I'm porting an existing script from BigInsights to Spark on Bluemix. I'm trying to run the following against Spark on Bluemix:
./spark-submit.sh --vcap ./vcap.json --deploy-mode cluster \
--master https://x.x.x.x:8443 --jars ./truststore.jar \
--packages org.elasticsearch:elasticsearch-spark_2.10:2.3.0 \
./export_to_elasticsearch.py ...
However, I get the following error:
Error: Unrecognized option: --packages
How can I pass the --packages parameter?
Bluemix uses a customized Spark version, with a customized spark-submit.sh script that only supports a subset of the original script parameters. You can see all the configuration properties and parameters you can use on its documentation.
Additionally, you can download the Bluemix version of the script from this link, and there you can see that there is no argument --packages.
Therefore, the problem with your approach is that the Bluemix version of spark-submit does not accept the --packages parameter, probably due to security reasons. However, alternatively, you can download the jar for the package you want (and maybe a fat jar for the dependencies) and upload them using the --jars parameter. Note: To avoid the necessity of uploading the jar files each time you call spark-submit, you can pre-upload them using curl. The details of this procedure can be found on this link.
Adding to Daniel's post, while using the method to pre-upload your package, you might want to upload your package to "${cluster_master_url}/tenant/data/libs", since Spark service sets these four spark properties "spark.driver.extraClassPath", "spark.driver.extraLibraryPath", "spark.executor.extraClassPath", and "spark.executor.extraLibraryPath" to ./data/libs/*
Reference: https://console.ng.bluemix.net/docs/services/AnalyticsforApacheSpark/index-gentopic3.html#spark-submit_properties
Related
I'm trying to deploy PySpark applications to an EMR cluster that have various, differing, third-party dependencies, and I am following this blog post, which describes a few approaches to packaging a virtual environment and distributing that across the cluster.
So, I've made a virtual environment with virtualenv, used venv-pack to create a tarball of the virtual environment, and I'm trying to pass that as an --archives argument to spark-submit:
spark-submit \
--deploy-mode cluster \
--master yarn \
--conf spark.pyspark.python=./venv/bin/python \
--archives s3://path/to/my/venv.tar.gz#venv \
s3://path/to/my/main.py
This fails with Cannot run program "./venv/bin/python": error=2, No such file or directory. Without the spark.pyspark.python option, the job fails with import errors, so my question is mainly about the syntax of this command when the archive is a remote object.
I can run a job that has no extra dependencies and a main method that's located remote to the cluster in S3, so I know at least something on S3 can be referenced (much like a Spark application JAR, which I'm much more familiar with). The problem is the virtual environment. I've found much literature about this, but it's all where the virtual environment archive is physically on the cluster. For many reasons, I would like to avoid having to copy virtual environments to the cluster every time a developer makes a new application.
Can I reference a remote archive? If so, what is the syntax for this and what other configuration options might I need?
I don't think it should matter, but just in case, I'm using a Livy client to submit this job remotely (the thing analogous to the above spark-submit).
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 have a “myprogram.py” and my “myprogram.scala” that I need to run on my spark machine. How Can I upload and launch them?
I have been using shell to do my transformation and calling actions, but now I want to launch a complete program on spark machine instead of entering single commands every time. Also I believe that will make it easy for me to make changes to my program instead of starting to enter commands in shell.
I did standalone installation in Ubuntu 14.04, on single machine, not a cluster, used spark 1.4.1.
I went through spark docs online, but I only find instruction on how to do that on cluster. Please help me on that.
Thank you.
The documentation to do this (as commented above) is available here: http://spark.apache.org/docs/latest/submitting-applications.html
However, the code you need is here:
# Run application locally on 8 cores
./bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master local[8] \
/path/to/examples.jar \
100
You'll need to compile the scala file using sbt (documentation here: http://www.scala-sbt.org/0.13/tutorial/index.html)
Here's some information on the build.sbt file you'll need in order to grab the right dependencies: http://spark.apache.org/docs/latest/quick-start.html
Once the scala file is compiled, you'll send the resulting jar using the above submit command.
Put it simply:
In Linux terminal, cd to the directory that spark is unpacked/installed
Note, this folder normally contains subfolders like “bin”, “conf”, “lib”, “logs” and so on.
To run the Python program locally with simple/default settings, type command
./bin/spark-submit --master local[*] myprogram.py
More complete descriptions are here like zero323 and ApolloFortyNine described.