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Containerized microservices have been widely deployed in industry. Meanwhile, security issues also arise. Many security enhancement mechanisms for containerized microservices require predefined rules and policies. However, it is challenging when it comes to thousands of microservices and a massive amount of real-time unstructured data. Hence, automatic policy generation becomes indispensable. In this paper, we focus on the automatic solution for the security problem: irregular traffic detection for RPCs. We propose Informer, which is a two-phase machine learning framework to track the traffic of each RPC and report anomalous points automatically. Firstly, we identify RPC chain patterns by density-based clustering techniques and build a graph for each critical pattern. Next, we solve the irregular RPC traffic detection problem as a prediction problem for time-series of attributed graphs by leveraging spatial-temporal graph convolution networks. Since the framework builds multiple models and makes individual predictions for each RPC chain pattern, it can be efficiently updated upon legitimate changes in any of the graphs. In evaluations, we applied Informer to a dataset containing more than 7 billion lines of raw RPC logs sampled from an large Kubernetes system for two weeks. We provide two case studies of detected real-world threats. As a result, our framework found fine-grained RPC chain patterns and accurately captured the anomalies in a dynamic and complicated microservice production scenario, which demonstrates the effectiveness of Informer.