Data streaming operations consume a significant portion of CPU resources in data centers. The Data Streaming Accelerator (DSA), integrated into modern Intel CPUs in datacenter, offers promising acceleration for these operations with user-friendly features. However, previous studies have overlooked DSA's internal mechanisms and the performance implications of these features, leaving key performance issues unresolved in real-world usage.

Learned indexes are viewed as promising substitutes for traditional indexes due to their excellent performance, especially in read-only workloads. Previous studies have shown that updatable learned indexes perform exceptionally well in many cases, suggesting they are nearly ready for real-world applications. However, unlike traditional indexes such as B+tree and ART, updatable learned indexes are prone to instability of real-time trained models, resulting in inherently uncertain structures. This raises skepticism about their robustness, hindering their broader adoption. In this paper, we conduct a systematic benchmark and analysis to address this concern, corroborating doubts about the lack of robustness in state-of-the-art updatable learned indexes. We demonstrate that, contrary to previous findings, updatable learned indexes cannot robustly surpass traditional indexes, even losing their expected advantage under read-intensive workloads. We further reveal the root causes, including overfitted models, unbalanced structures, ineffective adjustments, and excessive space reservation. In addition, we explore potential mitigation methods to address these challenges. We hope our findings will highlight the critical importance of robustness in the design of updatable learned indexes, ultimately paving the way for their real-world adoption.

Numerous high-performance updatable learned indexes have recently been designed to support the writing requirements in practical systems. Researchers have proposed various strategies to improve the availability of updatable learned indexes. However, it is unclear which strategy is more profitable. Therefore, we deconstruct the design of learned indexes into multiple dimensions and in-depth evaluate their impacts on the overall performance, respectively. Through the in-depth exploration of learned indexes, we reckon that the approximation algorithm is the most crucial design dimension for improving the performance of the learned indexes rather than the popular works that focus on the learned index structure. Moreover, this paper makes a comprehensive end-to-end evaluation based on a high-performance key-value store to answer people’s concerns about which learned index is better and whether learned indexes can outperform traditional ones. Finally, according to end-to-end and in-depth evaluation results, we give some constructive suggestions on designing a better learned index in these dimensions, especially how to design an excellent approximate algorithm to improve the lookup and insertion performance of learned indexes.

The growth in data storage capacity and the increasing demands for high performance have created several challenges for concurrent indexing structures. One promising solution is the learned index, which uses a learning-based approach to fit the distribution of stored data and predictively locate target keys, significantly improving lookup performance. Despite their advantages, prevailing learned indexes exhibit constraints and encounter issues of scalability on multi-core data storage. This paper introduces SALI, the Scalable Adaptive Learned Index framework, which incorporates two strategies aimed at achieving high scalability, improving efficiency, and enhancing the robustness of the learned index. Firstly, a set of node-evolving strategies is defined to enable the learned index to adapt to various workload skews and enhance its concurrency performance in such scenarios. Secondly, a lightweight strategy is proposed to maintain statistical information within the learned index, with the goal of further improving the scalability of the index. Furthermore, to validate their effectiveness, SALI applied the two strategies mentioned above to the learned index structure that utilizes fine-grained write locks, known as LIPP. The experimental results have demonstrated that SALI significantly enhances the insertion throughput with 64 threads by an average of 2.04x compared to the second-best learned index. Furthermore, SALI accomplishes a lookup throughput similar to that of LIPP+.