Hi Zhang, Could you paste your code in a gist? Not sure what you are doing inside the code to fill up memory.
Thanks Best Regards On Thu, May 28, 2015 at 10:08 AM, Ji ZHANG <zhangj...@gmail.com> wrote: > Hi, > > Yes, I'm using createStream, but the storageLevel param is by default > MEMORY_AND_DISK_SER_2. Besides, the driver's memory is also growing. I > don't think Kafka messages will be cached in driver. > > > On Thu, May 28, 2015 at 12:24 AM, Akhil Das <ak...@sigmoidanalytics.com> > wrote: > >> 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 >>> >> >> > > > -- > Jerry >
