  Innovative learning based structures have recently been proposed to tackle
index and cardinality estimation tasks, specifically learned indexes and data
driven cardinality estimators. These structures exhibit excellent performance
in capturing data distribution, making them promising for integration into AI
driven database kernels. However, accurate estimation for corner case queries
requires a large number of network parameters, resulting in higher computing
resources on expensive GPUs and more storage overhead. Additionally, the
separate implementation for CE and learned index result in a redundancy waste
by storage of single table distribution twice. These present challenges for
designing AI driven database kernels. As in real database scenarios, a compact
kernel is necessary to process queries within a limited storage and time
budget. Directly integrating these two AI approaches would result in a heavy
and complex kernel due to a large number of network parameters and repeated
storage of data distribution parameters. Our proposed CardIndex structure
effectively killed two birds with one stone. It is a fast multidim learned
index that also serves as a lightweight cardinality estimator with parameters
scaled at the KB level. Due to its special structure and small parameter size,
it can obtain both CDF and PDF information for tuples with an incredibly low
latency of 1 to 10 microseconds. For tasks with low selectivity estimation, we
did not increase the model's parameters to obtain fine grained point density.
Instead, we fully utilized our structure's characteristics and proposed a
hybrid estimation algorithm in providing fast and exact results.
