The emergence of Zoned Namespaces SSDs, known as ZNS SSDs, has introduced new challenges and opportunities for key-value stores. Since LSM-tree has been widely adopted in key-value stores, such as RocksDB and HBase. Compared with traditional SSDs, ZNS SSDs have the advantages of lower over-provisioning costs, higher and more stable performance, and better space efficiency. However, ZNS SSDs also have some limitations like sequential writes on zones. To make ZNS SSDs efficient in LSM-tree-based key-value stores, it is necessary to revisit the LSM-tree to make it adapt to ZNS SSDs. In this paper, we first analyze the challenges of adapting LSM-tree to ZNS SSDs. Then, we propose a high-level architecture for a ZNS-SSD-aware LSM-tree-based key-value store.

With the development of big data applications, online analytics systems that aim to offer decision support for various businesses and tasks have become a research focus, which calls for efficient approaches to handling OLAP queries. However, traditional OLAP systems suffer from the costly interactions with disks or SSDs, making them hard to deliver high performance for OLAP query processing. In this paper, we propose to use the emerging persistent memory to construct an efficient in-memory analytics system to improve the performance for OLAP query processing. We present the overall architecture as well as the detailed algorithms for the proposed system, which is named PM-Picker, and finally discuss the implementation issue of the system.

Recently, Zoned Namespaces (ZNS) SSDs have emerged as a hot topic in both academics and industries. Compared to conventional SSDs, ZNS SSDs have the advantages of less overhead of garbage collection and lower over-provisioning cost. However, ZNS SSDs only accept sequential writes, and the zones inside ZNS SSDs need to be carefully managed to maximize the advantages of ZNS SSDs. Therefore, how to make data management systems adapt to ZNS SSDs is becoming a challenging issue. Current database systems, either SQL databases or NoSQL data stores, are mainly designed toward magnetic disks or traditional SSDs (without zoned namespaces). In this paper, we explore the challenges and research opportunities of revising index structures for ZNS SSDs and focus on the B+-tree and LSM-tree, which represent the index structures for SQL databases and key-value stores. After summarizing the features of ZNS SSDs, we discuss the key issues of adapting the B+-tree to ZNS SSDs and the challenges of revising the LSM-tree (Log-Structured Merge tree) for ZNS SSDs. Finally, we suggest some future research work on this topic.