Any help? On 9 September 2015 at 00:57, Sajeevan Achuthan <achuthan.sajee...@gmail.com > wrote:
> > Similar bug reported before ZEPPELIN-108 > <https://issues.apache.org/jira/browse/ZEPPELIN-108> > > > On 8 September 2015 at 14:33, Sajeevan Achuthan < > achuthan.sajee...@gmail.com> wrote: > >> Hi Todd, >> Thanks for the quick reply. I tried that option too and I go the error >> below. Any idea? >> >> <console>:102: error: overloaded method constructor StreamingContext with >> alternatives: (path: String,sparkContext: >> org.apache.spark.org.apache.spark.org.apache.spark.org.apache.spark.org.apache.spark.SparkContext)org.apache.spark.streaming.StreamingContext >> <and> (path: String,hadoopConf: >> org.apache.hadoop.conf.Configuration)org.apache.spark.streaming.StreamingContext >> <and> (conf: org.apache.spark.SparkConf,batchDuration: >> org.apache.spark.streaming.Duration)org.apache.spark.streaming.StreamingContext >> <and> (sparkContext: >> org.apache.spark.org.apache.spark.org.apache.spark.org.apache.spark.org.apache.spark.SparkContext,batchDuration: >> org.apache.spark.streaming.Duration)org.apache.spark.streaming.StreamingContext >> cannot be applied to >> (org.apache.spark.org.apache.spark.org.apache.spark.org.apache.spark.org.apache.spark.SparkContext, >> org.apache.spark.streaming.Duration) val ssc = new StreamingContext(sc, >> Milliseconds(2000)) >> >> On 8 September 2015 at 13:53, Todd Nist <tsind...@gmail.com> wrote: >> >>> You are passing a new SparkConf to the StreamingContext, which will >>> cause the creation of a new SparkContext: >>> >>> *StreamingContext(conf: **SparkConf* >>> <https://spark.apache.org/docs/1.4.1/api/scala/org/apache/spark/SparkConf.html> >>> *, batchDuration: **Duration* >>> <https://spark.apache.org/docs/1.4.1/api/scala/org/apache/spark/streaming/Duration.html> >>> *)* >>> >>> Create a StreamingContext by providing the configuration necessary for a >>> new SparkContext. >>> >>> Is there a reason you can not use the existing SparkContext created by >>> Zeppelin? Then you can just do something like: >>> >>> val ssc = new StreamingContext(sc, Milliseconds( >>> SparkStreamingBatchInterval)) >>> >>> ssc.checkpoint(SparkCheckpointDir) >>> >>> ... >>> >>> Where "sc" is the Zeppelin provided SparkContext. >>> >>> -Todd >>> >>> >>> >>> On Tue, Sep 8, 2015 at 8:11 AM, Sajeevan Achuthan < >>> achuthan.sajee...@gmail.com> wrote: >>> >>>> Hi >>>> The problem is the Spark is allowing to create two contexts, See the >>>> log below. Could you please let me know , how to fix this problem? >>>> >>>> WARN [2015-09-08 13:09:01,191] ({pool-2-thread-5} >>>> Logging.scala[logWarning]:92) - Multiple running SparkContexts detected in >>>> the same JVM! >>>> org.apache.spark.SparkException: Only one SparkContext may be running >>>> in this JVM (see SPARK-2243). To ignore this error, set >>>> spark.driver.allowMultipleContexts = true. The currently running >>>> SparkContext was created at: >>>> org.apache.spark.SparkContext.<init>(SparkContext.scala:81) >>>> >>>> org.apache.zeppelin.spark.SparkInterpreter.createSparkContext(SparkInterpreter.java:301) >>>> >>>> org.apache.zeppelin.spark.SparkInterpreter.getSparkContext(SparkInterpreter.java:146) >>>> >>>> org.apache.zeppelin.spark.SparkInterpreter.open(SparkInterpreter.java:423) >>>> >>>> org.apache.zeppelin.interpreter.ClassloaderInterpreter.open(ClassloaderInterpreter.java:73) >>>> >>>> org.apache.zeppelin.interpreter.LazyOpenInterpreter.open(LazyOpenInterpreter.java:68) >>>> >>>> org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:92) >>>> >>>> org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:277) >>>> org.apache.zeppelin.scheduler.Job.run(Job.java:170) >>>> >>>> org.apache.zeppelin.scheduler.FIFOScheduler$1.run(FIFOScheduler.java:118) >>>> java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) >>>> java.util.concurrent.FutureTask.run(FutureTask.java:266) >>>> >>>> java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180) >>>> >>>> java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293) >>>> >>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) >>>> >>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) >>>> java.lang.Thread.run(Thread.java:745) >>>> at >>>> org.apache.spark.SparkContext$$anonfun$assertNoOtherContextIsRunning$1.apply(SparkContext.scala:2083) >>>> at >>>> org.apache.spark.SparkContext$$anonfun$assertNoOtherContextIsRunning$1.apply(SparkContext.scala:2065) >>>> at scala.Option.foreach(Option.scala:236) >>>> at >>>> org.apache.spark.SparkContext$.assertNoOtherContextIsRunning(SparkContext.scala:2065) >>>> at >>>> org.apache.spark.SparkContext$.setActiveContext(SparkContext.scala:2151) >>>> at org.apache.spark.SparkContext.<init>(SparkContext.scala:2023) >>>> at >>>> org.apache.spark.streaming.StreamingContext$.createNewSparkContext(StreamingContext.scala:834) >>>> at >>>> org.apache.spark.streaming.StreamingContext.<init>(StreamingContext.scala:80) >>>> at >>>> $line58.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:45) >>>> at >>>> $line58.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:50) >>>> at >>>> $line58.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:52) >>>> at >>>> $line58.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:54) >>>> at >>>> $line58.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:56) >>>> at >>>> $line58.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:58) >>>> at $line58.$ >>>> /Saj >>>> >>>> On 8 September 2015 at 12:25, Todd Nist <tsind...@gmail.com> wrote: >>>> >>>>> I do not see that your importing the following: >>>>> >>>>> import org.apache.spark.sql._ >>>>> >>>>> Which I believe is where you will find the DataFrame.toDF function. >>>>> >>>>> HTH. >>>>> >>>>> -Todd >>>>> >>>>> On Mon, Sep 7, 2015 at 5:49 PM, Sajeevan Achuthan < >>>>> achuthan.sajee...@gmail.com> wrote: >>>>> >>>>>> Hi Moon, >>>>>> Thanks for the reply, I tried that option too. Unfortunately, I >>>>>> tried that option too and I got the error >>>>>> data: org.apache.spark.streaming.dstream.DStream[CELL_KPIS] = >>>>>> org.apache.spark.streaming.dstream.MappedDStream@5f3ea8bb >>>>>> <console>:49: error: value toDF is not a member of >>>>>> org.apache.spark.rdd.RDD[CELL_KPIS] >>>>>> accessLogs.toDF.registerTempTable("RAS") ^ >>>>>> Any idea? >>>>>> >>>>>> On 7 September 2015 at 17:30, moon soo Lee <m...@apache.org> wrote: >>>>>> >>>>>>> Hi, >>>>>>> >>>>>>> I think you will need to convert RDD to data frame using .toDF(), >>>>>>> like >>>>>>> accessLogs.toDF.registerTempTable("RAS") >>>>>>> >>>>>>> Thanks, >>>>>>> moon >>>>>>> >>>>>>> On Mon, Sep 7, 2015 at 3:34 AM Sajeevan Achuthan < >>>>>>> achuthan.sajee...@gmail.com> wrote: >>>>>>> >>>>>>>> Zeppelin, an excellent tool. I am trying to implement a streaming >>>>>>>> application. I get an error while deploying my application. See my code >>>>>>>> below >>>>>>>> >>>>>>>> >>>>>>>> import org.apache.spark.SparkContext >>>>>>>> import org.apache.spark.SparkContext._ >>>>>>>> import org.apache.spark.SparkConf >>>>>>>> import org.apache.spark.streaming.StreamingContext >>>>>>>> import org.apache.spark.streaming.Seconds >>>>>>>> import org.apache.spark.sql.SQLContext >>>>>>>> val sparkConf = new >>>>>>>> SparkConf().setAppName("PEPA").setMaster("local[*]").set("spark.driver.allowMultipleContexts", >>>>>>>> "true") >>>>>>>> >>>>>>>> import org.apache.spark.streaming.kafka._ >>>>>>>> val ssc = new StreamingContext(sparkConf, Seconds(2)) >>>>>>>> >>>>>>>> ssc.checkpoint("checkpoint") >>>>>>>> val topicMap = Map("incoming"->1) >>>>>>>> >>>>>>>> val record = KafkaUtils.createStream(ssc, "localhost", "1", >>>>>>>> topicMap).map(_._2) >>>>>>>> record.print() >>>>>>>> case class >>>>>>>> CELL_KPIS(ECELL_Name:String,CGI:String,Number_of_Times_Interf:Double,TAOF:Double,PHL:Double,NPCCHL:Double,LRSRP:Double,NC:Double) >>>>>>>> val data = >>>>>>>> record.map(s=>s.split(",")).filter(s=>s(0)!