FOCUS: Boosting Schema-aware Access for KV Stores via Hierarchical Data Management

TL;DR

FOCUS is a hierarchical, schema-aware key-value store designed to improve performance by native support for structured data, significantly boosting throughput in NVM-backed environments for data-intensive applications.

Contribution

It introduces a novel hierarchical KV model and schema-aware access mechanism, optimizing integration of structured data into KV stores.

Findings

Increases throughput by 2.1-5.9x under YCSB SQL workloads.

Provides native support for structured data in KV stores.

Reduces I/O amplification and splitting in data-intensive applications.

Abstract

Persistent key-value (KV) stores are critical infrastructure for data-intensive applications. Leveraging high-performance Non-Volatile Memory (NVM) to enhance KV stores has gained traction. However, previous work has primarily focused on optimizing KV stores themselves, without adequately addressing their integration into applications. Consequently, existing applications, represented by NewSQL databases, still resort to a flat mapping approach, which simply maps structured records into flat KV pairs to use KV stores. Such semantic mismatch may cause significant I/O amplification and I/O splitting under production workloads, harming the performance. To this end, we propose FOCUS, a log-structured KV store optimized for fine-grained hierarchical data organization and schema-aware access. FOCUS introduces a hierarchical KV model to provide native support for upper-layer structured data. We implemented FOCUS from scratch. Experiments show that FOCUS can increase throughput by 2.1-5.9x compared to mainstream NVM-backed KV stores under YCSB SQL workloads.