spark部署之yarn模式
阿新 • • 發佈：2019-04-22
spark部署之yarn模式
- hadoop-3.0.0叢集搭建
- 配置相應環境
- java環境
- scala（可配可不配）
- hadoop環境
- 從官網下載spark
- 解壓
- 配置
/conf/spark-env.shexport JAVA_HOME=/usr/java/jdk1.8.0_45 export HADOOP_CONF_DIR=/usr/java/hadoop-3.0.0/etc/hadoop export SPARK_MASTER_HOST=master export SPARK_WORKER_MEMORY=1g - 啟動
- 啟動hdfs
start-dfs.sh- 啟動yarn
start-yarn.sh- 啟動spark-shell
./spark-shell --master yarn --deploy-mode client- 報錯
2019-04-22 11:15:44,640 ERROR spark.SparkContext: Error initializing SparkContext. org.apache.spark.SparkException: Yarn application has already ended! It might have been killed or unable to launch application master. at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.waitForApplication(YarnClientSchedulerBackend.scala:85) at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:62) at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:173) at org.apache.spark.SparkContext.<init>(SparkContext.scala:509) at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2516) at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:918) at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:910) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:910) at org.apache.spark.repl.Main$.createSparkSession(Main.scala:101) at $line3.$read$$iw$$iw.<init>(<console>:15) at $line3.$read$$iw.<init>(<console>:42) at $line3.$read.<init>(<console>:44) at $line3.$read$.<init>(<console>:48) at $line3.$read$.<clinit>(<console>) at $line3.$eval$.$print$lzycompute(<console>:7) at $line3.$eval$.$print(<console>:6) at $line3.$eval.$print(<console>) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:497) at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:786) at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1047) at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:638) at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:637) at scala.reflect.internal.util.ScalaClassLoader$class.asContext(ScalaClassLoader.scala:31) at scala.reflect.internal.util.AbstractFileClassLoader.asContext(AbstractFileClassLoader.scala:19) at scala.tools.nsc.interpreter.IMain$WrappedRequest.loadAndRunReq(IMain.scala:637) at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:569) at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:565) at scala.tools.nsc.interpreter.ILoop.interpretStartingWith(ILoop.scala:807) at scala.tools.nsc.interpreter.ILoop.command(ILoop.scala:681) at scala.tools.nsc.interpreter.ILoop.processLine(ILoop.scala:395) at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply$mcV$sp(SparkILoop.scala:38) at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:37) at org.apache.spark.repl.SparkILoop$$anonfun$initializeSpark$1.apply(SparkILoop.scala:37) at scala.tools.nsc.interpreter.IMain.beQuietDuring(IMain.scala:214) at org.apache.spark.repl.SparkILoop.initializeSpark(SparkILoop.scala:37) at org.apache.spark.repl.SparkILoop.loadFiles(SparkILoop.scala:98) at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply$mcZ$sp(ILoop.scala:920) at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:909) at scala.tools.nsc.interpreter.ILoop$$anonfun$process$1.apply(ILoop.scala:909) at scala.reflect.internal.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:97) at scala.tools.nsc.interpreter.ILoop.process(ILoop.scala:909) at org.apache.spark.repl.Main$.doMain(Main.scala:74) at org.apache.spark.repl.Main$.main(Main.scala:54) at org.apache.spark.repl.Main.main(Main.scala) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:497) at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:775) at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180) at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205) at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:119) at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) 2019-04-22 11:15:44,796 WARN cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Attempted to request executors before the AM has registered! 2019-04-22 11:15:45,030 WARN metrics.MetricsSystem: Stopping a MetricsSystem that is not running org.apache.spark.SparkException: Yarn application has already ended! It might have been killed or unable to launch application master. at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.waitForApplication(YarnClientSchedulerBackend.scala:85) at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:62) at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:173) at org.apache.spark.SparkContext.<init>(SparkContext.scala:509) at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2516) at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:918) at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:910) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:910) at org.apache.spark.repl.Main$.createSparkSession(Main.scala:101) ... 47 elided- 解決
- 停掉yarn
stop-yarn.sh- 新增配置hadoop下的
/hadoop-3.0.0/etc/hadoop/yarn-site.xml
<property> <name>yarn.nodemanager.vmem-check-enabled</name> <value>false</value> <description>Whether virtual memory limits will be enforced for containers</description> </property> <property> <name>yarn.nodemanager.vmem-pmem-ratio</name> <value>4</value> <description>Ratio between virtual memory to physical memory when setting memory limits for containers</description> </property>- 啟動yarn
- 啟動spark-shell
- 訪問web ui
http://master:4040