I use it as a standalone cluster. I run it through start-master, then start-slave. I only have one slave now, but I will probably have a few soon.
The "application" is run on a separate box. When everything was running on my mac, i was in local mode, but i never setup anything in local mode. Going "production" was a little more complex that I thought. > On Jul 13, 2016, at 10:35 PM, Chanh Le <giaosu...@gmail.com> wrote: > > 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 >> <mailto: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 >>>> >>>> >>>> >>>> >>> >> >
