Hi Akhil, Thanks for your help. Why do you put separator as "," ?
I have a parquet file which contains only json in each line. *Thanks*, <https://in.linkedin.com/in/ramkumarcs31> On Mon, Apr 4, 2016 at 2:34 PM, Akhil Das <ak...@sigmoidanalytics.com> wrote: > Something like this (in scala): > > val rdd = parquetFile.javaRDD().map(row => row.mkstring(",")) > > You can create a map operation over your javaRDD to convert the > org.apache.spark.sql.Row > <https://spark.apache.org/docs/1.4.0/api/java/org/apache/spark/sql/Row.html> > to String (the Row.mkstring() Operation) > > Thanks > Best Regards > > On Mon, Apr 4, 2016 at 12:02 PM, Ramkumar V <ramkumar.c...@gmail.com> > wrote: > >> Any idea on this ? How to convert parquet file into JavaRDD<String> ? >> >> *Thanks*, >> <https://in.linkedin.com/in/ramkumarcs31> >> >> >> On Thu, Mar 31, 2016 at 4:30 PM, Ramkumar V <ramkumar.c...@gmail.com> >> wrote: >> >>> Hi, >>> >>> Thanks for the reply. I tried this. It's returning JavaRDD<row> instead >>> of JavaRDD<String>. How to get JavaRDD<String> ? >>> >>> Error : >>> incompatible types: >>> org.apache.spark.api.java.JavaRDD<org.apache.spark.sql.Row> cannot be >>> converted to org.apache.spark.api.java.JavaRDD<java.lang.String> >>> >>> >>> >>> >>> >>> *Thanks*, >>> <https://in.linkedin.com/in/ramkumarcs31> >>> >>> >>> On Thu, Mar 31, 2016 at 2:57 PM, UMESH CHAUDHARY <umesh9...@gmail.com> >>> wrote: >>> >>>> From Spark Documentation: >>>> >>>> DataFrame parquetFile = sqlContext.read().parquet("people.parquet"); >>>> >>>> JavaRDD<String> jRDD= parquetFile.javaRDD() >>>> >>>> javaRDD() method will convert the DF to RDD >>>> >>>> On Thu, Mar 31, 2016 at 2:51 PM, Ramkumar V <ramkumar.c...@gmail.com> >>>> wrote: >>>> >>>>> Hi, >>>>> >>>>> I'm trying to read parquet log files in Java Spark. Parquet log files >>>>> are stored in hdfs. I want to read and convert that parquet file into >>>>> JavaRDD. I could able to find Sqlcontext dataframe api. How can I read if >>>>> it is sparkcontext and rdd ? what is the best way to read it ? >>>>> >>>>> *Thanks*, >>>>> <https://in.linkedin.com/in/ramkumarcs31> >>>>> >>>>> >>>> >>> >> >
