Yeah, the document may not be precisely aligned with latest code, so the best way is to check the code.
-----Original Message----- From: Haopu Wang [mailto:hw...@qilinsoft.com] Sent: Wednesday, July 23, 2014 5:56 PM To: user@spark.apache.org Subject: RE: "spark.streaming.unpersist" and "spark.cleaner.ttl" Jerry, thanks for the response. For the default storage level of DStream, it looks like Spark's document is wrong. In this link: http://spark.apache.org/docs/latest/streaming-programming-guide.html#memory-tuning It mentions: "Default persistence level of DStreams: Unlike RDDs, the default persistence level of DStreams serializes the data in memory (that is, StorageLevel.MEMORY_ONLY_SER for DStream compared to StorageLevel.MEMORY_ONLY for RDDs). Even though keeping the data serialized incurs higher serialization/deserialization overheads, it significantly reduces GC pauses." I will take a look at DStream.scala although I have no Scala experience. -----Original Message----- From: Shao, Saisai [mailto:saisai.s...@intel.com] Sent: 2014年7月23日 15:13 To: user@spark.apache.org Subject: RE: "spark.streaming.unpersist" and "spark.cleaner.ttl" Hi Haopu, Please see the inline comments. Thanks Jerry -----Original Message----- From: Haopu Wang [mailto:hw...@qilinsoft.com] Sent: Wednesday, July 23, 2014 3:00 PM To: user@spark.apache.org Subject: "spark.streaming.unpersist" and "spark.cleaner.ttl" I have a DStream receiving data from a socket. I'm using local mode. I set "spark.streaming.unpersist" to "false" and leave " spark.cleaner.ttl" to be infinite. I can see files for input and shuffle blocks under "spark.local.dir" folder and the size of folder keeps increasing, although JVM's memory usage seems to be stable. [question] In this case, because input RDDs are persisted but they don't fit into memory, so write to disk, right? And where can I see the details about these RDDs? I don't see them in web UI. [answer] Yes, if memory is not enough to put input RDDs, this data will be flush to disk, because the default storage level is "MEMORY_AND_DISK_SER_2" as you can see in StreamingContext.scala. Actually you cannot not see the input RDD in web UI, you can only see the cached RDD in web UI. Then I set "spark.streaming.unpersist" to "true", the size of "spark.local.dir" folder and JVM's used heap size are reduced regularly. [question] In this case, because I didn't change "spark.cleaner.ttl", which component is doing the cleanup? And what's the difference if I set "spark.cleaner.ttl" to some duration in this case? [answer] If you set "spark.streaming.unpersist" to true, old unused rdd will be deleted, as you can see in DStream.scala. While "spark.cleaner.ttl" is timer-based spark cleaner, not only clean streaming data, but also broadcast, shuffle and other data. Thank you!
