| dc.description.abstract | In this interactive demonstration, we show the current state of Protobase, our main-memory analytic document store that is designed from scratch to enable rapid prototyping of efficient microservices that perform analytics and explorations on (third-party) JSON-like documents stored in a novel columnar binary-encoded format, called the Cabin file format. In contrast to other solutions, our database system exposes neither a particular query language, nor a fixed REST API to its clients. Instead, the entire user-defined backend logic, whose user code is written in Python, is placed inside a sandbox that runs in the systems process. Protobase in turn exposes a user-defined REST API that the (frontend) application interacts with. Thus, our system acts as a backend server while at the same time avoids full exposure of its database to the clients. Consequently, a Protobase instance (database + user code + REST API) serves as (the entire) microservice -potentially minimizing the number of systems running in a typical analytic software stack. In terms of execution performance, Protobase therefore takes the inter-process communication overhead between backend and database system out of the picture and heavily utilizes columnar binary document storage to scale-up for analytic queries. Both features lead to a notable performance gain for non-trivial services, potentially minimizing the number of required nodes in a cloud setting, too. In our demo, we overview Protobases internals, spot major design decisions, and show how to prototype a scholarly search engine managing the Microsoft Academic Graph, a real-world scientific paper graph of roughly 154 mio. Documents. | en |
