This paper introduces a novel one-hop sub-query result cache for processing graph read transactions, gR-Txs, in a graph database system. The one-hop navigation is from a vertex using either its in-coming or out-going edges with selection predicates that filter edges and vertices. Its cache entry identifies a unique one-hop sub-query (key) and its result set consisting of immutable vertex ids (value). When processing a gR-Tx, the query processor identifies its sequence of individual one-hop sub-queries and looks up their results in the cache. A cache hit fetches less data from the storage manager and eliminates the requirement to process the one-hop sub-query. A cache miss populates the cache asynchronously and in a transactional manner, maintaining the separation of read and write paths of our transactional storage manager. A graph read and write transaction, gRW-Tx, identifies the impacted cache entries and either deletes or updates them. Our implementation of the cache is inside the graph query processing engine and transparent to a user application. We evaluate the cache using our eCommerce production workload and with rules that re-write graph queries to maximize the performance enhancements observed with the cache. Obtained results show the cache enhances 95th and 99th percentile of query response times by at least 2x and 1.63x, respectively. When combined with query re-writing, the enhancements are at least 2.33x and 4.48x, respectively. An interesting result is the significant performance enhancement observed by the indirect beneficiaries of the cache, gRW-Txs and gR-Txs that do not reference one-hop sub-queries. The cache frees system resources to expedite their processing significantly.

翻译：本文介绍了一种新颖的单跳子查询结果缓存技术，用于处理图数据库系统中的图读取事务（gR-Txs）。单跳导航从某个顶点出发，通过其入边或出边进行，并利用筛选边和顶点的选择谓词。缓存条目标识唯一的单跳子查询（键）及其由不可变顶点ID组成的结果集（值）。在处理gR-Tx时，查询处理器识别其包含的各个单跳子查询序列，并在缓存中查找它们的结果。缓存命中可从存储管理器获取更少数据，并消除处理单跳子查询的需求。缓存未命中则以异步且事务性的方式填充缓存，保持我们事务性存储管理器的读写路径分离。图读写事务（gRW-Tx）识别受影响的缓存条目，并对其进行删除或更新。我们在图查询处理引擎内部实现了该缓存，对用户应用程序透明。我们使用电子商务生产工作负载以及重写图查询的规则来评估缓存性能，以最大化缓存带来的性能提升。实验结果表明，该缓存将查询响应时间的第95和第99百分位数分别提升了至少2倍和1.63倍。结合查询重写技术后，提升幅度分别达到至少2.33倍和4.48倍。一个有趣的发现是，缓存的间接受益者（即不引用单跳子查询的gRW-Txs和gR-Txs）也观测到显著的性能提升，因为缓存释放了系统资源，从而大幅加速了它们的处理过程。