Wolfram Wingerath

NoSQL database systems: a survey and decision guidance

Today, data is generated and consumed at unprecedented scale. This has lead to novel approaches for scalable data management subsumed under the term “NoSQL” database systems to handle the ever-increasing data volume and request loads. However, the heterogeneity and diversity of the numerous existing systems impede the well-informed selection of a data store appropriate for a given application context. Therefore, this article gives a top-down overview of the field: instead of contrasting the implementation specifics of individual representatives, we propose a comparative classification model that relates functional and non-functional requirements to techniques and algorithms employed in NoSQL databases. This NoSQL Toolbox allows us to derive a simple decision tree to help practitioners and researchers filter potential system candidates based on central application requirements.

Real-time stream processing for Big Data

Abstract With the rise of the web 2.0 and the Internet of things, it has become feasible to track all kinds of information over time, in particular fine-grained user activities and sensor data on their environment and even their biometrics. However, while efficiency remains mandatory for any application trying to cope with huge amounts of data, only part of the potential of todays Big Data repositories can be exploited using traditional batch-oriented approaches as the value of data often decays quickly and high latency becomes unacceptable in some applications. In the last couple of years, several distributed data processing systems have emerged that deviate from the batch-oriented approach and tackle data items as they arrive, thus acknowledging the growing importance of timeliness and velocity in Big Data analytics. In this article, we give an overview over the state of the art of stream processors for low-latency Big Data analytics and conduct a qualitative comparison of the most popular contenders, namely Storm and its abstraction layer Trident, Samza and Spark Streaming. We describe their respective underlying rationales, the guarantees they provide and discuss the trade-offs that come with selecting one of them for a particular task.

Quaestor: query web caching for database-as-a-service providers

Today, web performance is primarily governed by round-trip latencies between end devices and cloud services. To improve performance, services need to minimize the delay of accessing data. In this paper, we propose a novel approach to low latency that relies on existing content delivery and web caching infrastructure. The main idea is to enable application-independent caching of query results and records with tunable consistency guarantees, in particular bounded staleness. Quaestor (Query Store) employs two key concepts to incorporate both expiration-based and invalidation-based web caches: (1) an Expiring Bloom Filter data structure to indicate potentially stale data, and (2) statistically derived cache expiration times to maximize cache hit rates. Through a distributed query invalidation pipeline, changes to cached query results are detected in real-time. The proposed caching algorithms offer a new means for data-centric cloud services to trade latency against staleness bounds, e.g. in a database-as-a-service. Quaestor is the core technology of the backend-as-a-service platform Baqend, a cloud service for low-latency websites. We provide empirical evidence for Quaestors scalability and performance through both simulation and experiments. The results indicate that for read-heavy workloads, up to tenfold speed-ups can be achieved through Quaestors caching.

BTW 2015 – Jubiläum an der Waterkant

Den Auftakt zur Konferenzwoche gaben die zwei parallel stattfindenden Tutorien „Distance-based Multimedia Indexing“ von Christian Beecks, Merih Seran Uysal und Thomas Seidl sowie „Skalierbare NoSQLund Cloud-Datenbanken in Forschung und Praxis“ von Felix Gessert. Am Nachmittag fanden die beiden Tutorien „Many-Core-Architekturen zur Datenbankbeschleunigung“ von Kai-Uwe Sattler, Jens Teubner, Felix Beier und Sebastian Breß sowie „Big-DataAnwendungsentwicklung mit SQL und NoSQL“ von Jens Albrecht und Uta Störl parallel zum Workshop „Second Workshop on Databases in Biometrics, Forensics and Security Applications“ (DBforBFS) organisiert durch Veit Koeppen, Gunter Saake und Claus Vielhauer statt. Über den gesamten zweiten Tag der Konferenzwoche erstreckte sich das von Andreas Thor organisierte Studierendenprogramm, welches nebenläufig zu den beiden Workshops „Data Streams and Event Processing“ (DSEP) organisiert durch Marco Grawunder und Daniela Nicklas und „Joint Workshop on Data Management for Science“ (DMS) organisiert durch Sebastian Dorok, Birgitta KönigRies, Matthias Lange, Erhard Rahm, Gunter Saake und Bernhard Seeger lief. 1 Die BTW 2015 im Überblick