  In this paper, we introduce LearnedKV, a novel tiered key-value (KV) store
that seamlessly integrates a Log-Structured Merge (LSM) tree with a Learned
Index. This integration yields superior read and write performance compared to
standalone indexing structures on SSDs. Our design capitalizes on the LSM
tree's high write/update throughput and the Learned Index's fast read
capabilities, enabling each component to leverage its strengths. We analyze the
impact of size on LSM tree performance and demonstrate how the tiered Learned
Index significantly mitigates the LSM tree's size-related performance
degradation, particularly by reducing the intensive I/O operations resulting
from re-insertions after Garbage Collection (GC). To maintain rapid read
performance for newly inserted keys, we introduce a non-blocking conversion
mechanism that efficiently transforms the existing LSM tree into a new Learned
Index with minimal overhead during GC. Our experimental results, conducted
across diverse workloads, show that LearnedKV outperforms state-of-the-art
solutions by up to 1.32x in read requests and 1.31x in write performance.
