Member since
12-15-2016
54
Posts
4
Kudos Received
1
Solution
My Accepted Solutions
| Title | Views | Posted |
|---|---|---|
| 477 | 01-19-2017 07:24 PM |
05-18-2018
10:21 PM
Hi! Im using Spark2 on YARN and I have some weird question maybe. I want to turn off Spark History server but I have Spark jobs running on YARN. Can you tell me, if I turn off Spark History server what will happen? Will my applications die or I wont be able to submit more jobs or? Thanks in advance I really appriciate any help 🙂
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01-31-2018
10:06 AM
@Gour Saha
Can you please help me?
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12-06-2017
12:23 PM
@Aravindan Vijayan Thank you! Is maybe known date or when will Ambari-3.0.0 be released?
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12-05-2017
10:05 AM
Hi I'm trying to make HA Hadoop client for Spark job (need for spark warehouse) which will switch from NN1 to NN2 if NN1 breaks down. public class ConfigFactoryTest {
public static void main(String [] args) throws IOException {
HdfsConfiguration conf = new HdfsConfiguration(true);
conf.set("fs.defaultFS", "hdfs://bigdata5.int.ch:8020");
conf.set("fs.default.name", conf.get("fs.defaultFS"));
conf.set("dfs.nameservices","hdfscluster");
conf.set("dfs.ha.namenodes.nameservice1", "nn1,nn2");
conf.set("dfs.namenode.rpc-address.hdfscluster.nn1","bigdata1.int.ch:8020");
conf.set("dfs.namenode.rpc-address.hdfscluster.nn2", "bigdata5.int.ch:8020");
conf.set("dfs.client.failover.proxy.provider.hdfscluster","org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider");
FileSystem fs = FileSystem.get(conf);
while(true){
FileStatus[] fsStatus = fs.listStatus(new Path("/"));
for(int i = 0; i < fsStatus.length; i++) {
System.out.println(fsStatus[i].getPath().toString());
}
}
}
}
Or I followed examples but when I tried to turn NN1 while this client is running, I'm getting exception that NN1 isnt available anymore and application is shutting down. Can someone point me in right direction? Thank you
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Labels:
- Labels:
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Apache Hadoop
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Apache Spark
11-22-2017
11:37 AM
Hi, Does anyone knows what will new HDP3 offer. When he will be out, will he offer new Grafana v4 which offers Alerts and do you have any alternative for alerts (e.g. on email for Kafka and etc.)? I would be grateful if someone post some answer/opinion/suggestion 🙂 Thanks, Ivan
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11-22-2017
10:38 AM
Do you need to pay for SmartSense or? @Artem Ervits
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11-06-2017
02:58 PM
Frequently, very frequently while I'm trying to run Spark Application this is kind of error I'm meeting with: 17/11/06 13:58:57 WARN DFSClient: DFSOutputStream ResponseProcessor exception for block BP-1246657973-10.60.213.61-1495788390217:blk_1076301910_2561450
java.io.EOFException: Premature EOF: no length prefix available
at org.apache.hadoop.hdfs.protocolPB.PBHelper.vintPrefixed(PBHelper.java:2464)
at org.apache.hadoop.hdfs.protocol.datatransfer.PipelineAck.readFields(PipelineAck.java:244)
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer$ResponseProcessor.run(DFSOutputStream.java:843)
17/11/06 13:58:57 WARN DFSClient: Error Recovery for block BP-1246657973-10.60.213.61-1495788390217:blk_1076301910_2561450 in pipeline DatanodeInfoWithStorage[xx.xxx.xx.xx6:50010,DS-dc399cb9-1705-4471-aad7-db328b1a4d94,DISK], DatanodeInfoWithStorage[xx.xxx.xx.xx6:50010,DS-0541333f-cf15-4c2b-af07-ce5aa75ef21a,DISK]: bad datanode DatanodeInfoWithStorage[xx.xxx.xx.xx6:50010,DS-dc399cb9-1705-4471-aad7-db328b1a4d94,DISK]
17/11/06 13:59:01 ERROR LiveListenerBus: Listener EventLoggingListener threw an exception
java.io.IOException: All datanodes DatanodeInfoWithStorage[xx.xxx.xx.xx6:50010,DS-0541333f-cf15-4c2b-af07-ce5aa75ef21a,DISK] are bad. Aborting...
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.setupPipelineForAppendOrRecovery(DFSOutputStream.java:1227)
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.processDatanodeError(DFSOutputStream.java:999)
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:506)
17/11/06 13:59:11 ERROR LiveListenerBus: Listener EventLoggingListener threw an exception
java.io.IOException: All datanodes DatanodeInfoWithStorage[xx.xxx.xx.xx6:50010,DS-0541333f-cf15-4c2b-af07-ce5aa75ef21a,DISK] are bad. Aborting...
