tableExists(SessionCatalog.scala:388) at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:398) at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:393) at com.stream.VoiceApplication2$2.call(VoiceApplication2.java:122) at com.stream.VoiceApplication2$2.call(VoiceApplication2.java:115) at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$2.apply(JavaDStreamLike.scala:280) at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$2.apply(JavaDStreamLike.scala:280) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51) at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:416) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:50) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50) at scala.util.Try$.apply(Try.scala:192) at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:257) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257) at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:256) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) 19/08/13 19:54:02 INFO ApplicationMaster: Final app status: FAILED, exitCode: 15, (reason: User class threw exception: org.apache.spark.sql.catalyst.analysis.NoSuchDatabaseException: Database 'meta_voice' not found; at org.apache.spark.sql.catalyst.catalog.ExternalCatalog.requireDbExists(ExternalCatalog.scala:40) at org.apache.spark.sql.catalyst.catalog.InMemoryCatalog.tableExists(InMemoryCatalog.scala:331) at org.apache.spark.sql.catalyst.catalog.SessionCatalog.tableExists(SessionCatalog.scala:388) at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:398) at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:393) at com.stream.VoiceApplication2$2.call(VoiceApplication2.java:122) at com.stream.VoiceApplication2$2.call(VoiceApplication2.java:115) at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$2.apply(JavaDStreamLike.scala:280) at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$2.apply(JavaDStreamLike.scala:280) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51) at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:416) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:50) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50) at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50) at scala.util.Try$.apply(Try.scala:192) at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:257) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257) at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:256) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) ) 19/08/13 19:54:02 INFO StreamingContext: Invoking stop(stopGracefully=true) from shutdown hook 19/08/13 19:54:02 INFO ReceiverTracker: Sent stop signal to all 1 receivers 19/08/13 19:54:02 ERROR ReceiverTracker: Deregistered receiver for stream 0: Stopped by driver 19/08/13 19:54:02 INFO TaskSetManager: Finished task 0.0 in stage 2.0 (TID 70) in 35055 ms on datanode01 (executor 2) (1/1) 19/08/13 19:54:02 INFO YarnClusterScheduler: Removed TaskSet 2.0, whose tasks have all completed, from pool 19/08/13 19:54:02 INFO DAGScheduler: ResultStage 2 (start at VoiceApplication2.java:128) finished in 35.086 s 19/08/13 19:54:02 INFO ReceiverTracker: Waiting for receiver job to terminate gracefully 19/08/13 19:54:02 INFO ReceiverTracker: Waited for receiver job to terminate gracefully 19/08/13 19:54:02 INFO ReceiverTracker: All of the receivers have deregistered successfully 19/08/13 19:54:02 INFO ReceiverTracker: ReceiverTracker stopped 19/08/13 19:54:02 INFO JobGenerator: Stopping JobGenerator gracefully 19/08/13 19:54:02 INFO JobGenerator: Waiting for all received blocks to be consumed for