Effizienz in Cluster-Datenbanksystemen - Dynamische und Arbeitslastberücksichtigende Skalierung und Allokation

Database systems have been vital in all forms of data processing for a long time. In recent years, the amount of processed data has been growing dramatically, even in small projects. Nevertheless, database management systems tend to be static in terms of size and performance which makes scaling a difficult and expensive task. Because of performance and especially cost advantages more and more installed systems have a shared nothing cluster architecture. Due to the massive parallelism of the hardware programming paradigms from high performance computing are translated into data processing. Database research struggles to keep up with this trend. A key feature of traditional database systems is to provide transparent access to the stored data. This introduces data dependencies and increases system complexity and inter process communication. Therefore, many developers are exchanging this feature for a better scalability. However, explicitly managing the data distribution and data flow requires a deep understanding of the distributed system and reduces the possibilities for automatic and autonomic optimization. In this thesis we present an approach for database system scaling and allocation that features good scalability although it keeps the data distribution transparent. The first part of this thesis analyzes the challenges and opportunities for self-scaling database management systems in cluster environments. Scalability is a major concern of Internet based applications. Access peaks that overload the application are a financial risk. Therefore, systems are usually configured to be able to process peaks at any given moment. As a result, server systems often have a very low utilization. In distributed systems the efficiency can be increased by adapting the number of nodes to the current workload. We propose a processing model and an architecture that allows efficient self-scaling of cluster database systems. In the second part we consider different allocation approaches. To increase the efficiency we present a workload-aware, query-centric model. The approach is formalized; optimal and heuristic algorithms are presented. The algorithms optimize the data distribution for local query execution and balance the workload according to the query history. We present different query classification schemes for different forms of partitioning. The approach is evaluated for OLTP and OLAP style workloads. It is shown that variants of the approach scale well for both fields of application. The third part of the thesis considers benchmarks for large, adaptive systems. First, we present a data generator for cloud-sized applications. Due to its architecture the data generator can easily be extended and configured. A key feature is the high degree of parallelism that makes linear speedup for arbitrary numbers of nodes possible. To simulate systems with user interaction, we have analyzed a productive online e-learning management system. Based on our findings, we present a model for workload generation that considers the temporal dependency of user interaction.Datenbanksysteme sind seit langem die Grundlage für alle Arten von Informationsverarbeitung. In den letzten Jahren ist das Datenaufkommen selbst in kleinen Projekten dramatisch angestiegen. Dennoch sind viele Datenbanksysteme statisch in Bezug auf ihre Kapazität und Verarbeitungsgeschwindigkeit was die Skalierung aufwendig und teuer macht. Aufgrund der guten Geschwindigkeit und vor allem aus Kostengründen haben immer mehr Systeme eine Shared-Nothing-Architektur, bestehen also aus unabhängigen, lose gekoppelten Rechnerknoten. Da dieses Konstruktionsprinzip einen sehr hohen Grad an Parallelität aufweist, werden zunehmend Programmierparadigmen aus dem klassischen Hochleistungsrechen für die Informationsverarbeitung eingesetzt. Dieser Trend stellt die Datenbankforschung vor große Herausforderungen. Eine der grundlegenden Eigenschaften traditioneller Datenbanksysteme ist der transparente Zugriff zu den gespeicherten Daten, der es dem Nutzer erlaubt unabhängig von der internen Organisation auf die Daten zuzugreifen. Die resultierende Unabhängigkeit führt zu Abhängigkeiten in den Daten und erhöht die Komplexität der Systeme und der Kommunikation zwischen einzelnen Prozessen. Daher wird Transparenz von vielen