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.setupPipelineForAppendOrRecovery(DFSOutputStream.java:1227)
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.processDatanodeError(DFSOutputStream.java:999)
at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:506)
Can someone please explain me why is this happening. I can't find any data or anything helpful because all nodes are fine on dashboard. All datanodes doesn't reporting any problem neither *hdfs fsck*. Any ideas, I'm really struggling 😕
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Labels:
- Labels:
-
Apache Hadoop
-
Apache Spark
11-03-2017
08:49 PM
Spark AM logs? Can you lead me please? :S
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10-30-2017
05:02 PM
I decreased yarn.scheduler.minimum-alocation-mb to 256MB Spark submit configs now are following: --executor-memory 256m --executor-cores 1 --num-executors 1 --driver-memory 512m I need it to set --driver-memory to 512MB since application wouldn't start. So, with this configs application is taking 2 GB of RAM and as you were asking => Job is as You assume across 2 containers and each is taking 1024MB UPDATE: In INFO of Spark job I can see this: 17/10/30 17:57:10 INFO Client: Will allocate AM container, with 896 MB memory including 384 MB overhead
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10-30-2017
04:49 PM
I really have feelling like YARN is overriding parameters I'm passing. Also I tried to set --num-executors to 2, he set 3 as you can see on the first picture above
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10-30-2017
04:43 PM
Yeah, yeah I did it of course, it was suggesting. I tested once more, and the same job is still taking 3GB, this s how my config looks like now screenshot-7.jpg
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10-30-2017
04:31 PM
Actually I did so, kill the app, submit the same app with same config
and he took 3GB again. I'll give it a shot again and give u feedback
asap
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10-30-2017
04:20 PM
Hi @Gour Saha Is it somehow possible to allocate just 512 because, apps jobs aren't that "expensive" that they need 3-4GBs of RAM? Thank you 🙂
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10-30-2017
01:35 PM
Hi, As my question is saying. Lets say I'm submiting spark-job like this: spark-submit --class streaming.test --master yarn --deploy-mode cluster --name some_name --executor-memory 512m --executor-cores 1 --driver-memory 512m some.jar The job is submited and it is running as you can see here: screenshot-6.jpg But as you can see, I gave to job 512MB of RAM, YARN gave 3GB and it is happening for every Spark job I'm submitting. Can someone lead me where I'm mistaking? UPDATE: I have 3 RMs. and yarn.scheduler.minimum-alocation-mb is set to 1024. Is that because this 1024 *(num of of RM) ?
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Labels:
- Labels:
-
Apache Hadoop
-
Apache Spark
-
Apache YARN
10-16-2017
02:15 PM
1 Kudo
I was on the edge with time, so I created Phoenix server on the other node and he created all SYSTEM tables he needed. On the other side, I haven't knew for that .py file. Does he really creates SYSTEM files? I don't want to run the same file because now its all working, or if I run it nothing will happen? If so, I can mark your answer as accepted. Also, thank you for your time 🙂
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10-15-2017
07:57 PM
I cannot find anything, but any little thing how to recreate system tables, can you please help me I' literally driving nuts 😞
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10-15-2017
07:06 PM
Hi @Ted Yu ng HDP2.6.0. So, when type "list" in shell or zeppelin I'm getting ZERO tables as a result. When I try to create table I'm getting this: org.apache.phoenix.exception.PhoenixIOException: Table 'SYSTEM.CATALOG' was not found, got: AFM_49_CLICKS_ANTIFRAUDD.
