Parallelizing Windowed Stream Joins in a Shared-Nothing Cluster

The availability of large number of processing nodes in a parallel and distributed computing environment enables sophisticated real time processing over high speed data streams, as required by many emerging applications. Sliding window stream joins are among the most important operators in a stream processing system. In this paper, we consider the issue of parallelizing a sliding window stream join operator over a shared nothing cluster. We propose a framework, based on fixed or predefined communication pattern, to distribute the join processing loads over the shared-nothing cluster. We consider various overheads while scaling over a large number of nodes, and propose solution methodologies to cope with the issues. We implement the algorithm over a cluster using a message passing system, and present the experimental results showing the effectiveness of the join processing algorithm.Comment: 11 page

Quality-Driven Disorder Handling for M-way Sliding Window Stream Joins

Sliding window join is one of the most important operators for stream applications. To produce high quality join results, a stream processing system must deal with the ubiquitous disorder within input streams which is caused by network delay, asynchronous source clocks, etc. Disorder handling involves an inevitable tradeoff between the latency and the quality of produced join results. To meet different requirements of stream applications, it is desirable to provide a user-configurable result-latency vs. result-quality tradeoff. Existing disorder handling approaches either do not provide such configurability, or support only user-specified latency constraints. In this work, we advocate the idea of quality-driven disorder handling, and propose a buffer-based disorder handling approach for sliding window joins, which minimizes sizes of input-sorting buffers, thus the result latency, while respecting user-specified result-quality requirements. The core of our approach is an analytical model which directly captures the relationship between sizes of input buffers and the produced result quality. Our approach is generic. It supports m-way sliding window joins with arbitrary join conditions. Experiments on real-world and synthetic datasets show that, compared to the state of the art, our approach can reduce the result latency incurred by disorder handling by up to 95% while providing the same level of result quality.Comment: 12 pages, 11 figures, IEEE ICDE 201

Saber: window-based hybrid stream processing for heterogeneous architectures

Modern servers have become heterogeneous, often combining multicore CPUs with many-core GPGPUs. Such heterogeneous architectures have the potential to improve the performance of data-intensive stream processing applications, but they are not supported by current relational stream processing engines. For an engine to exploit a heterogeneous architecture, it must execute streaming SQL queries with sufficient data-parallelism to fully utilise all available heterogeneous processors, and decide how to use each in the most effective way. It must do this while respecting the semantics of streaming SQL queries, in particular with regard to window handling. We describe SABER, a hybrid high-performance relational stream processing engine for CPUs and GPGPUs. SABER executes windowbased streaming SQL queries in a data-parallel fashion using all available CPU and GPGPU cores. Instead of statically assigning query operators to heterogeneous processors, SABER employs a new adaptive heterogeneous lookahead scheduling strategy, which increases the share of queries executing on the processor that yields the highest performance. To hide data movement costs, SABER pipelines the transfer of stream data between different memory types and the CPU/GPGPU. Our experimental comparison against state-ofthe-art engines shows that SABER increases processing throughput while maintaining low latency for a wide range of streaming SQL queries with small and large windows sizes

