Many data service platforms use local file systems as their backend storage. Although this approach offers advantages in portability, extensibility, and ease of development, it may suffer from severe performance degradation if the mapping between the services required by the data service platform and the functions provided by the local file system is not carefully managed. This paper presents in-depth analysis of performance problems in current data service platforms that use file systems as their backend storage and proposes three novel strategies that are essential to solving the current performance problems. We demonstrate the efficacy of our strategies by implementing a prototype object store in Ceph, called SwimStore (Shadowing with Immutable Metadata Store). We experimentally show that SwimStore provides high performance with little variation, as well as a large reduction in write traffic.