at org.apache.phoenix.util.ServerUtil.parseServerException(ServerUtil.java:111)
at org.apache.phoenix.query.ConnectionQueryServicesImpl.metaDataCoprocessorExec(ConnectionQueryServicesImpl.java:1303)
at org.apache.phoenix.query.ConnectionQueryServicesImpl.metaDataCoprocessorExec(ConnectionQueryServicesImpl.java:1268)
at org.apache.phoenix.query.ConnectionQueryServicesImpl.createTable(ConnectionQueryServicesImpl.java:1464)
at org.apache.phoenix.schema.MetaDataClient.createTableInternal(MetaDataClient.java:2190)
at org.apache.phoenix.schema.MetaDataClient.createTable(MetaDataClient.java:872)
at org.apache.phoenix.compile.CreateTableCompiler$2.execute(CreateTableCompiler.java:194)
at org.apache.phoenix.jdbc.PhoenixStatement$2.call(PhoenixStatement.java:343)
at org.apache.phoenix.jdbc.PhoenixStatement$2.call(PhoenixStatement.java:331)
at org.apache.phoenix.call.CallRunner.run(CallRunner.java:53)
at org.apache.phoenix.jdbc.PhoenixStatement.executeMutation(PhoenixStatement.java:329)
at org.apache.phoenix.jdbc.PhoenixStatement.execute(PhoenixStatement.java:1440)
at org.apache.commons.dbcp2.DelegatingStatement.execute(DelegatingStatement.java:291)
at org.apache.commons.dbcp2.DelegatingStatement.execute(DelegatingStatement.java:291)
at org.apache.zeppelin.jdbc.JDBCInterpreter.executeSql(JDBCInterpreter.java:580)
at org.apache.zeppelin.jdbc.JDBCInterpreter.interpret(JDBCInterpreter.java:692)
at org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:94)
at org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:489)
at org.apache.zeppelin.scheduler.Job.run(Job.java:175)
at org.apache.zeppelin.scheduler.ParallelScheduler$JobRunner.run(ParallelScheduler.java:162)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.hadoop.hbase.TableNotFoundException: Table 'SYSTEM.CATALOG' was not found, got: AFM_49_CLICKS_ANTIFRAUDD1.
at org.apache.hadoop.hbase.client.ConnectionManager$HConnectionImplementation.locateRegionInMeta(ConnectionManager.java:1284)
at org.apache.hadoop.hbase.client.ConnectionManager$HConnectionImplementation.locateRegion(ConnectionManager.java:1165)
at org.apache.hadoop.hbase.client.ConnectionManager$HConnectionImplementation.locateRegion(ConnectionManager.java:1149)
at org.apache.hadoop.hbase.client.ConnectionManager$HConnectionImplementation.locateRegion(ConnectionManager.java:1106)
at org.apache.hadoop.hbase.client.ConnectionManager$HConnectionImplementation.getRegionLocation(ConnectionManager.java:941)
at org.apache.hadoop.hbase.client.HRegionLocator.getRegionLocation(HRegionLocator.java:83)
at org.apache.hadoop.hbase.client.HTable.getRegionLocation(HTable.java:504)
at org.apache.hadoop.hbase.client.HTable.getKeysAndRegionsInRange(HTable.java:720)
at org.apache.hadoop.hbase.client.HTable.getKeysAndRegionsInRange(HTable.java:690)
at org.apache.hadoop.hbase.client.HTable.getStartKeysInRange(HTable.java:1757)
at org.apache.hadoop.hbase.client.HTable.coprocessorService(HTable.java:1712)
at org.apache.hadoop.hbase.client.HTable.coprocessorService(HTable.java:1692)
at org.apache.phoenix.query.ConnectionQueryServicesImpl.metaDataCoprocessorExec(ConnectionQueryServicesImpl.java:1286)
... 25 more The same table gets created, because I when I "list" its there but its not in SYSTEM.CATALOG since there is no such table in HBase. When I try to make SELECT query on the same table, this is what I'm getting: org.apache.phoenix.exception.PhoenixIOException: Table 'SYSTEM.CATALOG' was not found, got: AFM_49_CLICKS_ANTIFRAUDD.
