GTX: A Transactional Graph Data System For HTAP Workloads

TL;DR

GTX is a latch-free, high-throughput transactional graph system optimized for dynamic, real-world workloads, supporting millions of transactions per second while efficiently handling graph analytics and updates.

Contribution

GTX introduces a novel latch-free storage, delta-based multi-versioning, and adaptive concurrency control tailored for power-law graphs in HTAP workloads.

Findings

Supports millions of transactions per second.

Maintains performance with hotspots and temporal localities.

Outperforms existing transactional graph systems.

Abstract

Processing, managing, and analyzing dynamic graphs are the cornerstone in multiple application domains including fraud detection, recommendation system, graph neural network training, etc. This demo presents GTX, a latch-free write-optimized transactional graph data system that supports high throughput read-write transactions while maintaining competitive graph analytics. GTX has a unique latch-free graph storage and a transaction and concurrency control protocol for dynamic power-law graphs. GTX leverages atomic operations to eliminate latches, proposes a delta-based multi-version storage, and designs a hybrid transaction commit protocol to reduce interference between concurrent operations. To further improve its throughput, we design a delta-chains index to support efficient edge lookups. GTX manages concurrency control at delta-chain level, and provides adaptive concurrency according to the workload. Real-world graph access and updates exhibit temporal localities and hotspots. Unlike other transactional graph systems that experience significant performance degradation, GTX is the only system that can adapt to temporal localities and hotspots in graph updates and maintain million-transactions-per-second throughput. GTX is prototyped as a graph library and is evaluated using a graph library evaluation tool using real and synthetic datasets.