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.

翻译：处理、管理和分析动态图是欺诈检测、推荐系统、图神经网络训练等多个应用领域的基石。本演示展示了GTX——一种无锁、写优化的事务图数据系统，它在保持竞争力的图分析能力的同时，支持高吞吐量的读写事务。GTX拥有独特的无锁图存储空间以及针对动态幂律图的事务与并发控制协议。GTX利用原子操作消除锁机制，提出基于增量的多版本存储，并设计混合事务提交协议以减少并发操作间的干扰。为进一步提升吞吐量，我们设计了增量链索引以支持高效的边查找。GTX在增量链层面管理并发控制，并根据工作负载提供自适应并发能力。真实世界的图访问和更新表现出时间局部性和热点特性。与其他在热点访问下性能显著下降的事务图系统不同，GTX是唯一能够适应图更新中的时间局部性和热点问题，并保持每秒数百万笔事务吞吐量的系统。GTX以图库原型实现，并使用图库评估工具基于真实与合成数据集进行了评测。