Middleware-based Database Replication: The Gaps between Theory and Practice

The need for high availability and performance in data management systems has been fueling a long running interest in database replication from both academia and industry. However, academic groups often attack replication problems in isolation, overlooking the need for completeness in their solutions, while commercial teams take a holistic approach that often misses opportunities for fundamental innovation. This has created over time a gap between academic research and industrial practice. This paper aims to characterize the gap along three axes: performance, availability, and administration. We build on our own experience developing and deploying replication systems in commercial and academic settings, as well as on a large body of prior related work. We sift through representative examples from the last decade of open-source, academic, and commercial database replication systems and combine this material with case studies from real systems deployed at Fortune 500 customers. We propose two agendas, one for academic research and one for industrial R&D, which we believe can bridge the gap within 5-10 years. This way, we hope to both motivate and help researchers in making the theory and practice of middleware-based database replication more relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, June 200

Performance and reliability optimisation of a data acquisition and logging system in an integrated component-handling environment

Thesis (M. Tech.) - Central University of Technology, Free State, 201

Performance issues in mid-sized relational database machines

Relational database systems have provided end users and application programmers with an improved working environment over older hierarchial and networked database systems. End users now use interactive query languages to inspect and manage their data. And application programs are easier to write and maintain due to the separation of physical data storage information from the application program itself. These and other benefits do not come without a price however. System resource consumption has long been the perceived problem with relational systems. The additional resource demands usually force computing sites to upgrade existing systems or add additional facilities. One method of protecting the current investment in systems is to use specialized hardware designed specifically for relational database processing. \u27Database Machines\u27 provide that alternative. Since the commercial introduction of database machines in the early 1980\u27s, both software and hardware vendors of relational database systems have claimed superior performance over competing products. Without a STANDARD performance measurement technique, the database user community has been flooded with benchmarks and claims from vendors which are immediately discarded by some competitors as being biased towards a particular system design. This thesis discusses the issues of relational database performance measurement with an emphasis on database machines, however; these performance issues are applicable to both hardware and software systems. A discussion of hardware design, performance metrics, software and database design is included. Also provided are recommended guidelines to use in evaluating relational database systems in lieu of a standard benchmark methodology

A comparative analysis of leading relational database management systems

http://deepblue.lib.umich.edu/bitstream/2027.42/96903/1/MBA_JayaramanS_1996Final.pd

Three Denerations of DBMS

This paper describes the evolution of data base technology from early computing to the sophisticated systems of today. It presents an overview of the most popular data base management systems architectures such as hierarchical, network, relational and object-oriented. The last section of this paper presents a view of the factors that will influence the future of data base technology