Entwicklern für eine bessere Skalierbarkeit geopfert. Diese Entscheidung führt dazu, dass der die Datenorganisation und der Datenfluss explizit behandelt werden muss, was die Möglichkeiten für eine automatische und autonome Optimierung des Systems einschränkt. Der in dieser Arbeit vorgestellte Ansatz zur Skalierung und Allokation erhält den transparenten Zugriff und zeichnet sich dabei durch seine vollständige Automatisierbarkeit und sehr gute Skalierbarkeit aus. Im ersten Teil dieser Dissertation werden die Herausforderungen und Chancen für selbst-skalierende Datenbankmanagementsysteme behandelt, die in auf Computerclustern betrieben werden. Gute Skalierbarkeit ist eine notwendige Eigenschaft für Anwendungen, die über das Internet zugreifbar sind. Lastspitzen im Zugriff, die die Anwendung überladen stellen ein finanzielles Risiko dar. Deshalb werden Systeme so konfiguriert, dass sie eventuelle Lastspitzen zu jedem Zeitpunkt verarbeiten können. Das führt meist zu einer im Schnitt sehr geringen Auslastung der unterliegenden Systeme. Eine Möglichkeit dieser Ineffizienz entgegen zu steuern ist es die Anzahl der verwendeten Rechnerknoten an die vorliegende Last anzupassen. In dieser Dissertation werden ein Modell und eine Architektur für die Anfrageverarbeitung vorgestellt, mit denen es möglich ist Datenbanksysteme auf Clusterrechnern einfach und effizient zu skalieren. Im zweiten Teil der Arbeit werden verschieden Möglichkeiten für die Datenverteilung behandelt. Um die Effizienz zu steigern wird ein Modell verwendet, das die Lastverteilung im Anfragestrom berücksichtigt. Der Ansatz ist formalisiert und optimale und heuristische Lösungen werden präsentiert. Die vorgestellten Algorithmen optimieren die Datenverteilung für eine lokale Ausführung aller Anfragen und balancieren die Last auf den Rechnerknoten. Es werden unterschiedliche Arten der Anfrageklassifizierung vorgestellt, die zu verschiedenen Arten von Partitionierung führen. Der Ansatz wird sowohl für Onlinetransaktionsverarbeitung, als auch Onlinedatenanalyse evaluiert. Die Evaluierung zeigt, dass der Ansatz für beide Felder sehr gut skaliert. Im letzten Teil der Arbeit werden verschiedene Techniken für die Leistungsmessung von großen, adaptiven Systemen präsentiert. Zunächst wird ein Datengenerierungsansatz gezeigt, der es ermöglicht sehr große Datenmengen völlig parallel zu erzeugen. Um die Benutzerinteraktion von Onlinesystemen zu simulieren wurde ein produktives E-learningsystem analysiert. Anhand der Analyse wurde ein Modell für die Generierung von Arbeitslasten erstellt, das die zeitlichen Abhängigkeiten von Benutzerinteraktion berücksichtigt

Metadata-driven data integration

Cotutela: Universitat Politècnica de Catalunya i Université Libre de Bruxelles, IT4BI-DC programme for the joint Ph.D. degree in computer science.Data has an undoubtable impact on society. Storing and processing large amounts of available data is currently one of the key success factors for an organization. Nonetheless, we are recently witnessing a change represented by huge and heterogeneous amounts of data. Indeed, 90% of the data in the world has been generated in the last two years. Thus, in order to carry on these data exploitation tasks, organizations must first perform data integration combining data from multiple sources to yield a unified view over them. Yet, the integration of massive and heterogeneous amounts of data requires revisiting the traditional integration assumptions to cope with the new requirements posed by such data-intensive settings. This PhD thesis aims to provide a novel framework for data integration in the context of data-intensive ecosystems, which entails dealing with vast amounts of heterogeneous data, from multiple sources and in their original format. To this end, we advocate for an integration process consisting of sequential activities governed by a semantic layer, implemented via a shared repository of metadata. From an stewardship perspective, this activities are the deployment of a data integration architecture, followed by the population of such shared metadata. From a data consumption perspective, the activities are virtual and materialized data integration, the former an exploratory task and the latter a consolidation one. Following the proposed framework, we focus on providing contributions to each of the four activities. We begin proposing a software reference architecture