This paper shows that Julia provides sufficient performance to bridge the performance gap between productivity-oriented languages and low-level languages for complex memory intensive computation tasks such as graph traversal. We provide performance guidelines for using complex low-level data structures in high productivity languages and present the first parallel integration on the productivity-oriented language side for graph analysis. Performance on the Graph500 benchmark demonstrates that the Julia implementation is competitive with the native C/OpenMP implementation.