Scaling In-Memory databases on multicores

Current computer systems have evolved from featuring only a single processing unit and limited RAM, in the order of kilobytes or few megabytes, to include several multicore processors, o↵ering in the order of several tens of concurrent execution contexts, and have main memory in the order of several tens to hundreds of gigabytes. This allows to keep all data of many applications in the main memory, leading to the development of inmemory databases. Compared to disk-backed databases, in-memory databases (IMDBs) are expected to provide better performance by incurring in less I/O overhead. In this dissertation, we present a scalability study of two general purpose IMDBs on multicore systems. The results show that current general purpose IMDBs do not scale on multicores, due to contention among threads running concurrent transactions. In this work, we explore di↵erent direction to overcome the scalability issues of IMDBs in multicores, while enforcing strong isolation semantics. First, we present a solution that requires no modification to either database systems or to the applications, called MacroDB. MacroDB replicates the database among several engines, using a master-slave replication scheme, where update transactions execute on the master, while read-only transactions execute on slaves. This reduces contention, allowing MacroDB to o↵er scalable performance under read-only workloads, while updateintensive workloads su↵er from performance loss, when compared to the standalone engine. Second, we delve into the database engine and identify the concurrency control mechanism used by the storage sub-component as a scalability bottleneck. We then propose a new locking scheme that allows the removal of such mechanisms from the storage sub-component. This modification o↵ers performance improvement under all workloads, when compared to the standalone engine, while scalability is limited to read-only workloads. Next we addressed the scalability limitations for update-intensive workloads, and propose the reduction of locking granularity from the table level to the attribute level. This further improved performance for intensive and moderate update workloads, at a slight cost for read-only workloads. Scalability is limited to intensive-read and read-only workloads. Finally, we investigate the impact applications have on the performance of database systems, by studying how operation order inside transactions influences the database performance. We then propose a Read before Write (RbW) interaction pattern, under which transaction perform all read operations before executing write operations. The RbW pattern allowed TPC-C to achieve scalable performance on our modified engine for all workloads. Additionally, the RbW pattern allowed our modified engine to achieve scalable performance on multicores, almost up to the total number of cores, while enforcing strong isolation

An Access Control Model for NoSQL Databases

Current development platforms are web scale, unlike recent platforms which were just network scale. There has been a rapid evolution in computing paradigm that has created the need for data storage as agile and scalable as the applications they support. Relational databases with their joins and locks influence performance in web scale systems negatively. Thus, various types of non-relational databases have emerged in recent years, commonly referred to as NoSQL databases. To fulfill the gaps created by their relational counter-part, they trade consistency and security for performance and scalability. With NoSQL databases being adopted by an increasing number of organizations, the provision of security for them has become a growing concern. This research presents a context based abstract model by extending traditional role based access control for access control in NoSQL databases. The said model evaluates and executes security policies which contain versatile access conditions against the dynamic nature of data. The goal is to devise a mechanism for a forward looking, assertive yet flexible security feature to regulate access to data in the database system that is devoid of rigid structures and consistency, namely a document based database such as MongoDB

