In order to reduce cost, energy consumption and improve the utilization of cloud data center resources, many cloud data centers currently use a co-allocated pattern of online services and offline batch workload. Though the co- allocated approach can bring many benefits to the data center, it adds complexity to task scheduling and brings a range of challenges such as high reliability and low latency. This paper delves into the operation of all online services and offline batch workload for the Alibaba Data Center 4034 server cluster for a period of 8 days. From the results of the data analysis, following conclusions are drawn. Firstly, from the perspective of the operation of online service, the average CPU utilization of all containers has a cyclical change, which is maintained at a high level from 8:00 am to 9:00 pm every day, and falls back to the lowest point at 4 am every day. Secondly, for offline tasks, except the first and the eighth day, the peaks of task submissions for the remaining six days are concentrated at the same time each day. The running time of 95% of the instances is within 199 s, but there are 0.052% of the instances with running time of more than one hour or even a few days. Thirdly, for the application-related situation, there are large differences in the number of containers deployed in different applications. One application uses up to 629 containers and at least 1 container. Finally, cluster analysis is conducted on servers, online tasks and batch instances. Containers with relatively high resource utilization account for the vast majority of all containers, while instances with low resource utilization and short execution time account for the vast majority of all instances. The findings and recommendations in this paper can help data center managers understand the characteristics of colocated workloads more detailedly, thereby improving resource utilization and fault tolerance for each task. [ABSTRACT FROM AUTHOR]