Multi-Objective Materialized View Selection in Data-Intensive Flows

In this thesis we present Forge, a tool for automating multi-objective materialization of intermediate results in data-intensive flows, driven by a set of different quality objectives. We report initial evaluation results, showing the feasibility and efficiency of our approach

Cost-Based Optimization of Integration Flows

Integration flows are increasingly used to specify and execute data-intensive integration tasks between heterogeneous systems and applications. There are many different application areas such as real-time ETL and data synchronization between operational systems. For the reasons of an increasing amount of data, highly distributed IT infrastructures, and high requirements for data consistency and up-to-dateness of query results, many instances of integration flows are executed over time. Due to this high load and blocking synchronous source systems, the performance of the central integration platform is crucial for an IT infrastructure. To tackle these high performance requirements, we introduce the concept of cost-based optimization of imperative integration flows that relies on incremental statistics maintenance and inter-instance plan re-optimization. As a foundation, we introduce the concept of periodical re-optimization including novel cost-based optimization techniques that are tailor-made for integration flows. Furthermore, we refine the periodical re-optimization to on-demand re-optimization in order to overcome the problems of many unnecessary re-optimization steps and adaptation delays, where we miss optimization opportunities. This approach ensures low optimization overhead and fast workload adaptation

A Domain Specific Model for Generating ETL Workflows from Business Intents

Extract-Transform-Load (ETL) tools have provided organizations with the ability to build and maintain workflows (consisting of graphs of data transformation tasks) that can process the flood of digital data. Currently, however, the specification of ETL workflows is largely manual, human time intensive, and error prone. As these workflows become increasingly complex, the users that build and maintain them must retain an increasing amount of knowledge specific to how to produce solutions to business objectives using their domain\u27s ETL workflow system. A program that can reduce the human time and expertise required to define such workflows, producing accurate ETL solutions with fewer errors would therefore be valuable. This dissertation presents a means to automate the specification of ETL workflows using a domain-specific modeling language. To provide such a solution, the knowledge relevant to the construction of ETL workflows for the operations and objectives of a given domain is identified and captured. The approach provides a rich model of ETL workflow capable of representing such knowledge. This knowledge representation is leveraged by a domain-specific modeling language which maps declarative statements into workflow requirements. Users are then provided with the ability to assertionally express the intents that describe a desired ETL solution at a high-level of abstraction, from which procedural workflows satisfying the intent specification are automatically generated using a planner

Stubby: A Transformation-based Optimizer for MapReduce Workflows

There is a growing trend of performing analysis on large datasets using workflows composed of MapReduce jobs connected through producer-consumer relationships based on data. This trend has spurred the development of a number of interfaces--ranging from program-based to query-based interfaces--for generating MapReduce workflows. Studies have shown that the gap in performance can be quite large between optimized and unoptimized workflows. However, automatic cost-based optimization of MapReduce workflows remains a challenge due to the multitude of interfaces, large size of the execution plan space, and the frequent unavailability of all types of information needed for optimization. We introduce a comprehensive plan space for MapReduce workflows generated by popular workflow generators. We then propose Stubby, a cost-based optimizer that searches selectively through the subspace of the full plan space that can be enumerated correctly and costed based on the information available in any given setting. Stubby enumerates the plan space based on plan-to-plan transformations and an efficient search algorithm. Stubby is designed to be extensible to new interfaces and new types of optimizations, which is a desirable feature given how rapidly MapReduce systems are evolving. Stubby's efficiency and effectiveness have been evaluated using representative workflows from many domains.Comment: VLDB201

Cost-Based Vectorization of Instance-Based Integration Processes

The inefficiency of integration processes - as an abstraction of workflow-based integration tasks - is often reasoned by low resource utilization and significant waiting times for external systems. With the aim to overcome these problems, we proposed the concept of process vectorization. There, instance-based integration processes are transparently executed with the pipes-and-filters execution model. Here, the term vectorization is used in the sense of processing a sequence (vector) of messages by one standing process. Although it has been shown that process vectorization achieves a significant throughput improvement, this concept has two major drawbacks. First, the theoretical performance of a vectorized integration process mainly depends on the performance of the most cost-intensive operator. Second, the practical performance strongly depends on the number of available threads. In this paper, we present an advanced optimization approach that addresses the mentioned problems. Therefore, we generalize the vectorization problem and explain how to vectorize process plans in a cost-based manner. Due to the exponential complexity, we provide a heuristic computation approach and formally analyze its optimality. In conclusion of our evaluation, the message throughput can be significantly increased compared to both the instance-based execution as well as the rule-based process vectorization