at org.apache.phoenix.util.ServerUtil.parseServerException(ServerUtil.java:111)
at org.apache.phoenix.query.ConnectionQueryServicesImpl.metaDataCoprocessorExec(ConnectionQueryServicesImpl.java:1303)
at org.apache.phoenix.query.ConnectionQueryServicesImpl.metaDataCoprocessorExec(ConnectionQueryServicesImpl.java:1268)
at org.apache.phoenix.query.ConnectionQueryServicesImpl.getTable(ConnectionQueryServicesImpl.java:1493)
at org.apache.phoenix.schema.MetaDataClient.updateCache(MetaDataClient.java:514)
at org.apache.phoenix.schema.MetaDataClient.updateCache(MetaDataClient.java:437)
at org.apache.phoenix.schema.MetaDataClient.updateCache(MetaDataClient.java:429)
at org.apache.phoenix.schema.MetaDataClient.updateCache(MetaDataClient.java:425)
at org.apache.phoenix.compile.FromCompiler$BaseColumnResolver.createTableRef(FromCompiler.java:535)
at org.apache.phoenix.compile.FromCompiler$SingleTableColumnResolver.<init>(FromCompiler.java:365)
at org.apache.phoenix.compile.FromCompiler.getResolverForQuery(FromCompiler.java:213)
at org.apache.phoenix.jdbc.PhoenixStatement$ExecutableSelectStatement.compilePlan(PhoenixStatement.java:397)
at org.apache.phoenix.jdbc.PhoenixStatement$ExecutableSelectStatement.compilePlan(PhoenixStatement.java:378)
at org.apache.phoenix.jdbc.PhoenixStatement$1.call(PhoenixStatement.java:271)
at org.apache.phoenix.jdbc.PhoenixStatement$1.call(PhoenixStatement.java:266)
at org.apache.phoenix.call.CallRunner.run(CallRunner.java:53)
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10-15-2017
02:23 PM
Hi! I'm trying to recreate my SYSTEM tables since I deleted them from HDFS. So I cleaned everyhing from HDFS /apps/hbase + I did $ hbase zkCli clean -cleanAll + I delete everyhing from Zookeeper in /hbase-unsecure and afterwards start Hbase master. When I tried to query one new created table in Phoenix I got this org.apache.phoenix.exception.PhoenixIOException: Table 'SYSTEM.CATALOG' was not found, got: AFM_49_CLICKS_ANTIFRAUDD. In my zookeeper I have this: $ ls /hbase-unsecure/table
[hbase:meta, hbase:namespace, AFM_49_CLICKS_ANTIFRAUDD] So I don't know how to recreate all of the SYSTEM tables in my HBASE. I tried many things, with hbck options -repair, -fix/fixAssignments and many others, but nothing really happend. Can someone help me how to recreate this tables? Is that even possible? How to continue using Phoenix? Please don't suggest me to delete znode from zookeeper and etc because I tried everyhing and nothing is creating SYSTEM tables on HDFS.
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Labels:
- Labels:
-
Apache Hadoop
-
Apache HBase
-
Apache Phoenix
10-09-2017
12:11 PM
I'm running Zeppelin in new HDP2.6. and I'm dealing with
stacking processes and memory when running a note on scheduler every
5mins. So, let me explain. The note has about 3 little notes in it and
each one has task to query some table in MySQL. Afterwards he is making
some chart upon those data, 3 different queries and 3 different chart. I set scheduler to update that charts every 5minutes. This is
getting ugly. Each time job [python] is executed he just stacks or add
about 50MB or RAM more to that py interpreter note. How do get deal with
it, is there any way to clean RAM for that process besides that I don't
need to kill the process manually or etc?
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10-09-2017
11:55 AM
I'm running Zeppelin in new HDP2.6. and I'm dealing with
stacking processes and memory when running a note on scheduler every
5mins. So, let me explain. The note has about 3 little notes in it and
each one has task to query some table in MySQL. Afterwards he is making
some chart upon those data, 3 different queries and 3 different chart. I set scheduler to update that charts every 5minutes. This is
getting ugly. Each time job [python] is executed he just stacks or add
about 50MB or RAM more to that py interpreter note. How do get deal with
it, is there any way to clean RAM for that process besides that I don't
need to kill the process manually or etc?
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Labels:
10-06-2017
02:50 PM
I'm running Zeppelin in new HDP2.6. and I'm dealing with stacking processes and memory when running a note on scheduler every 5mins. So, let me explain. The note has about 3 little notes in it and each one has task to query some table in MySQL. Afterwards he is making some chart upon those data, 3 different queries and 3 different chart. I set scheduler to update that charts every 5minutes. This is getting ugly. Each time job [python] is executed he just stacks or add about 50MB or RAM more to that py interpreter note. How do get deal with it, is there any way to clean RAM for that process besides that I don't need to kill the process manually or etc?
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07-20-2017
08:48 AM
I'm using HDP2.6. where is installed oozie 4.2. and Spark2. After I tracked Hortonworks guide on this site: https://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.6.1/bk_spark-component-guide/content/ch_oozie-spark-action.html for adding libs for Spark2 in 4.2. version of Oozie. After I submit the job with this add-on: <code>oozie.action.sharelib.for.spark=spark2
The error I'm getting is this: <code> 2017-07-19 12:36:53,271 WARN SparkActionExecutor:523 - SERVER[] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000012-170717153234639-oozie-oozi-W] ACTION[0000012-170717153234639-oozie-oozi-W@spark_1] Launcher ERROR, reason: Main class [org.apache.oozie.action.hadoop.SparkMain], main() threw exception, Attempt to add (hdfs://:8020/user/oozie/share/lib/lib_20170613110051/oozie/aws-java-sdk-core-1.10.6.jar) multiple times to the distributed cache.
