I'm using Nifi 1.6.0.
I'm trying to write to HDFS and to Hive (cloudera) with nifi.
On "PutHDFS" I'm configure the "Hadoop Confiugration Resources" with hdfs-site.xml, core-site.xml files, set the directories and when I'm trying to Start it I got the following error:
"Failed to properly initialize processor, If still shcedule to run,
NIFI will attempt to initalize and run the Processor again after the
'Administrative Yield Duration' has elapsed. Failure is due to
java.lang.reflect.InvocationTargetException:
java.lang.reflect.InvicationTargetException"
On "PutHiveStreaming" I'm configure the "Hive Metastore URI" with
thrift://..., the database and the table name and on "Hadoop
Confiugration Resources" I'm put the Hive-site.xml location and when
I'm trying to Start it I got the following error:
"Hive streaming connect/write error, flow file will be penalized and routed to retry.
org.apache.nifi.util.hive.HiveWritter$ConnectFailure: Failed connectiong to EndPoint {metaStoreUri='thrift://myserver:9083', database='mydbname', table='mytablename', partitionVals=[]}:".
How can I solve the errors?
Thanks.
For #1, if you got your *-site.xml files from the cluster, it's possible that they are using internal IPs to refer to components like the DataNodes and you won't be able to reach them directly using that. Try setting dfs.client.use.datanode.hostname to true in your hdfs-site.xml on the client.
For #2, I'm not sure PutHiveStreaming will work against Cloudera, IIRC they use Hive 1.1.x and PutHiveStreaming is based on 1.2.x, so there may be some Thrift incompatibilities. If that doesn't seem to be the issue, make sure the client can connect to the metastore port (looks like 9083).
Related
I am getting this error which executing any query on hive which involves mapreduce.
“ Unable to retrieve URL for Hadoop Task logs. Does not contain a valid host:port authority: local”
The reported Exceptions is seen in older versions of Hadoop (i.e. before YARN). Mostly you are using older versions of Hadoop.
The exception is seen when value of mapred.job.tracker parameter is set to "local" in mapred-site.xml instead it should be <IP address of job tracker>:<port>.
we are trying to use talend batch (spark) jobs to access hive in a Kerberos cluster but we are getting the below "Can't get Master Kerberos principal for use as renewer" error.
By using the standard jobs(non spark) in talend we are able to access hive without any issue.
Below are the observation:
When we are running spark jobs talend could able to connect to hive
metastore and validating the syntax. ex if I provide the wrong table
name it does return "table not found".
when we select count(*) from table where there is no data it returns
"NULL" but if some data present in Hdfs(table) It failed with the error
"Can't get Master Kerberos principal for use as renewer".
I am not sure exactly what is the issue which is causing the token problem. could some one help us know the root cause.
One more thing to add instead of hive if I read / write to hdfs using spark batch jobs it works , So only problem is with hive and Kerberos.
You should include the hadoop config in the classpath (:/path/hadoop-configuration). You should include all configuration files in that hadoop configuration directory, not only the core-site.xml and hdfs-site.xml files. It happened to me and that solved the problem.
the same problem when I start spark on k8s,
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: java.io.IOException: Can't get Master Kerberos principal for use as renewer
at org.apache.hadoop.mapreduce.security.TokenCache.obtainTokensForNamenodesInternal(TokenCache.java:133)
at org.apache.hadoop.mapreduce.security.TokenCache.obtainTokensForNamenodesInternal(TokenCache.java:100)
at org.apache.hadoop.mapreduce.security.TokenCache.obtainTokensForNamenodes(TokenCache.java:80)
at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.listStatus(FileInputFormat.java:243)
at org.apache.spark.input.WholeTextFileInputFormat.setMinPartitions(WholeTextFileInputFormat.scala:52)
at org.apache.spark.rdd.WholeTextFileRDD.getPartitions(WholeTextFileRDD.scala:54)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:273)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:269)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:269)
and I just add yarn-site.xml to the HADOOP_CONFIG_DIR.
the yarn-site.xml only contains yarn.resourcemanager.principal
<?xml version="1.0" encoding="UTF-8"?>
<configuration>
<property>
<name>yarn.resourcemanager.principal</name>
<value>yarn/_HOST#DM.COM</value>
</property>
</configuration>
this working for me.
My Pig script works fine on its own, until I put it in an Oozie workflow, where I receive the following error:
ERROR 2043: Unexpected error during execution.
org.apache.pig.backend.executionengine.ExecException: ERROR 2043: Unexpected error during execution.
...
Caused by: java.io.IOException: No FileSystem for scheme: hbase
I registered the HBase and Zookeeper jars successfully, but received the same error.
I also attempted to set the Zookeeper Quorum by adding variation of these lines in the Pig script:
SET hbase.zookeeper.quorum 'vm-myhost-001,vm-myhost-002,vm-myhost-003'
Some searching on the internet instructed me to add this to the beginning of my workflow.xml:
SET mapreduce.fileoutputcommitter.marksuccessfuljobs false
This solved the problem. I was even able to remove the registration of the HBase and Zookeeper jars and the Zookeeper quorum.
