Document Type
Article
Publication Date
12-2020
Department
Department of Computer Science
Abstract
JavaScript Object Notation ( JSON) and its variants have gained great popularity in recent years. Unfortunately, the performance of their analytics is often dragged down by the expensive JSON parsing. To address this, recent work has shown that building bitwise indices on JSON data, called structural indices, can greatly accelerate querying. Despite its promise, the existing structural index construction does not scale well as records become larger and more complex, due to its (inherently) sequential construction process and the involvement of costly memory copies that grow as the nesting level increases. To address the above issues, this work introduces Pison – a more memory-efficient structural index constructor with supports of intra-record parallelism. First, Pison features a redesign of the bottleneck step in the existing solution. The new design is not only simpler but more memory-efficient. More importantly, Pison is able to build structural indices for a single bulky record in parallel, enabled by a group of customized parallelization techniques. Finally, Pison is also optimized for better data locality, which is especially critical in the scenario of bulky record processing. Our evaluation using real-world JSON datasets shows that Pison achieves 9.8X speedup (on average) over the existing structural index construction solution for bulky records and 4.6X speedup (on average) of end-to-end performance (indexing plus querying) over a state-of-the-art SIMD-based JSON parser on a 16-core machine.
Publication Title
Proceedings of the VLDB Endowment
Recommended Citation
Jiang, L.,
Qiu, J.,
&
Zhao, Z.
(2020).
Scalable structural index construction for json analytics.
Proceedings of the VLDB Endowment,
14(4), 694-707.
http://doi.org/10.14778/3436905.3436926
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/14568
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
This work is licensed under the Creative Commons BY-NC-ND 4.0 International License. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org. Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment. Publisher’s version of record: https://doi.org/10.14778/3436905.3436926