Comparison between different methods of executing SQL queries on Cassandra Column Families using spark

Question

As part of my project, I have to create a SQL query interface for a very large Cassandra Dataset, hence I have been looking at different methods for executing SQL queries on cassandra column families using Spark and I have come up with 3 different methods

1. using Spark SQLContext with a statically defined schema

   // statically defined in the application
   public static class TableTuple implements Serializable {
       private int id;
       private String line;
   
       TableTuple (int i, String l) {
           id = i;
           line = l;
       }
   
       // getters and setters
       ...
   }
   

   and I consume the definition as:

   SparkConf conf = new SparkConf(true)
           .set("spark.cassandra.connection.host", CASSANDRA_HOST)
           .setJars(jars);
   
   SparkContext sc = new SparkContext(HOST, APP_NAME, conf);
   SQLContext sqlContext = new SQLContext(sc);
   
   JavaRDD<CassandraRow> rowrdd = javaFunctions(sc).cassandraTable(CASSANDRA_KEYSPACE, CASSANDRA_COLUMN_FAMILY);
   JavaRDD<TableTuple> rdd = rowrdd.map(row -> new TableTuple(row.getInt(0), row.getString(1)));
   
   DataFrame dataFrame = sqlContext.createDataFrame(rdd, TableTuple.class);
   dataFrame.registerTempTable("lines");
   
   DataFrame resultsFrame = sqlContext.sql("Select line from lines where id=1");
   
   System.out.println(Arrays.asList(resultsFrame.collect()));
   

2. using Spark SQLContext with a dynamically defined schema

   SparkConf conf = new SparkConf(true)
           .set("spark.cassandra.connection.host", CASSANDRA_HOST)
           .setJars(jars);
   
   SparkContext sc = new SparkContext(HOST, APP_NAME, conf);
   SQLContext sqlContext = new SQLContext(sc);
   
   JavaRDD<CassandraRow> cassandraRdd = javaFunctions(sc).cassandraTable(CASSANDRA_KEYSPACE, CASSANDRA_COLUMN_FAMILY);
   JavaRDD<Row> rdd = cassandraRdd.map(row -> RowFactory.create(row.getInt(0), row.getString(1)));
   
   List<StructField> fields = new ArrayList<>();
   fields.add(DataTypes.createStructField("id", DataTypes.IntegerType, true));
   fields.add(DataTypes.createStructField("line", DataTypes.StringType, true));
   StructType schema = DataTypes.createStructType(fields);
   
   DataFrame dataFrame = sqlContext.createDataFrame(rdd, schema);
   dataFrame.registerTempTable("lines");
   
   DataFrame resultDataFrame = sqlContext.sql("select line from lines where id = 1");
   
   System.out.println(Arrays.asList(resultDataFrame.collect()));
   

3. using CassandraSQLContext from the spark-cassandra-connector

   SparkConf conf = new SparkConf(true)
           .set("spark.cassandra.connection.host", CASSANDRA_HOST)
           .setJars(jars);
   
   SparkContext sc = new SparkContext(HOST, APP_NAME, conf);
   
   CassandraSQLContext sqlContext = new CassandraSQLContext(sc);
   DataFrame resultsFrame = sqlContext.sql("Select line from " + CASSANDRA_KEYSPACE + "." + CASSANDRA_COLUMN_FAMILY + " where id = 1");
   
   System.out.println(Arrays.asList(resultsFrame.collect()));
   

I would like to know the advantages/disadvantages of one method over another. Also, for the CassandraSQLContext method, are queries limited to CQL, or is it fully compatible with Spark SQL. I would also like an analysis pertaining to my specific use case, I have a cassandra column family with ~17.6 million tuples having 62 columns. For querying such a large database, which method is most adequate ?