Arquitetura de elevada disponibilidade para bases de dados na cloud

Dissertação de mestrado em Computer ScienceCom a constante expansão de sistemas informáticos nas diferentes áreas de aplicação, a quantidade de dados que exigem persistência aumenta exponencialmente. Assim, por forma a tolerar faltas e garantir a disponibilidade de dados, devem ser implementadas técnicas de replicação. Atualmente existem várias abordagens e protocolos, tendo diferentes tipos de aplicações em vista. Existem duas grandes vertentes de protocolos de replicação, protocolos genéricos, para qualquer serviço, e protocolos específicos destinados a bases de dados. No que toca a protocolos de replicação genéricos, as principais técnicas existentes, apesar de completa mente desenvolvidas e em utilização, têm algumas limitações, nomeadamente: problemas de performance relativamente a saturação da réplica primária na replicação passiva e o determinismo necessário associado à replicação ativa. Algumas destas desvantagens são mitigadas pelos protocolos específicos de base de dados (e.g., com recurso a multi-master) mas estes protocolos não permitem efetuar uma separação entre a lógica da replicação e os respetivos dados. Abordagens mais recentes tendem a basear-se em técnicas de repli cação com fundamentos em mecanismos distribuídos de logging. Tais mecanismos propor cionam alta disponibilidade de dados e tolerância a faltas, permitindo abordagens inovado ras baseadas puramente em logs. Por forma a atenuar as limitações encontradas não só no mecanismo de replicação ativa e passiva, mas também nas suas derivações, esta dissertação apresenta uma solução de replicação híbrida baseada em middleware, o SQLware. A grande vantagem desta abor dagem baseia-se na divisão entre a camada de replicação e a camada de dados, utilizando um log distribuído altamente escalável que oferece tolerância a faltas e alta disponibilidade. O protótipo desenvolvido foi validado com recurso à execução de testes de desempenho, sendo avaliado em duas infraestruturas diferentes, nomeadamente, um servidor privado de média gama e um grupo de servidores de computação de alto desempenho. Durante a avaliação do protótipo, o standard da indústria TPC-C, tipicamente utilizado para avaliar sistemas de base de dados transacionais, foi utilizado. Os resultados obtidos demonstram que o SQLware oferece uma aumento de throughput de 150 vezes, comparativamente ao mecanismo de replicação nativo da base de dados considerada, o PostgreSQL.With the constant expansion of computational systems, the amount of data that requires durability increases exponentially. All data persistence must be replicated in order to provide high-availability and fault tolerance according to the surrogate application or use-case. Currently, there are numerous approaches and replication protocols developed supporting different use-cases. There are two prominent variations of replication protocols, generic protocols, and database specific ones. The two main techniques associated with generic replication protocols are the active and passive replication. Although generic replication techniques are fully matured and widely used, there are inherent problems associated with those protocols, namely: performance issues of the primary replica of passive replication and the determinism required by the active replication. Some of those disadvantages are mitigated by specific database replication protocols (e.g., using multi-master) but, those protocols do not allow a separation between logic and data and they can not be decoupled from the database engine. Moreover, recent strategies consider highly-scalable and fault tolerant distributed logging mechanisms, allowing for newer designs based purely on logs to power replication. To mitigate the shortcomings found in both active and passive replication mechanisms, but also in partial variations of these methods, this dissertation presents a hybrid replication middleware, SQLware. The cornerstone of the approach lies in the decoupling between the logical replication layer and the data store, together with the use of a highly scalable distributed log that provides fault-tolerance and high-availability. We validated the prototype by conducting a benchmarking campaign to evaluate the overall system’s performance under two distinct infrastructures, namely a private medium class server, and a private high performance computing cluster. Across the evaluation campaign, we considered the TPCC benchmark, a widely used benchmark in the evaluation of Online transaction processing (OLTP) database systems. Results show that SQLware was able to achieve 150 times more throughput when compared with the native replication mechanism of the underlying data store considered as baseline, PostgreSQL.This work was partially funded by FCT - Fundação para a Ciência e a Tecnologia, I.P., (Portuguese Foundation for Science and Technology) within project UID/EEA/50014/201

ARIADNE: A NOVEL HIGH AVAILABILITY CLOUD DATA STORE WITH TRANSACTIONAL GUARANTEES

Modern cloud data storage services have powerful capabilities for data-sets that can be indexed by a single key -- key-value stores -- and for data-sets that are characterized by multiple attributes (such as Google's BigTable). These data stores have non-ideal overheads, however, when graph data needs to be maintained; overheads are incurred because related (by graph edges) keys are managed in physically different host machines. We propose a new distributed data-storage paradigm, the key-key-value store, which extends the key-value model and significantly reduces these overheads by storing related keys in the same place. We provide a high-level description of our proposed system for storing large-scale, highly interconnected graph data -- such as social networks -- as well as an analysis of our key-key-value system in relation to existing work. In this thesis, we show how our novel data organization paradigm will facilitate improved levels of QoS in large graph data stores. Furthermore, we have built a system with our key-key-value system design -- Ariadne -- that is de-centralized, scalable, lightweight, relational and transactional. Such a system is unique among current systems in that it provides all qualities at once. This system was put to the test in the cloud using a strenuous concurrent workload and compared against the state of the art MySQL Cluster database system. Results show great promise for scalability and more consistent performance across workload types in Ariadne than in MySQL

10381 Summary and Abstracts Collection -- Robust Query Processing

Dagstuhl seminar 10381 on robust query processing (held 19.09.10 - 24.09.10) brought together a diverse set of researchers and practitioners with a broad range of expertise for the purpose of fostering discussion and collaboration regarding causes, opportunities, and solutions for achieving robust query processing. The seminar strove to build a unified view across the loosely-coupled system components responsible for the various stages of database query processing. Participants were chosen for their experience with database query processing and, where possible, their prior work in academic research or in product development towards robustness in database query processing. In order to pave the way to motivate, measure, and protect future advances in robust query processing, seminar 10381 focused on developing tests for measuring the robustness of query processing. In these proceedings, we first review the seminar topics, goals, and results, then present abstracts or notes of some of the seminar break-out sessions. We also include, as an appendix, the robust query processing reading list that was collected and distributed to participants before the seminar began, as well as summaries of a few of those papers that were contributed by some participants

Benchmarking Eventually Consistent Distributed Storage Systems

Cloud storage services and NoSQL systems typically offer only "Eventual Consistency", a rather weak guarantee covering a broad range of potential data consistency behavior. The degree of actual (in-)consistency, however, is unknown. This work presents novel solutions for determining the degree of (in-)consistency via simulation and benchmarking, as well as the necessary means to resolve inconsistencies leveraging this information