job generation 19/08/13 19:54:02 INFO JobGenerator: Waited for all received blocks to be consumed for job generation 19/08/13 19:54:12 WARN ShutdownHookManager: ShutdownHook '$anon$2' timeout, java.util.concurrent.TimeoutException java.util.concurrent.TimeoutException at java.util.concurrent.FutureTask.get(FutureTask.java:205) at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:67) 19/08/13 19:54:12 ERROR Utils: Uncaught exception in thread pool-1-thread-1 java.lang.InterruptedException at java.lang.Object.wait(Native Method) at java.lang.Thread.join(Thread.java:1252) at java.lang.Thread.join(Thread.java:1326) at org.apache.spark.streaming.util.RecurringTimer.stop(RecurringTimer.scala:86) at org.apache.spark.streaming.scheduler.JobGenerator.stop(JobGenerator.scala:137) at org.apache.spark.streaming.scheduler.JobScheduler.stop(JobScheduler.scala:123) at org.apache.spark.streaming.StreamingContext$$anonfun$stop$1.apply$mcV$sp(StreamingContext.scala:681) at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1357) at org.apache.spark.streaming.StreamingContext.stop(StreamingContext.scala:680) at org.apache.spark.streaming.StreamingContext.org$apache$spark$streaming$StreamingContext$$stopOnShutdown(StreamingContext.scala:714) at org.apache.spark.streaming.StreamingContext$$anonfun$start$1.apply$mcV$sp(StreamingContext.scala:599) at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:216) at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ShutdownHookManager.scala:188) at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188) at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188) at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1988) at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply$mcV$sp(ShutdownHookManager.scala:188) at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188) at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188) at scala.util.Try$.apply(Try.scala:192) at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188) at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178) at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) ```
C# Onvif协议获取到的视频流url如何使用
{rtsp://192.168.1.108:80/Streaming/Channels/101?transportmode=unicast&profile=Profile_1} 如上所示,我目前是通过onvif协议获取到了海康网络摄像头rtsp视频流的url,但是不知道这串url要怎么使用。。。我想实现的是通过onvif来预览摄像头的画面和录像,不使用海康本身提供的API接口
终于明白阿里百度这样的大公司,为什么面试经常拿ThreadLocal考验求职者了
点击上面↑「爱开发」关注我们每晚10点,捕获技术思考和创业资源洞察什么是ThreadLocalThreadLocal是一个本地线程副本变量工具类,各个线程都拥有一份线程私有的数
程序员必须掌握的核心算法有哪些?
由于我之前一直强调数据结构以及算法学习的重要性,所以就有一些读者经常问我,数据结构与算法应该要学习到哪个程度呢?,说实话,这个问题我不知道要怎么回答你,主要取决于你想学习到哪些程度,不过针对这个问题,我稍微总结一下我学过的算法知识点,以及我觉得值得学习的算法。这些算法与数据结构的学习大多数是零散的,并没有一本把他们全部覆盖的书籍。下面是我觉得值得学习的一些算法以及数据结构,当然,我也会整理一些看过...
《奇巧淫技》系列-python!!每天早上八点自动发送天气预报邮件到QQ邮箱
此博客仅为我业余记录文章所用,发布到此,仅供网友阅读参考,如有侵权,请通知我,我会删掉。 补充 有不少读者留言说本文章没有用,因为天气预报直接打开手机就可以收到了,为何要多此一举发送到邮箱呢!!!那我在这里只能说:因为你没用,所以你没用!!! 这里主要介绍的是思路,不是天气预报!不是天气预报!!不是天气预报!!!天气预报只是用于举例。请各位不要再刚了!!! 下面是我会用到的两个场景: 每日下
面试官问我:什么是消息队列?什么场景需要他?用了会出现什么问题?
你知道的越多,你不知道的越多 点赞再看,养成习惯 GitHub上已经开源 https://github.com/JavaFamily 有一线大厂面试点脑图、个人联系方式,欢迎Star和完善 前言 消息队列在互联网技术存储方面使用如此广泛,几乎所有的后端技术面试官都要在消息队列的使用和原理方面对小伙伴们进行360°的刁难。 作为一个在互联网公司面一次拿一次Offer的面霸,打败了无数
8年经验面试官详解 Java 面试秘诀
    作者 | 胡书敏 责编 | 刘静 出品 | CSDN(ID:CSDNnews) 本人目前在一家知名外企担任架构师,而且最近八年来,在多家外企和互联网公司担任Java技术面试官,前后累计面试了有两三百位候选人。在本文里,就将结合本人的面试经验,针对Java初学者、Java初级开发和Java开发,给出若干准备简历和准备面试的建议。   Java程序员准备和投递简历的实
究竟你适不适合买Mac?
我清晰的记得,刚买的macbook pro回到家,开机后第一件事情,就是上了淘宝网,花了500元钱,找了一个上门维修电脑的师傅,上门给我装了一个windows系统。。。。。。 表砍我。。。 当时买mac的初衷,只是想要个固态硬盘的笔记本,用来运行一些复杂的扑克软件。而看了当时所有的SSD笔记本后,最终决定,还是买个好(xiong)看(da)的。 已经有好几个朋友问我mba怎么样了,所以今天尽量客观
程序员一般通过什么途径接私活?
