This thesis presents BubbleStorm, which attempts to bridge the gap between peer-to-peer and databases. BubbleStorm is a peer-to-peer search system, which solves large-scale rendezvous problems over the unreliable global internet. It provides a concept of user-defined bubble types, loosely corresponding to table schemas. Queries follow the fully general black-box model, allowing powerful queries to be evaluated exhaustively. The system tracks usage statistics with a system-wide measurement service, used to automatically tune search performance. As strong consistency guarantees are impossible, BubbleStorm instead aims for user-controlled probabilistic guarantees.
The key contribution of this thesis is to develop rendezvous theory and reformulate the black-box query model within this framework. This reformulation allows us to interpret any black-box system as solving a rendezvous problem, allowing an elegant and tight lower-bound. BubbleStorm leverages rendezvous theory to substantially reduce bandwidth consumption (both practically and asymptotically) while simultaneously improving query latency. The resulting system, which has a full fledged implementation, sports a simple to understand interface, which abstracts away the underlying details, much like the database systems before it
