Alert is a kind of key data source in monitoring system for online service systems, which is used to record the anomalies in service components and report to engineers. In general, the occurrence of a service failure tends to be along with a large number of alerts, which is called alert storm. However, alert storm brings great challenges to diagnose the failure, because it is time-consuming and tedious for engineers to investigate such an overwhelming number of alerts manually. To help understand alert storm in practice, we conduct the first empirical study of alert storm based on large-scale real-world alert data and gain some valuable insights. Based on the findings obtained from the study, we propose a novel approach to handling alert storm. Specifically, this approach includes alert storm detection which aims to identify alert storm accurately, and alert storm summary which aims to recommend a small set of representative alerts to engineers for failure diagnosis. Our experimental study on real-world dataset demonstrates that our alert storm detection can achieve high F1-score (larger than 0.9). Besides, our alert storm summary can reduce the number of alerts that need to be examined by more than 98% and discover representative alerts accurately. We have successfully applied our approach to the service maintenance of a large commercial bank (China EverBright Bank), and we also share our success stories and lessons learned in industry.