how do i persist the RDD to HDFS ?
On Wed, Aug 5, 2015 at 8:32 PM, Philip Weaver <philip.wea...@gmail.com>
wrote:
> This message means that java.util.Date is not supported by Spark
> DataFrame. You'll need to use java.sql.Date, I believe.
>
> On Wed, Aug 5, 2015 at 8:29 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com> wrote:
>
>> That seem to be working. however i see a new exception
>>
>> Code:
>> def formatStringAsDate(dateStr: String) = new
>> SimpleDateFormat("yyyy-MM-dd").parse(dateStr)
>>
>>
>> //(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()
>> bank.registerTempTable("summary")
>>
>>
>> //Output
>> import java.text.SimpleDateFormat import java.util.Calendar import
>> java.util.Date formatStringAsDate: (dateStr: String)java.util.Date
>> rowStructText: org.apache.spark.rdd.RDD[String] =
>> /user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-00003.gz
>> MapPartitionsRDD[105] at textFile at <console>:60 defined class Summary x:
>> org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[106] at map at
>> <console>:61 java.lang.UnsupportedOperationException: Schema for type
>> java.util.Date is not supported at
>> org.apache.spark.sql.catalyst.ScalaReflection$class.schemaFor(ScalaReflection.scala:188)
>> at
>> org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:30)
>> at
>> org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$schemaFor$1.apply(ScalaReflection.scala:164)
>>
>>
>> Any suggestions
>>
>> On Wed, Aug 5, 2015 at 8:18 PM, Philip Weaver <philip.wea...@gmail.com>
>> wrote:
>>
>>> The parallelize method does not read the contents of a file. It simply
>>> takes a collection and distributes it to the cluster. In this case, the
>>> String is a collection 67 characters.
>>>
>>> Use sc.textFile instead of sc.parallelize, and it should work as you
>>> want.
>>>
>>> On Wed, Aug 5, 2015 at 8:12 PM, ÐΞ€ρ@Ҝ (๏̯͡๏) <deepuj...@gmail.com>
>>> wrote:
>>>
>>>> I have csv data that is embedded in gzip format on HDFS.
>>>>
>>>> *With Pig*
>>>>
>>>> a = load
>>>> '/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-00003.gz' using
>>>> PigStorage();
>>>>
>>>> b = limit a 10
>>>>
>>>>
>>>> (2015-07-27,12459,,31243,6,Daily,-999,2099-01-01,2099-01-02,4,0,0.1,0,1,,,,,203,4810370.0,1.4090459061723766,1.017458,-0.03,-0.11,0.05,0.468666,)
>>>>
>>>>
>>>> (2015-07-27,12459,,31241,6,Daily,-999,2099-01-01,2099-01-02,4,0,0.1,0,1,0,isGeo,,,203,7937613.0,1.1624841995932425,1.11562,-0.06,-0.15,0.03,0.233283,)
>>>>
>>>>
>>>> However with Spark
>>>>
>>>> val rowStructText =
>>>> sc.parallelize("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-00000.gz")
>>>>
>>>> val x = rowStructText.map(s => {
>>>>
>>>> println(s)
>>>>
>>>> s}
>>>>
>>>> )
>>>>
>>>> x.count
>>>>
>>>> Questions
>>>>
>>>> 1) x.count always shows 67 irrespective of the path i change in
>>>> sc.parallelize
>>>>
>>>> 2) It shows x as RDD[Char] instead of String
>>>>
>>>> 3) println() never emits the rows.
>>>>
>>>> Any suggestions
>>>>
>>>> -Deepak
>>>>
>>>>
>>>>
>>>> --
>>>> Deepak
>>>>
>>>>
>>>
>>
>>
>> --
>> Deepak
>>
>>
>
--
Deepak
