Large-scale social-media analytics on stratosphere

The importance of social-media platforms and online communities - in business as well as public context - is more and more acknowledged and appreciated by industry and researchers alike. Consequently, a wide range of analytics has been proposed to understand, steer, and exploit the mechanics and laws driving their functionality and creating the resulting benefits. However, analysts usually face significant problems in scaling existing and novel approaches to match the data volume and size of modern online communities. In this work, we propose and demonstrate the usage of the massively parallel data processing system Stratosphere, based on second order functions as an extended notion of the MapReduce paradigm, to provide a new level of scalability to such social-media analytics. Based on the popular example of role analysis, we present and illustrate how this massively parallel approach can be leveraged to scale out complex data-mining tasks, while providing a programming approach that eases the formulation of complete analytical workflows

Lightweight Asynchronous Snapshots for Distributed Dataflows

Distributed stateful stream processing enables the deployment and execution of large scale continuous computations in the cloud, targeting both low latency and high throughput. One of the most fundamental challenges of this paradigm is providing processing guarantees under potential failures. Existing approaches rely on periodic global state snapshots that can be used for failure recovery. Those approaches suffer from two main drawbacks. First, they often stall the overall computation which impacts ingestion. Second, they eagerly persist all records in transit along with the operation states which results in larger snapshots than required. In this work we propose Asynchronous Barrier Snapshotting (ABS), a lightweight algorithm suited for modern dataflow execution engines that minimises space requirements. ABS persists only operator states on acyclic execution topologies while keeping a minimal record log on cyclic dataflows. We implemented ABS on Apache Flink, a distributed analytics engine that supports stateful stream processing. Our evaluation shows that our algorithm does not have a heavy impact on the execution, maintaining linear scalability and performing well with frequent snapshots.Comment: 8 pages, 7 figure

Scalable and Declarative Information Extraction in a Parallel Data Analytics System

Informationsextraktions (IE) auf sehr großen Datenmengen erfordert hochkomplexe, skalierbare und anpassungsfähige Systeme. Obwohl zahlreiche IE-Algorithmen existieren, ist die nahtlose und erweiterbare Kombination dieser Werkzeuge in einem skalierbaren System immer noch eine große Herausforderung. In dieser Arbeit wird ein anfragebasiertes IE-System für eine parallelen Datenanalyseplattform vorgestellt, das für konkrete Anwendungsdomänen konfigurierbar ist und für Textsammlungen im Terabyte-Bereich skaliert. Zunächst werden konfigurierbare Operatoren für grundlegende IE- und Web-Analytics-Aufgaben definiert, mit denen komplexe IE-Aufgaben in Form von deklarativen Anfragen ausgedrückt werden können. Alle Operatoren werden hinsichtlich ihrer Eigenschaften charakterisiert um das Potenzial und die Bedeutung der Optimierung nicht-relationaler, benutzerdefinierter Operatoren (UDFs) für Data Flows hervorzuheben. Anschließend wird der Stand der Technik in der Optimierung nicht-relationaler Data Flows untersucht und herausgearbeitet, dass eine umfassende Optimierung von UDFs immer noch eine Herausforderung ist. Darauf aufbauend wird ein erweiterbarer, logischer Optimierer (SOFA) vorgestellt, der die Semantik von UDFs mit in die Optimierung mit einbezieht. SOFA analysiert eine kompakte Menge von Operator-Eigenschaften und kombiniert eine automatisierte Analyse mit manuellen UDF-Annotationen, um die umfassende Optimierung von Data Flows zu ermöglichen. SOFA ist in der Lage, beliebige Data Flows aus unterschiedlichen Anwendungsbereichen logisch zu optimieren, was zu erheblichen Laufzeitverbesserungen im Vergleich mit anderen Techniken führt. Als Viertes wird die Anwendbarkeit des vorgestellten Systems auf Korpora im Terabyte-Bereich untersucht und systematisch die Skalierbarkeit und Robustheit der eingesetzten Methoden und Werkzeuge beurteilt um schließlich die kritischsten Herausforderungen beim Aufbau eines IE-Systems für sehr große Datenmenge zu charakterisieren.Information extraction (IE) on very large data sets requires highly complex, scalable, and adaptive systems. Although numerous IE algorithms exist, their seamless and extensible combination in a scalable system still is a major challenge. This work presents a query-based IE system for a parallel data analysis platform, which is configurable for specific application domains and scales for terabyte-sized text collections. First, configurable operators are defined for basic IE and Web Analytics tasks, which can be used to express complex IE tasks in the form of declarative queries. All operators are characterized in terms of their properties to highlight the potential and importance of optimizing non-relational, user-defined operators (UDFs) for dataflows. Subsequently, we survey the state of the art in optimizing non-relational dataflows and highlight that a comprehensive optimization of UDFs is still a challenge. Based on this observation, an extensible, logical optimizer (SOFA) is introduced, which incorporates the semantics of UDFs into the optimization process. SOFA analyzes a compact set of operator properties and combines automated analysis with manual UDF annotations to enable a comprehensive optimization of data flows. SOFA is able to logically optimize arbitrary data flows from different application areas, resulting in significant runtime improvements compared to other techniques. Finally, the applicability of the presented system to terabyte-sized corpora is investigated. Hereby, we systematically evaluate scalability and robustness of the employed methods and tools in order to pinpoint the most critical challenges in building an IE system for very large data sets

Tupleware: Redefining Modern Analytics

There is a fundamental discrepancy between the targeted and actual users of current analytics frameworks. Most systems are designed for the data and infrastructure of the Googles and Facebooks of the world---petabytes of data distributed across large cloud deployments consisting of thousands of cheap commodity machines. Yet, the vast majority of users operate clusters ranging from a few to a few dozen nodes, analyze relatively small datasets of up to a few terabytes, and perform primarily compute-intensive operations. Targeting these users fundamentally changes the way we should build analytics systems. This paper describes the design of Tupleware, a new system specifically aimed at the challenges faced by the typical user. Tupleware's architecture brings together ideas from the database, compiler, and programming languages communities to create a powerful end-to-end solution for data analysis. We propose novel techniques that consider the data, computations, and hardware together to achieve maximum performance on a case-by-case basis. Our experimental evaluation quantifies the impact of our novel techniques and shows orders of magnitude performance improvement over alternative systems