Data deduplication techniques are often used by cloud storage systems to reduce network bandwidth and storage requirements. As a consequence, the current research literature tends to focus most of its algorithmic efforts on improving the Duplicate Elimination Ratio (DER), which reflects the compression achieved using a given algorithm. Yet, the importance of this indicator tends to be overestimated, while another key indicator, namely throughput, tends to be underestimated. To substantiate this claim, we reimplement a selection of popular Content-Defined Chunking algorithms (CDC) and perform a detailed performance analysis. On this basis, we show that the gain brought by algorithms that are aggressively focusing on DER often come at a significant cost in terms of throughput. As a consequence, we advocate for future optimizations taking throughput into account and for making balanced tradeoffs between DER and throughput.