Practical database replication

Tese de doutoramento em InformáticaSoftware-based replication is a cost-effective approach for fault-tolerance when combined with commodity hardware. In particular, shared-nothing database clusters built upon commodity machines and synchronized through eager software-based replication protocols have been driven by the distributed systems community in the last decade. The efforts on eager database replication, however, stem from the late 1970s with initial proposals designed by the database community. From that time, we have the distributed locking and atomic commitment protocols. Briefly speaking, before updating a data item, all copies are locked through a distributed lock, and upon commit, an atomic commitment protocol is responsible for guaranteeing that the transaction’s changes are written to a non-volatile storage at all replicas before committing it. Both these processes contributed to a poor performance. The distributed systems community improved these processes by reducing the number of interactions among replicas through the use of group communication and by relaxing the durability requirements imposed by the atomic commitment protocol. The approach requires at most two interactions among replicas and disseminates updates without necessarily applying them before committing a transaction. This relies on a high number of machines to reduce the likelihood of failures and ensure data resilience. Clearly, the availability of commodity machines and their increasing processing power makes this feasible. Proving the feasibility of this approach requires us to build several prototypes and evaluate them with different workloads and scenarios. Although simulation environments are a good starting point, mainly those that allow us to combine real (e.g., replication protocols, group communication) and simulated-code (e.g., database, network), full-fledged implementations should be developed and tested. Unfortunately, database vendors usually do not provide native support for the development of third-party replication protocols, thus forcing protocol developers to either change the database engines, when the source code is available, or construct in the middleware server wrappers that intercept client requests otherwise. The former solution is hard to maintain as new database releases are constantly being produced, whereas the latter represents a strenuous development effort as it requires us to rebuild several database features at the middleware. Unfortunately, the group-based replication protocols, optimistic or conservative, that had been proposed so far have drawbacks that present a major hurdle to their practicability. The optimistic protocols make it difficult to commit transactions in the presence of hot-spots, whereas the conservative protocols have a poor performance due to concurrency issues. In this thesis, we propose using a generic architecture and programming interface, titled GAPI, to facilitate the development of different replication strategies. The idea consists of providing key extensions to multiple DBMSs (Database Management Systems), thus enabling a replication strategy to be developed once and tested on several databases that have such extensions, i.e., those that are replication-friendly. To tackle the aforementioned problems in groupbased replication protocols, we propose using a novel protocol, titled AKARA. AKARA guarantees fairness, and thus all transactions have a chance to commit, and ensures great performance while exploiting parallelism as provided by local database engines. Finally, we outline a simple but comprehensive set of components to build group-based replication protocols and discuss key points in its design and implementation.A replicação baseada em software é uma abordagem que fornece um bom custo benefício para tolerância a falhas quando combinada com hardware commodity. Em particular, os clusters de base de dados “shared-nothing” construídos com hardware commodity e sincronizados através de protocolos “eager” têm sido impulsionados pela comunidade de sistemas distribuídos na última década. Os primeiros esforços na utilização dos protocolos “eager”, decorrem da década de 70 do século XX com as propostas da comunidade de base de dados. Dessa época, temos os protocolos de bloqueio distribuído e de terminação atómica (i.e. “two-phase commit”). De forma sucinta, antes de actualizar um item de dados, todas as cópias são bloqueadas através de um protocolo de bloqueio distribuído e, no momento de efetivar uma transacção, um protocolo de terminação atómica é responsável por garantir que as alterações da transacção são gravadas em todas as réplicas num sistema de armazenamento não-volátil. No entanto, ambos os processos contribuem para um mau desempenho do sistema. A comunidade de sistemas distribuídos melhorou esses processos, reduzindo o número de interacções entre réplicas, através do uso da comunicação em grupo e minimizando a rigidez os requisitos de durabilidade impostos pelo protocolo de terminação atómica. Essa abordagem requer no máximo duas interacções entre as réplicas e dissemina actualizações sem necessariamente aplicá-las antes de efectivar uma transacção. Para funcionar, a solução depende de um elevado número de máquinas para reduzirem a probabilidade de falhas e garantir a resiliência de dados. Claramente, a disponibilidade de hardware commodity e o seu poder de processamento crescente tornam essa abordagem possível. Comprovar a viabilidade desta abordagem obriga-nos a construir vários protótipos e a avaliálos com diferentes cargas de trabalho e cenários. Embora os ambientes de simulação sejam um bom ponto de partida, principalmente aqueles que nos permitem combinar o código real (por exemplo, protocolos de replicação, a comunicação em grupo) e o simulado (por exemplo, base de dados, rede), implementações reais devem ser desenvolvidas e testadas. Infelizmente, os fornecedores de base de dados, geralmente, não possuem suporte nativo para o desenvolvimento de protocolos de replicação de terceiros, forçando os desenvolvedores de protocolo a mudar o motor de base de dados, quando o código fonte está disponível, ou a construir no middleware abordagens que interceptam as solicitações do cliente. A primeira solução é difícil de manter já que novas “releases” das bases de dados estão constantemente a serem produzidas, enquanto a segunda representa um desenvolvimento árduo, pois obriga-nos a reconstruir vários recursos de uma base de dados no middleware. Infelizmente, os protocolos de replicação baseados em comunicação em grupo, optimistas ou conservadores, que foram propostos até agora apresentam inconvenientes que são um grande obstáculo à sua utilização. Com os protocolos optimistas é difícil efectivar transacções na presença de “hot-spots”, enquanto que os protocolos conservadores têm um fraco desempenho devido a problemas de concorrência. Nesta tese, propomos utilizar uma arquitetura genérica e uma interface de programação, intitulada GAPI, para facilitar o desenvolvimento de diferentes estratégias de replicação. A ideia consiste em fornecer extensões chaves para múltiplos SGBDs (Database Management Systems), permitindo assim que uma estratégia de replicação possa ser desenvolvida uma única vez e testada em várias bases de dados que possuam tais extensões, ou seja, aquelas que são “replicationfriendly”. Para resolver os problemas acima referidos nos protocolos de replicação baseados em comunicação em grupo, propomos utilizar um novo protocolo, intitulado AKARA. AKARA garante a equidade, portanto, todas as operações têm uma oportunidade de serem efectivadas, e garante um excelente desempenho ao tirar partido do paralelismo fornecido pelos motores de base de dados. Finalmente, propomos um conjunto simples, mas abrangente de componentes para construir protocolos de replicação baseados em comunicação em grupo e discutimos pontoschave na sua concepção e implementação

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author