二哥,你好,我想知道一般程序猿都如何接私活,我也想接,能告诉我一些方法吗? 上面是一个读者“烦不烦”问我的一个问题。其实不止是“烦不烦”,还有很多读者问过我类似这样的问题。 我接的私活不算多,挣到的钱也没有多少,加起来不到 20W。说实话,这个数目说出来我是有点心虚的,毕竟太少了,大家轻喷。但我想,恰好配得上“一般程序员”这个称号啊。毕竟苍蝇再小也是肉,我也算是有经验的人了。 唾弃接私活、做外
大学四年自学走来,这些珍藏的「实用工具/学习网站」我全贡献出来了
知乎高赞:文中列举了互联网一线大厂程序员都在用的工具集合,涉及面非常广,小白和老手都可以进来看看,或许有新收获。
《阿里巴巴开发手册》读书笔记-编程规约
Java编程规约命名风格 命名风格 类名使用UpperCamelCase风格 方法名,参数名,成员变量,局部变量都统一使用lowerCamelcase风格 常量命名全部大写,单词间用下划线隔开, 力求语义表达完整清楚,不要嫌名字长 ...
Python爬虫爬取淘宝,京东商品信息
小编是一个理科生,不善长说一些废话。简单介绍下原理然后直接上代码。 使用的工具(Python+pycharm2019.3+selenium+xpath+chromedriver)其中要使用pycharm也可以私聊我selenium是一个框架可以通过pip下载 pip install selenium -i https://pypi.tuna.tsinghua.edu.cn/simple/ 
阿里程序员写了一个新手都写不出的低级bug,被骂惨了。
你知道的越多,你不知道的越多 点赞再看,养成习惯 本文 GitHub https://github.com/JavaFamily 已收录,有一线大厂面试点思维导图,也整理了很多我的文档,欢迎Star和完善,大家面试可以参照考点复习,希望我们一起有点东西。 前前言 为啥今天有个前前言呢? 因为你们的丙丙啊,昨天有牌面了哟,直接被微信官方推荐,知乎推荐,也就仅仅是还行吧(心里乐开花)
Java工作4年来应聘要16K最后没要,细节如下。。。
前奏: 今天2B哥和大家分享一位前几天面试的一位应聘者,工作4年26岁,统招本科。 以下就是他的简历和面试情况。 基本情况: 专业技能: 1、&nbsp;熟悉Sping了解SpringMVC、SpringBoot、Mybatis等框架、了解SpringCloud微服务 2、&nbsp;熟悉常用项目管理工具:SVN、GIT、MAVEN、Jenkins 3、&nbsp;熟悉Nginx、tomca
Python爬虫精简步骤1 获取数据
爬虫的工作分为四步: 1.获取数据。爬虫程序会根据我们提供的网址,向服务器发起请求,然后返回数据。 2.解析数据。爬虫程序会把服务器返回的数据解析成我们能读懂的格式。 3.提取数据。爬虫程序再从中提取出我们需要的数据。 4.储存数据。爬虫程序把这些有用的数据保存起来,便于你日后的使用和分析。 这一篇的内容就是:获取数据。 首先,我们将会利用一个强大的库——requests来获取数据。 在电脑上安装
Python绘图,圣诞树,花,爱心 | Turtle篇
1.画圣诞树 import turtle screen = turtle.Screen() screen.setup(800,600) circle = turtle.Turtle() circle.shape('circle') circle.color('red') circle.speed('fastest') circle.up() square = turtle.Turtle()
作为一个程序员,CPU的这些硬核知识你必须会!
