The Border Gateway Protocol (BGP) has been used for decades as the de facto protocol to exchange reachability information among networks in the Internet. However, little is known about how this protocol is used to restrict reachability to selected destinations, e.g., that are under attack. While such a feature, BGP blackholing, has been available for some time, we lack a systematic study of its Internet-wide adoption, practices, and network ecacy, as well as the prole of blackholed destinations. In this paper, we develop and evaluate a methodology to automatically detect BGP blackholing activity in the wild. We apply our method to both public and private BGP datasets. We nd that hundreds of networks, including large transit providers, as well as about 50 Internet exchange points (IXPs) oer blackholing service to their customers, peers, and members. Between 2014-2017, the number of blackholed prexes increased by a factor of 6, peaking at 5K concurrently blackholed prexes by up to 400 Autonomous Systems. We assess the eect of blackholing on the data plane using both targeted active measurements as well as passive datasets, nding that blackholing is indeed highly eective in dropping trac before it reaches its destination, though it also discards legitimate trac. We augment our ndings with an analysis of the target IP addresses of blackholing. Our tools and insights are relevant for operators considering oering or using BGP blackholing services as well as for researchers studying DDoS mitigation in the Internet.