MapReduce is a widely used parallel computing paradigm for the big data realm on the scale of terabytes and higher. The introduction of minimal MapReduce algorithms promised efficiency in load balancing among participating machines by ensuring that partition skew (where some machines end up processing a significantly larger fraction of the input than other machines) is prevented. Despite minimal MapReduce algorithms guarantee of load-balancing within constant multiplicative factors, the constants are relatively large which severely diminishes the theoretical appeal for true efficiency at scale.