for semantic-aware data-intensive systems. Such architecture serves as a blueprint to deploy a stack of systems, its core being the metadata repository. Next, we propose a graph-based metadata model as formalism for metadata management. We focus on supporting schema and data source evolution, a predominant factor on the heterogeneous sources at hand. For virtual integration, we propose query rewriting algorithms that rely on the previously proposed metadata model. We additionally consider semantic heterogeneities in the data sources, which the proposed algorithms are capable of automatically resolving. Finally, the thesis focuses on the materialized integration activity, and to this end, proposes a method to select intermediate results to materialize in data-intensive flows. Overall, the results of this thesis serve as contribution to the field of data integration in contemporary data-intensive ecosystems.Les dades tenen un impacte indubtable en la societat. La capacitat d’emmagatzemar i processar grans quantitats de dades disponibles és avui en dia un dels factors claus per l’èxit d’una organització. No obstant, avui en dia estem presenciant un canvi representat per grans volums de dades heterogenis. En efecte, el 90% de les dades mundials han sigut generades en els últims dos anys. Per tal de dur a terme aquestes tasques d’explotació de dades, les organitzacions primer han de realitzar una integració de les dades, combinantles a partir de diferents fonts amb l’objectiu de tenir-ne una vista unificada d’elles. Per això, aquest fet requereix reconsiderar les assumpcions tradicionals en integració amb l’objectiu de lidiar amb els requisits imposats per aquests sistemes de tractament massiu de dades. Aquesta tesi doctoral té com a objectiu proporcional un nou marc de treball per a la integració de dades en el context de sistemes de tractament massiu de dades, el qual implica lidiar amb una gran quantitat de dades heterogènies, provinents de múltiples fonts i en el seu format original. Per això, proposem un procés d’integració compost d’una seqüència d’activitats governades per una capa semàntica, la qual és implementada a partir d’un repositori de metadades compartides. Des d’una perspectiva d’administració, aquestes activitats són el desplegament d’una arquitectura d’integració de dades, seguit per la inserció d’aquestes metadades compartides. Des d’una perspectiva de consum de dades, les activitats són la integració virtual i materialització de les dades, la primera sent una tasca exploratòria i la segona una de consolidació. Seguint el marc de treball proposat, ens centrem en proporcionar contribucions a cada una de les quatre activitats. La tesi inicia proposant una arquitectura de referència de software per a sistemes de tractament massiu de dades amb coneixement semàntic. Aquesta arquitectura serveix com a planell per a desplegar un conjunt de sistemes, sent el repositori de metadades al seu nucli. Posteriorment, proposem un model basat en grafs per a la gestió de metadades. Concretament, ens centrem en donar suport a l’evolució d’esquemes i fonts de dades, un dels factors predominants en les fonts de dades heterogènies considerades. Per a l’integració virtual, proposem algorismes de rescriptura de consultes que usen el model de metadades previament proposat. Com a afegitó, considerem heterogeneïtat semàntica en les fonts de dades, les quals els algorismes de rescriptura poden resoldre automàticament. Finalment, la tesi es centra en l’activitat d’integració materialitzada. Per això proposa un mètode per a seleccionar els resultats intermedis a materialitzar un fluxes de tractament intensiu de dades. En general, els resultats d’aquesta tesi serveixen com a contribució al camp d’integració de dades en els ecosistemes de tractament massiu de dades contemporanisLes données ont un impact indéniable sur la société. Le stockage et le traitement de grandes quantités de données disponibles constituent actuellement l’un des facteurs clés de succès d’une entreprise. Néanmoins, nous assistons récemment à un changement représenté par des quantités de données massives et hétérogènes. En effet, 90% des données dans le monde ont été générées au cours des deux dernières années. Ainsi, pour mener à bien ces tâches d’exploitation des données, les organisations doivent d’abord réaliser une intégration des données en combinant des données provenant de sources