s? repartition the input dataset to have less
>>>> partitions? I used df.rdd.getNumPartitions() to check the input data
>>>> partitions, they have 9 and 17 partitions respectively, should I decrease
>>>> them further? I also read a pos
xception-when-processing-big-data-se),
>>> saying increasing partitions may help.Which one makes more sense? I
>>> repartitioned the input data to 20 and 30 partitions, but still no luck.
>>>
>>> Any suggestions?
>>>
>>> 23/03/10 14
>
>> Any suggestions?
>>
>> 23/03/10 14:32:19 WARN TaskSetManager: Lost task 58.1 in stage 27.0 (TID
>> 3783) (10.1.0.116 executor 33): FetchFailed(BlockManagerId(72, 10.1.15.199,
>> 36791, None), shuffleId=24, mapIndex=77, mapId=3457, reduceId=58,
(TID
> 3783) (10.1.0.116 executor 33): FetchFailed(BlockManagerId(72, 10.1.15.199,
> 36791, None), shuffleId=24, mapIndex=77, mapId=3457, reduceId=58, message=
> org.apache.spark.shuffle.FetchFailedException
> at
> org.apache.spark.errors.SparkCoreErrors$.fetchFailedError(Spark
in stage 27.0
(TID 3783) (10.1.0.116 executor 33): FetchFailed(BlockManagerId(72,
10.1.15.199, 36791, None), shuffleId=24, mapIndex=77, mapId=3457,
reduceId=58, message=
org.apache.spark.shuffle.FetchFailedException
at
org.apache.spark.errors.SparkCoreErrors$.fetchFailedError
bove error in Spark SQL . I have increase (using 5000
>>> ) number of partitions but still getting the same error .
>>>
>>> My data most probably is skew.
>>>
>>>
>>>
>>> org.apache.spark.shuffle.FetchFailedException: Too large frame: 42
>> I am getting the above error in Spark SQL . I have increase (using 5000 )
>> number of partitions but still getting the same error .
>>
>> My data most probably is skew.
>>
>>
>>
>> org.apache.spark.shuffle.FetchFailedException: Too
increase (using 5000 )
> number of partitions but still getting the same error .
>
> My data most probably is skew.
>
>
>
> org.apache.spark.shuffle.FetchFailedException: Too large frame: 4247124829
> at
> org.apache.spark.storage.ShuffleBloc
Hi
I am getting the above error in Spark SQL . I have increase (using 5000 )
number of partitions but still getting the same error .
My data most probably is skew.
org.apache.spark.shuffle.FetchFailedException: Too large frame: 4247124829
che.org
> *Subject:* Re: Job keeps aborting because of
> org.apache.spark.shuffle.FetchFailedException:
> Failed to connect to server/ip:39232
>
>
> Thanks Juan for taking the time
>
> Here's more info:
> - This is running on Yarn in Master mode
>
> - See config para
ct: Re: Job keeps aborting because of
org.apache.spark.shuffle.FetchFailedException: Failed to connect to
server/ip:39232
Thanks Juan for taking the time
Here's more info:
- This is running on Yarn in Master mode
- See config params below
- This is a corporate environment. In general nodes
etwork.timeout=1000s ^
From: Juan Rodríguez Hortalá <juan.rodriguez.hort...@gmail.com>
Sent: Friday, July 28, 2017 4:20:40 PM
To: jeff saremi
Cc: user@spark.apache.org
Subject: Re: Job keeps aborting because of
org.apache.spark.shuffle.FetchFailedException: Faile
Hi Jeff,
Can you provide more information about how are you running your job? In
particular:
- which cluster manager are you using? It is YARN, Mesos, Spark
Standalone?
- with configuration options are you using to submit the job? In
particular are you using dynamic allocation or external
We have a not too complex and not too large spark job that keeps dying with
this error
I have researched it and I have not seen any convincing explanation on why
I am not using a shuffle service. Which server is the one that is refusing the
connection?
If I go to the server that is being
I had the same problem. One forum post elsewhere suggested that too much
network communication might be using up available ports. I reduced the
partition size via repartition(int) and it solved the problem.
--
View this message in context:
Also, this is the command I use to submit the Spark application:
**
where *recommendation_engine-0.1-py2.7.egg* is a Python egg of my own
library I've written for this application, and *'file'* and
*'/home/spark/enigma_analytics/tests/msg-epims0730_small.json'* are input
arguments for the
Slight update I suppose?
For some reason, sometimes it will connect and continue and the job will be
completed. But most of the time I still run into this error and the job is
killed and the application doesn't finish.
Still have no idea why this is happening. I could really use some help here.
attempts,
but I still get the same error. The error code that stands out to me is:
*org.apache.spark.shuffle.FetchFailedException: Failed to connect to
spark-mastr-1:xx*
The following is the error that I receive on my most recent attempted run of
the application:
Traceback (most recent call last
.
org.apache.spark.shuffle.FetchFailedException: Failed to connect to .
Now, when I restart the same worker or (2 workers were running on the
machine and I killed just one of them) then the execution resumes and the
process is completed.
Please help me in understanding why on a worker failure my
this query on a data size of 4 billion rows and
getting org.apache.spark.shuffle.FetchFailedException error.
select adid,position,userid,price
from (
select adid,position,userid,price,
dense_rank() OVER (PARTITION BY adlocationid ORDER BY price DESC) as rank
FROM trainInfo) as tmp
WHERE rank
Did you try increasing sql partitions?
On Tue, Aug 25, 2015 at 11:06 AM, kundan kumar iitr.kun...@gmail.com
wrote:
I am running this query on a data size of 4 billion rows and
getting org.apache.spark.shuffle.FetchFailedException error.
select adid,position,userid,price
from (
select adid
from Spark 1.2 to
Spark
1.3
15/05/18 18:22:39 WARN TaskSetManager: Lost task 0.0 in stage 1.6 (TID 84,
cloud8-server): FetchFailed(BlockManagerId(1, cloud4-server, 7337),
shuffleId=0, mapId=9, reduceId=1, message=
org.apache.spark.shuffle.FetchFailedException: java.lang.RuntimeException
=
org.apache.spark.shuffle.FetchFailedException: java.lang.RuntimeException:
Failed to open file:
/tmp/spark-fff63849-a318-4e48-bdea-2f563076ad5d/spark-40ba3a41-0f4d-446e-b806-e788e210d394/spark-a3d61f7a-22e9-4b3b-9346-ff3b70d0e43d/blockmgr-0e3b2b5d-f677-4e91-b98b-ed913adbd15f/39/shuffle_0_9_0.index
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