gParaKV: A GPGPU-accelerated Key-Value Separation-based KV Store with Optimized Compaction and Garbage Collection

LSM-tree-based key-value stores or KV stores are widely deployed in modern cloud storage systems thanks to high data storage efficiency and retrieval capabilities. The compaction process in the LSM-tree, however, results in severe performance bottlenecks, especially in scenarios involving large volumes of data. While key-value separation methods mitigate the performance bottlenecks caused by compaction, the existing methods do not fully address merge-sorting during compaction and expensive garbage collection (GC). We propose gParaKV, a GPGPU-empowered KV store with a KV separation mechanism, leveraging the GPGPU parallel technology to accelerate merge-sorting in compaction and GC. gParaKV embraces unique features like a GPGPU bitmap structure, parallel data marking, and a parallel GC mechanism. These critical components effectively curtail the overhead of merge-sorting and GC operations by virtue of parallel computing. We compare it with state-of-the-art KV stores (e.g., RocksDB, BlobDB, Wisckey, DiffKV, UniKV, and HPDK) under various workloads. The experimental results show that gParaKV can improve the write performance and GC efficiency compared to the existing key-value separation-based KV stores.