Semi-structured data, such as JSON, are fundamental to the Web and document data stores. Streaming analytics on semi-structured data combines parsing and query evaluation into one pass to avoid generating parse trees. Though promising, its conventional design requires to parse the data stream in detail character by character, which limits the efficiency of streaming analytics. This work reveals a wide range of opportunities to fast-forward the streaming over certain data substructures irrelevant to the query evaluation. However, identifying these substructures itself may need detailed parsing. To resolve this dilemma, this work designs a highly bit-parallel solution that intensively utilizes bitwise and SIMD operations to identify the irrelevant substructures during the streaming. It includes a new streaming model—recursive-descent streaming, for an easy adoption of fast-forward optimizations, a concept—structural intervals, for partitioning the data stream, and a group of bit-parallel algorithms implementing various fast-forward cases. The solution is implemented as a JSON streaming framework, called JSONSki. It offers a set of APIs that can be invoked during the streaming to dynamically fast-forward over different cases of irrelevant substructures. Evaluation using real-world datasets and standard path queries shows that JSONSki can achieve significant speedups over the state-of-the-art JSON processing tools while taking a minimum memory footprint.

@inproceedings{Jiang022,
  title = {JSONSki: streaming semi-structured data with bit-parallel fast-forwarding},
  author = {Lin Jiang and Zhijia Zhao},
  booktitle = {ASPLOS},
  year = {2022},
  doi = {10.1145/3503222.3507719}
}