Attribute reduction （feature selection） is an important part of data preprocessing. Most of attribute reduction methods use attribute dependence as the criterion for filtering attribute subsets. A Fast Dependence Calculation （FDC） method was designed to calculate the dependence by directly searching for the objects based on relative positive domains. It is not necessary to find the relative positive domain in advance， so that the method has a significant performance improvement in speed compared with the traditional methods. In addition， the Whale Optimization Algorithm （WOA） was improved to make the calculation method effective for rough set attribute reduction. Combining the above two methods， a distributed rough set attribute reduction algorithm based on Spark named SP-WOFRST was proposed， which was compared with a Spark-based rough set attribute reduction algorithm named SP-RST on two synthetical large data sets. Experimental results show that the proposed SP-WOFRST algorithm is superior to SP-RST in accuracy and speed.