Store countByKey result into Cassandra - cassandra

Question

I want to count the number of IndicatePresence messages for each user for any given day (out of a Cassandra table), and then store this in a separate Cassandra table to drive some dashboard pages. I managed to get the 'countByKey' working, but now cannot figure out how to use the Spark-Cassandra 'saveToCassandra' method with a Map (it only takes RDD).
JavaSparkContext sc = new JavaSparkContext(conf);
CassandraJavaRDD<CassandraRow> indicatePresenceTable = javaFunctions(sc).cassandraTable("mykeyspace", "indicatepresence");
JavaPairRDD<UserDate, CassandraRow> keyedByUserDate = indicatePresenceTable.keyBy(new Function<CassandraRow, UserDate>() {
private static final long serialVersionUID = 1L;
#Override
public UserDate call(CassandraRow cassandraIndicatePresenceRow) throws Exception {
SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd");
return new UserDate(cassandraIndicatePresenceRow.getString("userid"), sdf.format(cassandraIndicatePresenceRow.getDate("date")));
}
});
Map<UserDate, Object> countByKey = keyedByUserDate.countByKey();
writerBuilder("analytics", "countbykey", ???).saveToCassandra();
Is there a way use a Map directly in a writerBuilder? Or should I write my own custom reducer, that returns an RDD, but essentially does the same thing as the countByKey method? Or, should I convert each entry in the Map into a new POJO (eg UserDateCount, with user, date, and count) and use 'parallelize' to turn the list into an RDD and then store that?

Answer 1

The best thing to do would be to never return the result to the driver (by using countByKey). Instead do a reduceByKey to get another RDD back in the form of (key, count). Map that RDD to the row format of your table and then call saveToCassandra on that.
The most important strength of this approach is we never serialize the data back to the driver application. All the information is kept on the cluster and saved from their directly to C* rather than running through the bottleneck of the driver application.
Example (Very Similar to a Map Reduce Word Count):
Map each element to (key, 1)
Call reduceByKey to change (key, 1) -> (key, count)
Map each element to something writeable to C* (key,count)-> WritableObject
Call save to C*
In Scala this would be something like
keyedByUserDate
.map(_.1, 1) // Take the Key portion of the tuple and replace the value portion with 1
.reduceByKey( _ + _ ) // Combine the value portions for all elements which share a key
.map{ case (key, value) => your C* format} // Change the Tuple2 to something that matches your C* table
.saveToCassandra(ks,tab) // Save to Cassandra
In Java it is a little more convoluted (Insert your types in for K and V)
.mapToPair(new PairFunction<Tuple2<K,V>,K,Long>>, Tuple2<K, Long>(){
#Override
public Tuple2<K, Long> call(Tuple2<K, V> input) throws Exception {
return new Tuple2(input._1(),1)
}
}.reduceByKey(new Function2(Long,Long,Long)(){
#Override
public Long call(Long value1, Long value2) throws Exception {
return value1 + value2
}
}.map(new Function1(Tuple2<K, Long>, OutputTableClass)(){
#Override
public OutputTableClass call(Tuple2<K,Long> input) throws Exception {
//Do some work here
return new OutputTableClass(col1,col2,col3 ... colN)
}
}.saveToCassandra(ks,tab, mapToRow(OutputTableClass.class))