Hardware-conscious query processing for the many-core era

Die optimale Nutzung von moderner Hardware zur Beschleunigung von Datenbank-Anfragen ist keine triviale Aufgabe. Viele DBMS als auch DSMS der letzten Jahrzehnte basieren auf Sachverhalten, die heute kaum noch Gültigkeit besitzen. Ein Beispiel hierfür sind heutige Server-Systeme, deren Hauptspeichergröße im Bereich mehrerer Terabytes liegen kann und somit den Weg für Hauptspeicherdatenbanken geebnet haben. Einer der größeren letzten Hardware Trends geht hin zu Prozessoren mit einer hohen Anzahl von Kernen, den sogenannten Manycore CPUs. Diese erlauben hohe Parallelitätsgrade für Programme durch Multithreading sowie Vektorisierung (SIMD), was die Anforderungen an die Speicher-Bandbreite allerdings deutlich erhöht. Der sogenannte High-Bandwidth Memory (HBM) versucht diese Lücke zu schließen, kann aber ebenso wie Many-core CPUs jeglichen Performance-Vorteil negieren, wenn dieser leichtfertig eingesetzt wird. Diese Arbeit stellt die Many-core CPU-Architektur zusammen mit HBM vor, um Datenbank sowie Datenstrom-Anfragen zu beschleunigen. Es wird gezeigt, dass ein hardwarenahes Kostenmodell zusammen mit einem Kalibrierungsansatz die Performance verschiedener Anfrageoperatoren verlässlich vorhersagen kann. Dies ermöglicht sowohl eine adaptive Partitionierungs und Merge-Strategie für die Parallelisierung von Datenstrom-Anfragen als auch eine ideale Konfiguration von Join-Operationen auf einem DBMS. Nichtsdestotrotz ist nicht jede Operation und Anwendung für die Nutzung einer Many-core CPU und HBM geeignet. Datenstrom-Anfragen sind oft auch an niedrige Latenz und schnelle Antwortzeiten gebunden, welche von höherer Speicher-Bandbreite kaum profitieren können. Hinzu kommen üblicherweise niedrigere Taktraten durch die hohe Kernzahl der CPUs, sowie Nachteile für geteilte Datenstrukturen, wie das Herstellen von Cache-Kohärenz und das Synchronisieren von parallelen Thread-Zugriffen. Basierend auf den Ergebnissen dieser Arbeit lässt sich ableiten, welche parallelen Datenstrukturen sich für die Verwendung von HBM besonders eignen. Des Weiteren werden verschiedene Techniken zur Parallelisierung und Synchronisierung von Datenstrukturen vorgestellt, deren Effizienz anhand eines Mehrwege-Datenstrom-Joins demonstriert wird.Exploiting the opportunities given by modern hardware for accelerating query processing speed is no trivial task. Many DBMS and also DSMS from past decades are based on fundamentals that have changed over time, e.g., servers of today with terabytes of main memory capacity allow complete avoidance of spilling data to disk, which has prepared the ground some time ago for main memory databases. One of the recent trends in hardware are many-core processors with hundreds of logical cores on a single CPU, providing an intense degree of parallelism through multithreading as well as vectorized instructions (SIMD). Their demand for memory bandwidth has led to the further development of high-bandwidth memory (HBM) to overcome the memory wall. However, many-core CPUs as well as HBM have many pitfalls that can nullify any performance gain with ease. In this work, we explore the many-core architecture along with HBM for database and data stream query processing. We demonstrate that a hardware-conscious cost model with a calibration approach allows reliable performance prediction of various query operations. Based on that information, we can, therefore, come to an adaptive partitioning and merging strategy for stream query parallelization as well as finding an ideal configuration of parameters for one of the most common tasks in the history of DBMS, join processing. However, not all operations and applications can exploit a many-core processor or HBM, though. Stream queries optimized for low latency and quick individual responses usually do not benefit well from more bandwidth and suffer from penalties like low clock frequencies of many-core CPUs as well. Shared data structures between cores also lead to problems with cache coherence as well as high contention. Based on our insights, we give a rule of thumb which data structures are suitable to parallelize with focus on HBM usage. In addition, different parallelization schemas and synchronization techniques are evaluated, based on the example of a multiway stream join operation

State-Slice: A New Stream Query Optimization Paradigm for Multi-query and Distributed Processing

Modern stream applications necessitate the handling of large numbers of continuous queries specified over high volume data streams. This dissertation proposes novel solutions to continuous query optimization in three core areas of stream query processing, namely state-slice based multiple continuous query sharing, ring-based multi-way join query distribution and scalable distributed multi-query optimization. The first part of the dissertation proposes efficient optimization strategies that utilize the novel state-slicing concept to achieve maximum memory and computation sharing for stream join queries with window constraints. Extensive analytical and experimental evaluations demonstrate that our proposed strategies is capable to minimize the memory or CPU consumptions for multiple join queries. The second part of this dissertation proposes a novel scheme for the distributed execution of generic multi-way joins with window constraints. The proposed scheme partitions the states into disjoint slices in the time domain, and then distributes the fine-grained states in the cluster, forming a virtual computation ring. New challenges to support this distributed state-slicing processing are answered by numerous new techniques. The extensive experimental evaluations show that the proposed strategies achieve significant performance improvements in terms of response time and memory usages for a wide range of configurations and workloads on a real system. Ring based distributed stream query processing and multi-query sharing both are based on the state-slice concept. The third part of this dissertation combines the first two parts of this dissertation work and proposes a novel distributed multi-query optimization technique

