Hi Luis, The parameter “spark.cleaner.ttl” and “spark.streaming.unpersist” can be used to remove useless timeout streaming data, the difference is that “spark.cleaner.ttl” is time-based cleaner, it does not only clean streaming input data, but also Spark’s useless metadata; while “spark.streaming.unpersist” is reference-based cleaning mechanism, streaming data will be removed when out of slide duration.
Both these two parameter can alleviate the memory occupation of Spark Streaming. But if the data is flooded into Spark Streaming when start up like your situation using Kafka, these two parameters cannot well mitigate the problem. Actually you need to control the input data rate to not inject so fast, you can try “spark.straming.receiver.maxRate” to control the inject rate. Thanks Jerry From: Luis Ángel Vicente Sánchez [mailto:langel.gro...@gmail.com] Sent: Wednesday, September 10, 2014 5:21 AM To: user@spark.apache.org Subject: spark.cleaner.ttl and spark.streaming.unpersist The executors of my spark streaming application are being killed due to memory issues. The memory consumption is quite high on startup because is the first run and there are quite a few events on the kafka queues that are consumed at a rate of 100K events per sec. I wonder if it's recommended to use spark.cleaner.ttl and spark.streaming.unpersist together to mitigate that problem. And I also wonder if new RDD are being batched while a RDD is being processed. Regards, Luis
