Have you tried reading the spark documentation?

http://spark.apache.org/docs/latest/programming-guide.html



Thank you,
Ilya Ganelin



-----Original Message-----
From: ÐΞ€ρ@Ҝ (๏̯͡๏) [deepujain@gmail.com]
Sent: Thursday, August 06, 2015 12:41 AM Eastern Standard Time
To: Philip Weaver
Cc: user
Subject: Re: How to read gzip data in Spark - Simple question

how do i persist the RDD to HDFS ?

On Wed, Aug 5, 2015 at 8:32 PM, Philip Weaver <philip.weaver@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 (๏̯͡๏) <deepujain@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.weaver@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 (๏̯͡๏) <deepujain@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



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