Github user liancheng commented on the pull request:
https://github.com/apache/spark/pull/1715#issuecomment-50972156
Some examples:
1. For Python applications, we need a primary Python file to construct the
arguments for `PythonRunner`
1. Logic related to `spark-shell`, `pyspark-shell` and `spark-internal`
1. In standalone cluster mode, `org.apache.spark.deploy.Client` requires a
primary user jar to pass to `DriverRunner`
([1](https://github.com/apache/spark/blob/87738bfa4051771ddfb8c4a4c1eb142fd77e3a46/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala#L253),
[2](https://github.com/apache/spark/blob/87738bfa4051771ddfb8c4a4c1eb142fd77e3a46/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala#L71),
[3](https://github.com/apache/spark/blob/87738bfa4051771ddfb8c4a4c1eb142fd77e3a46/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala#L145))
I understand that "primary" is more a internal term, but it's still well
defined: for Java applications, conceptually the jar file containing the main
class that starts he Spark application should be the primary one.
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