Are you using the createStream or createDirectStream api? If its the former, you can try setting the StorageLevel to MEMORY_AND_DISK (it might slow things down though). Another way would be to try the later one.
Thanks Best Regards On Wed, May 27, 2015 at 1:00 PM, Ji ZHANG <zhangj...@gmail.com> wrote: > Hi Akhil, > > Thanks for your reply. Accoding to the Streaming tab of Web UI, the > Processing Time is around 400ms, and there's no Scheduling Delay, so I > suppose it's not the Kafka messages that eat up the off-heap memory. Or > maybe it is, but how to tell? > > I googled about how to check the off-heap memory usage, there's a tool > called pmap, but I don't know how to interprete the results. > > On Wed, May 27, 2015 at 3:08 PM, Akhil Das <ak...@sigmoidanalytics.com> > wrote: > >> After submitting the job, if you do a ps aux | grep spark-submit then you >> can see all JVM params. Are you using the highlevel consumer (receiver >> based) for receiving data from Kafka? In that case if your throughput is >> high and the processing delay exceeds batch interval then you will hit this >> memory issues as the data will keep on receiving and is dumped to memory. >> You can set StorageLevel to MEMORY_AND_DISK (but it slows things down). >> Another alternate will be to use the lowlevel kafka consumer >> <https://github.com/dibbhatt/kafka-spark-consumer> or to use the >> non-receiver based directStream >> <https://spark.apache.org/docs/1.3.1/streaming-kafka-integration.html#approach-2-direct-approach-no-receivers> >> that comes up with spark. >> >> Thanks >> Best Regards >> >> On Wed, May 27, 2015 at 11:51 AM, Ji ZHANG <zhangj...@gmail.com> wrote: >> >>> Hi, >>> >>> I'm using Spark Streaming 1.3 on CDH5.1 with yarn-cluster mode. I find >>> out that YARN is killing the driver and executor process because of >>> excessive use of memory. Here's something I tried: >>> >>> 1. Xmx is set to 512M and the GC looks fine (one ygc per 10s), so the >>> extra memory is not used by heap. >>> 2. I set the two memoryOverhead params to 1024 (default is 384), but the >>> memory just keeps growing and then hits the limit. >>> 3. This problem is not shown in low-throughput jobs, neither in >>> standalone mode. >>> 4. The test job just receives messages from Kafka, with batch interval >>> of 1, do some filtering and aggregation, and then print to executor logs. >>> So it's not some 3rd party library that causes the 'leak'. >>> >>> Spark 1.3 is built by myself, with correct hadoop versions. >>> >>> Any ideas will be appreciated. >>> >>> Thanks. >>> >>> -- >>> Jerry >>> >> >> > > > -- > Jerry >
