  Data warehouses organize data in a columnar format to enable faster scans and
better compression. Modern systems offer a variety of column encodings that can
reduce storage footprint and improve query performance. Selecting a good
encoding scheme for a particular column is an optimization problem that depends
on the data, the query workload, and the underlying hardware. We introduce
Learned Encoding Advisor (LEA), a learned approach to column encoding
selection. LEA is trained on synthetic datasets with various distributions on
the target system. Once trained, LEA uses sample data and statistics (such as
cardinality) from the user's database to predict the optimal column encodings.
LEA can optimize for encoded size, query performance, or a combination of the
two. Compared to the heuristic-based encoding advisor of a commercial column
store on TPC-H, LEA achieves 19% lower query latency while using 26% less
space.
