We consider single-sink network flow problems. An instance consists of a capacitated graph (directed or undirected), a sink node $t$ and a set of demands that we want to send to the sink. Here demand $i$ is located at a node $s_i$ and requests an amount $d_i$ of flow capacity in order to route successfully. Two standard objectives are to maximise (i) the number of demands (cardinality) and (ii) the total demand (throughput) that can be routed subject to the capacity constraints. Furthermore, we examine these maximisation problems for three specialised types of network flow: unsplittable, confluent and priority flows. In the {\em unsplittable flow} problem (UFP), we have edge capacities, and the demand for $s_i$ must be routed on a single path. In the {\em confluent flow} problem, we have node capacities, and the final flow must induce a tree. Both of these problems have been studied extensively, primarily in the single-sink setting. However, most of this work imposed the {\em no-bottleneck assumption} (that the maximum demand $d_{max}$ is at most the minimum capacity $u_{min}$). Given the no-bottleneck assumption (NBA), there is a factor $4.43$-approximation algorithm due to Dinitz et al. for the unsplittable flow problem. Under the stronger assumption of uniform capacities, there is a factor $3$-approximation algorithm due to Chen et al. for the confluent flow problem. However, unlike the UFP, we show that a constant factor approximation algorithm cannot be obtained for the single-sink confluent flows even {\bf with} the NBA. Without NBA, we show that maximum cardinality single-sink UFP is hard to approximate to within a factor $n^{.5-ε}$ even when all demands lie in a small interval $[1,1+Δ]$ where $Δ>0$ (but has polynomial input size). This is very sharp since when $Δ=0$, this becomes a maximum flow problem.