naa a22 4500 528783246 20180924065515.0 cr unu---uuuuu 180924e201706 xx s 100 0 eng 10.3929/ethz-b-000234616 doi (ETHRESEARCH)oai:www.research-collection.ethz.ch:20.500.11850/234616 Mison: A Fast JSON Parser for Data Analytics [Elektronische Daten] [Yinan Li, Nikos Katsipoulakis, Badrish Chandramouli, Jonathan Goldstein, Donald Kossman] Association for Computing Machinery 2017 Open access ethresearch The growing popularity of the JSON format has fueled increased interest in loading and processing JSON data within analytical data processing systems. However, in many applications, JSON pars- ing dominates performance and cost. In this paper, we present a new JSON parser called Mison that is particularly tailored to this class of applications, by pushing down both projection and filter operators of analytical queries into the parser. To achieve these features, we propose to deviate from the traditional approach of building parsers using finite state machines (FSMs). Instead, we follow a two-level approach that enables the parser to jump di- rectly to the correct position of a queried field without having to perform expensive tokenizing steps to find the field. At the upper level, Mison speculatively predicts the logical locations of queried fields based on previously seen patterns in a dataset. At the lower level, Mison builds structural indices on JSON data to map logi- cal locations to physical locations. Unlike all existing FSM-based parsers, building structural indices converts control flow into data flow, thereby largely eliminating inherently unpredictable branches in the program and exploiting the parallelism available in modern processors. We experimentally evaluate Mison using representative real-world JSON datasets and the TPC-H benchmark, and show that Mison produces significant performance benefits over the best existing JSON parsers; in some cases, the performance improve- ment is over one order of magnitude. 43rd International Conference on Very Large Data Bases (VLDB 2017) in Munich, Germany (August 28 - September 1, 2017) Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0 ethresearch Li Yinan joint author Katsipoulakis Nikos joint author Chandramouli Badrish joint author Goldstein Jonathan joint author Kossman Donald joint author Proceedings of the VLDB Endowment Association for Computing Machinery pp. 1118-1129 http://hdl.handle.net/20.500.11850/234616 text/html WWW-Backlink auf das Repository (Open access) 1 Conference Paper ethresearch ETHRESEARCH 856 40 http://hdl.handle.net/20.500.11850/234616 text/html WWW-Backlink auf das Repository (Open access) ETHRESEARCH 700 1- Li Yinan joint author ETHRESEARCH 700 1- Katsipoulakis Nikos joint author ETHRESEARCH 700 1- Chandramouli Badrish joint author ETHRESEARCH 700 1- Goldstein Jonathan joint author ETHRESEARCH 700 1- Kossman Donald joint author ETHRESEARCH 773 0- Proceedings of the VLDB Endowment Association for Computing Machinery pp. 1118-1129 BK010053 XK010053 XK010000 ETHRESEARCH ETHRESEARCH ETHRESEARCH Conference Paper Open access