paratext · Journal Of Big Data
Learned index structures replace traditional algorithmic data structures with machine learning models to improve database search efficiency. An analysis of 49 studies published between 2017 and 2025 outlines three development stages: foundational design, rapid diversification, and systematic optimisation. On standard central processing units, learned indexes consistently achieve lookup speed improvements of 1.4 to 5 times, while parallel batch processing on graphics hardware demonstrates speed increases of up to 174 times on benchmark datasets. Benefits in memory usage and training duration vary considerably based on the workload and data distribution. These systems function best in read-heavy and multi-dimensional analytical tasks, but they struggle under write-intensive demands. A persistent gap remains between algorithmic progress and system integration, with critical hurdles in reliability guarantees and dynamic workload management still requiring solutions.
Efficient data retrieval is central to modern software, enterprise storage, and cloud computing. By using machine learning models to navigate stored information, databases could process queries far more rapidly and consume fewer hardware resources. Clarifying where these approaches succeed and where they fail gives technologists a realistic view of how machine learning can transform core computing infrastructure.
The technology targets database management systems and analytical platforms used by enterprise infrastructure providers and data engineers. The technology remains at an early to mid-stage research level rather than being near-market. While benchmark evaluations show high read speeds, practical deployment is constrained by poor write handling, lack of reliability guarantees, and incomplete integration with existing database architectures, meaning significant infrastructural engineering is still needed before production adoption.
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Abstract Learned index structures use machine learning models, rather than traditional algorithmic data structures, to optimize database indexing performance. This systematic literature review applies the PICOC framework to 49 peer-reviewed articles (2017–2025) on learned index effectiveness versus traditional methods, using systematic multi-venue searches and strict, empirically grounded inclusion criteria. Three evolutionary phases emerge from the corpus: foundational development (2017–2018), rapid diversification (2019–2022), and systematic optimization (2023–2025). In terms of performance, learned indexes typically achieve 1.4–5× lookup speedups on standard CPU platforms. In contrast, substantially larger improvements (up to 174×) have only been reported under GPU-parallel batch inference on benchmark datasets. Likewise, reported memory reductions and training-time improvements depend on the specific workload, data distribution, and evaluation environment. Despite this progress, critical gaps remain in dynamic workload support, database integration, and reliability guarantees. Learned indexes perform best in read-heavy analytic and multi-dimensional workloads but degrade substantially under write-intensive conditions. This review contributes a comprehensive architectural taxonomy, an evidence-based performance framework, and a prioritized research roadmap, and in doing so identifies a persistent research-practice gap in which algorithmic innovation has outpaced system-level integration, underscoring the infrastructural work still required for production adoption.
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DOI: 10.1186/s40537-026-01542-1
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