Let me look into it
On Mon, Aug 24, 2015 at 3:41 PM Randy Gelhausen <rgel...@gmail.com> wrote:
> I am. Any suggested workarounds? I know I can add to ZEPPELIN_JAVA_OPTS,
> but seems less than ideal, given different users will have different
> dependencies.
>
> Doesn't seem wise to give end-users access to customize ZEPPELIN_JAVA_OPTS
> and restart zeppelin server.
>
> On Mon, Aug 24, 2015 at 6:14 PM, moon soo Lee <m...@apache.org> wrote:
>
>> Hi Randy,
>>
>> z.load() supposed to make dependencies available to all driver and
>> executors.
>>
>> However, it might not work correctly in yarn-client mode. Are you using
>> yarn-client mode?
>>
>> Best,
>> moon
>>
>> On Mon, Aug 24, 2015 at 9:12 AM Randy Gelhausen <rgel...@gmail.com>
>> wrote:
>>
>>> Any ideas?
>>>
>>> Is z.load supposed to make dependencies available to all Spark JVMs
>>> (driver AND executors)?
>>>
>>> Thanks,
>>> -Randy
>>>
>>> On Sun, Aug 23, 2015 at 2:41 PM, Randy Gelhausen <rgel...@gmail.com>
>>> wrote:
>>>
>>>> It seems Spark executors are not being provided with the requisite
>>>> dependencies. With spark-shell I can pass --jars /path/to/dep.jar. How can
>>>> we achieve this with Zeppelin, preferable inside a Note?
>>>>
>>>> %spark.dep
>>>> z.addRepo("hortonworks").url("
>>>> http://repo.hortonworks.com/content/repositories/releases/";)
>>>> z.load("org.apache.phoenix:phoenix-spark:4.4.0.2.3.0.0-2557")
>>>> z.load("org.apache.phoenix:phoenix-core:4.4.0.2.3.0.0-2557")
>>>> z.load("com.databricks:spark-csv_2.10:1.2.0")
>>>>
>>>> %spark
>>>> import org.apache.spark.sql._
>>>> import org.apache.phoenix.spark._
>>>> import java.sql.Connection
>>>> import java.sql.DriverManager
>>>>
>>>> val input = "/user/root/crimes/atlanta"
>>>> val zkUrl = "docker.dev:2181:/hbase-unsecure"
>>>> val table = "CRIMES"
>>>>
>>>> // Read CSV file, clean field names
>>>> var df =
>>>> sqlContext.read.format("com.databricks.spark.csv").option("header",
>>>> "true").option("DROPMALFORMED", "true").load(input)
>>>> val columns = df.columns.map(x => x.toUpperCase.replaceAll(" ", "_"))
>>>> df = df.toDF(columns:_*)
>>>>
>>>> df.save("org.apache.phoenix.spark", SaveMode.Overwrite, Map("table" ->
>>>> table, "zkUrl" -> zkUrl))
>>>>
>>>> Results:
>>>> org.apache.spark.SparkException: Job aborted due to stage failure: Task
>>>> 0 in stage 1.0 failed 4 times, most recent failure: Lost task 0.3 in stage
>>>> 1.0 (TID 5, docker.dev): java.lang.RuntimeException: java.sql.SQLException:
>>>> No suitable driver found for jdbc:phoenix:docker.dev:2181:/hbase-unsecure;
>>>> at
>>>> org.apache.phoenix.mapreduce.PhoenixOutputFormat.getRecordWriter(PhoenixOutputFormat.java:58)
>>>> at
>>>> org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1$$anonfun$12.apply(PairRDDFunctions.scala:1030)
>>>> at
>>>> org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1$$anonfun$12.apply(PairRDDFunctions.scala:1014)
>>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63) at
>>>> org.apache.spark.scheduler.Task.run(Task.scala:70) at
>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213) at
>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>>>> at
>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>>>> at java.lang.Thread.run(Thread.java:745) Caused by: java.sql.SQLException:
>>>> No suitable driver found for jdbc:phoenix:docker.dev:2181:/hbase-unsecure;
>>>> at java.sql.DriverManager.getConnection(DriverManager.java:689) at
>>>> java.sql.DriverManager.getConnection(DriverManager.java:208) at
>>>> org.apache.phoenix.mapreduce.util.ConnectionUtil.getConnection(ConnectionUtil.java:92)
>>>> at
>>>> org.apache.phoenix.mapreduce.util.ConnectionUtil.getOutputConnection(ConnectionUtil.java:80)
>>>> at
>>>> org.apache.phoenix.mapreduce.util.ConnectionUtil.getOutputConnection(ConnectionUtil.java:68)
>>>> at
>>>> org.apache.phoenix.mapreduce.PhoenixRecordWriter.<init>(PhoenixRecordWriter.java:49)
>>>> at
>>>> org.apache.phoenix.mapreduce.PhoenixOutputFormat.getRecordWriter(PhoenixOutputFormat.java:55)
>>>> ... 8 more Driver stacktrace: at
>>>> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1273)
>>>> at
>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1264)
>>>> at
>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1263)
>>>> at
>>>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>>>> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at
>>>> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1263)
>>>> at
>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:730)
>>>> at
>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:730)
>>>> at scala.Option.foreach(Option.scala:236) at
>>>> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:730)
>>>> at
>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1457)
>>>> at
>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1418)
>>>> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
>>>>
>>>
>>>
>