Automating User-Centered Design of Data-Intensive Processes

Business Intelligence (BI) enables organizations to collect and analyze internal and external business data to generate knowledge and business value, and provide decision support at the strategic, tactical, and operational levels. The consolidation of data coming from many sources as a result of managerial and operational business processes, usually referred to as Extract-Transform-Load (ETL) is itself a statically defined process and knowledge workers have little to no control over the characteristics of the presentable data to which they have access. There are two main reasons that dictate the reassessment of this stiff approach in context of modern business environments. The first reason is that the service-oriented nature of today’s business combined with the increasing volume of available data make it impossible for an organization to proactively design efficient data management processes. The second reason is that enterprises can benefit significantly from analyzing the behavior of their business processes fostering their optimization. Hence, we took a first step towards quality-aware ETL process design automation by defining through a systematic literature review a set of ETL process quality characteristics and the relationships between them, as well as by providing quantitative measures for each characteristic. Subsequently, we produced a model that represents ETL process quality characteristics and the dependencies among them and we showcased through the application of a Goal Model with quantitative components (i.e., indicators) how our model can provide the basis for subsequent analysis to reason and make informed ETL design decisions. In addition, we introduced our holistic view for a quality-aware design of ETL processes by presenting a framework for user-centered declarative ETL. This included the definition of an architecture and methodology for the rapid, incremental, qualitative improvement of ETL process models, promoting automation and reducing complexity, as well as a clear separation of business users and IT roles where each user is presented with appropriate views and assigned with fitting tasks. In this direction, we built a tool —POIESIS— which facilitates incremental, quantitative improvement of ETL process models with users being the key participants through well-defined collaborative interfaces. When it comes to evaluating different quality characteristics of the ETL process design, we proposed an automated data generation framework for evaluating ETL processes (i.e., Bijoux). To this end, we classified the operations based on the part of input data they access for processing, which facilitated Bijoux during data generation processes both for identifying the constraints that specific operation semantics imply over input data, as well as for deciding at which level the data should be generated (e.g., single field, single tuple, complete dataset). Bijoux offers data generation capabilities in a modular and configurable manner, which can be used to evaluate the quality of different parts of an ETL process. Moreover, we introduced a methodology that can apply to concrete contexts, building a repository of patterns and rules. This generated knowledge base can be used during the design and maintenance phases of ETL processes, automatically exposing understandable conceptual representations of the processes and providing useful insight for design decisions. Collectively, these contributions have raised the level of abstraction of ETL process components, revealing their quality characteristics in a granular level and allowing for evaluation and automated (re-)design, taking under consideration business users’ quality goals

Storage Format Selection and Optimization for Materialized Intermediate Results in Data-Intensive Flows

