summary: org.apache.spark.rdd.RDD[Summary] = MapPartitionsRDD[285] at map
at <console>:169 (1,517252)
What does that mean ?
On Wed, Aug 5, 2015 at 10:14 PM, Jeff Zhang <zjf...@gmail.com> wrote:
> 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
>
--
Deepak
