  Computer systems are full of heuristic rules which drive the decisions they
make. These rules of thumb are designed to work well on average, but ignore
specific information about the available context, and are thus sub-optimal. The
emerging field of machine learning for systems attempts to learn decision rules
with machine learning algorithms. In the database community, many recent
proposals have been made to improve selectivity estimation with batch machine
learning methods. Such methods are all batch methods which require retraining
and cannot handle concept drift, such as workload changes and schema
modifications. We present online machine learning as an alternative approach.
Online models learn on the fly and do not require storing data, they are more
lightweight than batch models, and finally may adapt to concept drift. As an
experiment, we teach models to improve the selectivity estimates made by
PostgreSQL's cost model. Our experiments make the case that simple online
models are able to compete with a recently proposed deep learning method.
