; >> >
> >> > There is a big table (5.6 Billion rows, 450Gb in memory) loaded into
> 300
> >> > executors's memory in SparkSQL, on which we would do some calculation
> >> > using
> >> > UDFs in pyspark.
> >> > If I run my SQL on on
n
>> > using
>> > UDFs in pyspark.
>> > If I run my SQL on only a portion of the data (filtering by one of the
>> > attributes), let's say 800 million records, then all works well. But
>> > when I
>> > run the same SQL on all the data, then I receive
&g
the data (filtering by one of the
> > attributes), let's say 800 million records, then all works well. But
> when I
> > run the same SQL on all the data, then I receive
> > "java.lang.OutOfMemoryError: GC overhead limit exceeded" from basically
> all
> > o
D_ACCOUNT_STATE_UNIV
> FROM ma""")
>
> results_df.registerTempTable("m")
> sqlContext.cacheTable("m")
>
> results_df = sqlContext.sql("""SELECT COUNT(*) FROM m""")
> print(results_df.take(1))
>
>
> - the error
a portion of the data (filtering by one of the
attributes), let's say 800 million records, then all works well. But when I
run the same SQL on all the data, then I receive "*java.lang.OutOfMemoryError:
GC overhead limit exceeded"* from basically all of the executors.
It seems to me that py
he partitioned dataset successfully. I can see the output in
HDFS once all Spark tasks are done.
After the spark tasks are done, the job appears to be running for
over an hour, until I get the following (full stack trace below):
java.lang.OutOfMemoryError: GC o
I can see the output in HDFS once all Spark tasks
>> are done.
>>
>> After the spark tasks are done, the job appears to be running for over an
>> hour, until I get the following (full stack trace below):
>>
>> java.lang.OutOfMemoryError: GC overhead limit exc
an
hour, until I get the following (full stack trace below):
java.lang.OutOfMemoryError: GC overhead limit exceeded
at
org.apache.parquet.format.converter.ParquetMetadataConverter.toParquetStatistics(ParquetMetadataConverter.java:238)
I had set the driver memory to be 20GB.
I attempted
'An error occurred while calling {0}{1}{2}.\n'.
--> 300 format(target_id, '.', name), value)
301 else:
302 raise Py4JError(
Py4JJavaError: An error occurred while calling o65.partitions.
: java.lang.OutOfMemoryError: GC overhead limit exceed
val Patel [mailto:dhaval1...@gmail.com]
Sent: Saturday, November 7, 2015 12:26 AM
To: Spark User Group
Subject: [sparkR] Any insight on java.lang.OutOfMemoryError: GC overhead limit
exceeded
I have been struggling through this error since past 3 days and have tried all
possible ways/suggest
cast_2_piece0 on
localhost:39562 in memory (size: 2.4 KB, free: 530.0 MB)
15/11/06 10:45:20 INFO ContextCleaner: Cleaned accumulator 2
15/11/06 10:45:53 WARN ServletHandler: Error for /static/timeline-view.css
java.lang.OutOfMemoryError: GC overhead limit exceeded
at java.util.zip.Zip
I get java.lang.OutOfMemoryError: GC overhead limit exceeded when trying
coutn action on a file.
The file is a CSV file 217GB zise
Im using a 10 r3.8xlarge(ubuntu) machines cdh 5.3.6 and spark 1.2.0
configutation:
spark.app.id:local-1443956477103
spark.app.name:Spark shell
spark.cores.max
1.2.0 is quite old.
You may want to try 1.5.1 which was released in the past week.
Cheers
> On Oct 4, 2015, at 4:26 AM, t_ras <marti...@netvision.net.il> wrote:
>
> I get java.lang.OutOfMemoryError: GC overhead limit exceeded when trying
> coutn action on a file.
>
>
: Samstag, 11. Juli 2015 03:58
An: Ted Yu; Robin East; user
Betreff: Re: Spark GraphX memory requirements + java.lang.OutOfMemoryError: GC
overhead limit exceeded
Hello again.
