Identifying heavy hitters in a network traffic stream is important for a variety of network applications ranging from traffic engineering to anomaly detection such as detection of denial-of-service attacks. Existing methods generally examine newly arriving items in the stream, perform a small number of operations using a small amount of memory, and still provide guarantees on the identifying accuracy. In high-speed network monitoring, the update speed per item is extremely critical. However, so far as we know, there are no identifying algorithms which can provide constant update time (O(1)) in a weighted data stream. In this paper, we present an algorithm named Weighted Lossy Counting (WLC) which is able to identify heavy hitters in a high-speed weighted data stream with constant update time. WLC employs a novel efficient partially ordered data structure which is able to provide a fast per-item update speed while keeping the memory cost relatively low. We compare WLC with state-of-the-art algorithms for finding heavy hitters in real traffic traces. The experimental results show that WLC performs well in accuracy (recall, precision and average relative error) as other algorithms; moreover it has a much higher update speed at the cost of relatively larger memory space used. A theoretical worst-case memory bound of WLC is also derived in this paper; however, experiments with long real traffic traces show that WLC requires much less space than the theoretical bound in practice. Identifying heavy hitters in a network traffic stream is important for a variety of network applications ranging from traffic engineering to anomaly detection such as detection of denial-of-service attacks. operations using a small amount of memory, and still provide offers on the identifying accuracy. In high-speed network monitoring, the update speed per item is extremely critical. However, so far as as we know, there are no identifying algorithms which can provide constant In this paper, we present an algorithm named Weighted Lossy Counting (WLC) which is capable of identifying heavy hitters in a high-speed weighted data stream with constant update time. WLC employs a novel efficient partially ordered data structure which is able to provide a fast per-item update speed while keeping the memory cost relatively low. We compare WLC with state-of-the- The experimental results show that WLC performs well in accuracy (recall, precision and average relative error) as other algorithms; moreover it has a much higher update speed at the cost of relatively more memory space used. A theoretical worst-case memory bound of WLC is also derived in this paper; however, experiments with long real traffic traces show that WLC requires much less space than the theoretical bound in practice.