The efficiency of datacenters is important consideration for cloud service providers to make their datacenters always ready for fulfilling the increasing demand for computing resources. Container-based virtualization is one approach to improving efficiency by reducing the overhead of virtualization. Resource overcommitment is another approach, but cloud providers tend to make conservative allocations of resources because there is no good understanding of the relationship between physical resource overcommitment and its impact on performance. This paper presents a quantitative study of performance degradation of containerized workloads due to memory overcommitment and a technique to mitigate it. We focused on physical memory overcommitment, where the sum of the working set memory is larger than the physical memory. We drove a small fraction of Docker containers at a high load level and the rest of them at a very low load level to emulate a common usage pattern of cloud datacenters. Detailed measurements revealed it is difficult to predict how many additional containers can be launched before thrashing hurts performance. We show that tuning the per-container swappiness of heavily loaded containers is effective for launching a larger number of containers and that it achieves an overcommitment of about three times.