Abstract:
Many NoSQL and SQL systems use log-structured merge (LSM) tree key-value (KV) stores for online big data applications like social networking, graph processing, machine learning, etc. In LSM-tree key-value stores, batch processing of sorted data merging (compaction) improves write efficiency, and lazy compaction methods can accumulate more data. Batched writing methods cause significant tail latency, which is unacceptable for online processing. We propose a novel Lower-level Driven Compaction (LDC) method that breaks the limitations of upper-level driven compaction and triggers practical compaction actions bottom-up, reducing compaction granularity for lower latency and write amplification for higher throughput. We also add an adaptive policy to ALDC to adjust the key compaction threshold to workload features. ALDC reduces tail latency and increases throughput compared to existing methods.
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