In LSM trees, background compaction tasks contend with foreground queries for CPU cycles, cache space, and also SAN bandwidth, if deployed in a disaggregated storage architecture. This study proposesNear-Data Compaction(NDC), which executes compaction on the storage node to utilize its underutilized computing resources. However, enabling NDC introduces several challenges. First, it has to support concurrent file access from both compute and storage nodes. In addition, it has to decide which compaction tasks to be executed on the storage node since the computing resources of a storage node are not unlimited. This study presentsTetherDB, an LSM tree for disaggregated storage architecture that addresses these challenges through lightweight dual-node coordination and selective NDC admission policies. Our evaluation demonstrates that TetherDB improves throughput by up to 2.1× compared to RocksDB in write-heavy workloads.