multiples pour obtenir une vue unifiée de ces dernières. Cependant, l’intégration de quantités de données massives et hétérogènes nécessite de revoir les hypothèses d’intégration traditionnelles afin de faire face aux nouvelles exigences posées par les systèmes de gestion de données massives. Cette thèse de doctorat a pour objectif de fournir un nouveau cadre pour l’intégration de données dans le contexte d’écosystèmes à forte intensité de données, ce qui implique de traiter de grandes quantités de données hétérogènes, provenant de sources multiples et dans leur format d’origine. À cette fin, nous préconisons un processus d’intégration constitué d’activités séquentielles régies par une couche sémantique, mise en oeuvre via un dépôt partagé de métadonnées. Du point de vue de la gestion, ces activités consistent à déployer une architecture d’intégration de données, suivies de la population de métadonnées partagées. Du point de vue de la consommation de données, les activités sont l’intégration de données virtuelle et matérialisée, la première étant une tâche exploratoire et la seconde, une tâche de consolidation. Conformément au cadre proposé, nous nous attachons à fournir des contributions à chacune des quatre activités. Nous commençons par proposer une architecture logicielle de référence pour les systèmes de gestion de données massives et à connaissance sémantique. Une telle architecture consiste en un schéma directeur pour le déploiement d’une pile de systèmes, le dépôt de métadonnées étant son composant principal. Ensuite, nous proposons un modèle de métadonnées basé sur des graphes comme formalisme pour la gestion des métadonnées. Nous mettons l’accent sur la prise en charge de l’évolution des schémas et des sources de données, facteur prédominant des sources hétérogènes sous-jacentes. Pour l’intégration virtuelle, nous proposons des algorithmes de réécriture de requêtes qui s’appuient sur le modèle de métadonnées proposé précédemment. Nous considérons en outre les hétérogénéités sémantiques dans les sources de données, que les algorithmes proposés sont capables de résoudre automatiquement. Enfin, la thèse se concentre sur l’activité d’intégration matérialisée et propose à cette fin une méthode de sélection de résultats intermédiaires à matérialiser dans des flux des données massives. Dans l’ensemble, les résultats de cette thèse constituent une contribution au domaine de l’intégration des données dans les écosystèmes contemporains de gestion de données massivesPostprint (published version

Arquitectura, técnicas y modelos para posibilitar la Ciencia de Datos en el Archivo de la Misión Gaia

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 26/05/2017.The massive amounts of data that the world produces every day pose new challenges to modern societies in terms of how to leverage their inherent value. Social networks, instant messaging, video, smart devices and scientific missions are just mere examples of the vast number of sources generating data every second. As the world becomes more and more digitalized, new needs arise for organizing, archiving, sharing, analyzing, visualizing and protecting the ever-increasing data sets, so that we can truly develop into a data-driven economy that reduces inefficiencies and increases sustainability, creating new business opportunities on the way. Traditional approaches for harnessing data are not suitable any more as they lack the means for scaling to the larger volumes in a timely and cost efficient manner. This has somehow changed with the advent of Internet companies like Google and Facebook, which have devised new ways of tackling this issue. However, the variety and complexity of the value chains in the private sector as well as the increasing demands and constraints in which the public one operates, needs an ongoing research that can yield newer strategies for dealing with data, facilitate the integration of providers and consumers of information, and guarantee a smooth and prompt transition when adopting these cutting-edge technological advances. This thesis aims at providing novel architectures and techniques that will help perform this transition towards Big Data in massive scientific archives. It highlights the common pitfalls that must be faced when embracing it and how to overcome them, especially when the data sets, their transformation pipelines and the tools used for the analysis are