joinWithCassandra 表从传递的rdd中提取分区/主键值，并将它们转换为针对cassandra中分区的单个请求。然后，除此之外，scc可能会应用额外的过滤，比如 where 条件。如果我没记错的话，但我可能错了，限制不会完全推到Cassandra身上——它仍然可能会有价值 limit 每个分区的行数。
您可以通过执行 result_rdd.toDebugString . 对于我的代码：

val df_for_join = Seq((2, 5),(5, 2)).toDF("col1", "col2")
val rdd_for_join = df_for_join.rdd

val result_rdd = rdd_for_join
.joinWithCassandraTable("test", "jt"
,selectedColumns = SomeColumns("col1","col2", "v")
,SomeColumns("col1", "col2")
).where("created_at >'2020-03-13T00:00:00Z' and created_at<= '2020-03-14T00:00:00Z'")
.limit(1)

它给出了以下内容：

scala> result_rdd.toDebugString
res7: String =
(2) CassandraJoinRDD[14] at RDD at CassandraRDD.scala:19 []
 |  MapPartitionsRDD[2] at rdd at <console>:45 []
 |  MapPartitionsRDD[1] at rdd at <console>:45 []
 |  ParallelCollectionRDD[0] at rdd at <console>:45 []

如果您执行“正常”联接，则会得到以下结果：

scala> val rdd1 = sc.parallelize(Seq((2, 5),(5, 2)))
rdd1: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[21] at parallelize at <console>:44
scala> val ct = sc.cassandraTable[(Int, Int)]("test", "jt").select("col1", "col2")
ct: com.datastax.spark.connector.rdd.CassandraTableScanRDD[(Int, Int)] = CassandraTableScanRDD[31] at RDD at CassandraRDD.scala:19

scala> rdd1.join(ct)
res15: org.apache.spark.rdd.RDD[(Int, (Int, Int))] = MapPartitionsRDD[34] at join at <console>:49
scala> rdd1.join(ct).toDebugString
res16: String =
(6) MapPartitionsRDD[37] at join at <console>:49 []
 |  MapPartitionsRDD[36] at join at <console>:49 []
 |  CoGroupedRDD[35] at join at <console>:49 []
 +-(3) ParallelCollectionRDD[21] at parallelize at <console>:44 []
 +-(6) CassandraTableScanRDD[31] at RDD at CassandraRDD.scala:19 []

更多信息见scc文件的相应章节。