Unbalanced class: class_weight for ML algorithms in Spark MLLib
Algorithms in MLLib are always used as baseline in production scenario , and they indeed can not handle some industrial problems , such as label imbalance . So if you want to use them , you have to balance your instances . 

Besides , mechanism of BSP in Spark , you can simply see as data parallel ,  might be the main reason why Spark does not cover that problem . It might be hard for Spark to dispatch instances to all nodes in cluster , while the partial instances of each node share the same label distribution as the whole . 

At last , you only have to weight the loss value for every minor labeled instance during your iteration process if you want to implement it . 

Hopes this will help you , good luck -)