Now after double checking, I noticed that my jobs actually do their job: they store the results in HBase as expected. But, Oozie claims that a failure occurred, when it didn't.
I don't think that setting the mapreduce.fileoutputcommitter.marksuccessfuljobs to false constitutes a solution.
Are there any other solutions?
It seems that there is currently no real solution for this.
However, this answer to a different question seems to indicate that the best workaround is to create the success flag 'manually'.
I have installed Whirr and created an EC2 cluster. The cluster is created correctly and I can ssh to the nodes and check that Hadoop is working correctly. However, whenever I try to use the hadoop-proxy.sh, I get the following message:
bind: Cannot assign requested address
And if I try to see the HDFS in a different shell (I have previously configured the HADOOP_CONF_DIR variable), I get the following error:
13/11/29 05:15:09 WARN conf.Configuration: DEPRECATED: hadoop-site.xml found in the classpath. Usage of hadoop-site.xml is deprecated. Instead use core-site.xml, mapred-site.xml and hdfs-site.xml to override properties of core-default.xml, mapred-default.xml and hdfs-default.xml respectively
Bad connection to FS. command aborted. exception: Server IPC version 7 cannot communicate with client version 4
I have tried with different properties files when setting up the cluster, using CDH, without using it... But I am still getting the same error. This is the properties file that I am currently using to launch the cluster with Whirr:
whirr.cluster-name=otrotest
whirr.instance-templates=1 hadoop-namenode+yarn-resourcemanager+mapreduce-historyserver,2 hadoop-datanode+yarn-nodemanager
whirr.provider=aws-ec2
whirr.identity=MY_ID
whirr.credential=MY_SECRET_KEY
whirr.private-key-file=/home/hduser/.ssh/whirr_id_rsa
whirr.public-key-file=/home/hduser/.ssh/whirr_id_rsa.pub
whirr.env.MAPREDUCE_VERSION=2
whirr.env.repo=cdh4
whirr.hadoop.install-function=install_cdh_hadoop
whirr.hadoop.configure-function=configure_cdh_hadoop
whirr.mr_jobhistory.start-function=start_cdh_mr_jobhistory
whirr.yarn.configure-function=configure_cdh_yarn
whirr.yarn.start-function=start_cdh_yarn
whirr.hardware-id=t1.micro
whirr.image-id=us-west-2/ami-6aad335a
whirr.location-id=us-west-2
whirr.java.install-function=install_openjdk
whirr.java.install-function=install_oab_java
I am new to Whirr and I guess I am missing something... But I don't know how to solve this. Any help would be much appreciated. Thanks in advance.
I am using Ubuntu 12.04, hadoop-0.23.5, hive-0.9.0.
I specified my metastore_db separately to some other place $HIVE_HOME/my_db/metastore_db in hive-site.xml
Hadoop runs fine, jps gives ResourceManager,NameNode,DataNode,NodeManager,SecondaryNameNode
Hive gets started perfectly,metastore_db & derby.log also created,and all hive commands run successfully,I can create databases,table,etc. But after few day later,when I run show databases,or show tables, get below error
FAILED: Error in metadata: MetaException(message:Got exception: java.net.ConnectException Call to localhost/127.0.0.1:54310 failed on connection exception: java.net.ConnectException: Connection refused) FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.DDLTask
I had this problem too and the accepted answer did not help me so will add my solution here for others:
My problem was I had a single machine with a pseudo distributed set up installed with hive. It was working fine with localhost as the host name. However when we decided to add multiple machines to the cluster we also decided to give the machines proper names "machine01, machine 02 etc etc".
I changed all the hadoop conf/*-site.xml files and the hive-site.xml file too but still had the error. After exhaustive research I realized that in the metastore db hive was picking up the URIs not from *-site files, but from the metastore tables in mysql. Where all the hive table meta data was saved are two tables SDS and DBS. Upon changing the DB_LOCATION_URI column and LOCATION in the tables DBS and SDS respectively to point to the latest namenode URI, I was back in business.
Hope this helps others.
reasons for this
If you changed your Hadoop/Hive version,you may be specifying previous hadoop version (which has ds.default.name=hdfs://localhost:54310 in core-site.xml) in your hive-0.9.0/conf/hive-env.sh
file
$HADOOP_HOME may be point to some other location
Specified version of Hadoop is not working
your namenode may be in safe mode ,run bin/hdfs dfsadmin -safemode leave or bin/hadoop dsfadmin -safemode leave
In case of fresh installation
the above problem can be the effect of a name node issue
try formatting the namenode using the command
hadoop namenode -format
1.Turn off your namenode from safe mode. Try the commands below:
hadoop dfsadmin -safemode leave
2.Restart your Hadoop daemons:
sudo service hadoop-master stop
sudo service hadoop-master start