spark-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Ganelin, Ilya" <>
Subject RE: How to read gzip data in Spark - Simple question
Date Thu, 06 Aug 2015 05:27:16 GMT
Have you tried reading the spark documentation?

Thank you,
Ilya Ganelin

-----Original Message-----
From: ÐΞ€ρ@Ҝ (๏̯͡๏) [<>]
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 <<>>
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 (๏̯͡๏) <<>>
That seem to be working. however i see a new exception

def formatStringAsDate(dateStr: String) = new SimpleDateFormat("yyyy-MM-dd").parse(dateStr)

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:

val summary  = => 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,
            s(9).replaceAll("\"", "").toInt,
            s(10).replaceAll("\"", "").toInt,
            s(11).replaceAll("\"", "").toFloat,
            s(12).replaceAll("\"", "").toInt,
            s(13).replaceAll("\"", "").toInt,
            s(14).replaceAll("\"", "")

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 <<>>
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 (๏̯͡๏) <<>>
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



However with Spark

val rowStructText = sc.parallelize("/user/zeppelin/aggregatedsummary/2015/08/03/regular/part-m-00000.gz")

val x = => {






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






The information contained in this e-mail is confidential and/or proprietary to Capital One
and/or its affiliates and may only be used solely in performance of work or services for Capital
One. The information transmitted herewith is intended only for use by the individual or entity
to which it is addressed. If the reader of this message is not the intended recipient, you
are hereby notified that any review, retransmission, dissemination, distribution, copying
or other use of, or taking of any action in reliance upon this information is strictly prohibited.
If you have received this communication in error, please contact the sender and delete the
material from your computer.
View raw message