An Efficient Framework for Order Optimization

Since the introduction of cost-based query optimization, the performance-critical role of interesting orders has been recognized. Some algebraic operators change interesting orders (e.g. sort and select), while others exploit interesting orders (e.g. merge join). The two operations performed by any query optimizer during plan generation are 1) computing the resulting order given an input order and an algebraic operator and 2) determining the compatibility between a given input order and the required order a given algebraic operator can beneficially exploit. Since these two operations are called millions of times during plan generation, they are highly performance-critical. The third crucial parameter is the space requirement for annotating every plan node with its output order. Lately, a powerful framework for reasoning about orders has been developed, which is based on functional dependencies. Within this framework, the current state-of-the-art algorithms for implementing the above operations both have a lower bound time requirement of Omega(n), where n is the number of functional dependencies involved. Further, the lower bound for the space requirement for every plan node is Omega(n). We improve these bounds by new algorithms with upper time bounds O(1). That is, our algorithms for both operations work in constant time during plan generation, after a one-time preparation step. Further, the upper bound for the space requirement for plan nodes is O(1) for our approach. Besides, our algorithm reduces the search space by detecting and ignoring irrelevant orderings. Experimental results with a full fledged query optimizer show that our approach significantly reduces the total time needed for plan generation. As a corollary of our experiments, it follows that the time spent for order processing is a non-neglectable part of plan generation

Transactional and analytical data management on persistent memory

Die zunehmende Anzahl von Smart-Geräten und Sensoren, aber auch die sozialen Medien lassen das Datenvolumen und damit die geforderte Verarbeitungsgeschwindigkeit stetig wachsen. Gleichzeitig müssen viele Anwendungen Daten persistent speichern oder sogar strenge Transaktionsgarantien einhalten. Die neuartige Speichertechnologie Persistent Memory (PMem) mit ihren einzigartigen Eigenschaften scheint ein natürlicher Anwärter zu sein, um diesen Anforderungen effizient nachzukommen. Sie ist im Vergleich zu DRAM skalierbarer, günstiger und dauerhaft. Im Gegensatz zu Disks ist sie deutlich schneller und direkt adressierbar. Daher wird in dieser Dissertation der gezielte Einsatz von PMem untersucht, um den Anforderungen moderner Anwendung gerecht zu werden. Nach der Darlegung der grundlegenden Arbeitsweise von und mit PMem, konzentrieren wir uns primär auf drei Aspekte der Datenverwaltung. Zunächst zerlegen wir mehrere persistente Daten- und Indexstrukturen in ihre zugrundeliegenden Entwurfsprimitive, um Abwägungen für verschiedene Zugriffsmuster aufzuzeigen. So können wir ihre besten Anwendungsfälle und Schwachstellen, aber auch allgemeine Erkenntnisse über das Entwerfen von PMem-basierten Datenstrukturen ermitteln. Zweitens schlagen wir zwei Speicherlayouts vor, die auf analytische Arbeitslasten abzielen und eine effiziente Abfrageausführung auf beliebigen Attributen ermöglichen. Während der erste Ansatz eine verknüpfte Liste von mehrdimensionalen gruppierten Blöcken verwendet, handelt es sich beim zweiten Ansatz um einen mehrdimensionalen Index, der Knoten im DRAM zwischenspeichert. Drittens zeigen wir unter Verwendung der bisherigen Datenstrukturen und Erkenntnisse, wie Datenstrom- und Ereignisverarbeitungssysteme mit transaktionaler Zustandsverwaltung verbessert werden können. Dabei schlagen wir ein neuartiges Transactional Stream Processing (TSP) Modell mit geeigneten Konsistenz- und Nebenläufigkeitsprotokollen vor, die an PMem angepasst sind. Zusammen sollen die diskutierten Aspekte eine Grundlage für die Entwicklung noch ausgereifterer PMem-fähiger Systeme bilden. Gleichzeitig zeigen sie, wie Datenverwaltungsaufgaben PMem ausnutzen können, indem sie neue Anwendungsgebiete erschließen, die Leistung, Skalierbarkeit und Wiederherstellungsgarantien verbessern, die Codekomplexität vereinfachen sowie die ökonomischen und ökologischen Kosten reduzieren.The increasing number of smart devices and sensors, but also social media are causing the volume of data and thus the demanded processing speed to grow steadily. At the same time, many applications need to store data persistently or even comply with strict transactional guarantees. The novel storage technology Persistent Memory (PMem), with its unique properties, seems to be a natural candidate to meet these requirements efficiently. Compared to DRAM, it is more scalable, less expensive, and durable. In contrast to disks, it is significantly faster and directly addressable. Therefore, this dissertation investigates the deliberate employment of PMem to fit the needs of modern applications. After presenting the fundamental work of and with PMem, we focus primarily on three aspects of data management. First, we disassemble several persistent data and index structures into their underlying design primitives to reveal the trade-offs for various access patterns. It allows us to identify their best use cases and vulnerabilities but also to gain general insights into the design of PMem-based data structures. Second, we propose two storage layouts that target analytical workloads and enable an efficient query execution on arbitrary attributes. While the first approach employs a linked list of multi-dimensional clustered blocks that potentially span several storage layers, the second approach is a multi-dimensional index that caches nodes in DRAM. Third, we show how to improve stream and event processing systems involving transactional state management using the preceding data structures and insights. In this context, we propose a novel Transactional Stream Processing (TSP) model with appropriate consistency and concurrency protocols adapted to PMem. Together, the discussed aspects are intended to provide a foundation for developing even more sophisticated PMemenabled systems. At the same time, they show how data management tasks can take advantage of PMem by opening up new application domains, improving performance, scalability, and recovery guarantees, simplifying code complexity, plus reducing economic and environmental costs

