Cephalocon 2024

LINE, a global mobile messenger, has adopted Ceph as its main object storage. It is used to store different kinds of data, such as log files and application data. Thanks to its scalability, billions of objects are stored in our clusters. However, over time, object deletions lead to the accumulation of tombstones in RocksDB, resulting in delays during iteration. Slow iteration not only impacts LIST operation but also stalls subsequent requests. To address this issue, we first collected RocksDB metric called "skip count", which indicates the total number of tombstones detected during iterations. We then deployed a new job which compacts OSDs with high skip counts to prevent stalls. Additionally, we analyzed the pattern of tombstones and found out that a few prefixes account for over 80% tombstones, throughout the entire OSD. Based on this observation, we propose range-based compaction. In this presentation, we will first explain the basics of RocksDB and its role in Ceph Object Storage. Then, we will share our experience how we handled the RocksDB issue. Lastly, we will discuss our proposal for range-based compaction, which could further optimize overall system performance.