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. >>>>>> >>>>> >>>> >>> >> >
