[ https://issues.apache.org/jira/browse/SPARK-15600?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Jeff Zhang updated SPARK-15600: ------------------------------- Description: Usually I would write spark application in IDE and will run it on small dataset locally. But I have to specify the master as local, otherwise will get the following error. But if I want to run it on large dataset in cluster, then I have to the remove the code of "SparkConf.setMaster(local[4])" which is inconvenient for me. so I create this ticket to propose that if there's no master specified, then use local mode. {code} org.apache.spark.SparkException: A master URL must be set in your configuration at org.apache.spark.SparkContext.<init>(SparkContext.scala:401) at com.zjffdu.tutorial.spark.kaggle.crime.SFCrime$.main(SFCrime.scala:14) at com.zjffdu.tutorial.spark.kaggle.crime.SFCrime.main(SFCrime.scala) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:497) at com.intellij.rt.execution.application.AppMain.main(AppMain.java:140) 16/05/27 14:31:33 INFO spark.SparkContext: Successfully stopped SparkContext {code} was: Usually I would write spark application in IDE and will run it on small dataset locally. But I have to specify the master as local, otherwise will get the following error. But if I want to run it on large dataset in cluster, then I have to the remove the code of "SparkConf.setMaster" which is inconvenient for me. so I create this ticket to propose that if there's no master specified, then use local mode. {code} org.apache.spark.SparkException: A master URL must be set in your configuration at org.apache.spark.SparkContext.<init>(SparkContext.scala:401) at com.zjffdu.tutorial.spark.kaggle.crime.SFCrime$.main(SFCrime.scala:14) at com.zjffdu.tutorial.spark.kaggle.crime.SFCrime.main(SFCrime.scala) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:497) at com.intellij.rt.execution.application.AppMain.main(AppMain.java:140) 16/05/27 14:31:33 INFO spark.SparkContext: Successfully stopped SparkContext {code} > Make local mode as default mode > ------------------------------- > > Key: SPARK-15600 > URL: https://issues.apache.org/jira/browse/SPARK-15600 > Project: Spark > Issue Type: Improvement > Components: Spark Core > Reporter: Jeff Zhang > Priority: Minor > > Usually I would write spark application in IDE and will run it on small > dataset locally. But I have to specify the master as local, otherwise will > get the following error. But if I want to run it on large dataset in cluster, > then I have to the remove the code of "SparkConf.setMaster(local[4])" which > is inconvenient for me. so I create this ticket to propose that if there's no > master specified, then use local mode. > {code} > org.apache.spark.SparkException: A master URL must be set in your > configuration > at org.apache.spark.SparkContext.<init>(SparkContext.scala:401) > at > com.zjffdu.tutorial.spark.kaggle.crime.SFCrime$.main(SFCrime.scala:14) > at com.zjffdu.tutorial.spark.kaggle.crime.SFCrime.main(SFCrime.scala) > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:497) > at com.intellij.rt.execution.application.AppMain.main(AppMain.java:140) > 16/05/27 14:31:33 INFO spark.SparkContext: Successfully stopped SparkContext > {code} -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org