The prevalence of big data has caused a notable surge in both the diversity and magnitude of data. Consequently, this has prompted the emergence and advancement of two distinct technologies: unstructured data management and data volume reduction. Key–value stores, such as Google’s LevelDB and Meta’s RocksDB, have emerged as a popular solution for managing unstructured data due to their ability to handle diverse data types with a simple key–value abstraction. Simultaneously, a multitude of data management tools have actively adopted compression techniques, such as Snappy and Zstd, to effectively reduce data volume. The objective of this study is to explore how these two technologies influence each other. For this purpose, we first examine a classification of compression techniques and discuss their strength and weakness, especially those adopted by modern key–value stores. We also investigate the internal structures and operations, such as batch writing and compaction, in order to grasp the characteristics of key–value stores. Then, we quantitatively evaluate the compression ratio and performance using RocksDB under diverse compression techniques, block sizes, value sizes, and workloads. Our evaluation shows that compression not only saves storage space but also decreases compaction overhead. It also reveals that compression techniques have their inherent trade-offs, meaning that some provide a better compression ratio, while others yield better compression performance. Based on our evaluation, a number of potential avenues for further research have been identified. These include the exploration of a compression-aware compaction mechanism, selective compression, and revisiting compression granularity.

This paper presents an approach that integrates tiered storage into the Log-Structured Merge (LSM)-tree to balance Key–Value Store (KVS) performance and storage financial cost trade-offs. The implementation focuses on applying tiered storage to LSM-tree-based KVS architectures, using both vertical and horizontal storage alignment strategies or a combination of both. Additionally, these configurations leverage key–value (KV) separation to further improve performance. Our experiments reveal that this approach reduces storage financial costs while offering trade-offs in write and read performance. For write-intensive workloads, our approach achieves competitive performance compared to a fast NVMe Solid State Drive (SSD)-only approach while storing 96% of data on more affordable SATA SSDs. Additionally, it exhibits lookup performance comparable to BlobDB, and improves range query performance by 1.8x over RocksDB on NVMe SSDs. Overall, the approach results in a 49.5% reduction in storage financial cost compared to RocksDB and BlobDB on NVMe SSDs. The integration of selective KV separation further advances these improvements, setting the stage for future research into offloading remote data in LSM-tree tiered storage systems.