Replication has been successfully employed and practiced to ensure high data availability in large-scale distributed storage systems. However, with the relentless growth of gen- erated and collected data, replication has become expensive not only in terms of storage cost but also in terms of network cost and hardware cost. Traditionally, erasure coding (EC) is employed as a cost-efficient alternative to replication when high access latency to the data can be tolerated. However, with the continuous reduction in its CPU overhead, EC is performed on the critical path of data access. For instance, EC has been integrated into the last major release of Hadoop Distributed File System (HDFS) which is the primary storage backend for data analytic frameworks (e.g., Hadoop, Spark, etc.). In this work, we measure and compare the performance of data accesses in HDFS under both replication and EC. Our analysis indicates that EC is a feasible solution for data- intensive applications and it can outperform replication in many scenarios. Furthermore, we demonstrate that it is the block placement algorithm in HDFS that mostly impacts the performance under EC.