I have an Apache Zeppelin notebook running and I'm trying to load the jdbc and/or postgres interpreter to my notebook in order to write to a postgres DB from Zeppelin.
The main resource to load new interpreters here tells me to run the code below to get other interpreters:
./bin/install-interpreter.sh --all
However, when I run this command in EMR terminal, I find that the EMR cluster does not come with an install-interpreter.sh executable file.
What is the recommended path?
1. Should I find the install-interpreter.sh file and load that to the EMR cluster under ./bin/?
2. Is there an EMR configuration on start time that would enable the install-interpreter.sh file?
Currently all tutorials and documentations assumes that you can run the install-interpreter.sh file.
The solution is to not run this code below in root (aka - ./ )
./bin/install-interpreter.sh --all
Instead in EMR, run the code above in Zeppelin, which in the EMR cluster, is in /usr/lib/zeppelin
Related
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 just installed Cloudera VM setup for hadoop. But when I open the command prompt and want to start all daemons for hadoop using command 'start-all.sh' , I get an error stating "bash : start-all.sh: command not found".
I have tried 'start-dfs.sh' too yet still gives the same error. When I use 'jps' command, I can see that none of the daemons have been started.
You can find start-all.sh and start-dfs.sh scripts in bin or sbin folders. You can use the following command to find that. Go to hadoop installation folder and run this command.
find . -name 'start-all.sh' # Finds files having name similar to start-all.sh
Then you can specify the path to start all the daemons using bash /path/to/start-all.sh
If you're using the QuickStart VM then the right way to start the cluster (as #cricket_007 hinted) is by restarting it in the Cloudera Manager UI. The start-all.sh scripts will not work since those only apply to the Hadoop servers (Name Node, Data Node, Resource Manager, Node Manager ...) but not all the services in the ecosystem (like Hive, Impala, Spark, Oozie, Hue ...).
You can refer to the YouTube video and the official documentation Starting, Stopping, Refreshing, and Restarting a Cluster
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.
Currently I’m doing some document similarity analysis using Mahout RowSimilarity Job. This can be easily done be running command ‘mahout rowsimilarity…’ from the console. However I noticed that this Job is also supported to be run on Spark engine. I wonder to know how I can run this Job on Spark Engine.
You can use MLlib alternate of mahout in spark. All library in MLlib are processing in distributed mode(Map-reduce in Hadoop).
In Mahout 0.10 provide job execution with spark.
More detail Link
http://mahout.apache.org/users/sparkbindings/play-with-shell.html
step to setup spark with mahout.
1 Goto the directory where you unpacked Spark and type sbin/start-all.sh to locally start Spark
2 Open a browser, point it to http://localhost:8080/ to check whether Spark successfully started. Copy the url of the spark master at the top of the page (it starts with spark://)
3 Define the following environment variables:
export MAHOUT_HOME=[directory into which you checked out Mahout]
export SPARK_HOME=[directory where you unpacked Spark]
export MASTER=[url of the Spark master]
4 Finally, change to the directory where you unpacked Mahout and type bin/mahout spark-shell, you should see the shell starting and get the prompt mahout>. Check FAQ for further troubleshooting.
Please visit link.It uses new mahout 0.10 and works uses spark server.
I have a small cluster (4 machines) set up with 3 slaves and a master node, all installed to /home/spark/spark. (I.e, $SPARK_HOME is /home/spark/spark)
When I use the spark shell: /home/spark/spark/bin/pyspark --master spark://192.168.0.11:7077 everything works fine. However I'd like for my colleagues to be able to connect to the cluster from a local instance of spark on their machine installed in whatever directory they wish.
Currently if somebody has spark installed in say /home/user12/spark and run /home/user12/spark/bin/pyspark --master spark://192.168.0.11:7077 the spark shell will connect to the master without problems but fails with an error when I try to run code:
class java.io.IOException: Cannot run program
"/home/user12/bin/compute-classpath.sh"
(in directory "."): error=2, No such file or directory)
The problem here is that Spark is looking for the spark installation in /home/user12/spark/, where as I'd like to just tell spark to look in /home/spark/spark/ instead.
How do I do this?
You need to edit three files, spark-submit, spark-class and pyspark (all in the bin folder).
Find the line
export SPARK_HOME = [...]
Then change it to
SPARK_HOME = [...]
Finally make sure you set SPARK_HOME to the directory where spark is installed on the cluster.
This works for me.
Here you can find a detailed explanation.
http://apache-spark-user-list.1001560.n3.nabble.com/executor-failed-cannot-find-compute-classpath-sh-td859.html