Modern organizations produce and collect large volumes of data, that need to be processed repeatedly and quickly for gaining business insights. For such processing, typically, Data-intensive Flows (DIFs) are deployed on distributed processing frameworks. The DIFs of different users have many computation overlaps (i.e., parts of the processing are duplicated), thus wasting computational resources and increasing the overall cost. The output of these computation overlaps (known as intermediate results) can be materialized for reuse, which helps in reducing the cost and saves computational resources if properly done. Furthermore, the way such outputs are materialized must be considered, as different storage layouts (i.e., horizontal, vertical, and hybrid) can be used to reduce the I/O cost. In this PhD work, we first propose a novel approach for automatically materializing the intermediate results of DIFs through a multi-objective optimization method, which can tackle multiple and conflicting quality metrics. Next, we study the behavior of different operators of DIFs that are the first to process the loaded materialized results. Based on this study, we devise a rule-based approach, that decides the storage layout for materialized results based on the subsequent operation types. Despite improving the cost in general, the heuristic rules do not consider the amount of data read while making the choice, which could lead to a wrong decision. Thus, we design a cost model that is capable of finding the right storage layout for every scenario. The cost model uses data and workload characteristics to estimate the I/O cost of a materialized intermediate results with different storage layouts and chooses the one which has minimum cost. The results show that storage layouts help to reduce the loading time of materialized results and overall, they improve the performance of DIFs. The thesis also focuses on the optimization of the configurable parameters of hybrid layouts. We propose ATUN-HL (Auto TUNing Hybrid Layouts), which based on the same cost model and given the workload and characteristics of data, finds the optimal values for configurable parameters in hybrid layouts (i.e., Parquet). Finally, the thesis also studies the impact of parallelism in DIFs and hybrid layouts. Our proposed cost model helps to devise an approach for fine-tuning the parallelism by deciding the number of tasks and machines to process the data. Thus, the cost model proposed in this thesis, enables in choosing the best possible storage layout for materialized intermediate results, tuning the configurable parameters of hybrid layouts, and estimating the number of tasks and machines for the execution of DIFs.Moderne Unternehmen produzieren und sammeln große Datenmengen, die wiederholt und schnell verarbeitet werden müssen, um geschäftliche Erkenntnisse zu gewinnen. Für die Verarbeitung dieser Daten werden typischerweise Datenintensive Prozesse (DIFs) auf verteilten Systemen wie z.B. MapReduce bereitgestellt. Dabei ist festzustellen, dass die DIFs verschiedener Nutzer sich in großen Teilen überschneiden, wodurch viel Arbeit mehrfach geleistet, Ressourcen verschwendet und damit die Gesamtkosten erhöht werden. Um diesen Effekt entgegenzuwirken, können die Zwischenergebnisse der DIFs für spätere Wiederverwendungen materialisiert werden. Hierbei müssen vor allem die unterschiedlichen Speicherlayouts (horizontal, vertikal und hybrid) berücksichtigt werden. In dieser Doktorarbeit wird ein neuartiger Ansatz zur automatischen Materialisierung der Zwischenergebnisse von DIFs durch eine mehrkriterielle Optimierungsmethode vorgeschlagen, der in der Lage ist widersprüchliche Qualitätsmetriken zu behandeln. Des Weiteren wird untersucht die Wechselwirkung zwischen verschiedenen peratortypen und unterschiedlichen Speicherlayouts untersucht. Basierend auf dieser Untersuchung wird ein regelbasierter Ansatz vorgeschlagen, der das Speicherlayout für materialisierte Ergebnisse, basierend auf den nachfolgenden Operationstypen, festlegt. Obwohl sich die Gesamtkosten für die Ausführung der DIFs im Allgemeinen verbessern, ist der heuristische Ansatz nicht in der Lage die gelesene Datenmenge bei der Auswahl des Speicherlayouts zu berücksichtigen. Dies kann in einigen Fällen zu falschen Entscheidung führen. Aus diesem Grund wird ein Kostenmodell entwickelt, mit dem für jedes Szenario das richtige Speicherlayout gefunden werden kann. Das Kostenmodell schätzt anhand von Daten und Auslastungsmerkmalen die E/A-Kosten eines materialisierten Zwischenergebnisses mit unterschiedlichen Speicherlayouts und wählt das kostenminimale aus. Die Ergebnisse zeigen, dass Speicherlayouts die Ladezeit materialisierter Ergebnisse verkürzen und insgesamt die Leistung von DIFs verbessern. Die Arbeit befasst sich auch mit der Optimierung der konfigurierbaren Parameter von hybriden Layouts. Konkret wird der sogenannte ATUN-HL Ansatz (Auto TUNing Hybrid Layouts) entwickelt, der auf der Grundlage des gleichen Kostenmodells und unter Berücksichtigung der Auslastung und der Merkmale der Daten die optimalen Werte für konfigurierbare Parameter in Parquet, d.h. eine Implementierung von hybrider Layouts. Schließlich werden in dieser Arbeit auch die Auswirkungen von Parallelität in DIFs und hybriden Layouts untersucht. Dazu wird ein Ansatz entwickelt, der in der Lage ist die Anzahl der Aufgaben und dafür notwendigen Maschinen automatisch zu bestimmen. Zusammengefasst lässt sich festhalten, dass das in dieser Arbeit vorgeschlagene Kostenmodell es ermöglicht, das bestmögliche Speicherlayout für materialisierte Zwischenergebnisse zu ermitteln, die konfigurierbaren Parameter hybrider Layouts festzulegen und die Anzahl der Aufgaben und Maschinen für die Ausführung von DIFs zu schätzen

Storage format selection and optimization for materialized intermediate results in data-intensive flows

