Share one Spark cluster for all tests #1290
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Draft implementation of having one Spark cluster for all the tests. This is implemented as two ctest fixtures, one to spawn a Spark master and one to spawn a Spark worker in the background. According to https://spark.apache.org/docs/latest/spark-standalone.html#resource-scheduling, the Spark standalone cluster supports a basic FIFO scheduling method. As such, the only way to benefit from having multiple Spark applications running concurrently is to have a Spark worker with many cores (the more the better), and then launch Spark applications that only use 2 cores each (through the spark.cores.max config option).
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Draft implementation of having one Spark cluster for all the tests. This is implemented as two ctest fixtures, one to spawn a Spark master and one to spawn a Spark worker in the background.
According to
https://spark.apache.org/docs/latest/spark-standalone.html#resource-scheduling, the Spark standalone cluster supports a basic FIFO scheduling method. As such, the only way to benefit from having multiple Spark applications running concurrently is to have a Spark worker with many cores (the more the better), and then launch Spark applications that only use 2 cores each (through the spark.cores.max config option).
This is a draft PR, just to document the progress done during the ROOT hackathon in March 2025. Anecdotally, I see some tangible improvement in the best case scenario on my laptop running only ctest, idle otherwise, with the following config:
test_allsuite in master: around 80s.pytest file concurrently, all internally creating a SparkContext that connects to the system process Spark worker: around 50s.The benefit is not yet completely clear, since these numbers would probably change heavily when put in the context of a real ROOT CI run. Specifically, creating a Spark worker process that takes all the cores of the CI machine means leaving little to no room for the other ctests.