already present in the organizations. Furthermore, a new perspective for facilitating a smoother transition is laid out. It involves the usage of higher-level and use case specific frameworks and models, which will naturally bridge the gap between the technological and scientific domains. This alternative will effectively widen the possibilities of scientific archives and therefore will contribute to the reduction of the time to science. The research will be applied to the European Space Agency cornerstone mission Gaia, whose final data archive will represent a tremendous discovery potential. It will create the largest and most precise three dimensional chart of our galaxy (the Milky Way), providing unprecedented position, parallax and proper motion measurements for about one billion stars. The successful exploitation of this data archive will depend to a large degree on the ability to offer the proper architecture, i.e. infrastructure and middleware, upon which scientists will be able to do exploration and modeling with this huge data set. In consequence, the approach taken needs to enable data fusion with other scientific archives, as this will produce the synergies leading to an increment in scientific outcome, both in volume and in quality. The set of novel techniques and frameworks presented in this work addresses these issues by contextualizing them with the data products that will be generated in the Gaia mission. All these considerations have led to the foundations of the architecture that will be leveraged by the Science Enabling Applications Work Package. Last but not least, the effectiveness of the proposed solution will be demonstrated through the implementation of some ambitious statistical problems that will require significant computational capabilities, and which will use Gaia-like simulated data (the first Gaia data release has recently taken place on September 14th, 2016). These ambitious problems will be referred to as the Grand Challenge, a somewhat grandiloquent name that consists in inferring a set of parameters from a probabilistic point of view for the Initial Mass Function (IMF) and Star Formation Rate (SFR) of a given set of stars (with a huge sample size), from noisy estimates of their masses and ages respectively. This will be achieved by using Hierarchical Bayesian Modeling (HBM). In principle, the HBM can incorporate stellar evolution models to infer the IMF and SFR directly, but in this first step presented in this thesis, we will start with a somewhat less ambitious goal: inferring the PDMF and PDAD. Moreover, the performance and scalability analyses carried out will also prove the suitability of the models for the large amounts of data that will be available in the Gaia data archive.Las grandes cantidades de datos que se producen en el mundo diariamente plantean nuevos retos a la sociedad en términos de cómo extraer su valor inherente. Las redes sociales, mensajería instantánea, los dispositivos inteligentes y las misiones científicas son meros ejemplos del gran número de fuentes generando datos en cada momento. Al mismo tiempo que el mundo se digitaliza cada vez más, aparecen nuevas necesidades para organizar, archivar, compartir, analizar, visualizar y proteger la creciente cantidad de datos, para que podamos desarrollar economías basadas en datos e información que sean capaces de reducir las ineficiencias e incrementar la sostenibilidad, creando nuevas oportunidades de negocio por el camino. La forma en la que se han manejado los datos tradicionalmente no es la adecuada hoy en día, ya que carece de los medios para escalar a los volúmenes más grandes de datos de una forma oportuna y eficiente. Esto ha cambiado de alguna manera con la llegada de compañías que operan en Internet como Google o Facebook, ya que han concebido nuevas aproximaciones para abordar el problema. Sin embargo, la variedad y complejidad de las cadenas de valor en el sector privado y las crecientes demandas y limitaciones en las que el sector público opera, necesitan una investigación continua en la materia que pueda proporcionar nuevas estrategias para procesar las enormes cantidades de datos, facilitar la integración de productores y consumidores de información, y garantizar una transición rápida y fluida a la hora de adoptar estos avances tecnológicos innovadores. Esta tesis tiene como objetivo proporcionar nuevas arquitecturas y técnicas