Association rule mining (ARM) has been paid more attention of both data mining users and database researchers in the last decade. Generation of various association rules from large distributed databases is the crucial task due to its intrinsic distribution of data sources. Identifying these type of distributed data sources requires a deep knowledge on data mining and planning for deployment in distributed environment. In this paper, a survey of the distributed framework for ARM is presented. It is observed that the parallelized nature of Apriori, Hadoop, MapReduce and Spark proves to be very efficient in Distributed association rule mining (DARM) environment. We expect that, the comprehensive review, references cited will convey foremost hypothetical issues and a guideline to the researcher in interesting research direction.