Key-value stores (KV stores) anchored on log-structured merge trees (LSM-tree) provide much improved performance under write-intensive workloads but exhibit significant write amplification (WA). The tiering compaction strategy is widely adopted in KV stores to reduce the WA. However, there are three imminent issues in tiering-based KV stores. First, excessive levels in the LSM-tree structure result in high read and write amplification. Second, without key partitioning support, these KV stores increase write tail latency. Relying solely on a hash-based key partitioning scheme is insufficient, as it does not support range queries. Furthermore, a KV store with only a key range-based partitioning scheme overlooks the distribution of keys within the key space. Third, existing KV stores with level optimization can incur high costs. A common approach to reducing the number of LSM-tree levels is to increase the MemTable size. While this reduces the number of levels, it requires more memory, leading to higher costs. Therefore, it is crucial to balance the tradeoff between level optimization and monetary expenses. To address these issues, we propose a tiering-based key-value store, leveraging high-performance hierarchical data management. The key space of our proposed KV store is partitioned into m ultiple partitions based on key ranges, with an LSM- tree in each partition. We refer to this KV store as MTree in this article. MTree reduces the number of levels in the LSM-Tree structure close to one without increasing the monetary cost. In MTree, a hierarchical structure that combines the log and LSM-tree curtails the number of levels in the LSM-tree by accumulating the data in the log without increasing the cost. With the hierarchical structure in place, we devise a long- and short-term key density distribution-aware partitioning scheme for the key range. This scheme dynamically adjusts the size of partitioning, balancing data in the partition and storing most data in large-sized logs. Then, MTree reduces the number of levels in the LSM-tree, thereby optimizing the read/write amplification. The experimental results show that MTree limits the WA to as low as two. MTree enhances the random write throughput of the state-of-the-art KV stores by up to 6.9×.