Online data services have stringent performance requirement and must tolerate workload fluctuation. This paper introduces P it S top , a new query language runtime design built on the idea of interruptible query processing : the time-consuming task of data inspection for processing each query or update may be interrupted and resumed later at the boundary of fine-grained data partitions. This counter-intuitive idea enables a novel form of fine-grained concurrency while preserving sequential consistency . We build P it S top through modifying the language runtime of Cypher, the query language of a state-of-the-art graph database, Neo4j. Our evaluation on the Google Cloud shows that P it S top can outperform unmodified Neo4j during workload fluctuation, with reduced latency and increased throughput.

@article{EymerDRL24,
  title = {A Runtime System for Interruptible Query Processing: When Incremental Computing Meets Fine-Grained Parallelism},
  author = {Jeff Eymer and Philip Dexter and Joseph Raskind and Yu David Liu},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {8},
  number = {OOPSLA2},
  year = {2024},
  doi = {10.1145/3689772}
}