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)
>>>
>>
>>
