For some reason the path of the HDFS is coming up in the data i am reading.
rowStructText*.filter(s => s.length != 1)*.map(s => {
println(s)
s.split("\t").size
}).countByValue foreach println
However the output (println()) on the executors still have the the
characters of the HDFS file , one character per line.
On Wed, Aug 5, 2015 at 10:30 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com> wrote:
> I see the spark job.
>
> The println statements has one character per line.
>
> 2
> 0
> 1
> 5
> /
> 0
> 8
> /
> 0
> 3
> /
> r
> e
> g
> u
> l
> a
> r
> /
> p
> a
> r
> t
> -
> m
>
>
> ....
>
>
> 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
>
>
--
Deepak
