  The emergence of learned indexes has caused a paradigm shift in our
perception of indexing by considering indexes as predictive models that
estimate keys' positions within a data set, resulting in notable improvements
in key search efficiency and index size reduction; however, a significant
challenge inherent in learned index modeling is its constrained support for
update operations, necessitated by the requirement for a fixed distribution of
records. Previous studies have proposed various approaches to address this
issue with the drawback of high overhead due to multiple model retraining. In
this paper, we present UpLIF, an adaptive self-tuning learned index that
adjusts the model to accommodate incoming updates, predicts the distribution of
updates for performance improvement, and optimizes its index structure using
reinforcement learning. We also introduce the concept of balanced model
adjustment, which determines the model's inherent properties (i.e. bias and
variance), enabling the integration of these factors into the existing index
model without the need for retraining with new data. Our comprehensive
experiments show that the system surpasses state-of-the-art indexing solutions
(both traditional and ML-based), achieving an increase in throughput of up to
3.12 times with 1000 times less memory usage.