que ayudarán a realizar esta transición hacia Big Data en archivos científicos masivos. La investigación destaca los escollos principales a encarar cuando se adoptan estas nuevas tecnologías y cómo afrontarlos, principalmente cuando los datos y las herramientas de transformación utilizadas en el análisis existen en la organización. Además, se exponen nuevas medidas para facilitar una transición más fluida. Éstas incluyen la utilización de software de alto nivel y específico al caso de uso en cuestión, que haga de puente entre el dominio científico y tecnológico. Esta alternativa ampliará de una forma efectiva las posibilidades de los archivos científicos y por tanto contribuirá a la reducción del tiempo necesario para generar resultados científicos a partir de los datos recogidos en las misiones de astronomía espacial y planetaria. La investigación se aplicará a la misión de la Agencia Espacial Europea (ESA) Gaia, cuyo archivo final de datos presentará un gran potencial para el descubrimiento y hallazgo desde el punto de vista científico. La misión creará el catálogo en tres dimensiones más grande y preciso de nuestra galaxia (la Vía Láctea), proporcionando medidas sin precedente acerca del posicionamiento, paralaje y movimiento propio de alrededor de mil millones de estrellas. Las oportunidades para la explotación exitosa de este archivo de datos dependerán en gran medida de la capacidad de ofrecer la arquitectura adecuada, es decir infraestructura y servicios, sobre la cual los científicos puedan realizar la exploración y modelado con esta inmensa cantidad de datos. Por tanto, la estrategia a realizar debe ser capaz de combinar los datos con otros archivos científicos, ya que esto producirá sinergias que contribuirán a un incremento en la ciencia producida, tanto en volumen como en calidad de la misma. El conjunto de técnicas e infraestructuras innovadoras presentadas en este trabajo aborda estos problemas, contextualizándolos con los productos de datos que se generarán en la misión Gaia. Todas estas consideraciones han conducido a los fundamentos de la arquitectura que se utilizará en el paquete de trabajo de aplicaciones que posibilitarán la ciencia en el archivo de la misión Gaia (Science Enabling Applications). Por último, la eficacia de la solución propuesta se demostrará a través de la implementación de dos problemas estadísticos que requerirán cantidades significativas de cómputo, y que usarán datos simulados en el mismo formato en el que se producirán en el archivo de la misión Gaia (la primera versión de datos recogidos por la misión está disponible desde el día 14 de Septiembre de 2016). Estos ambiciosos problemas representan el Gran Reto (Grand Challenge), un nombre grandilocuente que consiste en inferir una serie de parámetros desde un punto de vista probabilístico para la función de masa inicial (Initial Mass Function) y la tasa de formación estelar (Star Formation Rate) dado un conjunto de estrellas (con una muestra grande), desde estimaciones con ruido de sus masas y edades respectivamente. Esto se abordará utilizando modelos jerárquicos bayesianos (Hierarchical Bayesian Modeling). Enprincipio,losmodelospropuestos pueden incorporar otros modelos de evolución estelar para inferir directamente la función de masa inicial y la tasa de formación estelar, pero en este primer paso presentado en esta tesis, empezaremos con un objetivo algo menos ambicioso: la inferencia de la función de masa y distribución de edades actual (Present-Day Mass Function y Present-Day Age Distribution respectivamente). Además, se llevará a cabo el análisis de rendimiento y escalabilidad para probar la idoneidad de la implementación de dichos modelos dadas las enormes cantidades de datos que estarán disponibles en el archivo de la misión Gaia...Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Flexibility in Data Management

