You data might have format issue (with less fields than you expect)
Please try execute the following code to check whether all the lines with
14 fields:
rowStructText.map(s => s.split(",").size).countByValue foreach
println
On Thu, Aug 6, 2015 at 1:01 PM, Randy Gelhausen <rgelhau...@hortonworks.com>
wrote:
> You likely have a problem with your parsing logic. I can’t see the data to
> know for sure, but since Spark is lazily evaluated, it doesn’t try to run your
> map until you execute the SQL that applies it to the data.
>
> That’s why your first paragraph can run (it’s only defining metadata), but
> paragraph 2 throws an error.
>
> From: "ÐΞ€ρ@Ҝ (๏̯͡๏)"
> Reply-To: "users@zeppelin.incubator.apache.org"
> Date: Thursday, August 6, 2015 at 12:37 AM
> To: "users@zeppelin.incubator.apache.org"
> Subject: Re: Unable to run count(*)
>
> %sql
> select * from summary
>
> Throws same error
>
> On Wed, Aug 5, 2015 at 9:33 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com> wrote:
>
>> Para-1
>> import java.text.SimpleDateFormat
>> import java.util.Calendar
>> import java.sql.Date
>>
>> def formatStringAsDate(dateStr: String) = new java.sql.Date(new
>> SimpleDateFormat("yyyy-MM-dd").parse(dateStr).getTime())
>>
>>
>> //(2015-07-27,12459,,31242,6,Daily,-999,2099-01-01,2099-01-02,1,0,0.1,0,1,-1,isGeo,,,204,694.0,1.9236856708701322E-4,0.0,-4.48,0.0,0.0,0.0,)
>> val rowStructText =
>> sc.textFile("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-00003.gz")
>> case class Summary(f1: Date, f2: Long, f3: Long, f4: Integer, f5 :
>> String, f6: Integer, f7 : Date, f8: Date, f9: Integer, f10: Integer, f11:
>> Float, f12: Integer, f13: Integer, f14: String)
>>
>> val summary = rowStructText.map(s => s.split(",")).map(
>> {
>> s =>
>> Summary(formatStringAsDate(s(0)),
>> s(1).replaceAll("\"", "").toLong,
>> s(3).replaceAll("\"", "").toLong,
>> s(4).replaceAll("\"", "").toInt,
>> s(5).replaceAll("\"", ""),
>> s(6).replaceAll("\"", "").toInt,
>> formatStringAsDate(s(7)),
>> formatStringAsDate(s(8)),
>> s(9).replaceAll("\"", "").toInt,
>> s(10).replaceAll("\"", "").toInt,
>> s(11).replaceAll("\"", "").toFloat,
>> s(12).replaceAll("\"", "").toInt,
>> s(13).replaceAll("\"", "").toInt,
>> s(14).replaceAll("\"", "")
>> )
>> }
>> ).toDF()
>> summary.registerTempTable("summary")
>>
>>
>>
>> Output:
>> import java.text.SimpleDateFormat import java.util.Calendar import
>> java.sql.Date formatStringAsDate: (dateStr: String)java.sql.Date
>> rowStructText: org.apache.spark.rdd.RDD[String] =
>> /user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-00003.gz
>> MapPartitionsRDD[152] at textFile at <console>:100 defined class Summary
>> summary: org.apache.spark.sql.DataFrame = [f1: date, f2: bigint, f3:
>> bigint, f4: int, f5: string, f6: int, f7: date, f8: date, f9: int, f10:
>> int, f11: float, f12: int, f13: int, f14: string]
>>
>>
>> Para-2 (DOES NOT WORK)
>> %sql select count(*) from summary
>>
>> Output
>> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
>> in stage 29.0 failed 4 times, most recent failure: Lost task 0.3 in stage
>> 29.0 (TID 1844, datanode-6-3486.phx01.dev.ebayc3.com):
>> java.lang.ArrayIndexOutOfBoundsException: 1 at
>> $line184.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:109)
>> at
>> $line184.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:107)
>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) at
>> scala.collection.Iterator$$anon$1.next(Iterator.scala:853) at
>> scala.collection.Iterator$$anon$1.head(Iterator.scala:840) at
>> org.apache.spark.sql.execution.RDDConversions$$anonfun$productToRowRdd$1.apply(ExistingRDD.scala:42)
>> at
>> org.apache.spark.sql.execution.RDDConversions$$anonfun$productToRowRdd$1.apply(ExistingRDD.scala:37)
>> at org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:634) at
>> org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:634) at
>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) at
>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at
>> org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at
>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) at
>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at
>> org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at
>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) at
>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at
>> org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at
>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) at
>> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at
>> org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at
>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
>> at
>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
>> at org.apache.spark.scheduler.Task.run(Task.scala:64) at
>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203) 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)
>>
>>
>> Suggestions ?
>>
>> --
>> Deepak
>>
>>
>
>
> --
> Deepak
>
>
--
Best Regards
Jeff Zhang
