The constantly evolving demands of modern applications pose increasingly higher requirements for key-value (KV) stores, especially when dealing with update-intensive workloads. RocksDB, an LSM-tree-based KV store, has designed a merge operator to optimize update throughput by directly recording the partially modified value fields. However, this operator negatively affects read performance because it will result in the multiple coexistences of partial values for the same key, which thus requires multiple seeks. To address these issues, we propose a KV-store MoonKV designed for update-intensive workload, which implements Logical and Vertical Compaction techniques to efficiently and timely merge partial values, thereby minimizing their coexistence in the LSM-tree. Besides, MoonKV introduces an NVM-based Index and Estimate Search to enhance search efficiency, leveraging high-speed NVM. By incorporating the above techniques, MoonKV significantly reduces the number of partial keys required to search when reading the value of a specific key. Finally, our evaluation shows that MoonKV achieves a 1.6$\times$ −3.2$\times$ throughput improvement compared with other KV stores, such as RocksDB, BlobDB, and MatrixKV, under update-intensive workloads.