Zeppelin 0.6.2使用Spark的yarn-client模式

Zeppelin版本0.6.2

1. Export SPARK_HOME

In conf/zeppelin-env.sh, export SPARK_HOME environment variable with your Spark installation path.

You can optionally export HADOOP_CONF_DIR and SPARK_SUBMIT_OPTIONS

export SPARK_HOME=/usr/crh/4.9.2.5-1051/spark
export HADOOP_CONF_DIR=/etc/hadoop/conf
export JAVA_HOME=/opt/jdk1.7.0_79

这儿虽然添加了SPARK_HOME但是后面使用的时候还是找不到包。

2. Set master in Interpreter menu

After start Zeppelin, go to Interpreter menu and edit master property in your Spark interpreter setting. The value may vary depending on your Spark cluster deployment type.

spark解释器设置为yarn-client模式

FAQ

1.

ERROR [2016-07-26 16:46:15,999] ({pool-2-thread-2} Job.java[run]:189) - Job failed
java.lang.NoSuchMethodError: scala.reflect.api.JavaUniverse.runtimeMirror(Ljava/lang/ClassLoader;)Lscala/reflect/api/JavaMirrors$JavaMirror;
	at org.apache.spark.repl.SparkILoop.<init>(SparkILoop.scala:936)
	at org.apache.spark.repl.SparkILoop.<init>(SparkILoop.scala:70)
	at org.apache.zeppelin.spark.SparkInterpreter.open(SparkInterpreter.java:765)
	at org.apache.zeppelin.interpreter.LazyOpenInterpreter.open(LazyOpenInterpreter.java:69)
	at org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:93)
	at org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:341)
	at org.apache.zeppelin.scheduler.Job.run(Job.java:176)
	at org.apache.zeppelin.scheduler.FIFOScheduler$1.run(FIFOScheduler.java:139)
	at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
	at java.util.concurrent.FutureTask.run(FutureTask.java:262)
	at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
	at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
	at java.lang.Thread.run(Thread.java:745)

Solution

把SPARK_HOME/lib目录下的所有jar包都拷到zeppelin的lib下。

2.

%spark.sql
show tables

org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.security.AccessControlException): Permission denied: user=root, access=WRITE, inode="/user/root/.sparkStaging/application_1481857320971_0028":hdfs:hdfs:drwxr-xr-x
	at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.check(FSPermissionChecker.java:319)
	at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.check(FSPermissionChecker.java:292)
	at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkPermission(FSPermissionChecker.java:213)
	at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkPermission(FSPermissionChecker.java:190)
	at org.apache.hadoop.hdfs.server.namenode.FSDirectory.checkPermission(FSDirectory.java:1771)
	at org.apache.hadoop.hdfs.server.namenode.FSDirectory.checkPermission(FSDirectory.java:1755)
	at org.apache.hadoop.hdfs.server.namenode.FSDirectory.checkAncestorAccess(FSDirectory.java:1738)
	at org.apache.hadoop.hdfs.server.namenode.FSDirMkdirOp.mkdirs(FSDirMkdirOp.java:71)
	at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.mkdirs(FSNamesystem.java:3905)
	at org.apache.hadoop.hdfs.server.namenode.NameNodeRpcServer.mkdirs(NameNodeRpcServer.java:1048)
	at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolServerSideTranslatorPB.mkdirs(ClientNamenodeProtocolServerSideTranslatorPB.java:622)
	at org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$ClientNamenodeProtocol$2.callBlockingMethod(ClientNamenodeProtocolProtos.java)
	at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:616)
	at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:969)
	at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2151)
	at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2147)
	at java.security.AccessController.doPrivileged(Native Method)
	at javax.security.auth.Subject.doAs(Subject.java:415)
	at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1657)
	at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2145)

	at org.apache.hadoop.ipc.Client.call(Client.java:1427)
	at org.apache.hadoop.ipc.Client.call(Client.java:1358)
	at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:229)
	at com.sun.proxy.$Proxy24.mkdirs(Unknown Source)
	at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.mkdirs(ClientNamenodeProtocolTranslatorPB.java:558)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:606)
	at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:252)
	at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:104)
	at com.sun.proxy.$Proxy25.mkdirs(Unknown Source)
	at org.apache.hadoop.hdfs.DFSClient.primitiveMkdir(DFSClient.java:3018)
	at org.apache.hadoop.hdfs.DFSClient.mkdirs(DFSClient.java:2988)
	at org.apache.hadoop.hdfs.DistributedFileSystem$21.doCall(DistributedFileSystem.java:1057)
	at org.apache.hadoop.hdfs.DistributedFileSystem$21.doCall(DistributedFileSystem.java:1053)
	at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)
	at org.apache.hadoop.hdfs.DistributedFileSystem.mkdirsInternal(DistributedFileSystem.java:1053)
	at org.apache.hadoop.hdfs.DistributedFileSystem.mkdirs(DistributedFileSystem.java:1046)
	at org.apache.hadoop.fs.FileSystem.mkdirs(FileSystem.java:1877)
	at org.apache.hadoop.fs.FileSystem.mkdirs(FileSystem.java:598)
	at org.apache.spark.deploy.yarn.Client.prepareLocalResources(Client.scala:281)
	at org.apache.spark.deploy.yarn.Client.createContainerLaunchContext(Client.scala:634)
	at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:123)
	at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:57)
	at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:144)
	at org.apache.spark.SparkContext.<init>(SparkContext.scala:523)
	at org.apache.zeppelin.spark.SparkInterpreter.createSparkContext(SparkInterpreter.java:339)
	at org.apache.zeppelin.spark.SparkInterpreter.getSparkContext(SparkInterpreter.java:145)
	at org.apache.zeppelin.spark.SparkInterpreter.open(SparkInterpreter.java:465)
	at org.apache.zeppelin.interpreter.ClassloaderInterpreter.open(ClassloaderInterpreter.java:74)
	at org.apache.zeppelin.interpreter.LazyOpenInterpreter.open(LazyOpenInterpreter.java:68)
	at org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:92)
	at org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:300)
	at org.apache.zeppelin.scheduler.Job.run(Job.java:169)
	at org.apache.zeppelin.scheduler.FIFOScheduler$1.run(FIFOScheduler.java:134)
	at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
	at java.util.concurrent.FutureTask.run(FutureTask.java:262)
	at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178)
	at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
	at java.lang.Thread.run(Thread.java:745)

