只需使用点语法就可以对嵌套元素执行查询。例如，如果我有以下cql定义：

cqlsh> use test;
cqlsh:test> create type t1 (id int, t text);
cqlsh:test> create type t2 (id int, t1 frozen<t1>);
cqlsh:test> create table nudt (id int primary key, t2 frozen<t2>);
cqlsh:test> insert into nudt (id, t2) values (1, {id: 1, t1: {id: 1, t: 't1'}});
cqlsh:test> insert into nudt (id, t2) values (2, {id: 2, t1: {id: 2, t: 't2'}});
cqlsh:test> SELECT * from nudt;

 id | t2
----+-------------------------------
  1 | {id: 1, t1: {id: 1, t: 't1'}}
  2 | {id: 2, t1: {id: 2, t: 't2'}}

(2 rows)

然后我可以按如下方式加载数据：

scala> val data = spark.read.format("org.apache.spark.sql.cassandra").
     options(Map( "table" -> "nudt", "keyspace" -> "test")).load()
data: org.apache.spark.sql.DataFrame = [id: int, t2: struct<id: int, t1: struct<id: int, t: string>>]

scala> data.cache
res0: data.type = [id: int, t2: struct<id: int, t1: struct<id: int, t: string>>]

scala> data.show
+---+----------+
| id|        t2|
+---+----------+
|  1|[1,[1,t1]]|
|  2|[2,[2,t2]]|
+---+----------+

然后查询数据，只选择udt中字段的特定值：

scala> val res = spark.sql("select * from test.nudt where t2.t1.t = 't1'")
res: org.apache.spark.sql.DataFrame = [id: int, t2: struct<id: int, t1: struct<id: int, t: string>>]

scala> res.show
+---+----------+
| id|        t2|
+---+----------+
|  1|[1,[1,t1]]|
+---+----------+

你可以用任何一个 spark.sql ，或相应的 .filter 函数-取决于您的编程风格。这种技术适用于任何结构类型的数据，这些数据来自不同的源，如json等。
但要考虑到，您不会像按分区键/群集列查询时那样从cassandra连接器获得优化