Member since
12-14-2015
21
Posts
15
Kudos Received
3
Solutions
My Accepted Solutions
| Title | Views | Posted |
|---|---|---|
| 2337 | 12-15-2016 04:05 PM | |
| 7424 | 07-13-2016 09:39 AM | |
| 17430 | 01-11-2016 05:07 PM |
12-30-2016
05:28 PM
@Huahua Wei this is a spark specific configuration is not in hive-site.xml! set it in your application or from Ambari
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12-15-2016
04:05 PM
2 Kudos
It looks like your RM doesn't have write access to the root znode, and it can't create /yarn-leader-election Please ensure that you have proper ACL on /
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12-12-2016
10:36 PM
Thanks @bikas shc has it's own problems and I couldn't find it useful yet! please let me know if you've tried it? Here are the problems I faced: 1- The shc tech preview is released with HDP 2.5 but the Spark versions are slightly different! HDP 2.5 has Spark 1.6.2 but shc is built for 1.6.1; I actually tried to upgrade the Spark and rebuilt shc but it failed in few test cases! 2- Spark 1.6.2 it built based on Scala 2.10 as you can find http://repo.hortonworks.com/content/repositories/releases/org/apache/spark/spark-core_2.10 but shc for Spark 1.6.1 is built based on Scala 2.11 which sounds weird! http://repo.hortonworks.com/content/repositories/releases/com/hortonworks/shc-core/1.0.1-1.6-s_2.10/ The above link looks like Scala version is 2.10 but if you try it you will notice it was built and released based on 2.11!!!
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12-12-2016
10:24 PM
Thanks for the answer @Josh Elser My problem is not whether this is the best practice for handling authentication or not First I want my code work! I'm trying to find a way to make newAPIHadoopRDD authenticate to AD on the executors but there is no success yet!
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12-12-2016
03:40 PM
1 Kudo
Hi, Does anyone know how to let mr Spark talk to HBase on a secured cluster? I have a Keberized Hadoop cluster (HDP 2.5) and want to scan HBase tables from Spark using newAPIHadoopRDD! Spark application on local mode can easily authenticate to AD using a keytab and communicate with HBase! When I run it on YARN, driver can authenticate with AD and get the tgt in 2 ways: using --keytab --principal on spark-submit with the help of UserGroupInformation.loginUserFromKeytabAndReturnUGI But executors fail and can't get the Kerberos tgt although the keytab is available to Spark on all the nodes! The problem is executors are handled by newAPIHadoopRDD and I can't find a way to make them use my user and its headless keytab! Then I get the following famous exception on all the executors: avax.security.sasl.SaslException: GSS initiate failed [Caused by GSSException: No valid credentials provided (Mechanism level: Failed to find any Kerberos tgt)]
at com.sun.security.sasl.gsskerb.GssKrb5Client.evaluateChallenge(GssKrb5Client.java:211)
at org.apache.hadoop.hbase.security.HBaseSaslRpcClient.saslConnect(HBaseSaslRpcClient.java:179)
at org.apache.hadoop.hbase.ipc.RpcClientImpl$Connection.setupSaslConnection(RpcClientImpl.java:611)
at org.apache.hadoop.hbase.ipc.RpcClientImpl$Connection.access$600(RpcClientImpl.java:156)
at org.apache.hadoop.hbase.ipc.RpcClientImpl$Connection$2.run(RpcClientImpl.java:737)
at org.apache.hadoop.hbase.ipc.RpcClientImpl$Connection$2.run(RpcClientImpl.java:734)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1724)
at org.apache.hadoop.hbase.ipc.RpcClientImpl$Connection.setupIOstreams(RpcClientImpl.java:734)
at org.apache.hadoop.hbase.ipc.RpcClientImpl$Connection.writeRequest(RpcClientImpl.java:887)
at org.apache.hadoop.hbase.ipc.RpcClientImpl$Connection.tracedWriteRequest(RpcClientImpl.java:856)
at org.apache.hadoop.hbase.ipc.RpcClientImpl.call(RpcClientImpl.java:1199)
at org.apache.hadoop.hbase.ipc.AbstractRpcClient.callBlockingMethod(AbstractRpcClient.java:213)
at org.apache.hadoop.hbase.ipc.AbstractRpcClient$BlockingRpcChannelImplementation.callBlockingMethod(AbstractRpcClient.java:287)
at org.apache.hadoop.hbase.protobuf.generated.ClientProtos$ClientService$BlockingStub.scan(ClientProtos.java:32741)
at org.apache.hadoop.hbase.client.ClientSmallScanner$SmallScannerCallable.call(ClientSmallScanner.java:201)
at org.apache.hadoop.hbase.client.ClientSmallScanner$SmallScannerCallable.call(ClientSmallScanner.java:180)
at org.apache.hadoop.hbase.client.RpcRetryingCaller.callWithoutRetries(RpcRetryingCaller.java:200)
at org.apache.hadoop.hbase.client.ScannerCallableWithReplicas$RetryingRPC.call(ScannerCallableWithReplicas.java:364)
at org.apache.hadoop.hbase.client.ScannerCallableWithReplicas$RetryingRPC.call(ScannerCallableWithReplicas.java:338)
at org.apache.hadoop.hbase.client.RpcRetryingCaller.callWithRetries(RpcRetryingCaller.java:126)
at org.apache.hadoop.hbase.client.ResultBoundedCompletionService$QueueingFuture.run(ResultBoundedCompletionService.java:65)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: GSSException: No valid credentials provided (Mechanism level: Failed to find any Kerberos tgt)
at sun.security.jgss.krb5.Krb5InitCredential.getInstance(Krb5InitCredential.java:147)
at sun.security.jgss.krb5.Krb5MechFactory.getCredentialElement(Krb5MechFactory.java:122)
at sun.security.jgss.krb5.Krb5MechFactory.getMechanismContext(Krb5MechFactory.java:187)
at sun.security.jgss.GSSManagerImpl.getMechanismContext(GSSManagerImpl.java:224)
at sun.security.jgss.GSSContextImpl.initSecContext(GSSContextImpl.java:212)
at sun.security.jgss.GSSContextImpl.initSecContext(GSSContextImpl.java:179)
at com.sun.security.sasl.gsskerb.GssKrb5Client.evaluateChallenge(GssKrb5Client.java:192)