With the ongoing expansion of information technology, new fields of application requiring data management emerge virtually every day. In our knowledge culture increasing amounts of data and work force organized in more creativity-oriented ways also radically change traditional fields of application and question established assumptions about data management. For instance, investigative analytics and agile software development move towards a very agile and flexible handling of data. As the primary facilitators of data management, database systems have to reflect and support these developments. However, traditional database management technology, in particular relational database systems, is built on assumptions of relatively stable application domains. The need to model all data up front in a prescriptive database schema earned relational database management systems the reputation among developers of being inflexible, dated, and cumbersome to work with. Nevertheless, relational systems still dominate the database market. They are a proven, standardized, and interoperable technology, well-known in IT departments with a work force of experienced and trained developers and administrators. This thesis aims at resolving the growing contradiction between the popularity and omnipresence of relational systems in companies and their increasingly bad reputation among developers. It adapts relational database technology towards more agility and flexibility. We envision a descriptive schema-comes-second relational database system, which is entity-oriented instead of schema-oriented; descriptive rather than prescriptive. The thesis provides four main contributions: (1)~a flexible relational data model, which frees relational data management from having a prescriptive schema; (2)~autonomous physical entity domains, which partition self-descriptive data according to their schema properties for better query performance; (3)~a freely adjustable storage engine, which allows adapting the physical data layout used to properties of the data and of the workload; and (4)~a self-managed indexing infrastructure, which autonomously collects and adapts index information under the presence of dynamic workloads and evolving schemas. The flexible relational data model is the thesis\' central contribution. It describes the functional appearance of the descriptive schema-comes-second relational database system. The other three contributions improve components in the architecture of database management systems to increase the query performance and the manageability of descriptive schema-comes-second relational database systems. We are confident that these four contributions can help paving the way to a more flexible future for relational database management technology

Resiliency Mechanisms for In-Memory Column Stores

The key objective of database systems is to reliably manage data, while high query throughput and low query latency are core requirements. To date, database research activities mostly concentrated on the second part. However, due to the constant shrinking of transistor feature sizes, integrated circuits become more and more unreliable and transient hardware errors in the form of multi-bit flips become more and more prominent. In a more recent study (2013), in a large high-performance cluster with around 8500 nodes, a failure rate of 40 FIT per DRAM device was measured. For their system, this means that every 10 hours there occurs a single- or multi-bit flip, which is unacceptably high for enterprise and HPC scenarios. Causes can be cosmic rays, heat, or electrical crosstalk, with the latter being exploited actively through the RowHammer attack. It was shown that memory cells are more prone to bit flips than logic gates and several surveys found multi-bit flip events in main memory modules of today's data centers. Due to the shift towards in-memory data management systems, where all business related data and query intermediate results are kept solely in fast main memory, such systems are in great danger to deliver corrupt results to their users. Hardware techniques can not be scaled to compensate the exponentially increasing error rates. In other domains, there is an increasing interest in software-based solutions to this problem, but these proposed methods come along with huge runtime and/or storage overheads. These are unacceptable for in-memory data management systems. In this thesis, we investigate how to integrate bit flip detection mechanisms into in-memory data management systems. To achieve this goal, we first build an understanding of bit flip detection techniques and select two error codes, AN codes and XOR checksums, suitable to the requirements of in-memory data management systems. The most important requirement is effectiveness of the codes to detect bit flips. We meet this goal through AN codes, which exhibit better and adaptable error detection capabilities than those found in