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Protobase: It's About Time for Backend/Database Co-Design

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dc.contributor.authorPinnecke, Marcus
dc.contributor.authorCampero, Gabriel
dc.contributor.authorZoun, Roman
dc.contributor.authorBroneske, David
dc.contributor.authorSaake, Gunter
dc.contributor.editorGrust, Torsten
dc.contributor.editorNaumann, Felix
dc.contributor.editorBöhm, Alexander
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorHärder, Theo
dc.contributor.editorRahm, Erhard
dc.contributor.editorHeuer, Andreas
dc.contributor.editorKlettke, Meike
dc.contributor.editorMeyer, Holger
dc.date.accessioned2019-04-11T07:21:29Z
dc.date.available2019-04-11T07:21:29Z
dc.date.issued2019
dc.description.abstractIn 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
dc.identifier.doi10.18420/btw2019-35
dc.identifier.isbn978-3-88579-683-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/21721
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofBTW 2019
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) – Proceedings, Volume P-289
dc.subjectNoSQL
dc.subjectDocument Stores
dc.subjectAnalytics
dc.subjectRapid Prototyping
dc.subjectBackend/Database Co-Design
dc.titleProtobase: It's About Time for Backend/Database Co-Designen
dc.title.subtitleA Demo on Rapid Microservice Prototyping for Third-Party Dataset Analyticsen
gi.citation.endPage518
gi.citation.startPage515
gi.conference.date4.-8. März 2019
gi.conference.locationRostock
gi.conference.sessiontitleDemonstrationen

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P289 - BTW2019 - Datenbanksysteme für Business, Technologie und Web