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
