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 (๏̯͡๏) [deepuj...@gmail.com<mailto:deepuj...@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.wea...@gmail.com<mailto: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<mailto: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<mailto: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<mailto: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 ________________________________________________________ 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.
