Re: Spark Streaming: Custom Receiver OOM consistently

[Manish Malhotra]

Thanks !

On Mon, May 22, 2017 at 5:58 PM kant kodali  wrote:

> Well there are few things here.
>
> 1. What is the Spark Version?
>
cdh 1.6

2. You said there is OOM error but what is the cause that appears in the
> log message or stack trace? OOM can happen for various reasons and JVM
> usually specifies the cause in the error message.
>
GC heap reached. Will send some logs as well.

>
> 3. What is the driver and executor memory?
>
Driver : 4g
Executor: 40g

> 4. What is the receive throughput per second and what is the data size of
> an average message?
>
Msg size : 2KB
1/sec per receiver. Running 2 receivers.

5. What OS you are using ?
>

Red hat Linux.

StorageLevel.MEMORY_AND_DISK_SER_2 This means that after the receiver
> receives the data is replicated across worker nodes.
>
yes but after batch is finished or after few batches receiver and worker
nodes should discard the old data ?


>
>
>
> On Mon, May 22, 2017 at 5:20 PM, Manish Malhotra <
> manish.malhotra.w...@gmail.com> wrote:
>
>> thanks Alonso,
>>
>> Sorry, but there are some security reservations.
>>
>> But we can assume the receiver, is equivalent to writing a JMS based
>> custom receiver, where we register a message listener and for each message
>> delivered by JMS will be stored by calling store method of listener.
>>
>>
>> Something like :
>> https://github.com/tbfenet/spark-jms-receiver/blob/master/src/main/scala/org/apache/spark/streaming/jms/JmsReceiver.scala
>>
>> Though the diff is here this JMS receiver is using block generator and
>> the calling store.
>> I m calling store when I receive message.
>> And also I'm not using block generator.
>> Not sure if that something will make the memory to balloon up.
>>
>> Btw I also run the same message consumer code from standalone map and
>> never seen this memory issue.
>>
>> On Sun, May 21, 2017 at 10:20 AM, Alonso Isidoro Roman <
>> alons...@gmail.com> wrote:
>>
>>> could you share the code?
>>>
>>> Alonso Isidoro Roman
>>> [image: https://]about.me/alonso.isidoro.roman
>>>
>>> 
>>>
>>> 2017-05-20 7:54 GMT+02:00 Manish Malhotra <
>>> manish.malhotra.w...@gmail.com>:
>>>
 Hello,

 have implemented Java based custom receiver, which consumes from
 messaging system say JMS.
 once received message, I call store(object) ... Im storing spark Row
 object.

 it run for around 8 hrs, and then goes OOM, and OOM is happening in
 receiver nodes.
 I also tried to run multiple receivers, to distribute the load but
 faces the same issue.

 something fundamentally we are doing wrong, which tells custom 
 receiver/spark
 to release the memory.
 but Im not able to crack that, atleast till now.

 any help is appreciated !!

 Regards,
 Manish


>>>
>>
>

Re: Spark Streaming: Custom Receiver OOM consistently

[kant kodali]

Well there are few things here.

1. What is the Spark Version?
2. You said there is OOM error but what is the cause that appears in the
log message or stack trace? OOM can happen for various reasons and JVM
usually specifies the cause in the error message.
3. What is the driver and executor memory?
4. What is the receive throughput per second and what is the data size of
an average message?
5. What OS you are using ?

StorageLevel.MEMORY_AND_DISK_SER_2 This means that after the receiver
receives the data is replicated across worker nodes.




On Mon, May 22, 2017 at 5:20 PM, Manish Malhotra <
manish.malhotra.w...@gmail.com> wrote:

> thanks Alonso,
>
> Sorry, but there are some security reservations.
>
> But we can assume the receiver, is equivalent to writing a JMS based
> custom receiver, where we register a message listener and for each message
> delivered by JMS will be stored by calling store method of listener.
>
>
> Something like : https://github.com/tbfenet/spark-jms-receiver/blob/
> master/src/main/scala/org/apache/spark/streaming/jms/JmsReceiver.scala
>
> Though the diff is here this JMS receiver is using block generator and the
> calling store.
> I m calling store when I receive message.
> And also I'm not using block generator.
> Not sure if that something will make the memory to balloon up.
>
> Btw I also run the same message consumer code from standalone map and
> never seen this memory issue.
>
> On Sun, May 21, 2017 at 10:20 AM, Alonso Isidoro Roman  > wrote:
>
>> could you share the code?
>>
>> Alonso Isidoro Roman
>> [image: https://]about.me/alonso.isidoro.roman
>>
>> 
>>
>> 2017-05-20 7:54 GMT+02:00 Manish Malhotra > >:
>>
>>> Hello,
>>>
>>> have implemented Java based custom receiver, which consumes from
>>> messaging system say JMS.
>>> once received message, I call store(object) ... Im storing spark Row
>>> object.
>>>
>>> it run for around 8 hrs, and then goes OOM, and OOM is happening in
>>> receiver nodes.
>>> I also tried to run multiple receivers, to distribute the load but
>>> faces the same issue.
>>>
>>> something fundamentally we are doing wrong, which tells custom 
>>> receiver/spark
>>> to release the memory.
>>> but Im not able to crack that, atleast till now.
>>>
>>> any help is appreciated !!
>>>
>>> Regards,
>>> Manish
>>>
>>>
>>
>

Re: Spark Streaming: Custom Receiver OOM consistently

[Manish Malhotra]

thanks Alonso,

Sorry, but there are some security reservations.

But we can assume the receiver, is equivalent to writing a JMS based custom
receiver, where we register a message listener and for each message
delivered by JMS will be stored by calling store method of listener.


Something like :
https://github.com/tbfenet/spark-jms-receiver/blob/master/src/main/scala/org/apache/spark/streaming/jms/JmsReceiver.scala

Though the diff is here this JMS receiver is using block generator and the
calling store.
I m calling store when I receive message.
And also I'm not using block generator.
Not sure if that something will make the memory to balloon up.

Btw I also run the same message consumer code from standalone map and never
seen this memory issue.

On Sun, May 21, 2017 at 10:20 AM, Alonso Isidoro Roman 
wrote:

> could you share the code?
>
> Alonso Isidoro Roman
> [image: https://]about.me/alonso.isidoro.roman
>
> 
>
> 2017-05-20 7:54 GMT+02:00 Manish Malhotra 
> :
>
>> Hello,
>>
>> have implemented Java based custom receiver, which consumes from
>> messaging system say JMS.
>> once received message, I call store(object) ... Im storing spark Row
>> object.
>>
>> it run for around 8 hrs, and then goes OOM, and OOM is happening in
>> receiver nodes.
>> I also tried to run multiple receivers, to distribute the load but faces
>> the same issue.
>>
>> something fundamentally we are doing wrong, which tells custom receiver/spark
>> to release the memory.
>> but Im not able to crack that, atleast till now.
>>
>> any help is appreciated !!
>>
>> Regards,
>> Manish
>>
>>
>

Re: Spark Streaming: Custom Receiver OOM consistently

[Alonso Isidoro Roman]

could you share the code?

Alonso Isidoro Roman
[image: https://]about.me/alonso.isidoro.roman


2017-05-20 7:54 GMT+02:00 Manish Malhotra :

> Hello,
>
> have implemented Java based custom receiver, which consumes from
> messaging system say JMS.
> once received message, I call store(object) ... Im storing spark Row
> object.
>
> it run for around 8 hrs, and then goes OOM, and OOM is happening in
> receiver nodes.
> I also tried to run multiple receivers, to distribute the load but faces
> the same issue.
>
> something fundamentally we are doing wrong, which tells custom receiver/spark
> to release the memory.
> but Im not able to crack that, atleast till now.
>
> any help is appreciated !!
>
> Regards,
> Manish
>
>

Spark Streaming: Custom Receiver OOM consistently

[Manish Malhotra]

Hello,

have implemented Java based custom receiver, which consumes from messaging
system say JMS.
once received message, I call store(object) ... Im storing spark Row object.

it run for around 8 hrs, and then goes OOM, and OOM is happening in receiver
 nodes.
I also tried to run multiple receivers, to distribute the load but faces
the same issue.

something fundamentally we are doing wrong, which tells custom receiver/spark
to release the memory.
but Im not able to crack that, atleast till now.

any help is appreciated !!

Regards,
Manish