Spontananfragen auf Datenströmen

Many modern applications require processing large amounts of data in a real-time fashion. As a result, distributed stream processing engines (SPEs) have gained significant attention as an important new class of big data processing systems. The central design principle of these SPEs is to handle queries that potentially run forever on data streams with a query-at-a-time model, i.e., each query is optimized and executed separately. However, in many real applications, not only long-running queries but also many short-running queries are processed on data streams. In these applications, multiple stream queries are created and deleted concurrently, in an ad-hoc manner. The best practice to handle ad-hoc stream queries is to fork input stream and add additional resources for each query. However, this approach leads to redundant computation and data copy. This thesis lays the foundation for efficient ad-hoc stream query processing. To bridge the gap between stream data processing and ad-hoc query processing, we follow a top-down approach. First, we propose a benchmarking framework to analyze state-of-the-art SPEs. We provide a definition of latency and throughput for stateful operators. Moreover, we carefully separate the system under test and the driver, to correctly represent the open-world model of typical stream processing deployments. This separation enables us to measure the system performance under realistic conditions. Our solution is the first benchmarking framework to define and test the sustainable performance of SPEs. Throughout our analysis, we realize that the state-of-the-art SPEs are unable to execute stream queries in an ad-hoc manner. Second, we propose the first ad-hoc stream query processing engine for distributed data processing environments. We develop our solution based on three main requirements: (1) Integration: Ad-hoc query processing should be a composable layer that can extend stream operators, such as join, aggregation, and window operators; (2) Consistency: Ad-hoc query creation and deletion must be performed consistently and ensure exactly-once semantics and correctness; (3) Performance: In contrast to modern SPEs, ad-hoc SPEs should not only maximize data throughput but also query throughout via incremental computation and resource sharing. Third, we propose an ad-hoc stream join processing framework that integrates dynamic query processing and query re-optimization techniques with ad-hoc stream query processing. Our solution comprises an optimization layer and a stream data processing layer. The optimization layer periodically re-optimizes the query execution plan, performing join reordering and vertical and horizontal scaling at runtime without stopping the execution. The data processing layer enables incremental and consistent query processing, supporting all the actions triggered by the optimizer. The result of the second and the third contributions forms a complete ad-hoc SPE. We utilize the first contribution not only for benchmarking modern SPEs but also for evaluating the ad-hoc SPE.Eine Vielzahl moderner Anwendungen setzten die Echtzeitverarbeitung großer Datenmengen voraus. Aus diesem Grund haben neuerdings verteilte Systeme zur Verarbeitung von Datenströmen (sog. Datenstrom-Verarbeitungssysteme, abgek. "DSV") eine wichtige Bedeutung als neue Kategorie von Massendaten-Verarbeitungssystemen erlangt. Das zentrale Entwurfsprinzip dieser DSVs ist es, Anfragen, die potenziell unendlich lange auf einem Datenstrom laufen, jeweils Eine nach der Anderen zu verarbeiten (Englisch: "query-at-a-time model"). Das bedeutet, dass jede Anfrage eigenständig vom System optimiert und ausgeführt wird. Allerdings stellen vielen reale Anwendungen nicht nur lang laufende Anfragen auf Datenströmen, sondern auch kurz laufende Spontananfragen. Solche Anwendungen können mehrere Anfragen spontan und zeitgleich erstellen und entfernen. Das bewährte Verfahren, um Spontananfragen zu bearbeiten, zweigt den eingehenden Datenstrom ab und belegt zusätzliche Ressourcen für jede neue Anfrage. Allerdings ist dieses Verfahren ineffizient, weil Spontananfragen damit redundante Berechnungen und Daten-Kopieroperationen verursachen. In dieser Arbeit legen wir das Fundament für die effiziente Verarbeitung von Spontananfragen auf Datenströmen. Wir schließen in den folgenden drei Schritten die Lücke zwischen verteilter Datenstromanfrage-Verarbeitung und Spontananfrage-Verarbeitung. Erstens stellen wir ein Benchmark-Framework zur Analyse von modernen DSVs vor. In diesem Framework stellen wir eine neue Definition für die Latenz und den Durchsatz von zustandsbehafteten Operatoren vor. Zudem unterscheiden wir genau zwischen dem zu testenden System und dem Treibersystem, um das offene-Welt Modell, welches den typischen Anwendungsszenarien in der Datenstromverabeitung entspricht, korrekt zu repräsentieren. Diese strikte Unterscheidung ermöglicht es, die Systemleistung unter realen Bedingungen zu messen. Unsere Lösung ist damit das erste Benchmark-Framework, welches die dauerhaft durchhaltbare Systemleistung von DSVs definiert und testet. Durch eine systematische Analyse aktueller DSVs stellen wir fest, dass aktuelle DSVs außerstande sind, Spontananfragen effizient zu verarbeiten. Zweitens stellen wir das erste verteilte DSV zur Spontananfrageverarbeitung vor. Wir entwickeln unser Lösungskonzept basierend auf drei Hauptanforderungen: (1) Integration: Spontananfrageverarbeitung soll ein modularer Baustein sein, mit dem Datenstrom-Operatoren wie z.B. Join, Aggregation, und Zeitfenster-Operatoren erweitert werden können; (2) Konsistenz: die Erstellung und Entfernung von Spontananfragen müssen konsistent ausgeführt werden, die Semantik für einmalige Nachrichtenzustellung erhalten, sowie die Korrektheit des Anfrage-Ergebnisses sicherstellen; (3) Leistung: Im Gegensatz zu modernen DSVs sollen DSVs zur Spontananfrageverarbeitung nicht nur den Datendurchsatz, sondern auch den Anfragedurchsatz maximieren. Dies ermöglichen wir durch inkrementelle Kompilation und der Ressourcenteilung zwischen Anfragen. Drittens stellen wir ein Programmiergerüst zur Verbeitung von Spontananfragen auf Datenströmen vor. Dieses integriert die dynamische Anfrageverarbeitung und die Nachoptimierung von Anfragen mit der Spontananfrageverarbeitung auf Datenströmen. Unser Lösungsansatz besteht aus einer Schicht zur Anfrageoptimierung und einer Schicht zur Anfrageverarbeitung. Die Optimierungsschicht optimiert periodisch den Anfrageverarbeitungsplan nach, wobei sie zur Laufzeit Joins neu anordnet und vertikal sowie horizontal skaliert, ohne die Verarbeitung anzuhalten. Die Verarbeitungsschicht ermöglicht eine inkrementelle und konsistente Anfrageverarbeitung und unterstützt alle zuvor beschriebenen Eingriffe der Optimierungsschicht in die Anfrageverarbeitung. Zusammengefasst ergeben unsere zweiten und dritten Lösungskonzepte eine vollständige DSV zur Spontananfrageverarbeitung. Wir verwenden hierzu unseren ersten Beitrag nicht nur zur Bewertung moderner DSVs, sondern auch zur Evaluation unseres DSVs zur Spontananfrageverarbeitung

