iLSM-SSD: An Intelligent LSM-Tree Based Key-Value SSD for Data Analytics

Several key-value stores such as RocksDB and MongoDB are implemented on the file system using the Log-Structured Merge-Tree (LSM-tree). The LSM-tree involves high compaction overhead. To minimize this overhead, WiscKey, the state-of-the-art LSM-tree, separates key and value, appends the value to the Value Log file, and LSM-tree manages only the key and Value Log offset. This minimizes the compaction overhead by reducing the number of SSTables managed by the LSM-tree. However, WiscKey still has a high I/O stack overhead that must go through the OS file system and block-layer. Therefore, this paper proposes iLSM-SSD that implements WiscKey in SSD and supports near-data processing. iLSM-SSD has the following features: (i) iLSM-SSD implements a key-value separation based LSM-tree in a limited memory space inside the SSD. (ii) The Value Log offset update management overhead incurred during the Value Log cleaning has a significant performance impact on CPU and memory-constrained SSD environments. To minimize this overhead, iLSM-SSD implements Scattered Logging, which reuses invalidated Value Log pages on the Value Log. (iii) iLSM-SSD manages the data layout internally. This enables iLSM-SSD to eliminate the need for file system interactions to obtain the data layout for in-storage processing on traditional block-interface-based SSDs. We prototyped the iLSM-SSD on the Cosmos+ OpenSSD platform in a Linux environment. Extensive evaluations with synthetic benchmarks have shown that the PUT performance of iLSM-SSD is 1.6-4 times higher than that of WiscKey implemented in RocksDB.