Sampling is a fundamental class of computational problems. The problem of generating random samples from a solution space according to certain probability distribution has numerous important applications in approximate counting, probability inference, statistical learning, etc. In the big data era, the distributed sampling attracts considerably more attentions. In recent years, there is a line of research works that systematically study the theory of distributed sampling. This study surveys important results on distributed sampling, including distributed sampling algorithms with theoretically provable guarantees, the computational complexity of sampling in the distributed computing model, and the mutual relation between sampling and inference in the distributed computing model.