DeepBench: Benchmarking JSON Document Stores.
ACM Conference on Management of Data(2022)
摘要
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 work, we specify 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 evaluation of well-known document stores with a prototypical DeepBench implementation shows its versatility and gives new insights into potential weaknesses that were not found by existing, non-JSON benchmarks
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要