Remove Duplicates without shuffle Spark

Question

I have a Cassandra table XYX with columns( id uuid, insert a timestamp, header text)

Where id and insert are composite primary key.

I'm using Dataframe and in my spark shell I'm fetching id and header column. I want to have distinct rows based on id and header column.

I'm seeing lot of shuffles which not be the case since Spark Cassandra connector ensures that all rows for a given Cassandra partition are in same spark partition.

After fetching I'm using dropDuplicates to get distinct records.

Answer 1

Spark Dataframe API does not support custom partitioners yet. So the Connector could not introduce the C* partitioner to Dataframe engine. An RDD Spark API supports custom partitioner from other hand. Thus you could load your data into RDD and then covert it to df. Here is a Connector doc about C* partitioner usage: https://github.com/datastax/spark-cassandra-connector/blob/master/doc/16_partitioning.md

keyBy() function allow you to define key columns to use for grouping

Here is working example. It is not short, so I expect someone could improve it:

//load data into RDD and define a group key
val rdd = sc.cassandraTable[(String, String)] ("test", "test")
   .select("id" as "_1", "header" as "_2")
   .keyBy[Tuple1[Int]]("id")
// check that partitioner is CassandraPartitioner
rdd.partitioner
// call distinct for each group, flat it, get two column DF
val df = rdd.groupByKey.flatMap {case (key,group) => group.toSeq.distinct}
    .toDF("id", "header")