CPU对每个程序员来说,是个既熟悉又陌生的东西? 如果你只知道CPU是中央处理器的话,那可能对你并没有什么用,那么作为程序员的我们,必须要搞懂的就是CPU这家伙是如何运行的,尤其要搞懂它里面的寄存器是怎么一回事,因为这将让你从底层明白程序的运行机制。 随我一起,来好好认识下CPU这货吧 把CPU掰开来看 对于CPU来说,我们首先就要搞明白它是怎么回事,也就是它的内部构造,当然,CPU那么牛的一个东
破14亿,Python分析我国存在哪些人口危机!
2020年1月17日,国家统计局发布了2019年国民经济报告,报告中指出我国人口突破14亿。 猪哥的朋友圈被14亿人口刷屏,但是很多人并没有看到我国复杂的人口问题:老龄化、男女比例失衡、生育率下降、人口红利下降等。 今天我们就来分析一下我们国家的人口数据吧! 更多有趣分析教程,扫描下方二维码关注vx公号「裸睡的猪」 即可查看! 一、背景 1.人口突破14亿 2020年1月17日,国家统计局发布
听说想当黑客的都玩过这个Monyer游戏(1~14攻略)
第零关 进入传送门开始第0关(游戏链接) 请点击链接进入第1关: 连接在左边→ ←连接在右边 看不到啊。。。。(只能看到一堆大佬做完的留名,也能看到菜鸡的我,在后面~~) 直接fn+f12吧 &lt;span&gt;连接在左边→&lt;/span&gt; &lt;a href="first.php"&gt;&lt;/a&gt; &lt;span&gt;←连接在右边&lt;/span&gt; o
在家远程办公效率低?那你一定要收好这个「在家办公」神器!
相信大家都已经收到国务院延长春节假期的消息,接下来,在家远程办公可能将会持续一段时间。 但是问题来了。远程办公不是人在电脑前就当坐班了,相反,对于沟通效率,文件协作,以及信息安全都有着极高的要求。有着非常多的挑战,比如: 1在异地互相不见面的会议上,如何提高沟通效率? 2文件之间的来往反馈如何做到及时性?如何保证信息安全? 3如何规划安排每天工作,以及如何进行成果验收? ......
作为一个程序员,内存和磁盘的这些事情,你不得不知道啊!!!
截止目前,我已经分享了如下几篇文章: 一个程序在计算机中是如何运行的?超级干货!!! 作为一个程序员,CPU的这些硬核知识你必须会! 作为一个程序员,内存的这些硬核知识你必须懂! 这些知识可以说是我们之前都不太重视的基础知识,可能大家在上大学的时候都学习过了,但是嘞,当时由于老师讲解的没那么有趣,又加上这些知识本身就比较枯燥,所以嘞,大家当初几乎等于没学。 再说啦,学习这些,也看不出来有什么用啊!
别低估自己的直觉,也别高估自己的智商
所有群全部吵翻天,朋友圈全部沦陷,公众号疯狂转发。这两周没怎么发原创,只发新闻,可能有人注意到了。我不是懒,是文章写了却没发,因为大家的关注力始终在这次的疫情上面,发了也没人看。当然,我...
这个世界上人真的分三六九等,你信吗?
偶然间,在知乎上看到一个问题 一时间,勾起了我深深的回忆。 以前在厂里打过两次工,做过家教,干过辅导班,做过中介。零下几度的晚上,贴过广告,满脸、满手地长冻疮。   再回首那段岁月,虽然苦,但让我学会了坚持和忍耐。让我明白了,在这个世界上,无论环境多么的恶劣,只要心存希望,星星之火,亦可燎原。   下文是原回答,希望能对你能有所启发。   如果我说,这个世界上人真的分三六九等,
为什么听过很多道理,依然过不好这一生?
记录学习笔记是一个重要的习惯,不希望学习过的东西成为过眼云烟。做总结的同时也是一次复盘思考的过程。 本文是根据阅读得到 App上《万维钢·精英日课》部分文章后所做的一点笔记和思考。学习是一个系统的过程,思维模型的建立需要相对完整的学习和思考过程。以下观点是在碎片化阅读后总结的一点心得总结。
B 站上有哪些很好的学习资源?
