Hi,
We are running a long running Spark application ( which executes lots of
quick jobs using our scheduler ) on Spark stand-alone cluster 2.4.0.
We see that old shuffle files ( a week old for example ) are not deleted
during the execution of the application, which leads to out of disk space
This is the job of ContextCleaner. There are few a property that you can tweak
to see if that helps:
spark.cleaner.periodicGC.interval
spark.cleaner.referenceTracking
spark.cleaner.referenceTracking.blocking.shuffle
Regards
Prathmesh Ranaut
> On Jul 21, 2019, at 11:36 AM, Prathmesh Ranaut
Thanks,
I looked into these options, the cleaner periodic interval is set to 30 min
by default.
The block option for shuffle -
*spark.cleaner.referenceTracking.blocking.shuffle* - is set to false by
default.
What are the implications of setting it to true?
Will it make the driver slower?
Thanks,
I dug some of my old stuff using Spark as ETL.
Regarding the question
"Any reason why Spark's SaveMode doesn't have mode that ignore any Primary
Key/Unique constraint violations?"
There is no way Spark can determine if PK constraint is violated until it
receives such message from Oracle through
Hi , I wrote a code in future block which read data from dataset and cache
it which is used later in the code. I faced a issue that data.cached() data
will be replaced by concurrent running thread . Is there any way we can
avoid this condition.
val dailyData = callDetailsDS.collect.toList
val
Hi Alex,
Shuffle files in spark are deleted when the object holding a reference to
the shuffle file on disk goes out of scope (is garbage collected by the
JVM). Could it be the case that you are keeping these objects alive?
Regards,
Keith.
http://keith-chapman.com
On Sun, Jul 21, 2019 at
Hi Spark communities,
I just found out that in
https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.fullOuterJoin,
the documentation is "Perform a right outer join of self and other." It
should be a full outer join, not a right outer join, as shown in the
example and the