So I could compute triangle numbers when run the code from spark shell without
workers (with --driver-memory 15g option
:
15/07/15 18:24:05 WARN scheduler.TaskSetManager: Lost task 267.0 in stage
0.0 (TID 267, psh-11.nse.ir): java.lang.OutOfMemoryError: GC overhead limit
exceeded
It seems that the map function keeps the hashDocs RDD in the memory and when
the memory is filled in an executor, the application
the html that has the shortest URL. However, after
running for 2-3 hours the application crashes due to memory issue. Here is
the exception:
15/07/15 18:24:05 WARN scheduler.TaskSetManager: Lost task 267.0 in stage 0.0
(TID 267, psh-11.nse.ir): java.lang.OutOfMemoryError: GC overhead limit
): java.lang.OutOfMemoryError: GC
overhead limit exceeded
It seems that the map function keeps the hashDocs RDD in the memory
and when the memory is filled in an executor, the application crashes.
Persisting the map output to disk solves the problem. Adding the
following line between map and reduce solve
Hello again.
So I could compute triangle numbers when run the code from spark shell
without workers (with --driver-memory 15g option), but with workers I have
errors. So I run spark shell:
./bin/spark-shell --master spark://192.168.0.31:7077 --executor-memory
6900m --driver-memory 15g
and workers
Ok, but what does it means? I did not change the core files of spark, so is
it a bug there?
PS: on small datasets (500 Mb) I have no problem.
Am 25.06.2015 18:02 schrieb Ted Yu yuzhih...@gmail.com:
The assertion failure from TriangleCount.scala corresponds with the
following lines:
You’ll get this issue if you just take the first 2000 lines of that file. The
problem is triangleCount() expects srdId dstId which is not the case in the
file (e.g. vertex 28). You can get round this by calling
graph.convertToCanonical Edges() which removes bi-directional edges and ensures
Yep, I already found it. So I added 1 line:
val graph = GraphLoader.edgeListFile(sc, , ...)
val newgraph = graph.convertToCanonicalEdges()
and could successfully count triangles on newgraph. Next will test it on
bigger (several Gb) networks.
I am using Spark 1.3 and 1.4 but haven't seen
Hello!
I am trying to compute number of triangles with GraphX. But get memory
error or heap size, even though the dataset is very small (1Gb). I run the
code in spark-shell, having 16Gb RAM machine (also tried with 2 workers on
separate machines 8Gb RAM each). So I have 15x more memory than the
The assertion failure from TriangleCount.scala corresponds with the
following lines:
g.outerJoinVertices(counters) {
(vid, _, optCounter: Option[Int]) =
val dblCount = optCounter.getOrElse(0)
// double count should be even (divisible by two)
assert((dblCount 1)
java.lang.OutOfMemoryError: GC overhead
limit exceeded.
The job is trying to process a filesize 4.5G.
I've tried following spark configuration:
--num-executors 6 --executor-memory 6G --executor-cores 6 --driver-memory 3G
I tried increasing more cores and executors which sometime works
Hello All,
I have a Spark job that throws java.lang.OutOfMemoryError: GC overhead
limit exceeded.
The job is trying to process a filesize 4.5G.
I've tried following spark configuration:
--num-executors 6 --executor-memory 6G --executor-cores 6 --driver-memory 3G
I tried increasing more
kras...@gmail.com
Cc: Sandy Ryza sandy.r...@cloudera.com, user@spark.apache.org
user@spark.apache.org
Subject: Re: java.lang.OutOfMemoryError: GC overhead limit exceeded
I have yarn configured with yarn.nodemanager.vmem-check-enabled=false and
yarn.nodemanager.pmem-check-enabled=false to avoid
Hi Antony,
If you look in the YARN NodeManager logs, do you see that it's killing the
executors? Or are they crashing for a different reason?
-Sandy
On Tue, Jan 27, 2015 at 12:43 PM, Antony Mayi antonym...@yahoo.com.invalid
wrote:
Hi,
I am using spark.yarn.executor.memoryOverhead=8192 yet
Hi,
I am using spark.yarn.executor.memoryOverhead=8192 yet getting executors
crashed with this error.
does that mean I have genuinely not enough RAM or is this matter of config
tuning?
