In recent years, edge networks have been deployed broadly at large scale, hosting a wide variety of services. Among these, microservices have emerged as one of the predominant service paradigms. Typically, microservices run on edge servers with varying configurations, while new microservice instances are usually online generated and join in edge networks due to the dynamic attributes of requests and networks. In those cases, an effective microservice online deployment solution are expected to be vital to system performance. So it becomes a critical issue to design online deployment solutions for microservices in edge. Existing research has always focused on offline deployment of microservices. However, edge networks are characterized by dynamics, real time, and concurrency, and when the environments or requests change, traditional offline deployment solutions usually cannot handle the deployment task in such cases. To address these issues, we carry out a comprehensive investigation on those potential influencing factors in edge, fully covering deployment cost, load balance, packet loss, and network delay. We further develop an innovative holistic online deployment solution that encompasses a system model, constraint analysis, multiobjective optimization, and a deep reinforcement learning algorithm. We conducted extensive experiments and evaluated our solution over a set of metrics using a real-world microservice prototype system. The results show that our online deployment solution produces superior performance, for example, reducing deployment cost by an average of 70.14% compared to all baselines. We also evaluated our solution under varying volumes of requests and gave analysis for performance stability and parameter sensitivity. We have released the code on GitHub.