Tesi en modalitat de cotuela: Universitat Politècnica de Catalunya i Technische Universität DresdenModern organizations produce and collect large volumes of data, that need to be processed repeatedly and quickly for gaining business insights. For such processing, typically, Data-intensive Flows (DIFs) are deployed on distributed processing frameworks. The DIFs of different users have many computation overlaps (i.e., parts of the processing are duplicated), thus wasting computational resources and increasing the overall cost. The output of these computation overlaps (known as intermediate results) can be materialized for reuse, which helps in reducing the cost and saves computational resources if properly done. Furthermore, the way such outputs are materialized must be considered, as different storage layouts (i.e., horizontal, vertical, and hybrid) can be used to reduce the I/O cost. In this PhD work, we first propose a novel approach for automatically materializing the intermediate results of DIFs through a multi-objective optimization method, which can tackle multiple and conflicting quality metrics. Next, we study the behavior of different operators of DIFs that are the first to process the loaded materialized results. Based on this study, we devise a rule-based approach, that decides the storage layout for materialized results based on the subsequent operation types. Despite improving the cost in general, the heuristic rules do not consider the amount of data read while making the choice, which could lead to a wrong decision. Thus, we design a cost model that is capable of finding the right storage layout for every scenario. The cost model uses data and workload characteristics to estimate the I/O cost of a materialized intermediate results with different storage layouts and chooses the one which has minimum cost. The results show that storage layouts help to reduce the loading time of materialized results and overall, they improve the performance of DIFs. The thesis also focuses on the optimization of the configurable parameters of hybrid layouts. We propose ATUN-HL (Auto TUNing Hybrid Layouts), which based on the same cost model and given the workload and characteristics of data, finds the optimal values for configurable parameters in hybrid layouts (i.e., Parquet). Finally, the thesis also studies the impact of parallelism in DIFs and hybrid layouts. Our proposed cost model helps to devise an approach for fine-tuning the parallelism by deciding the number of tasks and machines to process the data. Thus, the cost model proposed in this thesis, enables in choosing the best possible storage layout for materialized intermediate results, tuning the configurable parameters of hybrid layouts, and estimating the number of tasks and machines for the execution of DIFs.Las organizaciones producen y recopilan grandes volúmenes de datos, que deben procesarse de forma repetitiva y rápida para obtener información relevante para la empresa. Para tal procesamiento, por lo general, se emplean flujos intensivos de datos (DIFs por sussiglas en inglés) en entornos de procesamiento distribuido. Los DIFs de diferentes usuarios tienen elementos comunes (es decir, se duplican partes del procesamiento, lo que desperdicia recursos computacionales y aumenta el coste en general). Los resultados intermedios de varios DIFs pueden pues coincidir y se pueden por tanto materializar para facilitar su reutilización, lo que ayuda a reducir el coste y ahorrar recursos si se realiza correctamente. Además, la forma en qué se materializan dichos resultados debe ser considerada. Por ejemplo, diferentes tipos de diseño lógico de los datos (es decir, horizontal, vertical o híbrido) se pueden utilizar para reducir el coste de E/S. En esta tesis doctoral, primero proponemos un enfoque novedoso para materializar automáticamente los resultados intermedios de los DIFs a través de un método de optimización multi-objetivo, que puede considerar múltiples y contradictorias métricas de calidad. A continuación, estudiamos el comportamiento de diferentes operadores de DIF que acceden directamente a los resultados materializados. Sobre la base de este estudio, ideamos un enfoque basado en reglas, que decide el diseño del almacenamiento para los resultados materializados en función de los tipos de operaciones que los utilizan directamente. A pesar de mejorar el coste en general, las reglas heurísticas no consideran estadísticas sobre la cantidad de datos leídos al hacer la elección, lo que podría llevar a una decisión errónea. Consecuentemente, diseñamos un