other config options used:spark.storage.memoryFraction=0.3
SPARK_EXECUTOR_MEMORY=14G
running spark 1.2.0 as
sandy.r...@cloudera.com
Date: Tuesday, January 27, 2015 at 3:33 PM
To: Antony Mayi antonym...@yahoo.com
Cc: user@spark.apache.org user@spark.apache.org
Subject: Re: java.lang.OutOfMemoryError: GC overhead limit exceeded
Hi Antony,
If you look in the YARN NodeManager logs, do you see that it's
, 2015 at 3:33 PM
To: Antony Mayi antonym...@yahoo.com
Cc: user@spark.apache.org user@spark.apache.org
Subject: Re: java.lang.OutOfMemoryError: GC overhead limit exceeded
Hi Antony,
If you look in the YARN NodeManager logs, do you see that it's killing the
executors? Or are they crashing
: Re: java.lang.OutOfMemoryError: GC overhead limit exceeded
Since it's an executor running OOM it doesn't look like a container being
killed by YARN to me. As a starting point, can you repartition your job
into smaller tasks?
-Sven
On Tue, Jan 27, 2015 at 2:34 PM, Guru Medasani gdm...@outlook.com
17:02:53 ERROR executor.Executor: Exception in task 21.0 in
stage 12.0 (TID 1312)java.lang.OutOfMemoryError: GC overhead limit exceeded
at java.lang.Integer.valueOf(Integer.java:642) at
scala.runtime.BoxesRunTime.boxToInteger(BoxesRunTime.java:70
Hi guys,
My Spark Streaming application have this java.lang.OutOfMemoryError: GC
overhead limit exceeded error in SparkStreaming driver program. I have
done the following to debug with it：
1. improved the driver memory from 1GB to 2GB, this error came after 22
hrs. When the memory was 1GB
I got a 40 node cdh 5.1 cluster and attempting to run a simple spark app that
processes about 10-15GB raw data but I keep running into this error:
java.lang.OutOfMemoryError: GC overhead limit exceeded
Each node has 8 cores and 2GB memory. I notice the heap size on the
executors is set
to run a simple spark app that
processes about 10-15GB raw data but I keep running into this error:
java.lang.OutOfMemoryError: GC overhead limit exceeded
Each node has 8 cores and 2GB memory. I notice the heap size on the
executors is set to 512MB with total heap size on each executor
Hi Yifan
This works for me:
export SPARK_JAVA_OPTS=-Xms10g -Xmx40g -XX:MaxPermSize=10g
export ADD_JARS=/home/abel/spark/MLI/target/MLI-assembly-1.0.jar
export SPARK_MEM=40g
./spark-shell
Regards
On Mon, Jul 21, 2014 at 7:48 AM, Yifan LI iamyifa...@gmail.com wrote:
Hi,
I am trying to load
Thanks, Abel.
Best,
Yifan LI
On Jul 21, 2014, at 4:16 PM, Abel Coronado Iruegas acoronadoirue...@gmail.com
wrote:
Hi Yifan
This works for me:
export SPARK_JAVA_OPTS=-Xms10g -Xmx40g -XX:MaxPermSize=10g
export ADD_JARS=/home/abel/spark/MLI/target/MLI-assembly-1.0.jar
export
Hi all,
I faced with the next exception during map step:
java.lang.OutOfMemoryError (java.lang.OutOfMemoryError: GC overhead limit
exceeded)
java.lang.reflect.Array.newInstance(Array.java:70)
com.esotericsoftware.kryo.serializers.DefaultArraySerializers$ObjectArraySerializer.read
.
On Tue, Jul 8, 2014 at 9:27 AM, Konstantin Kudryavtsev
kudryavtsev.konstan...@gmail.com wrote:
Hi all,
I faced with the next exception during map step:
java.lang.OutOfMemoryError (java.lang.OutOfMemoryError: GC overhead limit
exceeded)
java.lang.reflect.Array.newInstance(Array.java:70
kudryavtsev.konstan...@gmail.com wrote:
Hi all,
I faced with the next exception during map step:
java.lang.OutOfMemoryError (java.lang.OutOfMemoryError: GC overhead limit
exceeded)
java.lang.reflect.Array.newInstance(Array.java:70
to caching or serializing.
On Tue, Jul 8, 2014 at 9:27 AM, Konstantin Kudryavtsev
kudryavtsev.konstan...@gmail.com wrote:
Hi all,
I faced with the next exception during map step:
java.lang.OutOfMemoryError (java.lang.OutOfMemoryError: GC overhead
limit exceeded
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