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
>
>