modelo de costos que es capaz de encontrar el diseño de almacenamiento adecuado para cada escenario dependiendo de las características de los datos almacenados. El modelo de costes usa estadísticas y características de acceso para estimar el coste de E/S de un resultado intervii medio materializado con diferentes diseños de almacenamiento y elige el de menor coste. Los resultados muestran que los diseños de almacenamiento ayudan a reducir el tiempo de carga de los resultados materializados y, en general, mejoran el rendimiento de los DIF. La tesis también presta atención a la optimización de los parámetros configurables de diseños híbridos. Proponemos así ATUN-HL (Auto TUNing Hybrid Layouts), que, basado en el mismo modelo de costes, las características de los datos y el tipo de acceso que se está haciendo, encuentra los valores óptimos para los parámetros de configuración en disponibles Parquet (una implementación de diseños híbridos para Hadoop Distributed File System). Finalmente, esta tesis estudia el impacto del paralelismo en DIF y diseños híbridos. El modelo de coste propuesto ayuda a idear un enfoque para ajustar el paralelismo al decidir la cantidad de tareas y máquinas para procesar los datos. En resumen, el modelo de costes propuesto permite elegir el mejor diseño de almacenamiento posible para los resultados intermedios materializados, ajustar los parámetros configurables de diseños híbridos y estimar el número de tareas y máquinas para la ejecución de DIF.Moderne Unternehmen produzieren und sammeln große Datenmengen, die wiederholt und schnell verarbeitet werden müssen, um geschäftliche Erkenntnisse zu gewinnen. Für die Verarbeitung dieser Daten werden typischerweise Datenintensive Prozesse (DIFs) auf verteilten Systemen wie z.B. MapReduce bereitgestellt. Dabei ist festzustellen, dass die DIFs verschiedener Nutzer sich in großen Teilen überschneiden, wodurch viel Arbeit mehrfach geleistet, Ressourcen verschwendet und damit die Gesamtkosten erhöht werden. Um diesen Effekt entgegenzuwirken, können die Zwischenergebnisse der DIFs für spätere Wiederverwendungen materialisiert werden. Hierbei müssen vor allem die unterschiedlichen Speicherlayouts (horizontal, vertikal und hybrid) berücksichtigt werden. In dieser Doktorarbeit wird ein neuartiger Ansatz zur automatischen Materialisierung der Zwischenergebnisse von DIFs durch eine mehrkriterielle Optimierungsmethode vorgeschlagen, der in der Lage ist widersprüchliche Qualitätsmetriken zu behandeln. Des Weiteren wird untersucht die Wechselwirkung zwischen verschiedenen Operatortypen und unterschiedlichen Speicherlayouts untersucht. Basierend auf dieser Untersuchung wird ein regelbasierter Ansatz vorgeschlagen, der das Speicherlayout für materialisierte Ergebnisse, basierend auf den nachfolgenden Operationstypen, festlegt. Obwohl sich die Gesamtkosten für die Ausführung der DIFs im Allgemeinen verbessern, ist der heuristische Ansatz nicht in der Lage die gelesene Datenmenge bei der Auswahl des Speicherlayouts zu berücksichtigen. Dies kann in einigen Fällen zu falschen Entscheidung führen. Aus diesem Grund wird ein Kostenmodell entwickelt, mit dem für jedes Szenario das richtige Speicherlayout gefunden werden kann. Das Kostenmodell schätzt anhand von Daten und Auslastungsmerkmalen die E/A-Kosten eines materialisierten Zwischenergebnisses mit unterschiedlichen Speicherlayouts und wählt das kostenminimale aus. Die Ergebnisse zeigen, dass Speicherlayouts die Ladezeit materialisierter Ergebnisse verkürzen und insgesamt die Leistung von DIFs verbessern. Die Arbeit befasst sich auch mit der Optimierung der konfigurierbaren Parameter von hybriden Layouts. Konkret wird der sogenannte ATUN-HLAnsatz (Auto TUNing Hybrid Layouts) entwickelt, der auf der Grundlage des gleichen Kostenmodells und unter Berücksichtigung der Auslastung und der Merkmale der Daten die optimalen Werte für konfigurierbare Parameter in Parquet, d.h. eine Implementierung von hybrider Layouts. Schließlich werden in dieser Arbeit auch die Auswirkungen von Parallelität in DIFs und hybriden Layouts untersucht. Dazu wird ein Ansatz entwickelt, der in der Lage ist die Anzahl der Aufgaben und dafür notwendigen Maschinen automatisch zu bestimmen. Zusammengefasst lässt sich festhalten, dass das in dieser Arbeit vorgeschlagene Kostenmodell es ermöglicht, das bestmögliche Speicherlayout für materialisierte Zwischenergebnisse zu ermitteln, die konfigurierbaren Parameter hybrider Layouts festzulegen und die Anzahl der Aufgaben und Maschinen für die Ausführung von DIFs zu schätzenPostprint (published version