2017-07-19 12:36:53,275 WARN SparkActionExecutor:523 - SERVER[] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000012-170717153234639-oozie-oozi-W] ACTION[0000012-170717153234639-oozie-oozi-W@spark_1] Launcher exception: Attempt to add (hdfs://:8020/user/oozie/share/lib/lib_20170613110051/oozie/aws-java-sdk-core-1.10.6.jar) multiple times to the distributed cache.
java.lang.IllegalArgumentException: Attempt to add (hdfs://:8020/user/oozie/share/lib/lib_20170613110051/oozie/aws-java-sdk-core-1.10.6.jar) multiple times to the distributed cache.
at org.apache.spark.deploy.yarn.Client$anonfun$prepareLocalResources$13$anonfun$apply$8.apply(Client.scala:629)
at org.apache.spark.deploy.yarn.Client$anonfun$prepareLocalResources$13$anonfun$apply$8.apply(Client.scala:620)
at scala.collection.mutable.ArraySeq.foreach(ArraySeq.scala:74)
at org.apache.spark.deploy.yarn.Client$anonfun$prepareLocalResources$13.apply(Client.scala:620)
at org.apache.spark.deploy.yarn.Client$anonfun$prepareLocalResources$13.apply(Client.scala:619)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.deploy.yarn.Client.prepareLocalResources(Client.scala:619)
at org.apache.spark.deploy.yarn.Client.createContainerLaunchContext(Client.scala:892)
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:171)
at org.apache.spark.deploy.yarn.Client.run(Client.scala:1228)
at org.apache.spark.deploy.yarn.Client$.main(Client.scala:1287)
at org.apache.spark.deploy.yarn.Client.main(Client.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:745)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
at org.apache.oozie.action.hadoop.SparkMain.runSpark(SparkMain.java:311)
at org.apache.oozie.action.hadoop.SparkMain.run(SparkMain.java:232)
at org.apache.oozie.action.hadoop.LauncherMain.run(LauncherMain.java:58)
at org.apache.oozie.action.hadoop.SparkMain.main(SparkMain.java:62)
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.oozie.action.hadoop.LauncherMapper.map(LauncherMapper.java:239)
at org.apache.hadoop.mapred.MapRunner.run(MapRunner.java:54)
at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:453)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:343)
at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:170)
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:1866)
at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:164)
I have read that new Spark2 will not work with Spark 2.1 (via oozie anyway) due to a change in how Spark handles multiple files found in distributed cache, as mentioned here: see here Keep in mind that I'm using Ambari and HDP2.6. How can I deal with this? @Tom Shields
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Labels:
- Labels:
-
Apache Oozie
-
Apache Spark
07-19-2017
10:47 AM
I updated my question after your answer, can you please check it @yvora 🙂
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07-18-2017
08:30 AM
1 Kudo
I'm trying to make spark action in oozie and I uploaded my .jar on hdfs. While I'm trying to submit the job on oozie I'm getting this error: 2017-07-18 10:24:32,726 INFO ActionStartXCommand:520 - SERVER[bigdata3.int.ch] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@:start:] Start action [0000007-170717153234639-oozie-oozi-W@:start:] with user-retry state : userRetryCount [0], userRetryMax [0], userRetryInterval [10]
2017-07-18 10:24:32,728 INFO ActionStartXCommand:520 - SERVER[bigdata3.int.ch] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@:start:] [***0000007-170717153234639-oozie-oozi-W@:start:***]Action status=DONE
2017-07-18 10:24:32,728 INFO ActionStartXCommand:520 - SERVER[bigdata3.int.ch] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@:start:] [***0000007-170717153234639-oozie-oozi-W@:start:***]Action updated in DB!
2017-07-18 10:24:32,778 INFO WorkflowNotificationXCommand:520 - SERVER[bigdata3.int.ch] USER[-] GROUP[-] TOKEN[-] APP[-] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@:start:] No Notification URL is defined. Therefore nothing to notify for job 0000007-170717153234639-oozie-oozi-W@:start:
2017-07-18 10:24:32,779 INFO WorkflowNotificationXCommand:520 - SERVER[bigdata3.int.ch] USER[-] GROUP[-] TOKEN[-] APP[-] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[] No Notification URL is defined. Therefore nothing to notify for job 0000007-170717153234639-oozie-oozi-W
2017-07-18 10:24:32,798 INFO ActionStartXCommand:520 - SERVER[bigdata3.int.ch] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@spark_1] Start action [0000007-170717153234639-oozie-oozi-W@spark_1] with user-retry state : userRetryCount [0], userRetryMax [0], userRetryInterval [10]
2017-07-18 10:24:35,045 INFO SparkActionExecutor:520 - SERVER[bigdata3.int.ch] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@spark_1] Trying to get job [job_1500298352706_0009], attempt [1]
2017-07-18 10:24:35,074 INFO SparkActionExecutor:520 - SERVER[bigdata3.int.ch] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@spark_1] checking action, hadoop job ID [job_1500298352706_0009] status [RUNNING]
2017-07-18 10:24:35,076 INFO ActionStartXCommand:520 - SERVER[bigdata3.int.ch] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@spark_1] [***0000007-170717153234639-oozie-oozi-W@spark_1***]Action status=RUNNING
2017-07-18 10:24:35,076 INFO ActionStartXCommand:520 - SERVER[bigdata3.int.ch] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@spark_1] [***0000007-170717153234639-oozie-oozi-W@spark_1***]Action updated in DB!