Toward timely, predictable and cost-effective data analytics

Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely, predictable and cost-effective analytical processing of such large data sets in order to extract deep insights is now a key ingredient for success. Traditional database systems (DBMS) are, however, not the first choice for servicing these modern applications, despite 40 years of database research. This is due to the fact that modern applications exhibit different behavior from the one assumed by DBMS: a) timely data exploration as a new trend is characterized by ad-hoc queries and a short user interaction period, leaving little time for DBMS to do good performance tuning, b) accurate statistics representing relevant summary information about distributions of ever increasing data are frequently missing, resulting in suboptimal plan decisions and consequently poor and unpredictable query execution performance, and c) cloud service providers - a major winner in the data analytics game due to the low cost of (shared) storage - have shifted the control over data storage from DBMS to the cloud providers, making it harder for DBMS to optimize data access. This thesis demonstrates that database systems can still provide timely, predictable and cost-effective analytical processing, if they use an agile and adaptive approach. In particular, DBMS need to adapt at three levels (to workload, data and hardware characteristics) in order to stabilize and optimize performance and cost when faced with requirements posed by modern data analytics applications. Workload-driven data ingestion is introduced with NoDB as a means to enable efficient data exploration and reduce the data-to-insight time (i.e., the time to load the data and tune the system) by doing these steps lazily and incrementally as a side-effect of posed queries as opposed to mandatory first steps. Data-driven runtime access path decision making introduced with Smooth Scan alleviates suboptimal query execution, postponing the decision on access paths from query optimization, where statistics are heavily exploited, to query execution, where the system can obtain more details about data distributions. Smooth Scan uses access path morphing from one physical alternative to another to fit the observed data distributions, which removes the need for a priori access path decisions and substantially improves the predictability of DBMS. Hardware-driven query execution introduced with Skipper enables the usage of cold storage devices (CSD) as a cost-effective solution for storing the ever increasing customer data. Skipper uses an out-of-order CSD-driven query execution model based on multi-way joins coupled with efficient cache and I/O scheduling policies to hide the non-uniform access latencies of CSD. This thesis advocates runtime adaptivity as a key to dealing with raising uncertainty about workload characteristics that modern data analytics applications exhibit. Overall, the techniques introduced in this thesis through the three levels of adaptivity (workload, data and hardware-driven adaptivity) increase the usability of database systems and the user satisfaction in the case of big data exploration, making low-cost data analytics reality