With the deployment of modern hardware such as Flash-based SSDs and the high-speed network in distributed systems, the distributed consensus and consistency module (e.g., Raft) is typically the most time-consuming part. The reason lies in that Raft introduces some very strict constraints to ensure the linearizability. Therefore, in this paper, we rethink these constraints in-depth and find that some of them are not necessary, and can be broken to accelerate the performance significantly without breaking the linear consistency for distributed systems. An improved distributed consensus algorithm calledBUC-Raft(Breaking Unnecessary Constraints of Raft) is proposed in this paper and implemented in an industry-level distributed system. The experimental results suggest that both the write and the read performance can be accelerated significantly by BUC-Raft.

Log-structured merge (LSM) tree key-value (KV) stores have been widely deployed in many NoSQL and SQL systems, serving online big data applications such as social networking, graph processing, machine learning, etc. The batch processing of sorted data merging (i.e., compaction) in LSM-tree key-value stores improves the write efficiency, and some lazy compaction methods have been proposed to accumulate more data within a batch. However, these batched writing methods lead to significant tail latency, which is unacceptable for online processing. Aiming to optimize both latency and throughput, we propose a novel Lower-level Driven Compaction (LDC) method which breaks the limitations of the traditional upper-level driven compaction manner and triggers practical compaction actions bottom-up, with the benefits of both decreasing the compaction granularity for smaller latency and reducing write amplification for higher throughput. Furthermore, we extend LDC to Adaptive LDC (ALDC) by adding an adaptive policy to adjust the key compaction threshold to fit the changes of workloads’ features. The experimental results indicate that ALDC reduces the tail latency significantly and meanwhile achieves a much higher and stable throughput compared with existing approaches.

Distributed key-value stores have been widely used as NoSQL systems or the storage layer of distributed relational databases for various big data applications (e.g., social networking, graph processing, machine learning, etc.) due to their excellent scalability and adaptability. Although modern hardware such as Flash-based SSDs and the high-speed network is commonly deployed in key-value stores to promote performance, the distributed consensus and consistency module (e.g., Raft) is typically the most time-consuming part in distributed systems. The reason lies in that Raft introduces some very strict constraints to ensure the linearizability. Therefore, in this paper, we rethink these constraints in-depth and find that some of them are not necessary, and can be broken to accelerate the performance significantly without breaking the linear consistency for distributed key-value storage systems. An improved distributed consensus algorithm called KV-Raft is proposed in this paper and implemented in an industry-level distributed key-value system, i.e., TiKV. The experimental results suggest that both the write and the read performance can be accelerated significantly by KV-Raft. For example, in the typical read/write-balanced case, KV-Raft promotes the system throughput by 53.6%, and reduce the average write and read latency by 37.8% and 29.4%, respectively.