I am following this guide to run up a zeppelin container in a local kubernetes cluster set up using minikube.
https://zeppelin.apache.org/docs/0.9.0-SNAPSHOT/quickstart/kubernetes.html
I am able to set up zeppelin and run some sample code there. I have downloaded spark 2.4.5 & 2.4.0 source code and built it for kubernetes support with the following command:
./build/mvn -Pkubernetes -DskipTests clean package
Once spark is built I created a docker container as explained in the article:
bin/docker-image-tool.sh -m -t 2.4.X build
I configured zeppelin to use the spark image which was built with kubernetes support. The article above explains that the spark interpreter will auto configure spark on kubernetes to run in client mode and run the job.
But whenever I try to run any parahgraph with spark I receive the following error
Exception in thread "main" java.lang.IllegalArgumentException: basedir must be absolute: ?/.ivy2/local
I tried setting the spark configuration spark.jars.ivy in zeppelin to point to a temp directory but that does not work either.
I found a similar issue here:
basedir must be absolute: ?/.ivy2/local
But I can't seem to configure spark to run with the spark.jars.ivy /tmp/.ivy config. I tried building spark with the spark-defaults.conf when building spark but that does not seems to be working either.
Quite stumped at this problem and how to solve it any guidance would be appreciated.
Thanks!
I have also run into this problem, but a work-around I used for setting spark.jars.ivy=/tmp/.ivy is to rather set it is as an environment variable.
In your spark interpreter settings, add the following property: SPARK_SUBMIT_OPTIONS and set its value to --conf spark.jars.ivy=/tmp/.ivy.
This should pass additional options to spark submit and your job should continue.
Related
I have an exist ambari cluster with spark2.3.0, which have problem to execute the program I developed with pyspark3, so I'm considering to install another spark3 on one of the servers and only run in YARN mode.
Could someone tell me what I should do?
I tried to extract spark3 package on a server and added HADOOP_CONF_DIR & YARN_CONF_DIR & SCALA_HOME in spark-env.sh, after trying spark-submit, below error popup:
"Failed to find Spark jars directory (/usr/localSpark/spark-3.0.0/assembly/target/scala-2.12/jars). You need to build Spark with the target "package" before running this program.
"
Thanks!
I am a complete beginner to all this stuff in general so pardon if I'm missing some totally obvious step. I installed spark 3.1.2 and cassandra 3.11.11 and I'm trying to connect both of them through this guide I found where I made a fat jar for execution. In the link I posted when they execute the spark-shell command with the jar file, there's a line which occurs at the start.
INFO SparkContext: Added JAR file:/home/chbatey/dev/tmp/spark-cassandra-connector/spark-cassandra-connector-java/target/scala-2.10/spark-cassandra-connector-java-assembly-1.2.0-SNAPSHOT.jar at http://192.168.0.34:51235/jars/spark-15/01/26 16:16:10 INFO SparkILoop: Created spark context..
I followed all of the steps properly but it doesn't show any line like that in my shell. To confirm that it hasn't been added I try the sample program on that website and it throws an error
java.lang.NoClassDefFoundError: com/datastax/spark/connector/util/Logging
What should I do? I'm using spark-cassandra-connector-3.1.0
You don't need to compile it yourself, just follow official documentation - use --packages to automatically download all dependencies:
spark-shell --packages com.datastax.spark:spark-cassandra-connector_2.12:3.1.0
Your error is that connector file doesn't contain dependencies, you need to list all things, like, java driver, etc. So if you still want to use --jars option, then just download assembly version of it (link to jar) - it will contain all necessary dependencies.
I would like to run a PySpark job locally, using a specific version of Hadoop (let's say hadoop-aws 2.8.5) because of some features.
PySpark versions seem to be aligned with Spark versions.
Here I use PySpark 2.4.5 which seems to wrap a Spark 2.4.5.
When submitting my PySpark Job, using spark-submit --local[4] ..., with the option --conf spark.jars.packages=org.apache.hadoop:hadoop-aws:2.8.5, I encounter the following error:
py4j.protocol.Py4JJavaError: An error occurred while calling o32.sql
With the following java exceptions:
java.lang.NoClassDefFoundError: org/apache/hadoop/fs/StorageStatistics
Or:
java.lang.IllegalAccessError: tried to access method org.apache.hadoop.metrics2.lib.MutableCounterLong.<init (Lorg/apache/hadoop/metrics2/MetricsInfo;J)V from class org.apache.hadoop.fs.s3a.S3AInstrumentation
I suppose that the Pyspark Job Hadoop version is unaligned with the one I pass to the spark-submit option spark.jars.packages.
But I have not any idea of how I could make it work? :)
Default spark disto has hadoop libraries included. Spark use system (its own) libraries first. So you should either set --conf spark.driver.userClassPathFirst=true and for cluster add --conf spark.executor.userClassPathFirst=true or download spark distro without hadoop. Probably you will have to put your hadoop distro into spark disto jars directory.
Ok, I found a solution:
1 - Install Hadoop in the expected version (2.8.5 for me)
2 - Install a Hadoop Free version of Spark (2.4.4 for me)
3 - Set SPARK_DIST_CLASSPATH environment variable, to make Spark uses the custom version of Hadoop.
(cf. https://spark.apache.org/docs/2.4.4/hadoop-provided.html)
4 - Add the PySpark directories to PYTHONPATH environment variable, like the following:
export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.10.7-src.zip:$SPARK_HOME/python:$SPARK_HOME/python/build:$PYTHONPATH
(Note that the py4j version my differs)
That's it.
i am listening to eventhub stream and have seen article to attach library to cluster(databricks) and my code runs file.
For debugging i am running the code on local machine/cluster, but it fails for missing library. How can i add library when running on local machine.
i tried sparkcontext.addfile(fullpathtojar), but still same error.
You can use spark-submit --packages
Example: spark-submit --packages org.postgresql:postgresql:42.1.1
You would need to find the package that you are using and check the compatibility with spark.
With a single jar file you'd use spark-submit --jars instead.
i used spark-submit --packages {package} and it works.
I want to install Zeppelin to use my existing Spark cluster. I used the following way:
Spark Master (Spark 1.5.0 for Hadoop 2.4):
Zeppelin 0.5.5
Spark Slave
I downladed the Zeppelin v0.5.5 and installed it via:
mvn clean package -Pspark-1.5 -Dspark.version=1.5.0 -Dhadoop.version=2.4.0 -Phadoop-2.4 -DskipTests
I saw, that the local[*] master setting works also without my Spark Cluster (notebook also runnable when shutted down the Spark cluster).
My problem: When I want to use my Spark Cluster for a Streaming application, it seems not to work correctly. My SQL-Table is empty when I use spark://my_server:7077 as master - in local mode everything works fine!
See also my other question which describes the problem: Apache Zeppelin & Spark Streaming: Twitter Example only works local
Did I something wrong
on installation via "mvn clean packge"?
on setting the master url?
Spark and/or Hadoop version (any limitations???)
Do I have to set something special in zeppelin-env.sh file (is actually back on defaults)???
The problem was caused by a missing library dependency! So before searching around too long, first check the dependencies, whether one is missing!
%dep
z.reset
z.load("org.apache.spark:spark-streaming-twitter_2.10:1.5.1")