[
https://issues.apache.org/jira/browse/KUDU-2633?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Grant Henke resolved KUDU-2633.
-------------------------------
Fix Version/s: NA
Resolution: Duplicate
> Missing documentation about Spark's KuduContext API
> ---------------------------------------------------
>
> Key: KUDU-2633
> URL: https://issues.apache.org/jira/browse/KUDU-2633
> Project: Kudu
> Issue Type: Improvement
> Components: api, integration, spark
> Affects Versions: 1.6.0, 1.7.0, 1.8.0, 1.7.1
> Reporter: Ricardo Gaspar
> Priority: Major
> Labels: documentation, spark, usability
> Fix For: NA
>
>
> Right now there's no place to check the documentation about methods belonging
> to KuduContext.
> The only resources available only show some examples:
>
> [https://kudu.apache.org/docs/developing.html#_spark_integration_best_practices]
> Even when including the dependency in the IDE there are no documentation for
> each method.
> For example, I was getting a SparkException (which does not describe the
> actual error) when, accidentally, inserting rows in a table that already had
> the same rows. And the method *insertRows* from KuduContext does not mention
> that an exception can be thrown.
> Exception example:
> {code:java}
> 18/12/05 11:26:35 ERROR core.JobRunShell: Job DEFAULT.EventKpisConsumer threw
> an unhandled Exception:
> org.apache.spark.SparkException: Job aborted due to stage failure: Aborting
> TaskSet 109.0 because task 3 (partition 3) cannot run anywhere due to node
> and executor blacklist. Blacklisting behavior can be configured via
> spark.blacklist.*.
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1524)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1512)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1511)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1511)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
> at scala.Option.foreach(Option.scala:257)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1739)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1694)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1683)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2031)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2052)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2071)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2096)
> at
> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:926)
> at
> org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:924)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
> at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:924)
> at
> org.apache.spark.sql.Dataset$$anonfun$foreachPartition$1.apply$mcV$sp(Dataset.scala:2340)
> at
> org.apache.spark.sql.Dataset$$anonfun$foreachPartition$1.apply(Dataset.scala:2340)
> at
> org.apache.spark.sql.Dataset$$anonfun$foreachPartition$1.apply(Dataset.scala:2340)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
> at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2827)
> at org.apache.spark.sql.Dataset.foreachPartition(Dataset.scala:2339)
> at
> org.apache.kudu.spark.kudu.KuduContext.writeRows(KuduContext.scala:246)
> at
> org.apache.kudu.spark.kudu.KuduContext.insertRows(KuduContext.scala:197)
> at
> com.xpandit.bdu.altice.EventKpisKafkaConsumer.run(EventKpisKafkaConsumer.scala:193)
> at
> com.xpandit.bdu.altice.scheduling.RunnableInterruptableJob.execute(CronScheduler.scala:73)
> at org.quartz.core.JobRunShell.run(JobRunShell.java:207)
> at
> org.quartz.simpl.SimpleThreadPool$WorkerThread.run(SimpleThreadPool.java:560)
> {code}
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