  Anyone in need of a data system today is confronted with numerous complex
options in terms of system architectures, such as traditional relational
databases, NoSQL and NewSQL solutions as well as several sub-categories like
column-stores, row-stores etc. This overwhelming array of choices makes
bootstrapping data-driven applications difficult and time consuming, requiring
expertise often not accessible due to cost issues (e.g., to scientific labs or
small businesses). In this paper, we present the vision of evolutionary data
systems that free systems architects and application designers from the
complex, cumbersome and expensive process of designing and tuning specialized
data system architectures that fit only a single, static application scenario.
Setting up an evolutionary system is as simple as identifying the data. As new
data and queries come in, the system automatically evolves so that its
architecture matches the properties of the incoming workload at all times.
Inspired by the theory of evolution, at any given point in time, an
evolutionary system may employ multiple competing solutions down at the low
level of database architectures -- characterized as combinations of data
layouts, access methods and execution strategies. Over time, "the fittest wins"
and becomes the dominant architecture until the environment (workload) changes.
In our initial prototype, we demonstrate solutions that can seamlessly evolve
(back and forth) between a key-value store and a column-store architecture in
order to adapt to changing workloads.