Dynamic Optimization and Migration of Continuous Queries Over Data Streams

Continuous queries process real-time streaming data and output results in streams for a wide range of applications. Due to the fluctuating stream characteristics, a streaming database system needs to dynamically adapt query execution. This dissertation proposes novel solutions to continuous query adaptation in three core areas, namely dynamic query optimization, dynamic plan migration and partitioned query adaptation. Runtime query optimization needs to efficiently generate plans that satisfy both CPU and memory resource constraints. Existing work focus on minimizing intermediate query results, which decreases memory and CPU usages simultaneously. However, doing so cannot assure that both resource constraints are being satisfied, because memory and CPU can be either positively or negatively correlated. This part of the dissertation proposes efficient optimization strategies that utilize both types of correlations to search the entire query plan space in polynomial time when a typical exhaustive search would take at least exponential time. Extensive experimental evaluations have demonstrated the effectiveness of the proposed strategies. Dynamic plan migration is concerned with on-the-fly transition from one continuous plan to a semantically equivalent yet more efficient plan. It is a must to guarantee the continuation and repeatability of dynamic query optimization. However, this research area has been largely neglected in the current literature. The second part of this dissertation proposes migration strategies that dynamically migrate continuous queries while guaranteeing the integrity of the query results, meaning there are no missing, duplicate or incorrect results. The extensive experimental evaluations show that the proposed strategies vary significantly in terms of output rates and memory usages given distinct system configurations and stream workloads. Partitioned query processing is effective to process continuous queries with large stateful operators in a distributed system. Dynamic load redistribution is necessary to balance uneven workload across machines due to changing stream properties. However, existing solutions generally assume static query plans without runtime query optimization. This part of the dissertation evaluates the benefits of applying query optimization in partitioned query processing and shows dramatic performance improvement of more than 300%. Several load balancing strategies are then proposed to consider the heterogeneity of plan shapes across machines caused by dynamic query optimization. The effectiveness of the proposed strategies is analyzed through extensive experiments using a cluster

Data Provenance and Management in Radio Astronomy: A Stream Computing Approach

New approaches for data provenance and data management (DPDM) are required for mega science projects like the Square Kilometer Array, characterized by extremely large data volume and intense data rates, therefore demanding innovative and highly efficient computational paradigms. In this context, we explore a stream-computing approach with the emphasis on the use of accelerators. In particular, we make use of a new generation of high performance stream-based parallelization middleware known as InfoSphere Streams. Its viability for managing and ensuring interoperability and integrity of signal processing data pipelines is demonstrated in radio astronomy. IBM InfoSphere Streams embraces the stream-computing paradigm. It is a shift from conventional data mining techniques (involving analysis of existing data from databases) towards real-time analytic processing. We discuss using InfoSphere Streams for effective DPDM in radio astronomy and propose a way in which InfoSphere Streams can be utilized for large antennae arrays. We present a case-study: the InfoSphere Streams implementation of an autocorrelating spectrometer, and using this example we discuss the advantages of the stream-computing approach and the utilization of hardware accelerators