哇说起B站,在小九眼里就是宝藏般的存在,放年假宅在家时一天刷6、7个小时不在话下,更别提今年的跨年晚会,我简直是跪着看完的!! 最早大家聚在在B站是为了追番,再后来我在上面刷欧美新歌和漂亮小姐姐的舞蹈视频,最近两年我和周围的朋友们已经把B站当作学习教室了,而且学习成本还免费,真是个励志的好平台ヽ(.◕ฺˇд ˇ◕ฺ;)ノ 下面我们就来盘点一下B站上优质的学习资源: 综合类 Oeasy: 综合
如何优雅地打印一个Java对象?
你好呀,我是沉默王二,一个和黄家驹一样身高,和刘德华一样颜值的程序员。虽然已经写了十多年的 Java 代码,但仍然觉得自己是个菜鸟(请允许我惭愧一下)。 在一个月黑风高的夜晚,我思前想后,觉得再也不能这么蹉跎下去了。于是痛下决心,准备通过输出的方式倒逼输入,以此来修炼自己的内功,从而进阶成为一名真正意义上的大神。与此同时,希望这些文章能够帮助到更多的读者,让大家在学习的路上不再寂寞、空虚和冷。 ...
雷火神山直播超两亿,Web播放器事件监听是怎么实现的?
Web播放器解决了在手机浏览器和PC浏览器上播放音视频数据的问题,让视音频内容可以不依赖用户安装App,就能进行播放以及在社交平台进行传播。在视频业务大数据平台中,播放数据的统计分析非常重要,所以Web播放器在使用过程中,需要对其内部的数据进行收集并上报至服务端,此时,就需要对发生在其内部的一些播放行为进行事件监听。 那么Web播放器事件监听是怎么实现的呢? 01 监听事件明细表 名
3万字总结,Mysql优化之精髓
本文知识点较多,篇幅较长,请耐心学习 MySQL已经成为时下关系型数据库产品的中坚力量,备受互联网大厂的青睐,出门面试想进BAT,想拿高工资,不会点MySQL优化知识,拿offer的成功率会大大下降。 为什么要优化 系统的吞吐量瓶颈往往出现在数据库的访问速度上 随着应用程序的运行,数据库的中的数据会越来越多,处理时间会相应变慢 数据是存放在磁盘上的,读写速度无法和内存相比 如何优化 设计
Linux 命令(122)—— watch 命令
1.命令简介 2.命令格式 3.选项说明 4.常用示例 参考文献 [1] watch(1) manual
Linux 命令(121)—— cal 命令
1.命令简介 2.命令格式 3.选项说明 4.常用示例 参考文献 [1] cal(1) manual
记jsp+servlet+jdbc实现的新闻管理系统
1.工具:eclipse+SQLyog 2.介绍:实现的内容就是显示新闻的基本信息,然后一个增删改查的操作。 3.数据库表设计 列名 中文名称 数据类型 长度 非空 newsId 文章ID int 11 √ newsTitle 文章标题 varchar 20 √ newsContent 文章内容 text newsStatus 是否审核 varchar 10 news...
Python新型冠状病毒疫情数据自动爬取+统计+发送报告+数据屏幕(三)发送篇
今天介绍的项目是使用 Itchat 发送统计报告 项目功能设计: 定时爬取疫情数据存入Mysql 进行数据分析制作疫情报告 使用itchat给亲人朋友发送分析报告(本文) 基于Django做数据屏幕 使用Tableau做数据分析 来看看最终效果 目前已经完成,预计2月12日前更新 使用 itchat 发送数据统计报告 itchat 是一个基于 web微信的一个框架,但微信官方并不允
相关热词 c#时间格式化 不带- c#替换字符串中指定位置 c# rdlc 动态报表 c# 获取txt编码格式 c#事件主动调用 c#抽象工厂模式 c# 如何添加类注释 c# static块 c#处理浮点数 c# 生成字母数字随机数
立即提问