2017-07-18 10:24:35,084 INFO WorkflowNotificationXCommand:520 - SERVER[bigdata3.int.ch] USER[-] GROUP[-] TOKEN[-] APP[-] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@spark_1] No Notification URL is defined. Therefore nothing to notify for job 0000007-170717153234639-oozie-oozi-W@spark_1
2017-07-18 10:25:25,159 INFO CallbackServlet:520 - SERVER[bigdata3.int.ch] USER[-] GROUP[-] TOKEN[-] APP[-] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@spark_1] callback for action [0000007-170717153234639-oozie-oozi-W@spark_1]
2017-07-18 10:25:25,201 INFO SparkActionExecutor:520 - SERVER[bigdata3.int.ch] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@spark_1] Trying to get job [job_1500298352706_0009], attempt [1]
2017-07-18 10:25:25,289 INFO SparkActionExecutor:520 - SERVER[bigdata3.int.ch] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@spark_1] action completed, external ID [job_1500298352706_0009]
2017-07-18 10:25:25,425 WARN SparkActionExecutor:523 - SERVER[bigdata3.int.ch] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@spark_1] Launcher ERROR, reason: Main class [org.apache.oozie.action.hadoop.SparkMain], main() threw exception, File file:/spark-examples_2.11-2.1.0.2.6.0.3-8.jar does not exist
2017-07-18 10:25:25,427 WARN SparkActionExecutor:523 - SERVER[bigdata3.int.ch] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000007-170717153234639-oozie-oozi-W] ACTION[0000007-170717153234639-oozie-oozi-W@spark_1] Launcher exception: File file:/spark-examples_2.11-2.1.0.2.6.0.3-8.jar does not exist
java.io.FileNotFoundException: File file:/spark-examples_2.11-2.1.0.2.6.0.3-8.jar does not exist
at org.apache.hadoop.fs.RawLocalFileSystem.deprecatedGetFileStatus(RawLocalFileSystem.java:624)
at org.apache.hadoop.fs.RawLocalFileSystem.getFileLinkStatusInternal(RawLocalFileSystem.java:850)
at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:614)
at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:422)
at org.apache.hadoop.fs.FileUtil.copy(FileUtil.java:340)
at org.apache.hadoop.fs.FileUtil.copy(FileUtil.java:292)
at org.apache.spark.deploy.yarn.Client.copyFileToRemote(Client.scala:371)
at org.apache.spark.deploy.yarn.Client.org$apache$spark$deploy$yarn$Client$$distribute$1(Client.scala:487)
at org.apache.spark.deploy.yarn.Client$$anonfun$prepareLocalResources$12.apply(Client.scala:598)
at org.apache.spark.deploy.yarn.Client$$anonfun$prepareLocalResources$12.apply(Client.scala:597)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.deploy.yarn.Client.prepareLocalResources(Client.scala:597)
at org.apache.spark.deploy.yarn.Client.createContainerLaunchContext(Client.scala:892)
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:171)
at org.apache.spark.deploy.yarn.Client.run(Client.scala:1228)
at org.apache.spark.deploy.yarn.Client$.main(Client.scala:1287)
at org.apache.spark.deploy.yarn.Client.main(Client.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:497)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:745)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
at org.apache.oozie.action.hadoop.SparkMain.runSpark(SparkMain.java:311)
at org.apache.oozie.action.hadoop.SparkMain.run(SparkMain.java:232)
at org.apache.oozie.action.hadoop.LauncherMain.run(LauncherMain.java:58)
at org.apache.oozie.action.hadoop.SparkMain.main(SparkMain.java:62)
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.oozie.action.hadoop.LauncherMapper.map(LauncherMapper.java:239)
at org.apache.hadoop.mapred.MapRunner.run(MapRunner.java:54)
at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:453)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:343)
at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:170)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1866)
at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:164) Does someone know how to fix it or some workaround because I have read that spark-submit cannot read jars which are on hdfs. This is my preview of workflow: <workflow-app name="Workflow2"
