On Atomic Batch Executions in Stream Processing

AbstractStream processing is about processing continuous streams of data by programs in a workflow. Continuous execution is discretized by grouping input stream tuples into batches and using one batch at a time for the execution of programs. As source input batches arrive continuously, several batches may be processed in the workflow simultaneously. A general requirement is that each batch be processed completely in the workflow. That is, all the programs triggered by the batch, directly and transitively, in the workflow must be executed successfully. Executing only a prefix of the workflow amounts to dropping (discarding) the batches that were derived by the executed part and were supposed to be input to the rest of the workflow. In some cases, such partial executions may not be acceptable and may have to be rolled back, amounting to dropping the source input batches that were processed by the partial execution. We refer to this property of processing the batches either completely or not at all as atomic execution of the batches. We also attribute the property to the batches themselves, calling them atomic batches, meaning that the property applies to the set of transactions that are executed due to that batch. If batches are processed in isolation in the workflow, preserving atomicity is fairly straightforward. When batches are split or merged along the workflow computation, the problem becomes complicated. In this paper, we study issues relating to the atomicity of batches. We illustrate that, in general, preserving atomicity of some batches may affect the atomicity of some other batches, and suggest trade-offs

A Transaction Model for Executions of Compositions of Internet of Things Services

AbstractInternet of Things (IoT) is about making “things” smart in some functionality, and connecting and enabling them to perform complex tasks by themselves. The functionality can be encapsulated as services and the task executed by composing the services. Two noteworthy functionalities of IoT services are monitoring and actuation. Monitoring implies continuous executions, and actuation is by triggering. Continuous executions typically involve stream processing. Stream input data are accumulated into batches and each batch is subjected to a sequence of computations, structured as a dataflow graph. The composition may be processing several batches simultaneously. Additionally, some non-stream OLTP transactions may also be executing concurrently. Thus, several composite transactions may be executing concurrently. This is in contrast to a typical Web services composition, where just one composite transaction is executed on each invocation. Therefore, defining transactional properties for executions of IoT service compositions is much more complex than for those of conventional Web service compositions. In this paper, we propose a transaction model and a correctness criterion for executions of IoT service compositions. Our proposal defines relaxed atomicity and isolation properties for transactions in a flexible manner and can be adapted for a variety of IoT applications

S-Store: Streaming Meets Transaction Processing

Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees

A Survey on Transactional Stream Processing

Transactional stream processing (TSP) strives to create a cohesive model that merges the advantages of both transactional and stream-oriented guarantees. Over the past decade, numerous endeavors have contributed to the evolution of TSP solutions, uncovering similarities and distinctions among them. Despite these advances, a universally accepted standard approach for integrating transactional functionality with stream processing remains to be established. Existing TSP solutions predominantly concentrate on specific application characteristics and involve complex design trade-offs. This survey intends to introduce TSP and present our perspective on its future progression. Our primary goals are twofold: to provide insights into the diverse TSP requirements and methodologies, and to inspire the design and development of groundbreaking TSP systems

HeTM: Transactional Memory for Heterogeneous Systems

Modern heterogeneous computing architectures, which couple multi-core CPUs with discrete many-core GPUs (or other specialized hardware accelerators), enable unprecedented peak performance and energy efficiency levels. Unfortunately, though, developing applications that can take full advantage of the potential of heterogeneous systems is a notoriously hard task. This work takes a step towards reducing the complexity of programming heterogeneous systems by introducing the abstraction of Heterogeneous Transactional Memory (HeTM). HeTM provides programmers with the illusion of a single memory region, shared among the CPUs and the (discrete) GPU(s) of a heterogeneous system, with support for atomic transactions. Besides introducing the abstract semantics and programming model of HeTM, we present the design and evaluation of a concrete implementation of the proposed abstraction, which we named Speculative HeTM (SHeTM). SHeTM makes use of a novel design that leverages on speculative techniques and aims at hiding the inherently large communication latency between CPUs and discrete GPUs and at minimizing inter-device synchronization overhead. SHeTM is based on a modular and extensible design that allows for easily integrating alternative TM implementations on the CPU's and GPU's sides, which allows the flexibility to adopt, on either side, the TM implementation (e.g., in hardware or software) that best fits the applications' workload and the architectural characteristics of the processing unit. We demonstrate the efficiency of the SHeTM via an extensive quantitative study based both on synthetic benchmarks and on a porting of a popular object caching system.Comment: The current work was accepted in the 28th International Conference on Parallel Architectures and Compilation Techniques (PACT'19

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

Extract, Transform, and Load data from Legacy Systems to Azure Cloud

Internship report presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business IntelligenceIn a world with continuously evolving technologies and hardened competitive markets, organisations need to continually be on guard to grasp cutting edge technology and tools that will help them to surpass any competition that arises. Modern data platforms that incorporate cloud technologies, support organisations to strive and get ahead of their competitors by providing solutions that help them capture and optimally use untapped data, and scalable storages to adapt to ever-growing data quantities. Also, adopt data processing and visualisation tools that help to improve the decision-making process. With many cloud providers available in the market, from small players to major technology corporations, this offers much flexibility to organisations to choose the best cloud technology that will align with their use cases and overall products and services strategy. This internship came up at the time when one of Accenture’s significant client in the financial industry decided to migrate from legacy systems to a cloud-based data infrastructure that is Microsoft Azure cloud. During this internship, development of the data lake, which is a core part of the MDP, was done to understand better the type of challenges that can be faced when migrating data from on-premise legacy systems to a cloud-based infrastructure. Also, provided in this work, are the main recommendations and guidelines when it comes to performing a large scale data migration

Data Management Solutions for Tackling Big Data Variety

Variety is one of the three defining characteristics of Big Data; the others being Volume and Velocity. There are several aspects of this data variety: diversity in data formats (text, video, audio) and structure (relational, graph etc), variety in access methodologies(OLTP, OLAP), and distribution heterogeneity within the workloads (read-heavy, high contention). Data management solutions for modern-day applications need to tackle this variety.This dissertation provides an understanding of the challenges associated with the different elements of variety, and proposes several solutions for efficiently handling its various aspects. First, the dissertation studies the challenges related to variety in data structure and access methodologies, and the resultant heterogeneity at the data infrastructure level. Applications now employ several data-processing engines with different underlying representations, like row, column, graph etc., to process their data. We propose Janus, which introduces a novel data-movement pipeline, which enables the use of different representations to support both high throughput of transactions and diverse analytics, while still ensuring consistent real-time analytics in a scale-out setting. Janus partitions the data at different representations, and allows distributed transactions and diverse partitioning strategies at the representations. Then, we propose Typhon and Cerberus, which define and enforce consistency semantics for application data spread across representations. Second, this dissertation proposes solutions for handling distribution heterogeneity within the workloads. Workloads can have have skewed distribution in terms of operation-type, data access or temporal variation. We propose strongly-consistent quorum reads for Raft-like consensus protocols, which can be utilized to scale read-heavy workloads. For supporting high contention transaction workloads, we integrate an existing dynamic timestamp allocation based concurrency control mechanism in a distributed OLTP setting, and analyze its performance. Third, we study IoT applications, which have to deal with both physical heterogeneity of the sensors, as well asdiverse data-processing demands. We propose a multi-representation based architecture catering to IoT applications, and also present the initial design of M-stream, a computation framework for enabling integration and monitoring of uncertain data from multiplesensors. Through analysis, illustrative examples and extensive evaluation of the proposed protocols, this dissertation demonstrates that the proposed solutions can be employed for efficiently handling the different aspects of variety of data-intensive applications