Hi,
This problem is very annoying for me and I'm tired of surfing the network
without any good advice to follow.

I have a complex job. It has been worked fine until I needed to save
partial results (RDDs) to files.
So I tried to cache the RDDs and then call a saveAsText method and follow
the workflow as usual.
The first problem I noticed was that RDD was not totally cached .
So I changed the cache() method for a
 persist(StorageLevel.MEMORY_AND_DISK_SER())
hoping this should persist 100% of RDD. But it didn't at all.
That not make any sense to me. Is not suposed that with that storage level,
the fractions which don't fit in memory will be persisted in disk?

[image: Imagen integrada 3]

Even insignificant RDDs of about ~5MB was cached only at 82%.

The last one in the previous image, which had 6628 cached partitions, is
distributed in the following way:


[image: Imagen integrada 4]

The executors Storage Memory wee far away from be filled:
[image: Imagen integrada 5]


[image: Imagen integrada 6]


The only thing I noticed is near to be exhausted is "Memory" in hadoop
Cluster Memory:

[image: Imagen integrada 7]

I don't know the relation between this "memory used" column and the memory
described in Spark UI (Storage memory was almost empty).


Finally, the job accumulated a lot of  stages (~100) for recalculation of
RDDs not cached  and the cluster failed with an enigmatic and apparently
known error:

16/12/14 21:03:54 ERROR LiveListenerBus: Dropping SparkListenerEvent
because no remaining room in event queue. This likely means one of the
SparkListeners is too slow and cannot keep up with the rate at which
tasks are being started by the scheduler.
16/12/14 21:03:54 WARN LiveListenerBus: Dropped 1 SparkListenerEvents
since Thu Jan 01 01:00:00 CET 1970


 mentioned here <https://issues.apache.org/jira/browse/SPARK-18838> .


Please, any clue, any comment indeed, will be much appreciated.

Thanks,
Pedro


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