Hi,
Thanks for you information. I'll give spark1.4 a try when it's released.
On Wed, Jun 3, 2015 at 11:31 AM, Tathagata Das <t...@databricks.com> wrote:
> Could you try it out with Spark 1.4 RC3?
>
> Also pinging, Cloudera folks, they may be aware of something.
>
> BTW, the way I have debugged memory leaks in the past is as follows.
>
> Run with a small driver memory, say 1 GB. Periodically (maybe a script),
> take snapshots of histogram and also do memory dumps. Say every hour. And
> then compare the difference between two histo/dumps that are few hours
> separated (more the better). Diffing histo is easy. Diff two dumps can be
> done in JVisualVM, it will show the diff in the objects that got added in
> the later dump. That makes it easy to debug what is not getting cleaned.
>
> TD
>
>
> On Tue, Jun 2, 2015 at 7:33 PM, Ji ZHANG <zhangj...@gmail.com> wrote:
>
>> Hi,
>>
>> Thanks for you reply. Here's the top 30 entries of jmap -histo:live
>> result:
>>
>> num #instances #bytes class name
>> ----------------------------------------------
>> 1: 40802 145083848 [B
>> 2: 99264 12716112 <methodKlass>
>> 3: 99264 12291480 <constMethodKlass>
>> 4: 8472 9144816 <constantPoolKlass>
>> 5: 8472 7625192 <instanceKlassKlass>
>> 6: 186 6097824
>> [Lscala.concurrent.forkjoin.ForkJoinTask;
>> 7: 7045 4804832 <constantPoolCacheKlass>
>> 8: 139168 4453376 java.util.HashMap$Entry
>> 9: 9427 3542512 <methodDataKlass>
>> 10: 141312 3391488
>> io.netty.buffer.PoolThreadCache$MemoryRegionCache$Entry
>> 11: 135491 3251784 java.lang.Long
>> 12: 26192 2765496 [C
>> 13: 813 1140560 [Ljava.util.HashMap$Entry;
>> 14: 8997 1061936 java.lang.Class
>> 15: 16022 851384 [[I
>> 16: 16447 789456 java.util.zip.Inflater
>> 17: 13855 723376 [S
>> 18: 17282 691280 java.lang.ref.Finalizer
>> 19: 25725 617400 java.lang.String
>> 20: 320 570368
>> [Lio.netty.buffer.PoolThreadCache$MemoryRegionCache$Entry;
>> 21: 16066 514112
>> java.util.concurrent.ConcurrentHashMap$HashEntry
>> 22: 12288 491520
>> org.jboss.netty.util.internal.ConcurrentIdentityHashMap$Segment
>> 23: 13343 426976
>> java.util.concurrent.locks.ReentrantLock$NonfairSync
>> 24: 12288 396416
>> [Lorg.jboss.netty.util.internal.ConcurrentIdentityHashMap$HashEntry;
>> 25: 16447 394728 java.util.zip.ZStreamRef
>> 26: 565 370080 [I
>> 27: 508 272288 <objArrayKlassKlass>
>> 28: 16233 259728 java.lang.Object
>> 29: 771 209232
>> [Ljava.util.concurrent.ConcurrentHashMap$HashEntry;
>> 30: 2524 192312 [Ljava.lang.Object;
>>
>> But as I mentioned above, the heap memory seems OK, the extra memory is
>> consumed by some off-heap data. I can't find a way to figure out what is in
>> there.
>>
>> Besides, I did some extra experiments, i.e. run the same program in
>> difference environments to test whether it has off-heap memory issue:
>>
>> spark1.0 + standalone = no
>> spark1.0 + yarn = no
>> spark1.3 + standalone = no
>> spark1.3 + yarn = yes
>>
>> I'm using CDH5.1, so the spark1.0 is provided by cdh, and
>> spark-1.3.1-bin-hadoop2.3 is downloaded from the official website.
>>
>> I could use spark1.0 + yarn, but I can't find a way to handle the logs,
>> level and rolling, so it'll explode the harddrive.
>>
>> Currently I'll stick to spark1.0 + standalone, until our ops team decides
>> to upgrade cdh.
>>
>>
>>
>> On Tue, Jun 2, 2015 at 2:58 PM, Tathagata Das <t...@databricks.com>
>> wrote:
>>
>>> While you are running is it possible for you login into the YARN node
>>> and get histograms of live objects using "jmap -histo:live". That may
>>> reveal something.
>>>
>>>
>>> On Thursday, May 28, 2015, Ji ZHANG <zhangj...@gmail.com> wrote:
>>>
>>>> Hi,
>>>>
>>>> Unfortunately, they're still growing, both driver and executors.
>>>>
>>>> I run the same job with local mode, everything is fine.
>>>>
>>>> On Thu, May 28, 2015 at 5:26 PM, Akhil Das <ak...@sigmoidanalytics.com>
>>>> wrote:
>>>>
>>>>> Can you replace your counting part with this?
>>>>>
>>>>> logs.filter(_.s_id > 0).foreachRDD(rdd => logger.info(rdd.count()))
>>>>>
>>>>>
>>>>>
>>>>> Thanks
>>>>> Best Regards
>>>>>
>>>>> On Thu, May 28, 2015 at 1:02 PM, Ji ZHANG <zhangj...@gmail.com> wrote:
>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> I wrote a simple test job, it only does very basic operations. for
>>>>>> example:
>>>>>>
>>>>>> val lines = KafkaUtils.createStream(ssc, zkQuorum, group,
>>>>>> Map(topic -> 1)).map(_._2)
>>>>>> val logs = lines.flatMap { line =>
>>>>>> try {
>>>>>> Some(parse(line).extract[Impression])
>>>>>> } catch {
>>>>>> case _: Exception => None
>>>>>> }
>>>>>> }
>>>>>>
>>>>>> logs.filter(_.s_id > 0).count.foreachRDD { rdd =>
>>>>>> rdd.foreachPartition { iter =>
>>>>>> iter.foreach(count => logger.info(count.toString))
>>>>>> }
>>>>>> }
>>>>>>
>>>>>> It receives messages from Kafka, parse the json, filter and count the
>>>>>> records, and then print it to logs.
>>>>>>
>>>>>> Thanks.
>>>>>>
>>>>>>
>>>>>> On Thu, May 28, 2015 at 3:07 PM, Akhil Das <
>>>>>> ak...@sigmoidanalytics.com> wrote:
>>>>>>
>>>>>>> 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
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> Jerry
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> Jerry
>>>>
>>>
>>
>>
>> --
>> Jerry
>>
>
>
--
Jerry
