Hi,
I set spark.shuffle.io.preferDirectBufs to false in SparkConf and this
setting can be seen in web ui's environment tab. But, it still eats memory,
i.e. -Xmx set to 512M but RES grows to 1.5G in half a day.
On Wed, Jun 3, 2015 at 12:02 PM, Shixiong Zhu <zsxw...@gmail.com> wrote:
> Could you set "spark.shuffle.io.preferDirectBufs" to false to turn off the
> off-heap allocation of netty?
>
> Best Regards,
> Shixiong Zhu
>
> 2015-06-03 11:58 GMT+08:00 Ji ZHANG <zhangj...@gmail.com>:
>
>> 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
>>
>
>
--
Jerry