xmlns="uri:oozie:workflow:0.5">
<start to="spark_1"/>
<action name="spark_1">
<spark
xmlns="uri:oozie:spark-action:0.2">
<job-tracker>${resourceManager}</job-tracker>
<name-node>${nameNode}</name-node>
<master>yarn-cluster</master>
<name>ScalaPi</name>
<class>org.apache.spark.examples.ScalaPi</class>
<jar>/spark-examples_2.11-2.1.0.2.6.0.3-8.jar</jar>
</spark>
<ok to="end"/>
<error to="kill"/>
</action>
<kill name="kill">
<message>${wf:errorMessage(wf:lastErrorNode())}</message>
</kill>
<end name="end"/>
</workflow-app> UPDATE 1: I'm using HDP2.6. where is installed oozie 4.2. and Spark2 I also tried to add full path on hdfs like hdfs://xxxx.xxx:8020/spark-examples_2.11-2.1.0.2.6.0.3-8.jar where I afterwards got this: 2017-07-19 12:36:53,271 WARN SparkActionExecutor:523 - SERVER[] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000012-170717153234639-oozie-oozi-W] ACTION[0000012-170717153234639-oozie-oozi-W@spark_1] Launcher ERROR, reason: Main class [org.apache.oozie.action.hadoop.SparkMain], main() threw exception, Attempt to add (hdfs://:8020/user/oozie/share/lib/lib_20170613110051/oozie/aws-java-sdk-core-1.10.6.jar) multiple times to the distributed cache.
2017-07-19 12:36:53,275 WARN SparkActionExecutor:523 - SERVER[] USER[admin] GROUP[-] TOKEN[] APP[Workflow2] JOB[0000012-170717153234639-oozie-oozi-W] ACTION[0000012-170717153234639-oozie-oozi-W@spark_1] Launcher exception: Attempt to add (hdfs://:8020/user/oozie/share/lib/lib_20170613110051/oozie/aws-java-sdk-core-1.10.6.jar) multiple times to the distributed cache.
java.lang.IllegalArgumentException: Attempt to add (hdfs://:8020/user/oozie/share/lib/lib_20170613110051/oozie/aws-java-sdk-core-1.10.6.jar) multiple times to the distributed cache.
at org.apache.spark.deploy.yarn.Client$$anonfun$prepareLocalResources$13$$anonfun$apply$8.apply(Client.scala:629)
at org.apache.spark.deploy.yarn.Client$$anonfun$prepareLocalResources$13$$anonfun$apply$8.apply(Client.scala:620)
at scala.collection.mutable.ArraySeq.foreach(ArraySeq.scala:74)
at org.apache.spark.deploy.yarn.Client$$anonfun$prepareLocalResources$13.apply(Client.scala:620)
at org.apache.spark.deploy.yarn.Client$$anonfun$prepareLocalResources$13.apply(Client.scala:619)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.deploy.yarn.Client.prepareLocalResources(Client.scala:619)
at org.apache.spark.deploy.yarn.Client.createContainerLaunchContext(Client.scala:892)
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:171)
at org.apache.spark.deploy.yarn.Client.run(Client.scala:1228)
at org.apache.spark.deploy.yarn.Client$.main(Client.scala:1287)
at org.apache.spark.deploy.yarn.Client.main(Client.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:745)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
at org.apache.oozie.action.hadoop.SparkMain.runSpark(SparkMain.java:311)
at org.apache.oozie.action.hadoop.SparkMain.run(SparkMain.java:232)
at org.apache.oozie.action.hadoop.LauncherMain.run(LauncherMain.java:58)
at org.apache.oozie.action.hadoop.SparkMain.main(SparkMain.java:62)
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.oozie.action.hadoop.LauncherMapper.map(LauncherMapper.java:239)
at org.apache.hadoop.mapred.MapRunner.run(MapRunner.java:54)
at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:453)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:343)
at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:170)
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:1866)
at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:164) Is anyone familiar? P.S. I erased URLs of hdfs so you don't get confused 🙂
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Labels:
- Labels:
-
Apache Hadoop
-
Apache Oozie
-
Apache YARN
07-17-2017
02:31 PM
I'm using HDP2.6.0 where I have installed 4.2.0v of Oozie and 2.1.1. of Spark. My question is, can I manage and adjust those 2 so I can submit job of Spark2 application to Oozie or I need to wait for 5.0 version of Oozie. Does anyone had any of problems like this and how did he fix it?
