Modern systems generate a massive amount of logs to detect and diagnose system faults, which incurs expensive storage cost and runtime overhead. After investigating real-world production logs, we observe that most of the logging overhead is due to a small number of log templates, referred to as log hotspots. Therefore, we conduct a systematical study about log hotspots in an industrial system $\mathcal{W}$, which motivates us to identify log hotspots and reduce them on the fly. In this paper, we propose \textit{LogReducer}, a non-intrusive and language-independent log reduction framework based on eBPF (Extended Berkeley Packet Filter), consisting of both online and offline processes. After two months of serving the offline process of \textit{LogReducer} in $\mathcal{W}$, the log storage overhead has dropped from 19.7 PB per day to 12.0 PB (i.e., about a 39.08% decrease). Practical implementation and experimental evaluations in the test environment demonstrate that the online process of \textit{LogReducer} can control the logging overhead of hotspots while preserving logging effectiveness. Moreover, the log hotspot handling time can be reduced from average 9 days in production to 10 minutes in the test with the help of \textit{LogReducer}.