How to specify the address of ResourceManager to bin/yarn-session.sh? - hadoop

I am a newbie in Flink.
I'm confused about how to specify the address of ResourceManager when run bin/yarn-session.sh?

When starting a Flink Yarn session via bin/yarn-session.sh then it will create a .yarn-properties-USER file in your tmp directory. This file will contain the connection information for the Flink cluster. When trying to submit a job via bin/flink run <JOB_JAR>, the client will use the connection information from this file.

Related

Hadoop job submission using Apache Ignite Hadoop Accelerators

Disclaimer: I am new to both Hadoop and Apache Ignite. sorry for the lengthy background info.
Setup:
I have installed and configured Apache Ignite Hadoop Accelerator. Start-All.sh brings up the below services. I can submit Hadoop jobs. They complete and I can see results as expected. The start all uses traditional core-site, hdfs-site, mapred-site, and yarn-site configuration files.
28336 NodeManager
28035 ResourceManager
27780 SecondaryNameNode
27429 NameNode
28552 Jps
27547 DataNode
I also have installed Apache Ignite 2.6.0. I am able to start ignite nodes, connect to it using web console. I was able to load the cache from MySQL and run SQL queries and java programs against this cache.
For running Hadoop jobs using ignited Hadoop, I created a separate ignite-config directory, in which I have customized core-site and mapred-site configurations as per the instructions in the Apache ignite web site.
Issue:
When I run a Hadoop job using the command:
hadoop --config ~/ignite-conf jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.0.jar wordcount input output1
I get the below error (Note, the same job ran successfully against the Hadoop/without ignite):
java.io.IOException: Failed to get new job ID.
...
...
Caused by: class org.apache.ignite.internal.client.GridClientDisconnectedException: Latest topology update failed.
...
...
Caused by: class org.apache.ignite.internal.client.GridServerUnreachableException: Failed to connect to any of the servers in list: [/:13500]
...
...
It looks like, there was attempt made to lookup the jobtracker (13500) and it was not able to find. From the service list above, it's obvious that job tracker is not running. However, the job ran just fine on non-ignited hadoop over YARN.
Can you help please?
This is resolved in my case.
The job tracker here meant the Apache Ignite memory cache services listening on port 11211.
After making this change in mapred-site.xml, the job ran!

Write to HDFS/Hive using NiFi

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).

How to connect Apache Spark with Yarn from the SparkContext?

I have developed a Spark application in Java using Eclipse.
So far, I am using the standalone mode by configuring the master's address to 'local[*]'.
Now I want to deploy this application on a Yarn cluster.
The only official documentation I found is http://spark.apache.org/docs/latest/running-on-yarn.html
Unlike the documentation for deploying on a mesos cluster or in standalone (http://spark.apache.org/docs/latest/running-on-mesos.html), there is not any URL to use within SparkContext for the master's adress.
Apparently, I have to use line commands to deploy spark on Yarn.
Do you know if there is a way to configure the master's adress in the SparkContext like the standalone and mesos mode?
There actually is a URL.
Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory which contains the (client side) configuration files for the Hadoop cluster. These configs are used to write to HDFS and connect to the YARN ResourceManager
You should have at least hdfs-site.xml, yarn-site.xml, and core-site.xml files that specify all the settings and URLs for the Hadoop cluster you connect to.
Some properties from yarn-site.xml include yarn.nodemanager.hostname and yarn.nodemanager.address.
Since the address has a default of ${yarn.nodemanager.hostname}:0, you may only need to set the hostname.

Job Tracker web interface

I followed the tutorialshttp://hadoop.apache.org/docs/r2.4.1/hadoop-project-dist/hadoop-common/SingleCluster.html and installed hadoop 2.4.1 as pseudo distributed cluster. I created a ubuntu VM using OracleVM and installed hadoop as mentioned in the link. It was setup fine and able to run the examples. However the job tracker URL is not working. :50030 gives page not found. I also tried netstat on the server and there is no process waiting on 50030 port . Do i need to start any other service ? What are the possible reasons ?
You need to execute this:
$HADOOP_HOME/sbin/mr-jobhistory-daemon.sh start historyserver
Or JobTracker won't start.
(In my case, $HADOOP_HOME is in /usr/local/hadoop)
Check the value of mapred.job.tracker.http.address in mapred-site.xml
If the port is different, use that.
Also check if jobtracker is running. Check the jobtracker logs.
You need to enter the following command
http://localhost:50030/
Job Tracker web UI.

where is the hadoop task manager UI

I installed the hadoop 2.2 system on my ubuntu box using this tutorial
http://codesfusion.blogspot.com/2013/11/hadoop-2x-core-hdfs-and-yarn-components.html
Everything worked fine for me and now when I do
http://localhost:50070
I can see the management UI for HDFS. Very good!!
But the I am going through another tutorial which tells me that there must be a task manager UI running at http://mymachine.com:50030 and http://mymachine.com:50060
on my machine I cannot open these ports.
I have already done
start-dfs.sh
start-yarn.sh
start-all.sh
is something wrong? why can't I see the task manager UI?
You have installed YARN (MRv2) which runs the ResourceManager. The URL http://mymachine.com:50030 is the web address for the JobTracker daemon that comes with MRv1 and hence you are not able to see it.
To see the ResourceManager UI, check your yarn-site.xml file for the following property:
yarn.resourcemanager.webapp.address
By default, it should point to : resource_manager_hostname:8088
Assuming your ResourceManager runs on mymachine, you should see the ResourceManager UI at http://mymachine.com:8088/
Make sure all your deamons are up and running before you visit the URL for the ResourceManager.
For Hadoop 2[aka YARN/MRV2] - Any hadoop installation version-ed 2.x or higher its at port number 8088. eg. localhost:8088
For Hadoop 1 - Any hadoop installation version-ed lower than 2.x[eg 1.x or 0.x] its at port number 50030. eg localhost:50030
By default HadoopUI location is as below
http://mymachine.com:50070

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