Hi all,

above topic has been mentioned before in this list between March - June 2016, again mentioned in July 2016 and got asked similarly in early September 2017 - none of which had a conclusion on how to limit effectively the number of Python processes spawned by PySparks respectively the number of actual cores used per executor.

Does anyone have tips or solutions at hand? Thanks!

Bolding for the skim-readers, I'm not shouting ;)

Problem on my side, example setup:
Mesos 1.3.1, Spark 2.1.1, 
Coarse mode, dynamicAllocation off, shuffle service off
spark.cores.max=112
spark.executor.cores=8 (machines have 32)
spark.executor.memory=50G (machines have 250G)

Stage 1 goes okyish, after setting spark.task.cpus=2. Without this setting, there was 8 python processes per executor (using 8 CPUs) plus 2-4 CPUs of the java processes, ending up with 10-14 cores per executor instead of the 8. This JVM overhead is ok to handle with this setting I believe.
val df = spark.read.parquet(path) 
val grpd = df.rdd.map(lambda x: (x[0], list(x[1:]))).groupByKey()
This stage runs 3 hours, writes 990G of shuffle.

Stage 2 is roughly speaking a 
grpd.mapValues(sklearn.DBSCAN(n_jobs=1).fit_predict(_)).write.parquet(path)
which runs much more (sometimes dozens!) than 4 python processes per executor, which would be the expected number given 8 executor cores with task.cpus=2. Runs for about 15 hours.

We are fairly sure that the mapValues function doesn't apply multi-processing. Actually this would probably result in single Python processes use more than 100% CPU - something which is never observed.

Unfortunately these Spark tasks then overuse their allocated Mesos resources by 100-150% (hitting the physical limit of the machine).

Any tipps much appreciated!

Best,
Fabian