How can I run uncompiled Spark Scala/spark-shell code as a Dataproc job?

Question

Normally, if I'm using Scala for Spark jobs I'll compile a jarfile and submit it with gcloud dataproc jobs submit spark, but sometimes for very lightweight jobs I might be using uncompiled Scala code in a notebook or using the spark-shell REPL, where I assume a SparkContext is already available.

For some of these lightweight use cases I can equivalently use PySpark and submit with gcloud dataproc jobs submit pyspark but sometimes I need easier access to Scala/Java libraries such as directly creating a org.apache.hadoop.fs.FileSystem object inside of map functions. Is there any easy way to submit such "spark-shell" equivalent jobs directly from a command-line using Dataproc Jobs APIs?

Answer 1

At the moment, there isn't a specialized top-level Dataproc Job type for uncompiled Spark Scala, but under the hood, spark-shell is just using the same mechanisms as spark-submit to run a specialized REPL driver: org.apache.spark.repl.Main. Thus, combining this with the --files flag available in gcloud dataproc jobs submit spark, you can just write snippets of Scala that you may have tested in a spark-shell or notebook session, and run that as your entire Dataproc job, assuming job.scala is a local file on your machine:

gcloud dataproc jobs submit spark --cluster ${CLUSTER} \
    --class org.apache.spark.repl.Main \
    --files job.scala \
    -- -i job.scala

Just like any other file, you can also specify any Hadoop-compatible path in the --files argument as well, such as gs:// or even hdfs://, assuming you've already placed your job.scala file there:

gcloud dataproc jobs submit spark --cluster ${CLUSTER} \
    --class org.apache.spark.repl.Main \
    --files gs://${BUCKET}/job.scala \
    -- -i job.scala

gcloud dataproc jobs submit spark --cluster ${CLUSTER} \
    --class org.apache.spark.repl.Main \
    --files hdfs:///tmp/job.scala \
    -- -i job.scala

If you've staged your job file onto the Dataproc master node via an init action, you'd use file:/// to specify that the file is found on the cluster's local filesystem instead of your local filesystem where you're running gcloud:

gcloud dataproc jobs submit spark --cluster ${CLUSTER} \
    --class org.apache.spark.repl.Main \
    --files file:///tmp/job.scala \
    -- -i job.scala

Note in all cases, the file becomes a local file in the working-directory of the main driver job, so the argument to "-i" can just be a relative path to the filename.