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Labels:
- Labels:
-
Apache Oozie
-
Apache Spark
06-12-2017
12:01 PM
Hello! I'm using new HDP2.6. and Ambari. On it I have installed Yarn, MapReduce, Spark2, Hadoop and etc. I'm trying to enter spark shell with --master yarn but I'm constantly getting this kind of error: bin/spark-shell --master yarn --deploy-mode client
Warning: Ignoring non-spark config property: spark-executor.memory=4g
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
17/06/12 13:38:38 ERROR SparkContext: Error initializing SparkContext.
java.lang.IllegalArgumentException: Required executor memory (8192+819 MB) is above the max threshold (8192 MB) of this cluster! Please check the values of 'yarn.scheduler.maximum-allocation-mb' and/or 'yarn.nodemanager.resource.memory-mb'.
at org.apache.spark.deploy.yarn.Client.verifyClusterResources(Client.scala:334)
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:168)
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56)
at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:156)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:509)
at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2320)
at org.apache.spark.sql.SparkSession$Builder$anonfun$6.apply(SparkSession.scala:868)
at org.apache.spark.sql.SparkSession$Builder$anonfun$6.apply(SparkSession.scala:860)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:860)
at org.apache.spark.repl.Main$.createSparkSession(Main.scala:96)
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$1.apply(SparkILoop.scala:37)
at scala.tools.nsc.interpreter.IMain.beQuietDuring(IMain.scala:214)
at org.apache.spark.repl.SparkILoop.initializeSpark(SparkILoop.scala:37)
at org.apache.spark.repl.SparkILoop.loadFiles(SparkILoop.scala:105)
at scala.tools.nsc.interpreter.ILoop$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$.doMain(Main.scala:69)
at org.apache.spark.repl.Main$.main(Main.scala:52)
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:745)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) Also I tried with this line of code: bin/spark-shell --conf spark-executor.memory=4g --conf spark.executor.cores=2 --master yarn --deploy-mode client But still getting exactly the same error. This is my Yarn resources: And this are apps that succeded on Ambari test: Can someone tell me what I'm doing wrong here because I'm running nuts. Trying to fix this already one week and I can't anymore. Please someone. 😞 @Wynner @Matt Clarke @Jay SenSharma
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Labels:
- Labels:
-
Apache Spark
-
Apache YARN
06-12-2017
12:00 PM
1 Kudo
Hello! I'm using new HDP2.6. and Ambari. On it I have installed Yarn, MapReduce, Spark2, Hadoop and etc. I'm trying to enter spark shell with --master yarn but I'm constantly getting this kind of error: bin/spark-shell --master yarn --deploy-mode client
Warning: Ignoring non-spark config property: spark-executor.memory=4g
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
17/06/12 13:38:38 ERROR SparkContext: Error initializing SparkContext.
java.lang.IllegalArgumentException: Required executor memory (8192+819 MB) is above the max threshold (8192 MB) of this cluster! Please check the values of 'yarn.scheduler.maximum-allocation-mb' and/or 'yarn.nodemanager.resource.memory-mb'.
at org.apache.spark.deploy.yarn.Client.verifyClusterResources(Client.scala:334)
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:168)
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56)
at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:156)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:509)
at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2320)
at org.apache.spark.sql.SparkSession$Builder$anonfun$6.apply(SparkSession.scala:868)
at org.apache.spark.sql.SparkSession$Builder$anonfun$6.apply(SparkSession.scala:860)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:860)
at org.apache.spark.repl.Main$.createSparkSession(Main.scala:96)
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$1.apply(SparkILoop.scala:37)
at scala.tools.nsc.interpreter.IMain.beQuietDuring(IMain.scala:214)
at org.apache.spark.repl.SparkILoop.initializeSpark(SparkILoop.scala:37)
at org.apache.spark.repl.SparkILoop.loadFiles(SparkILoop.scala:105)
at scala.tools.nsc.interpreter.ILoop$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$.doMain(Main.scala:69)
at org.apache.spark.repl.Main$.main(Main.scala:52)
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:745)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) Also I tried with this line of code: bin/spark-shell --conf spark-executor.memory=4g --conf spark.executor.cores=2 --master yarn --deploy-mode client But still getting exactly the same error. This is my Yarn resources: And this are apps that succeded on Ambari test: Can someone tell me what I'm doing wrong here because I'm running nuts. Trying to fix this already one week and I can't anymore. Please someone. 😞 @Wynner @Matt Clarke @Jay SenSharma
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Apache Spark
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Apache YARN
