tal of 100 TB RAM and 100TB disk. So If I do something like
>> this
>>
>> spark.read.option("header","true").csv(filepath).show(false)
>>
>> Will it lead to an OOM error since it doesn't have enough memory? or it
>> will spill data onto the disk and process it?
>>
>> Thanks,
>> Sid
>>
>
path).show(false)
>
> Will it lead to an OOM error since it doesn't have enough memory? or it
> will spill data onto the disk and process it?
>
> Thanks,
> Sid
>
might lead to OOM error?
Thanks,
Sid
On Wed, Jun 22, 2022 at 6:40 PM Enrico Minack
wrote:
The RAM and disk memory consumtion depends on what you do with the
data after reading them.
Your particular action will read 20 lines from the first partition
and show them. So it will no
Hi Enrico,
Thanks for the insights.
Could you please help me to understand with one example of compressed files
where the file wouldn't be split in partitions and will put load on a
single partition and might lead to OOM error?
Thanks,
Sid
On Wed, Jun 22, 2022 at 6:40 PM Enrico Minack
quot;true").csv(filepath).show(false)
Will it lead to an OOM error since it doesn't have enough memory?
or it will spill data onto the disk and process it?
Thanks,
Sid
--
Thanks
Deepak
www.bigdatabig.com <http://www.bigdatabig.com>
www.keosha.net <http://www.keosha.net>
v(filepath).show(false)
>
> Will it lead to an OOM error since it doesn't have enough memory? or it
> will spill data onto the disk and process it?
>
> Thanks,
> Sid
>
--
Thanks
Deepak
www.bigdatabig.com
www.keosha.net
I have a 150TB CSV file.
I have a total of 100 TB RAM and 100TB disk. So If I do something like this
spark.read.option("header","true").csv(filepath).show(false)
Will it lead to an OOM error since it doesn't have enough memory? or it
will spill data onto the disk and process it?
Thanks,
Sid
s
>>>>>>> Ankit Khettry
>>>>>>>
>>>>>>> On Sat, 7 Sep, 2019, 6:52 AM Upasana Sharma, <
>>>>>>> 028upasana...@gmail.com> wrote:
>>>>>>>
>>>>>>>> Is it a streaming job?
&g
:
>>>>>>>
>>>>>>>> I have a Spark job that consists of a large number of Window
>>>>>>>> operations and hence involves large shuffles. I have roughly 900 GiBs
>>>>>>>> of
>>>>>>&
;>>>> wrote:
>>>>>>
>>>>>>> I have a Spark job that consists of a large number of Window
>>>>>>> operations and hence involves large shuffles. I have roughly 900 GiBs of
>>>>>>> data, although I am using a large enough clust
gt;>>>> have tried various other combinations without any success.
>>>>>
>>>>> spark.yarn.driver.memoryOverhead 6g
>>>>> spark.storage.memoryFraction 0.1
>>>>> spark.executor.cores 6
>>>>> spark.executor.memory 36g
cess.
>>>>
>>>> spark.yarn.driver.memoryOverhead 6g
>>>> spark.storage.memoryFraction 0.1
>>>> spark.executor.cores 6
>>>> spark.executor.memory 36g
>>>> spark.memory.offHeap.size 8g
>>>> spark.
;> spark.executor.cores 6
>>> spark.executor.memory 36g
>>> spark.memory.offHeap.size 8g
>>> spark.memory.offHeap.enabled true
>>> spark.executor.instances 10
>>> spark.driver.memory 14g
>>> spark.yarn.executor.memoryOverhead 10g
>>>
>>&
es 10
>> spark.driver.memory 14g
>> spark.yarn.executor.memoryOverhead 10g
>>
>> I keep running into the following OOM error:
>>
>> org.apache.spark.memory.SparkOutOfMemoryError: Unable to acquire 16384
>> bytes of memory, got 0
>> at
>> org.ap
> spark.memory.offHeap.size 8g
> spark.memory.offHeap.enabled true
> spark.executor.instances 10
> spark.driver.memory 14g
> spark.yarn.executor.memoryOverhead 10g
>
> I keep running into the following OOM error:
>
> org.apache.spark.memory.SparkOutOfMemoryErr
spark.yarn.executor.memoryOverhead 10g
I keep running into the following OOM error:
org.apache.spark.memory.SparkOutOfMemoryError: Unable to acquire 16384
bytes of memory, got 0
at org.apache.spark.memory.MemoryConsumer.throwOom(MemoryConsumer.java:157)
at
org.apache.spark.memory.MemoryConsumer.allocateArray
hese maps into a two-level Map i.e.
>> Map[String, Map[String, Int]] ? Or would this still count against me?
>>
>> What if I manually split them up into numerous Map variables?
>>
>> On Mon, Aug 15, 2016 at 2:12 PM, Arun Luthra
>> wrote:
>>
>>> I got t
variables?
>
> On Mon, Aug 15, 2016 at 2:12 PM, Arun Luthra
> wrote:
>
>> I got this OOM error in Spark local mode. The error seems to have been at
>> the start of a stage (all of the stages on the UI showed as complete, there
>> were more stages to do but had not sho
ainst me?
What if I manually split them up into numerous Map variables?
On Mon, Aug 15, 2016 at 2:12 PM, Arun Luthra wrote:
> I got this OOM error in Spark local mode. The error seems to have been at
> the start of a stage (all of the stages on the UI showed as complete, there
> were
I got this OOM error in Spark local mode. The error seems to have been at
the start of a stage (all of the stages on the UI showed as complete, there
were more stages to do but had not showed up on the UI yet).
There appears to be ~100G of free memory at the time of the error.
Spark 2.0.0
200G
My workers are going OOM over time. I am running a streaming job in spark
1.4.0.
Here is the heap dump of workers.
*16,802 instances of "org.apache.spark.deploy.worker.ExecutorRunner",
loaded by "sun.misc.Launcher$AppClassLoader @ 0xdff94088" occupy
488,249,688 (95.80%) bytes. These instance
s from Kafka
topic and are pending to be scheduled because of delay in processing... Will
my force killing the streaming job lose that data which is not yet
scheduled?
Please help ASAP.
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/OOM-error-in-Spark-worker
llelism", "300").set("spark.serializer",
> "org.apache.spark.serializer.KryoSerializer").set("spark.kryoserializer.buffer.mb",
> "500").set("spark.akka.frameSize", "256").set("spark.akka.timeout", "300")
>
> Howev
uot;).set("spark.akka.frameSize", "256").set("spark.akka.timeout", "300")
However, at the aggregate step (Line 168)
val sums = breezeData.aggregate(ExpectationSum.zero(k, d))(compute.value, _
+= _)
I get OOM error and the application hangs indefini
Thanks for the pointer it led me to
http://spark.apache.org/docs/1.2.0/tuning.html increasing parallelism
resolved the issue.
On Mon, Feb 16, 2015 at 11:57 PM, Akhil Das
wrote:
> Increase your executor memory, Also you can play around with increasing
> the number of partitions/parallelism etc.
Increase your executor memory, Also you can play around with increasing the
number of partitions/parallelism etc.
Thanks
Best Regards
On Tue, Feb 17, 2015 at 3:39 AM, Harshvardhan Chauhan
wrote:
> Hi All,
>
>
> I need some help with Out Of Memory errors in my application. I am using
> Spark 1.1
Hi All,
I need some help with Out Of Memory errors in my application. I am using
Spark 1.1.0 and my application is using Java API. I am running my app on
EC2 25 m3.xlarge (4 Cores 15GB Memory) instances. The app only fails
sometimes. Lots of mapToPair tasks a failing. My app is configured to ru
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