I followed that guidance and installed the spark-1.6.1 and hadoop, but when I run it, it hangs here. (I had to reboot the machines, before it complains JVM doesn't start in 10 seconds)
sc <- sparkR.init(master = "local")
Launching java with spark-submit command C:/spark-1.6.1/bin/spark-submit.cmd "--packages" "com.databricks:spark-csv_2.11:1.2.0" "sparkr-shell" C:\Users\i50194\AppData\Local\Temp\RtmpUnK05t\backend_portff47fcb7e27
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I have set up a Hadoop single node cluster with pseudo distributed operations, and YARN running. I am able to use Spark JAVA API to run queries as a YARN-client. I wanted to go one step further and try Apache Drill on this "cluster". I installed Zookeeper that is running smoothly but I am not able to start drill and I get this log:
nohup: ignoring input
Error: Could not find or load main class
org.apache.drill.exec.server.Drillbit
Any idea?
I am on Windows 10 with JDK 1.8.
DRILL CLASSPATH is not initialized in the process of running drillbit on your machine.
For the purpose to start Drill on Windows machine it is necessary to run sqlline.bat script, for example:
C:\bin\sqlline sqlline.bat –u "jdbc:drill:zk=local;schema=dfs"
See more info: https://drill.apache.org/docs/starting-drill-on-windows/
I have a 5 node hadoop cluster running HDP 2.3.0. I setup a H2O cluster on Yarn as described here.
On running following command
hadoop jar h2odriver_hdp2.2.jar water.hadoop.h2odriver -libjars ../h2o.jar -mapperXmx 512m -nodes 3 -output /user/hdfs/H2OTestClusterOutput
I get the following ouput
H2O cluster (3 nodes) is up
(Note: Use the -disown option to exit the driver after cluster formation)
(Press Ctrl-C to kill the cluster)
Blocking until the H2O cluster shuts down...
When I try to execute the command
h2o.init(ip="10.113.57.98", port=54321)
The process remains stuck at this stage.On trying to connect to the web UI using the ip:54321, the browser tries to endlessly load the H2O admin page but nothing ever displays.
On forcefully terminating the init process I get the following error
No instance found at ip and port: 10.113.57.98:54321. Trying to start local jar...
However if I try and use H2O with python without setting up a H2O cluster, everything runs fine.
I executed all commands as the root user. Root user has permissions to read and write from the /user/hdfs hdfs directory.
I'm not sure if this is a permissions error or that the port is not accessible.
Any help would be greatly appreciated.
It looks like you are using H2O2 (H2O Classic). I recommend upgrading your H2O to the latest (H2O 3). There is a build specifically for HDP2.3 here: http://www.h2o.ai/download/h2o/hadoop
Running H2O3 is a little cleaner too:
hadoop jar h2odriver.jar -nodes 1 -mapperXmx 6g -output hdfsOutputDirName
Also, 512mb per node is tiny - what is your use case? I would give the nodes some more memory.
Is there any? I could find only how to run the tasks in windows scheduler. No utility to run it as job, i.e. via CreateJobObject() - AssignProcessToJobObject().
I need my application killed if it consumes > 1.5 Gb RAM, job would be perfect to do that...
I am new to the spark, After installing the spark using parcels available in the cloudera manager.
I have configured the files as shown in the below link from cloudera enterprise:
http://www.cloudera.com/content/cloudera-content/cloudera-docs/CM4Ent/4.8.1/Cloudera-Manager-Installation-Guide/cmig_spark_installation_standalone.html
After this setup, I have started all the nodes in the spark by running /opt/cloudera/parcels/SPARK/lib/spark/sbin/start-all.sh. But I couldn't run the worker nodes as I got the specified error below.
[root#localhost sbin]# sh start-all.sh
org.apache.spark.deploy.master.Master running as process 32405. Stop it first.
root#localhost.localdomain's password:
localhost.localdomain: starting org.apache.spark.deploy.worker.Worker, logging to /var/log/spark/spark-root-org.apache.spark.deploy.worker.Worker-1-localhost.localdomain.out
localhost.localdomain: failed to launch org.apache.spark.deploy.worker.Worker:
localhost.localdomain: at java.lang.ClassLoader.loadClass(libgcj.so.10)
localhost.localdomain: at gnu.java.lang.MainThread.run(libgcj.so.10)
localhost.localdomain: full log in /var/log/spark/spark-root-org.apache.spark.deploy.worker.Worker-1-localhost.localdomain.out
localhost.localdomain:starting org.apac
When I run jps command, I got:
23367 Jps
28053 QuorumPeerMain
28218 SecondaryNameNode
32405 Master
28148 DataNode
7852 Main
28159 NameNode
I couldn't run the worker node properly. Actually I thought to install a standalone spark where the master and worker work on a single machine. In slaves file of spark directory, I given the address as "localhost.localdomin" which is my host name. I am not aware of this settings file. Please any one cloud help me out with this installation process. Actually I couldn't run the worker nodes. But I can start the master node.
Thanks & Regards,
bips
Please notice error info below:
localhost.localdomain: at java.lang.ClassLoader.loadClass(libgcj.so.10)
I met the same error when I installed and started Spark master/workers on CentOS 6.2 x86_64 after making sure that libgcj.x86_64 and libgcj.i686 had been installed on my server, finally I solved it. Below is my solution, wish it can help you.
It seem as if your JAVA_HOME environment parameter didn't set correctly.
Maybe, your JAVA_HOME links to system embedded java, e.g. java version "1.5.0".
Spark needs java version >= 1.6.0. If you are using java 1.5.0 to start Spark, you will see this error info.
Try to export JAVA_HOME="your java home path", then start Spark again.
I'm deploying Hadoop CDH3 in pseudo-distributed mode on a VPS.
So i have installed CDH3, then i have executed
sudo apt-get install hadoop-0.20-conf-pseudo
but if i try to start all daemons with
for service in /etc/init.d/hadoop-0.20-*; do sudo $service start; done
it throws
ERROR. Could not start Hadoop datanode daemon
The same installation and starting commands works on my notebook.
I don't understand the cause. In fact the log file is empty. The available RAM is about 900MB, with 98G of available disk space.
Which can be the cause or how can i discover it? I'm excluding that the error is from the configuration files.
Consider using Cloudera Manager, it could save you some time (especially if you use multiple nodes). There is a nice video on Youtube which shows deployment process