I need to change the python that is being used with my CDH5.5.1 cluster. My research pointed me to set PYSPARK_PYTHON in spark-env.sh. I tried that manually without success. I then used Cloudera Manager to set the variable in both the 'Spark Service Environment Advanced Configuration Snippet' and 'Spark Service Advanced Configuration Snippet' & about everywhere else that referenced spark-env-sh. This hasn't worked and I'm at a lost where to go next.
You need to add the PYSPARK_PYTHON variable to the YARN configuration :
YARN (MR2 Included) Service Environment Advanced Configuration Snippet (Safety Valve)
Do that, restart the cluster and you are good to go.
Related
I have setup on my PC a miniconda python environment where I have installed the databricks-connect package and configured the tool with databricks-connect configure to connect to a databricks instance I want to use when developing code in the US.
I have a need to connect to a different a different databricks instance for developing code in the EU and I thought I could do this by setting up a different miniconda environment and installing databricks-connect in that environment and setting the configuration in that environment to point to the new databricks instance.
Alas, this did not work. When I look at databricks-connect configure in either miniconda environment, I see the same configuration in both which is the configuration I last configured.
My question therefore is: Is there a way to have multiple databricks-connect connections at the same time and toggle between the two without having to reconfigure each time?
Thank you for your time.
Right now, databricks-connect relies on the central configuration file, and this causes problems. There are two approaches to workaround that:
Use environment variables as described in the documentation, but they should be set somehow, plus you need to have different python environments for different versions of databricks-connect
Specify parameters as spark configuration (see in the same documentation)
For each DB cluster, do following:
separate python environment with name <name> & activate it
install databricks-connect into it
configure databricks-connect
move ~/.databricks-connect into ~/.databricks-connect-<name>
write wrapper script, that will activate python environment & symlink ~/.databricks-connect-<name> into ~/.databricks-connect (I have such script for Zsh, it could be too long to paste it here.)
I am building a a demo/training environment for one of our products which work with Hive & Spark. I am using HDP 2.6.5 and If I configure the hive settings I need (primarily these: ACID Settings) through the Ambari GUI it works fine. But I want to automate this and setting these in hive-site.xml is not working (I have found many copies of this file, so it could simply be I am using the wrong one? )
How can I change from the command line what changes when I make changes in Dashboard->Hive->Configs ?
Where are these changes stored? I am sure I have missed something obvious in the docs, but I can't find it.
Thanks!
#Leigh K You should check out the Ambari REST API to make changes to hive. I did not find a quick link to official documentation, but I was able to find this post that goes into detail using PIG:
https://markobigdata.com/2018/07/22/adding-service-to-hdp-using-rest-api/2/
I install spark according to all available tutorials I found on internet. Set up all environmental variables yet I am still not able to launch it. Please see the attached report.
make sure your environment variables are setup properly for spark home and path, for example:
SPARK_HOME = D:\Spark\spark-2.3.0-bin-hadoop2.7
PATH += D:\Spark\spark-2.3.0-bin-hadoop2.7\bin
I have a spark cluster set up on 2 CentOS machines. I want to secure the web UI of my cluster (master node). I have made a BasicAuthenticationFilter servlet. I am unable to understand:
how should I use spark.ui.filter to secure my web UI.
Where should I place the servlet/jar file.
Kindly help.
I also needed to handle this security problem to prevent unauthorized access to spark standalone UI. At last I fixed it after surfing on the web, the procedure is :
code and compile a java filter using standard basic authentication protocol, I refered to this [blog]: http://lambda.fortytools.com/post/26977061125/servlet-filter-for-http-basic-auth
packaged above filter class as a jar file, put it in $spark_home/jars/
add config lines in $spark_home/conf/spark-default.conf as :
spark.ui.filters xxx.BasicAuthFilter # the full class name
spark.test.BasicAuthFilter.params user=foo,password=cool,realm=some
the username and password need to provide to access the spark UI, “realm” is insignificant whatever you typed
restart all slave and master process and test to find it works
Hi place the jar file in all the nodes in the folder /opt/spark/conf/. In terminal, type the following commands:
Navigate to the directory /usr/local/share/jupyter/kernels/pyspark/kernel.json
Edit the file kernel.json
Add the following argument to the PYSPARK_SUBMIT_ARGS --jars /opt/spark/conf/filterauth.jar –conf spark.ui.filters=authenticate.MyFilter
Here, filterauth.jar is the jar file created and authenticate.MyFilter represents <package name>.<class name>
Hope this answers your query. :)
I have written an application in Scala that uses Spark.
The application consists of two modules - the App module which contains classes with different logic, and the Env module which contains environment and system initialization code, as well as utility functions.
The entry point is located in Env, and after initialization, it creates a class in App (according to args, using Class.forName) and the logic is executed.
The modules are exported into 2 different JARs (namely, env.jar and app.jar).
When I run the application locally, it executes well. The next step is to deploy the application to my servers. I use Cloudera's CDH 5.4.
I used Hue to create a new Oozie workflow with a Spark task with the following parameters:
Spark Master: yarn
Mode: cluster
App name: myApp
Jars/py files: lib/env.jar,lib/app.jar
Main class: env.Main (in Env module)
Arguments: app.AggBlock1Task
I then placed the 2 JARs inside the lib folder in the workflow's folder (/user/hue/oozie/workspaces/hue-oozie-1439807802.48).
When I run the workflow, it throws a FileNotFoundException and the application does not execute:
java.io.FileNotFoundException: File file:/cloudera/yarn/nm/usercache/danny/appcache/application_1439823995861_0029/container_1439823995861_0029_01_000001/lib/app.jar,lib/env.jar does not exist
However, when I leave the Spark master and mode parameters empty, it all works properly, but when I check spark.master programmatically it is set to local[*] and not yarn. Also, when observing the logs, I encountered this under Oozie Spark action configuration:
--master
null
--name
myApp
--class
env.Main
--verbose
lib/env.jar,lib/app.jar
app.AggBlock1Task
I assume I'm not doing it right - not setting Spark master and mode parameters and running the application with spark.master set to local[*]. As far as I understand, creating a SparkConf object within the application should set the spark.master property to whatever I specify in Oozie (in this case yarn) but it just doesn't work when I do that..
Is there something I'm doing wrong or missing?
Any help will be much appreciated!
I managed to solve the problem by putting the two JARs in the user directory /user/danny/app/ and specifying the Jar/py files parameter as ${nameNode}/user/danny/app/env.jar. Running it caused a ClassNotFoundException to be thrown, even though the JAR was located at the same folder in HDFS. To work around that, I had to go to the settings and add the following to the options list: --jars ${nameNode}/user/danny/app/app.jar. This way the App module is referenced as well and the application runs successfully.