Hi Jean, How do you run your Spark Application? Local Mode, Cluster Mode? If you run in local mode did you use —driver-memory and —executor-memory because in local mode your setting about executor and driver didn’t work that you expected.
> On Jul 14, 2016, at 8:43 AM, Jean Georges Perrin <j...@jgp.net> wrote: > > Looks like replacing the setExecutorEnv() by set() did the trick... let's see > how fast it'll process my 50x 10ˆ15 data points... > >> On Jul 13, 2016, at 9:24 PM, Jean Georges Perrin <j...@jgp.net >> <mailto:j...@jgp.net>> wrote: >> >> I have added: >> >> SparkConf conf = new >> SparkConf().setAppName("app").setExecutorEnv("spark.executor.memory", "8g") >> .setMaster("spark://10.0.100.120:7077 >> <spark://10.0.100.120:7077>"); >> >> but it did not change a thing >> >>> On Jul 13, 2016, at 9:14 PM, Jean Georges Perrin <j...@jgp.net >>> <mailto:j...@jgp.net>> wrote: >>> >>> Hi, >>> >>> I have a Java memory issue with Spark. The same application working on my >>> 8GB Mac crashes on my 72GB Ubuntu server... >>> >>> I have changed things in the conf file, but it looks like Spark does not >>> care, so I wonder if my issues are with the driver or executor. >>> >>> I set: >>> >>> spark.driver.memory 20g >>> spark.executor.memory 20g >>> And, whatever I do, the crash is always at the same spot in the app, which >>> makes me think that it is a driver problem. >>> >>> The exception I get is: >>> >>> 16/07/13 20:36:30 WARN TaskSetManager: Lost task 0.0 in stage 7.0 (TID 208, >>> micha.nc.rr.com): java.lang.OutOfMemoryError: Java heap space >>> at java.nio.HeapCharBuffer.<init>(HeapCharBuffer.java:57) >>> at java.nio.CharBuffer.allocate(CharBuffer.java:335) >>> at java.nio.charset.CharsetDecoder.decode(CharsetDecoder.java:810) >>> at org.apache.hadoop.io.Text.decode(Text.java:412) >>> at org.apache.hadoop.io.Text.decode(Text.java:389) >>> at org.apache.hadoop.io.Text.toString(Text.java:280) >>> at >>> org.apache.spark.sql.execution.datasources.json.JSONRelation$$anonfun$org$apache$spark$sql$execution$datasources$json$JSONRelation$$createBaseRdd$1.apply(JSONRelation.scala:105) >>> at >>> org.apache.spark.sql.execution.datasources.json.JSONRelation$$anonfun$org$apache$spark$sql$execution$datasources$json$JSONRelation$$createBaseRdd$1.apply(JSONRelation.scala:105) >>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) >>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) >>> at scala.collection.Iterator$class.foreach(Iterator.scala:727) >>> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157) >>> at >>> scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:144) >>> at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1157) >>> at >>> scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:201) >>> at scala.collection.AbstractIterator.aggregate(Iterator.scala:1157) >>> at >>> org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1$$anonfun$23.apply(RDD.scala:1135) >>> at >>> org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1$$anonfun$23.apply(RDD.scala:1135) >>> at >>> org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1$$anonfun$24.apply(RDD.scala:1136) >>> at >>> org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1$$anonfun$24.apply(RDD.scala:1136) >>> at >>> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710) >>> at >>> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710) >>> at >>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) >>> at org.apache.spark.scheduler.Task.run(Task.scala:89) >>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227) >>> at >>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) >>> at >>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) >>> at java.lang.Thread.run(Thread.java:745) >>> >>> I have set a small memory "dumper" in my app. At the beginning, it says: >>> >>> ** Free ......... 1,413,566 >>> ** Allocated .... 1,705,984 >>> ** Max .......... 16,495,104 >>> **> Total free ... 16,202,686 >>> Just before the crash, it says: >>> >>> ** Free ......... 1,461,633 >>> ** Allocated .... 1,786,880 >>> ** Max .......... 16,495,104 >>> **> Total free ... 16,169,857 >>> >>> >>> >>> >> >