Solution

hadoop fs -chown root:hdfs /user/root

3.

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Row, SQLContext}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.ml.feature.RFormula
import org.apache.spark.ml.regression.LinearRegression
conf: org.apache.spark.SparkConf = org.apache.spark.SparkConf@6a79f5df
sc: org.apache.spark.SparkContext = org.apache.spark.SparkContext@59b2aabc
spark: org.apache.spark.sql.SQLContext = org.apache.spark.sql.SQLContext@129d0b9b
org.apache.spark.sql.AnalysisException: Specifying database name or other qualifiers are not allowed for temporary tables. If the table name has dots (.) in it, please quote the table name with backticks (`).;
    at org.apache.spark.sql.catalyst.analysis.Catalog$class.checkTableIdentifier(Catalog.scala:97)
    at org.apache.spark.sql.catalyst.analysis.SimpleCatalog.checkTableIdentifier(Catalog.scala:104)
    at org.apache.spark.sql.catalyst.analysis.SimpleCatalog.lookupRelation(Catalog.scala:134)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.getTable(Analyzer.scala:257)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$7.applyOrElse(Analyzer.scala:268)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$7.applyOrElse(Analyzer.scala:264)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:57)
    at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:51)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:56)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:54)
    at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:249)

val dataset = spark.sql("select knife_dish_power,penetration,knife_dish_torque,total_propulsion,knife_dish_speed_readings,propulsion_speed1 from `tbm.tbm_test` where knife_dish_power!=0 and penetration!=0")

如上sql中给表名和库名添加``。

然后又报如下错：

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Row, SQLContext}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.ml.feature.RFormula
import org.apache.spark.ml.regression.LinearRegression
conf: org.apache.spark.SparkConf = org.apache.spark.SparkConf@4dd69db0
sc: org.apache.spark.SparkContext = org.apache.spark.SparkContext@4072dd9
spark: org.apache.spark.sql.SQLContext = org.apache.spark.sql.SQLContext@238ac654
java.lang.RuntimeException: Table Not Found: tbm.tbm_test
	at scala.sys.package$.error(package.scala:27)
	at org.apache.spark.sql.catalyst.analysis.SimpleCatalog.lookupRelation(Catalog.scala:139)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.getTable(Analyzer.scala:257)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$7.applyOrElse(Analyzer.scala:268)

原因：我用的是org.apache.spark.sql.SQLContext对象spark查询hive中的数据，查询hive的数据需要org.apache.spark.sql.hive.HiveContext对象sqlContext或sqlc。

实例：

 

顺便记录一下spark-shell使用HiveContext：

集群环境是HDP2.3.4.0

spark版本是1.5.2

spark-shell
scala> val hiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
scala> hiveContext.sql("show tables").collect().foreach(println)
[gps_p1,false]
scala> hiveContext.sql("select * from g").collect().foreach(println)
[1,li]                                                                          
[1,li]
[1,li]
[1,li]
[1,li]

4.

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Row, SQLContext}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.ml.feature.RFormula
import org.apache.spark.ml.regression.LinearRegression
conf: org.apache.spark.SparkConf = org.apache.spark.SparkConf@4d66e4f8
org.apache.spark.SparkException: Only one SparkContext may be running in this JVM (see SPARK-2243). To ignore this error, set spark.driver.allowMultipleContexts = true. The currently running SparkContext was created at:
org.apache.spark.SparkContext.<init>(SparkContext.scala:82)
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:46)
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:51)
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:53)
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:55)
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:57)
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:59)
$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:61)
$iwC$$iwC$$iwC$$iwC.<init>(<console>:63)
$iwC$$iwC$$iwC.<init>(<console>:65)
$iwC$$iwC.<init>(<console>:67)
$iwC.<init>(<console>:69)
<init>(<console>:71)
.<init>(<console>:75)
.<clinit>(<console>)
.<init>(<console>:7)
.<clinit>(<console>)
$print(<console>)

Solution：

val conf = new SparkConf().setAppName("test").set("spark.driver.allowMultipleContexts", "true")
    val sc = new SparkContext(conf)
    val spark = new SQLContext(sc)

在上面添加set("spark.driver.allowMultipleContexts", "true")。