The growing popularity of JSON as exchange and storage format in business and analytical applications led to its rapid dissemination, thus making a timely storage and processing of JSON documents crucial for organizations. Consequently, specialized JSON document stores are ubiquitously used for diverse domain-specific workloads, while a JSON-specific benchmark is missing.
In this repository, we provide an example implementation of DeepBench, an extensible, scalable benchmark that addresses nested JSON data, as well as queries over JSON documents. DeepBench features configurable domain-independent (e. g., varying document sizes, concurrent users) and JSON-specific scale levels (e. g., object, array nesting).
The package json_data_and_query_generator contains tools to generate random json data and corresponding SQL queries.
Each of these tools needs as an input a configuration in form of a json document describing the fixed structure of the data and the characteristic of the generated queries.
Install prerequisites:
pip install .
To execute data and query generation based on the example scenario in examples (default):
python -m json_data_and_query_generator --num-proc 5
with five processes.
If other scenarios should be run, then specify paths to schema.txt, data.txt, and config.json as described in pipeline.py --help.
This project is open to feature requests/suggestions, bug reports etc. via GitHub issues. Contribution and feedback are encouraged and always welcome. For more information about how to contribute, the project structure, as well as additional contribution information, see our Contribution Guidelines.
We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone. By participating in this project, you agree to abide by its Code of Conduct at all times.
Copyright 2022-2023 SAP SE or an SAP affiliate company and contributors. Please see our LICENSE for copyright and license information. Detailed information including third-party components and their licensing/copyright information is available via the REUSE tool.
For more documentation read the following documents. If you find this work useful for your research, please cite:
@inproceedings{DBLP:conf/dbtest-ws/Belloni0SR22,
author = {Stefano Belloni and
Daniel Ritter and
Marco Schr{\"{o}}der and
Nils R{\"{o}}rup},
editor = {Manuel Rigger and
Pinar T{\"{o}}z{\"{u}}n},
title = {DeepBench: Benchmarking {JSON} Document Stores},
booktitle = {DBTest@SIGMOD '22: Proceedings of the 9th International Workshop of
Testing Database Systems, Philadelphia, PA, USA, 17 June 2022},
pages = {1--9},
publisher = {{ACM}},
year = {2022},
url = {https://doi.org/10.1145/3531348.3532176},
doi = {10.1145/3531348.3532176},
timestamp = {Sun, 02 Oct 2022 15:58:56 +0200},
biburl = {https://dblp.org/rec/conf/dbtest-ws/Belloni0SR22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
and / or the usage in systems:
@article{DBLP:journals/dbsk/BelloniR22,
author = {Stefano Belloni and
Daniel Ritter},
title = {Benchmarking {JSON} Document Stores in Practice},
journal = {Datenbank-Spektrum},
volume = {22},
number = {3},
pages = {217--226},
year = {2022},
url = {https://doi.org/10.1007/s13222-022-00425-y},
doi = {10.1007/s13222-022-00425-y},
timestamp = {Sat, 25 Feb 2023 21:35:08 +0100},
biburl = {https://dblp.org/rec/journals/dbsk/BelloniR22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}