• Designing systems that embrace nonrelational data models, rather than shoehorning them into tables;

• Trading off consistency and availability for better performance and thousands of machines; and

•Designing power-aware DBMSs that limit energy costs without sacrificing scalability.

This list is not exhaustive. One industrial participant at the Claremont meeting noted that this is a time of opportunity for academic researchers; the landscape has shifted enough that access to industrial legacy code provides little advantage, and large-scale clustered hardware is rentable in the cloud at low cost. Moreover, industrial players and investors are aggressively looking for bold new ideas. This opportunity for academics to lead in system design is a major change in the research environment.

Declarative programming for emerging platforms. Programmer productivity is a key long-acknowledged challenge in computing, with its most notable mention in the database context in Jim Gray’s 1998 Turing lecture. Today, the urgency of the challenge is increasing exponentially as programmers target ever more complex environments, including many-core chips, distributed services, and cloud computing platforms.

Nonexpert programmers must be able to write robust code that scales out across processors in both loosely and tightly coupled architectures. Although developing new programming paradigms is not a database problem per se, ideas of data independence, declarative programming, and cost-based optimization provide a promising angle of attack. There is significant evidence that data-centric approaches will have significant influence on programming in the near term.

The recent popularity of the MapReduce programming framework for manipulating big data sets is an example of this potential. MapReduce is attractively simple, building on language and data-parallelism techniques that have been known for decades. For database researchers, the significance of MapReduce is in demonstrating the benefits of data-parallel programming to new classes of developers.

this is a unique
opportunity for
a fundamental
“reformation”
of the notion of
data management,
not as a single
system but as
a set of services
that can be
embedded, as
needed, in many
computing contexts.

This opens opportunities for the database community to extend its contribution to the broader community, developing more powerful and efficient languages and runtime mechanisms that help these developers address more complex problems.

As another example of declarative programming, in the past five years a variety of new declarative languages, often grounded in Datalog, have been developed for domain-specific systems in fields as diverse as networking and distributed systems, computer games, machine learning and robotics, compilers, security protocols, and information extraction. In many of these scenarios, the use of a declarative language has reduced code size by orders of magnitude while also enabling distributed or parallel execution. Surprisingly, the groups behind these efforts have coordinated very little with one another; the move to revive declarative languages in these new contexts has grown up organically.

A third example arises in enter-prise-application programming. Recent language extensions (such as Ruby on Rails and LINQ) encourage query-like logic in programmer design patterns. But these packages have yet to address the challenge of enterprise-style programming across multiple machines; the closest effort here is DryadLINQ, focusing on parallel analytics rather than on distributed application development. For enterprise applications, a key distributed design decision is the partitioning of logic and data across multiple “tiers,” including Web clients, Web servers, application servers, and a backend DBMS. Data independence is particularly valuable here, allowing programs to be specified without making a priori permanent decisions about physical deployment across tiers. Automatic optimization processes could make these decisions and move data and code as needed to achieve efficiency and correctness. XQuery has been proposed as an existing language that would facilitate this kind of declarative programming, in part because XML is often used in cross-tier protocols.

It is unusual to see this much energy surrounding new data-centric programming techniques, but the opportunity brings challenges as