  Index structures are important for efficient data access, which have been
widely used to improve the performance in many in-memory systems. Due to high
in-memory overheads, traditional index structures become difficult to process
the explosive growth of data, let alone providing low latency and high
throughput performance with limited system resources. The promising learned
indexes leverage deep-learning models to complement existing index structures
and obtain significant memory savings. However, the learned indexes fail to
become scalable due to the heavy inter-model dependency and expensive
retraining. To address these problems, we propose a scalable learned index
scheme to construct different linear regression models according to the data
distribution. Moreover, the used models are independent so as to reduce the
complexity of retraining and become easy to partition and store the data into
different pages, blocks or distributed systems. Our experimental results show
that compared with state-of-the-art schemes, AIDEL improves the insertion
performance by about 2$\times$ and provides comparable lookup performance,
while efficiently supporting scalability.
