I see the spark job.
The println statements has one character per line.
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On Wed, Aug 5, 2015 at 10:27 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com> wrote:
> val summary = rowStructText.map(s => s.split(",")).map(
> {
> s =>
> *println(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("\"", "")
> )
> }
> )
>
> summary.count
>
> AND
>
> rowStructText.map(s => {
> * println(s)*
> s.split(",").size
>
> }).countByValue foreach println
>
>
> DOES NOT PRINT THE OUTPUT.
>
> When i open up the spark history server it does not launch new SPARK JOBS
> for countByValue . Why is that and when does it actually start a new job ?
>
>
> On Wed, Aug 5, 2015 at 10:19 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com>
> wrote:
>
>> 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
>>
>>
>
>
> --
> Deepak
>
>
--
Deepak
