Due to individual unreliable commodity components, failures are common in large-scale distributed storage systems. Erasure codes are widely deployed in practical storage systems to provide fault tolerance with low storage overhead. However, random data distribution (RDD), commonly used in erasure-coded storage systems, induces heavy cross-rack traffic, load imbalance, and random access, which adversely affects failure recovery. In this article, with orthogonal arrays, we define a Deterministic Data Distribution (D ³ ) to uniformly distribute data/parity blocks among nodes, and propose an efficient failure recovery approach based on D ³ , which minimizes the cross-rack repair traffic against a single node failure. Thanks to the uniformity of D ³ , the proposed recovery approach balances the repair traffic not only among nodes within a rack but also among racks. We implement D ³ over Reed-Solomon codes and Locally Repairable Codes in Hadoop Distributed File System (HDFS) with a cluster of 28 machines. Compared with RDD, our experiments show that D3 significantly speeds up the failure recovery up to 2.49 times for RS codes and 1.38 times for LRCs. Moreover, D ³ supports front-end applications better than RDD in both of normal and recovery states.