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07-13-2016
09:39 AM
2 Kudos
Now I know what is going on, there is a ticket in progress at moment called "Enable OrcRelation even when connecting via spark thrift server", https://issues.apache.org/jira/browse/SPARK-12998 But you can find it as a release improvement on HDP 2.4.2, https://github.com/hortonworks/spark-release/blob/HDP-2.4.2.0-tag/HDP-CHANGES.txt To conclude, Spark in Hortonworks could be slightly different from main Spark! Now this is a workaround to disable the improvement and directly read the schema from Hive metastore: sqlContext.setConf("spark.sql.hive.convertMetastoreOrc", "false")
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07-11-2016
08:02 PM
I think this bug fix is also related to the issue https://issues.apache.org/jira/browse/SPARK-8501
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07-11-2016
07:25 PM
There is also an open jira ticket since March https://issues.apache.org/jira/browse/SPARK-14286
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07-11-2016
07:23 PM
3 Kudos
A simple scenario, create an empty orc table using Hive, then try to query the table using Spark Hive:
create table tbl(name string) stored as orc;
Spark:
sqlContext.sql("select * from tbl") // even collect is not needed to see the error! Here is the error: 16/07/11 15:09:21 INFO ParseDriver: Parsing command: select * from tbl
16/07/11 15:09:22 INFO ParseDriver: Parse Completed
java.lang.IllegalArgumentException: orcFileOperator: path hdfs://dobbindata/apps/hive/warehouse/tbl does not have valid orc files matching the pattern
at org.apache.spark.sql.hive.orc.OrcFileOperator$.listOrcFiles(OrcFileOperator.scala:104)
at org.apache.spark.sql.hive.orc.OrcFileOperator$.getFileReader(OrcFileOperator.scala:69)
at org.apache.spark.sql.hive.orc.OrcFileOperator$.readSchema(OrcFileOperator.scala:77)
at org.apache.spark.sql.hive.orc.OrcRelation$$anonfun$2.apply(OrcRelation.scala:185)
at org.apache.spark.sql.hive.orc.OrcRelation$$anonfun$2.apply(OrcRelation.scala:185)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.sql.hive.orc.OrcRelation.<init>(OrcRelation.scala:184)
at org.apache.spark.sql.hive.HiveMetastoreCatalog$$anonfun$20.apply(HiveMetastoreCatalog.scala:580)
at org.apache.spark.sql.hive.HiveMetastoreCatalog$$anonfun$20.apply(HiveMetastoreCatalog.scala:578)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.sql.hive.HiveMetastoreCatalog.org$apache$spark$sql$hive$HiveMetastoreCatalog$$convertToOrcRelation(HiveMetastoreCatalog.scala:578)
at org.apache.spark.sql.hive.HiveMetastoreCatalog$OrcConversions$$anonfun$apply$2.applyOrElse(HiveMetastoreCatalog.scala:647)
at org.apache.spark.sql.hive.HiveMetastoreCatalog$OrcConversions$$anonfun$apply$2.applyOrElse(HiveMetastoreCatalog.scala:643)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:335)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:335)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:334)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:332)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:332)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:281)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:321)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:332)
at org.apache.spark.sql.hive.HiveMetastoreCatalog$OrcConversions$.apply(HiveMetastoreCatalog.scala:643)
at org.apache.spark.sql.hive.HiveMetastoreCatalog$OrcConversions$.apply(HiveMetastoreCatalog.scala:637)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:83)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:80)
at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:80)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:72)
at scala.collection.immutable.List.foreach(List.scala:318)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:72)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:36)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:36)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:34)
at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:133)
at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:817)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:26)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:31)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:33)
at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:35)
at $iwC$$iwC$$iwC$$iwC.<init>(<console>:37)
at $iwC$$iwC$$iwC.<init>(<console>:39)
at $iwC$$iwC.<init>(<console>:41)
at $iwC.<init>(<console>:43)
at <init>(<console>:45)
at .<init>(<console>:49)
at .<clinit>(<console>)
at .<init>(<console>:7)
at .<clinit>(<console>)
at $print(<console>)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1346)
at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:857)
at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:902)
at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:814)
at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:657)
at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:665)
at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$loop(SparkILoop.scala:670)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply$mcZ$sp(SparkILoop.scala:997)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$process(SparkILoop.scala:945)
at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1059)
at org.apache.spark.repl.Main$.main(Main.scala:31)
at org.apache.spark.repl.Main.main(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
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02-02-2016
06:24 PM
I see no sign of Kafka on Zeppelin, but apparently TwitterUtils is available to use!
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