Finding connected components is a fundamental problem in graph analysis. We develop a novel minimum- mapping based Contour algorithm to solve the connectivity problem. The Contour algorithm can identify all connected components of an undirected graph within O (log 𝑑𝑚𝑎𝑥 ) iterations on 𝑚 parallel processors, where 𝑑𝑚𝑎𝑥 is the largest diameter of all components in a given graph and 𝑚 is the total number of edges of the given graph. Furthermore, each iteration can easily be parallelized by employing the highly efficient minimum-mapping operator on all edges. To improve performance, the Contour algorithm is further optimized through asynchronous updates and simplified atomic operations. Our algorithm has been integrated into an open-source framework, Arachne, that extends Arkouda for large-scale interactive graph analytics with a Python API powered by the high-productivity parallel language Chapel. Experimental results on real-world and synthetic graphs show that the proposed Contour algorithm needs less number of iterations and can achieve 5.26 folds of speedup on average compared with the state-of-the-art connected component method FastSV implemented in Chapel. All code is publicly available on GitHub (https://github.com/Bears-R-Us/arkouda-njit).