  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 learned indexes, which use 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+.
