to doubly sure if you don't mind?.
>>>
>>>
>>> Lastly, do you mind if I ask to open an issue in
>>> https://github.com/databricks/spark-xml/issues if you still face this
>>> problem?
>>>
>>> I will try to take a look at my best
//github.com/databricks/spark-xml/issues if you still face this
>> problem?
>>
>> I will try to take a look at my best.
>>
>>
>> Thank you.
>>
>>
>> 2016-11-16 9:12 GMT+09:00 Arun Patel :
>>
>>> I am trying to read an XML file w
f you still face this problem?
>
> I will try to take a look at my best.
>
>
> Thank you.
>
>
> 2016-11-16 9:12 GMT+09:00 Arun Patel :
>
>> I am trying to read an XML file which is 1GB is size. I am getting an
>> error 'java.lang.OutOfMemoryErr
ricks/spark-xml/issues if you still face this
problem?
I will try to take a look at my best.
Thank you.
2016-11-16 9:12 GMT+09:00 Arun Patel :
> I am trying to read an XML file which is 1GB is size. I am getting an
> error 'java.lang.OutOfMemoryError: Requested array size exceeds V
I am trying to read an XML file which is 1GB is size. I am getting an
error 'java.lang.OutOfMemoryError: Requested array size exceeds VM limit'
after reading 7 partitions in local mode. In Yarn mode, it
throws 'java.lang.OutOfMemoryError: Java heap space' error after readin
esn't work, throws java.lang.OutOfMemoryError: Requested array size
exceeds VM limit
df.write.mode('overwrite').partitionBy('e_dt','c_dt').parquet("/path/to/file/")
Thanks,
Bijay
On Wed, May 4, 2016 at 3:02 PM, Prajwal Tuladhar wrote:
> If you are run
;.format(part_dt))
> sqlcontext.read.parquet("/path/to/partion/")
>
> #
> # java.lang.OutOfMemoryError: Requested array size exceeds VM limit
> # -XX:OnOutOfMemoryError="kill -9 %p"
> # Executing /bin/sh -c "kill -9 16953"...
>
>
> What cou
Have you seen this thread ?
http://search-hadoop.com/m/q3RTtyXr2N13hf9O&subj=java+lang+OutOfMemoryError+Requested+array+size+exceeds+VM+limit
On Wed, May 4, 2016 at 2:44 PM, Bijay Kumar Pathak wrote:
> Hi,
>
> I am reading the parquet file around 50+ G which has 4013 partitio
using hive SQL but for the both cases, it throws me below
error with no further description on error.
hive_context.sql("select * from test.base_table where
date='{0}'".format(part_dt))
sqlcontext.read.parquet("/path/to/partion/")
#
# java.lang.OutOfMemoryError: R
For uniform partitioning, you can try custom Partitioner.
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> COUNT(*)
> FROM
>p_all_tx
> WHERE
>date_prefix >= "20150500"
>AND date_prefix <= "20150700"
>AND sanitizeddetails.merchantaccountid = 'Rvr7StMZSTQj';
java.lan
ry.
On Mon, Feb 23, 2015 at 6:44 PM, insperatum wrote:
> Hi, I'm using MLLib to train a random forest. It's working fine to depth 15,
> but if I use depth 20 I get a java.lang.OutOfMemoryError: Requested array
> size exceeds VM limit on the driver, from the collectAsMap opera
Hi,I'm using MLLib to train a random forest. It's working fine to depth 15,
but if I use depth 20 I get a*java.lang.OutOfMemoryError: Requested array
size exceeds VM limit* on the driver, from the collectAsMap operation in
DecisionTree.scala, around line 642.It doesn't happen un
.set("spark.serializer",
"org.apache.spark.serializer.KryoSerializer").set("spark.kryoserializer.buffer.mb",
"256")
java.lang.OutOfMemoryError: Requested array size exceeds VM limit
at java.util.Arrays.copyOf(Arrays.java:2271)
at
java.i
can provide.
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st 4
14/11/03 20:46:00 WARN BlockManager: Putting block rdd_19_5 failed
14/11/03 20:46:00 ERROR Executor: Exception in task 5.0 in stage 3.0 (TID
70)
java.lang.OutOfMemoryError: Requested array size exceeds VM limit
at java.util.Arrays.copyOf(Arrays.java:2271)
at
java.io.ByteArrayO
Hi,
>
> The array size you (or the serializer) tries to allocate is just too big
> for the JVM. No configuration can help :
>
> https://plumbr.eu/outofmemoryerror/requested-array-size-exceeds-vm-limit
>
> The only option is to split you problem further by increasing parallelism.
Hi,
The array size you (or the serializer) tries to allocate is just too big
for the JVM. No configuration can help :
https://plumbr.eu/outofmemoryerror/requested-array-size-exceeds-vm-limit
The only option is to split you problem further by increasing parallelism.
Guillaume
Hi,
I’m using
erIterator$BasicBlockFetcherIterator:
>>> Getting 1566 non-empty blocks out of 1566 blocks
>>> 14/10/11 13:00:16 INFO BlockFetcherIterator$BasicBlockFetcherIterator:
>>> Started 0 remote fetches in 4 ms
>>> 14/10/11 13:02:06 INFO ExternalAppendOnlyMap:
of
> memory where as it only has 5Gb and hence it exceeds the VM Limit.
>
>
>
> Thanks
> Best Regards
>
> On Mon, Oct 20, 2014 at 4:42 PM, Arian Pasquali
> wrote:
>
>> Hi,
>> I’m using Spark 1.1.0 and I’m having some issues to setup memory options.
&g
8Gb of memory
where as it only has 5Gb and hence it exceeds the VM Limit.
Thanks
Best Regards
On Mon, Oct 20, 2014 at 4:42 PM, Arian Pasquali
wrote:
> Hi,
> I’m using Spark 1.1.0 and I’m having some issues to setup memory options.
> I get “Requested array size exceeds VM limit” and I’m
Hi,
I’m using Spark 1.1.0 and I’m having some issues to setup memory options.
I get “Requested array size exceeds VM limit” and I’m probably missing
something regarding memory configuration
(https://spark.apache.org/docs/1.1.0/configuration.html).
My server has 30G of memory and this are my
kSetManager: Lost task 0.0 in stage 3.0
(TID 2028, idp11.foo.bar): java.lang.OutOfMemoryError: Requested array size
exceeds VM limit
java.util.Arrays.copyOf(Arrays.java:3230)
java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:113)
java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayO
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