="\"ECELL_Name\"").map( >>>>>>>> s=>CELL_KPIS(s(0), s(1), s(2).toDouble, s(3).toDouble, >>>>>>>> s(5).toDouble,s(6).toDouble, s(7).toDouble, s(8).toDouble) >>>>>>>> ) >>>>>>>> data.foreachRDD {accessLogs => >>>>>>>> import sqlContext.implicits._ >>>>>>>> accessLogs.registerTempTable("RAS") >>>>>>>> >>>>>>>> } >>>>>>>> ssc.start() >>>>>>>> ssc.awaitTermination() >>>>>>>> >>>>>>>> And I get error >>>>>>>> import org.apache.spark.SparkContext import >>>>>>>> org.apache.spark.SparkContext._ import org.apache.spark.SparkConf >>>>>>>> import >>>>>>>> org.apache.spark.streaming.StreamingContext import >>>>>>>> org.apache.spark.streaming.Seconds import >>>>>>>> org.apache.spark.sql.SQLContext >>>>>>>> sparkConf: org.apache.spark.SparkConf = >>>>>>>> org.apache.spark.SparkConf@2e5779a >>>>>>>> import org.apache.spark.streaming.kafka._ ssc: >>>>>>>> org.apache.spark.streaming.StreamingContext = >>>>>>>> org.apache.spark.streaming.StreamingContext@48621ee1 topicMap: >>>>>>>> scala.collection.immutable.Map[String,Int] = Map(incoming -> 1) record: >>>>>>>> org.apache.spark.streaming.dstream.DStream[String] = >>>>>>>> org.apache.spark.streaming.dstream.MappedDStream@6290e75e defined >>>>>>>> class CELL_KPIS data: >>>>>>>> org.apache.spark.streaming.dstream.DStream[CELL_KPIS] >>>>>>>> = org.apache.spark.streaming.dstream.MappedDStream@4bda38c3 >>>>>>>> >>>>>>>> <console>:55: error: value registerTempTable is not a member of >>>>>>>> org.apache.spark.rdd.RDD[CELL_KPIS] accessLogs.registerTempTable("RAS") >>>>>>>> >>>>>>>> *My configuration for Zeppelin* >>>>>>>> >>>>>>>> >>>>>>>> export MASTER=spark://localhost:7077 >>>>>>>> export JAVA_HOME=/usr/lib/jvm/jdk1.8.0_05 >>>>>>>> export ZEPPELIN_PORT=9090 >>>>>>>> export ZEPPELIN_SPARK_CONCURRENTSQL=false >>>>>>>> export ZEPPELIN_SPARK_USEHIVECONTEXT=false >>>>>>>> #'export MASTER=local[*] >>>>>>>> export SPARK_HOME=/home/anauser/spark-1.3/spark-1.3.0-bin-cdh4 >>>>>>>> >>>>>>>> *Interpreter configuration for spark * >>>>>>>> >>>>>>>> "2AW247KM7": { "id": "2AW247KM7", "name": "spark", "group": >>>>>>>> "spark", "properties": { "spark.cores.max": "", "spark.yarn.jar": "", >>>>>>>> "master": "local[*]", "zeppelin.spark.maxResult": "1000", >>>>>>>> "zeppelin.dep.localrepo": "local-repo", "spark.app.name": "APP3", >>>>>>>> "spark.executor.memory": "5G", "zeppelin.spark.useHiveContext": >>>>>>>> "false", >>>>>>>> "spark.driver.allowMultipleContexts": "true", "args": "", "spark.home": >>>>>>>> "/home/anauser/spark-1.3/spark-1.3.0-bin-cdh4", >>>>>>>> "zeppelin.spark.concurrentSQL": "true", "zeppelin.pyspark.python": >>>>>>>> "python" >>>>>>>> }, "interpreterGroup": [ { "class": >>>>>>>> "org.apache.zeppelin.spark.SparkInterpreter", "name": "spark" }, { >>>>>>>> "class": >>>>>>>> "org.apache.zeppelin.spark.PySparkInterpreter", "name": "pyspark" }, { >>>>>>>> "class": "org.apache.zeppelin.spark.SparkSqlInterpreter", "name": >>>>>>>> "sql" }, >>>>>>>> { "class": "org.apache.zeppelin.spark.DepInterpreter", "name": "dep" } >>>>>>>> ], >>>>>>>> "option": { "remote": true } } >>>>>>>> Is there any problem in my code or setup ? >>>>>>>> Any help very much appreciated. >>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> >