  We study (Euclidean) $k$-median and $k$-means with constraints in the
streaming model.
  There have been recent efforts to design unified algorithms to solve
constrained $k$-means problems without using knowledge of the specific
constraint at hand aside from mild assumptions like the polynomial
computability of feasibility under the constraint (compute if a clustering
satisfies the constraint) or the presence of an efficient assignment oracle
(given a set of centers, produce an optimal assignment of points to the centers
which satisfies the constraint). These algorithms have a running time
exponential in $k$, but can be applied to a wide range of constraints.
  We demonstrate that a technique proposed in 2019 for solving a specific
constrained streaming $k$-means problem, namely fair $k$-means clustering,
actually implies streaming algorithms for all these constraints. These work for
low dimensional Euclidean space. [Note that there are more algorithms for
streaming fair $k$-means today, in particular they exist for high dimensional
spaces now as well.]
