  Query workloads and database schemas in OLAP applications are becoming
increasingly complex. Moreover, the queries and the schemas have to continually
\textit{evolve} to address business requirements. During such repetitive
transitions, the \textit{order} of index deployment has to be considered while
designing the physical schemas such as indexes and MVs.
  An effective index deployment ordering can produce (1) a prompt query runtime
improvement and (2) a reduced total deployment time. Both of these are
essential qualities of design tools for quickly evolving databases, but
optimizing the problem is challenging because of complex index interactions and
a factorial number of possible solutions.
  We formulate the problem in a mathematical model and study several techniques
for solving the index ordering problem. We demonstrate that Constraint
Programming (CP) is a more flexible and efficient platform to solve the problem
than other methods such as mixed integer programming and A* search. In addition
to exact search techniques, we also studied local search algorithms to find
near optimal solution very quickly.
  Our empirical analysis on the TPC-H dataset shows that our pruning techniques
can reduce the size of the search space by tens of orders of magnitude. Using
the TPC-DS dataset, we verify that our local search algorithm is a highly
scalable and stable method for quickly finding a near-optimal solution.
