Aaand, the error! :)
Exception in thread "org.apache.hadoop.hdfs.PeerCache@4e000abf"
Exception: java.lang.OutOfMemoryError thrown from the
UncaughtExceptionHandler in thread
"org.apache.hadoop.hdfs.PeerCache@4e000abf"
Exception in thread "Thread-7"
Exception: java.lang.OutOfMemoryError thrown
Hey, I'd try to debug, profile ResolvedDataSource. As far as I know, your
write will be performed by the JVM.
On Mon, Sep 7, 2015 at 4:11 PM Tóth Zoltán wrote:
> Unfortunately I'm getting the same error:
> The other interesting things are that:
> - the parquet files got
Hi,
Can you try to using save method instead of write?
ex: out_df.save("path","parquet")
b0c1
--
Skype: boci13, Hangout: boci.b...@gmail.com
On Mon, Sep 7, 2015 at
Unfortunately I'm getting the same error:
The other interesting things are that:
- the parquet files got actually written to HDFS (also with
.write.parquet() )
- the application gets stuck in the RUNNING state for good even after the
error is thrown
15/09/07 10:01:10 INFO spark.ContextCleaner:
Hi,
I ran your example on Spark-1.4.1 and 1.5.0-rc3. It succeeds on 1.4.1 but
throws the OOM on 1.5.0. Do any of you know which PR introduced this
issue?
Zsolt
2015-09-07 16:33 GMT+02:00 Zoltán Zvara :
> Hey, I'd try to debug, profile ResolvedDataSource. As far as I
Did you try increasing the parallelism?
Thanks
Best Regards
On Fri, Jan 16, 2015 at 10:41 AM, Anand Mohan chinn...@gmail.com wrote:
We have our Analytics App built on Spark 1.1 Core, Parquet, Avro and Spray.
We are using Kryo serializer for the Avro objects read from Parquet and we
are using
Hi ,
How to increase the heap size?
What is the difference between spark executor memory and heap size?
Thanks Regards,
Meethu M
On Monday, 18 August 2014 12:35 PM, Akhil Das ak...@sigmoidanalytics.com
wrote:
I believe spark.shuffle.memoryFraction is the one you are looking for.
/configuration.html
Thanks
Jerry
From: MEETHU MATHEW [mailto:meethu2...@yahoo.co.in]
Sent: Wednesday, August 20, 2014 4:48 PM
To: Akhil Das; Ghousia
Cc: user@spark.apache.org
Subject: Re: OutOfMemory Error
Hi ,
How to increase the heap size?
What is the difference between spark executor memory and heap
Hi,
Any further info on this??
Do you think it would be useful if we have a in memory buffer implemented
that stores the content of the new RDD. In case the buffer reaches a
configured threshold, content of the buffer are spilled to the local disk.
This saves us from OutOfMememory Error.
Hi Ghousia,
You can try the following:
1. Increase the heap size
https://spark.apache.org/docs/0.9.0/configuration.html
2. Increase the number of partitions
http://stackoverflow.com/questions/21698443/spark-best-practice-for-retrieving-big-data-from-rdd-to-local-machine
3. You could try
Thanks for the answer Akhil. We are right now getting rid of this issue by
increasing the number of partitions. And we are persisting RDDs to
DISK_ONLY. But the issue is with heavy computations within an RDD. It would
be better if we have the option of spilling the intermediate transformation
I believe spark.shuffle.memoryFraction is the one you are looking for.
spark.shuffle.memoryFraction : Fraction of Java heap to use for aggregation
and cogroups during shuffles, if spark.shuffle.spill is true. At any given
time, the collective size of all in-memory maps used for shuffles is
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