today's hardware. The second most important goal is efficiency in terms of coding latency. We meet this by introducing a fundamental performance improvements to AN codes, and by vectorizing both chosen codes' operations. We integrate bit flip detection mechanisms into the lowest storage layer and the query processing layer in such a way that the remaining data management system and the user can stay oblivious of any error detection. This includes both base columns and pointer-heavy index structures such as the ubiquitous B-Tree. Additionally, our approach allows adaptable, on-the-fly bit flip detection during query processing, with only very little impact on query latency. AN coding allows to recode intermediate results with virtually no performance penalty. We support our claims by providing exhaustive runtime and throughput measurements throughout the whole thesis and with an end-to-end evaluation using the Star Schema Benchmark. To the best of our knowledge, we are the first to present such holistic and fast bit flip detection in a large software infrastructure such as in-memory data management systems. Finally, most of the source code fragments used to obtain the results in this thesis are open source and freely available.:1 INTRODUCTION 1.1 Contributions of this Thesis 1.2 Outline 2 PROBLEM DESCRIPTION AND RELATED WORK 2.1 Reliable Data Management on Reliable Hardware 2.2 The Shift Towards Unreliable Hardware 2.3 Hardware-Based Mitigation of Bit Flips 2.4 Data Management System Requirements 2.5 Software-Based Techniques For Handling Bit Flips 2.5.1 Operating System-Level Techniques 2.5.2 Compiler-Level Techniques 2.5.3 Application-Level Techniques 2.6 Summary and Conclusions 3 ANALYSIS OF CODING TECHNIQUES 3.1 Selection of Error Codes 3.1.1 Hamming Coding 3.1.2 XOR Checksums 3.1.3 AN Coding 3.1.4 Summary and Conclusions 3.2 Probabilities of Silent Data Corruption 3.2.1 Probabilities of Hamming Codes 3.2.2 Probabilities of XOR Checksums 3.2.3 Probabilities of AN Codes 3.2.4 Concrete Error Models 3.2.5 Summary and Conclusions 3.3 Throughput Considerations 3.3.1 Test Systems Descriptions 3.3.2 Vectorizing Hamming Coding 3.3.3 Vectorizing XOR Checksums 3.3.4 Vectorizing AN Coding 3.3.5 Summary and Conclusions 3.4 Comparison of Error Codes 3.4.1 Effectiveness 3.4.2 Efficiency 3.4.3 Runtime Adaptability 3.5 Performance Optimizations for AN Coding 3.5.1 The Modular Multiplicative Inverse 3.5.2 Faster Softening 3.5.3 Faster Error Detection 3.5.4 Comparison to Original AN Coding 3.5.5 The Multiplicative Inverse Anomaly 3.6 Summary 4 BIT FLIP DETECTING STORAGE 4.1 Column Store Architecture 4.1.1 Logical Data Types 4.1.2 Storage Model 4.1.3 Data Representation 4.1.4 Data Layout 4.1.5 Tree Index Structures 4.1.6 Summary 4.2 Hardened Data Storage 4.2.1 Hardened Physical Data Types 4.2.2 Hardened Lightweight Compression 4.2.3 Hardened Data Layout 4.2.4 UDI Operations 4.2.5 Summary and Conclusions 4.3 Hardened Tree Index Structures 4.3.1 B-Tree Verification Techniques 4.3.2 Justification For Further Techniques 4.3.3 The Error Detecting B-Tree 4.4 Summary 5 BIT FLIP DETECTING QUERY PROCESSING 5.1 Column Store Query Processing 5.2 Bit Flip Detection Opportunities 5.2.1 Early Onetime Detection 5.2.2 Late Onetime Detection 5.2.3 Continuous Detection 5.2.4 Miscellaneous Processing Aspects 5.2.5 Summary and Conclusions 5.3 Hardened Intermediate Results 5.3.1 Materialization of Hardened Intermediates 5.3.2 Hardened Bitmaps 5.4 Summary 6 END-TO-END EVALUATION 6.1 Prototype Implementation 6.1.1 AHEAD Architecture 6.1.2 Diversity of Physical Operators 6.1.3 One Concrete Operator Realization 6.1.4 Summary and Conclusions 6.2 Performance of Individual Operators 6.2.1 Selection on One Predicate 6.2.2 Selection on Two Predicates 6.2.3 Join Operators 6.2.4 Grouping and Aggregation 6.2.5 Delta Operator 6.2.6 Summary and Conclusions 6.3 Star Schema Benchmark Queries 6.3.1 Query Runtimes 6.3.2 Improvements Through Vectorization 6.3.3 Storage Overhead 6.3.4 Summary and Conclusions 6.4 Error Detecting B-Tree 6.4.1 Single Key Lookup 6.4.2 Key Value-Pair Insertion 6.5 Summary 7 SUMMARY AND CONCLUSIONS 7.1 Future Work A APPENDIX A.1 List of Golden As A.2 More on Hamming Coding A.2.1 Code examples A.2.2 Vectorization BIBLIOGRAPHY LIST OF FIGURES LIST OF TABLES LIST OF LISTINGS LIST OF ACRONYMS LIST OF SYMBOLS LIST OF DEFINITION

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