Unfortunately, I couldn’t figure it out without involving Avro.

Here is something that may be useful since it uses Avro generic records (so no case classes needed) and transforms to Parquet.

http://blog.cloudera.com/blog/2014/05/how-to-convert-existing-data-into-parquet/

HTH,
Mahesh

From: "Anita Tailor [via Apache Spark User List]" <[hidden email]>
Date: Thursday, June 19, 2014 at 12:53 PM
To: Mahesh Padmanabhan <[hidden email]>
Subject: Re: Spark streaming RDDs to Parquet records

I have similar case where I have RDD [List[Any], List[Long] ] and wants to save it as Parquet file. 
My understanding is that only RDD of case classes can be converted to SchemaRDD. So is there any way I can save this RDD as Parquet file without using Avro? 

Thanks in advance
Anita   


On 18 June 2014 05:03, Michael Armbrust <[hidden email]> wrote:
If you convert the data to a SchemaRDD you can save it as Parquet: http://spark.apache.org/docs/latest/sql-programming-guide.html#using-parquet


On Tue, Jun 17, 2014 at 11:47 PM, Padmanabhan, Mahesh (contractor) <[hidden email]> wrote:
Thanks Krishna. Seems like you have to use Avro and then convert that to Parquet. I was hoping to directly convert RDDs to Parquet files. I’ll look into this some more.

Thanks,
Mahesh

From: Krishna Sankar <[hidden email]>
Reply-To: "[hidden email]" <[hidden email]>
Date: Tuesday, June 17, 2014 at 2:41 PM
To: "[hidden email]" <[hidden email]>
Subject: Re: Spark streaming RDDs to Parquet records

Mahesh,
Cheers
<k/>


On Tue, Jun 17, 2014 at 12:52 PM, maheshtwc <[hidden email]> wrote:
Hello,

Is there an easy way to convert RDDs within a DStream into Parquet records?
Here is some incomplete pseudo code:

// Create streaming context
val ssc = new StreamingContext(...)

// Obtain a DStream of events
val ds = KafkaUtils.createStream(...)

// Get Spark context to get to the SQL context
val sc = ds.context.sparkContext

val sqlContext = new org.apache.spark.sql.SQLContext(sc)

// For each RDD
ds.foreachRDD((rdd: RDD[Array[Byte]]) => {

    // What do I do next?
})

Thanks,
Mahesh



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