Currently we use `RDD[Vector]` to store point cost during k-means|| initialization, where each `Vector` has size `runs`. This is not storage-efficient because `runs` is usually 1 and then each record is a Vector of size 1. What we need is just the 8 bytes to store the cost, but we introduce two objects (DenseVector and its values array), which could cost 16 bytes. That is 200% overhead. Thanks Grace Huang and Jiayin Hu from Intel for reporting this issue!
There are several solutions:
1. Use `RDD[Array[Double]]` instead of `RDD[Vector]`, which saves 8 bytes per record.
2. Use `RDD[Array[Double]]` but batch the values for storage, e.g. each `Array[Double]` object covers 1024 instances, which could remove most of the overhead.
Besides, using MEMORY_AND_DISK instead of MEMORY_ONLY could prevent cost RDDs kicking out the training dataset from memory.