Distributed hash tables (DHT) bring the promise of increased availability of data to wide-area systems, under the assumption of uniform request load. However, they do not intrinsically offer protection against the hotspot phenomenon, when an item suddenly becomes popular and the node hosting the item must handle a high surge in demand. Therefore, most DHT-based systems employ techniques such as path caching or caching on the client to alleviate this problem. In this paper, we analyze the efficiency of path caching for DHT that offer convergence of routing paths, and compare it to probabilistic forwarding and congestion control techniques, which help balance the load among the nodes holding caches, while also providing superior availability and reducing the number of denied requests. We show that probabilistic forwarding can simultaneously reduce the request drop rate and the number of caches. We compare four algorithms for probabilistic forwarding and conclude that geometric and adaptive mechanisms are superior to the other approaches.