Set similarity joins, which compute pairs of similar sets, constitute an important operator primitive in a variety of applications, including applications that must process large amounts of data. To handle these data volumes, several distributed set similarity join algorithms have been proposed. Unfortunately, little is known about the relative performance, strengths and weaknesses of these techniques. Previous comparisons are limited to a small subset of relevant algorithms, and the large differences in the various test setups make it hard to draw overall conclusions. In this paper we survey ten recent, distributed set similarity join algorithms, all based on the MapReduce paradigm. We empirically compare the algorithms in a uniform test environment on twelve datasets that expose different characteristics and represent a broad range of applications. Our experiments yield a surprising result: All algorithms in our test fail to scale for at least one dataset and are sensitive to long sets, frequent set elements, low similarity thresholds, or a combination thereof. Interestingly, some algorithms even fail to handle the small datasets that can easily be processed in a non-distributed setting. Our analytic investigation of the algorithms pinpoints the reasons for the poor performance and targeted experiments confirm our analytic findings. Based on our investigation, we suggest directions for future research in the area.