JSON Tiles: Fast Analytics on Semi-Structured Data (2021)
Abstract: Developers often prefer flexibility over upfront schema design, making semi-structured data formats such as JSON increasingly popular. Large amounts of JSON data are therefore stored and analyzed by relational database systems. In existing systems, however, JSON’s lack of a fixed schema results in slow analytics. In this paper, we present JSON tiles, which, without losing the…
Developers now favor flexibility over strict schema design, leading to a surge in semi-structured data formats like JSON. Consequently, large quantities of JSON data are now stored and analyzed using relational database systems. However, existing systems face challenges due to JSON's schema-less nature, resulting in slow analytics.
To address this issue, researchers introduce JSON tiles. This innovative solution retains JSON's flexibility while enabling relational systems to perform analytics on JSON data at native speeds. JSON tiles automatically identifies the most critical keys and extracts them seamlessly, often achieving scan performance equivalent to columnar storage. Additionally, JSON tiles can manage heterogeneous and evolving data.
Moreover, JSON tiles automatically gathers statistics that assist the query optimizer in devising optimal execution plans. To validate the effectiveness of this approach, the researchers conducted an extensive experimental evaluation, comparing JSON tiles against other state-of-the-art systems and research proposals. The results demonstrate that JSON tiles is both robust and efficient, making it a promising solution for handling semi-structured data in relational databases.
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