Fast Algorithms for Maintaining Replica Consistency in Lazy Master Replicated Databases

Projet RODIN, Projet REFLECSIn a lazy master replicated database, a transaction can commit after updating one replica copy (primary copy) at some master node. After the transaction commits, the updates are propagated towards the other replicas (secondary copies), which are updated in separate refresh transactions. A central problem is the design of algorithms that maintain replica's consistency while at the same time minimizing the performance degradation due to the synchronization of refresh transactions. In this paper, we propose a simple and general refreshment algorithm that solves this problem and we prove its correctness. The principle of the algorithm is to let refresh transactions wait for a certain «deliver time» before being executed at a node having secondary copies. We then present two main optimizations to this algorithm. One is based on specific properties of the topology of replica distribution across nodes. In particular, we characterize the nodes for which the deliver time can be null. The other improves the refreshment algorithm by using an immediate update propagation strategy. Our performance evaluation demonstrate the effectiveness of this optimization

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

Eventual Consistency: Origin and Support

Eventual consistency is demanded nowadays in geo-replicated services that need to be highly scalable and available. According to the CAP constraints, when network partitions may arise, a distributed service should choose between being strongly consistent or being highly available. Since scalable services should be available, a relaxed consistency (while the network is partitioned) is the preferred choice. Eventual consistency is not a common data-centric consistency model, but only a state convergence condition to be added to a relaxed consistency model. There are still several aspects of eventual consistency that have not been analysed in depth in previous works: 1. which are the oldest replication proposals providing eventual consistency, 2. which replica consistency models provide the best basis for building eventually consistent services, 3. which mechanisms should be considered for implementing an eventually consistent service, and 4. which are the best combinations of those mechanisms for achieving different concrete goals. This paper provides some notes on these important topics

Efficient middleware for database replication

Dissertação de Mestrado em Engenharia InformáticaDatabase systems are used to store data on the most varied applications, like Web applications, enterprise applications, scientific research, or even personal applications. Given the large use of database in fundamental systems for the users, it is necessary that database systems are efficient e reliable. Additionally, in order for these systems to serve a large number of users, databases must be scalable, to be able to process large numbers of transactions. To achieve this, it is necessary to resort to data replication. In a replicated system, all nodes contain a copy of the database. Then, to guarantee that replicas converge, write operations must be executed on all replicas. The way updates are propagated leads to two different replication strategies. The first is known as asynchronous or optimistic replication, and the updates are propagated asynchronously after the conclusion of an update transaction. The second is known as synchronous or pessimistic replication, where the updates are broadcasted synchronously during the transaction. In pessimistic replication, contrary to the optimistic replication, the replicas remain consistent. This approach simplifies the programming of the applications, since the replication of the data is transparent to the applications. However, this approach presents scalability issues, caused by the number of exchanged messages during synchronization, which forces a delay to the termination of the transaction. This leads the user to experience a much higher latency in the pessimistic approach. On this work is presented the design and implementation of a database replication system, with snapshot isolation semantics, using a synchronous replication approach. The system is composed by a primary replica and a set of secondary replicas that fully replicate the database- The primary replica executes the read-write transactions, while the remaining replicas execute the read-only transactions. After the conclusion of a read-write transaction on the primary replica the updates are propagated to the remaining replicas. This approach is proper to a model where the fraction of read operations is considerably higher than the write operations, allowing the reads load to be distributed over the multiple replicas. To improve the performance of the system, the clients execute some operations speculatively, in order to avoid waiting during the execution of a database operation. Thus, the client may continue its execution while the operation is executed on the database. If the result replied to the client if found to be incorrect, the transaction will be aborted, ensuring the correctness of the execution of the transactions

Distributed replicated macro-components

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaIn recent years, several approaches have been proposed for improving application performance on multi-core machines. However, exploring the power of multi-core processors remains complex for most programmers. A Macro-component is an abstraction that tries to tackle this problem by allowing to explore the power of multi-core machines without requiring changes in the programs. A Macro-component encapsulates several diverse implementations of the same specification. This allows to take the best performance of all operations and/or distribute load among replicas, while keeping contention and synchronization overhead to the minimum. In real-world applications, relying on only one server to provide a service leads to limited fault-tolerance and scalability. To address this problem, it is common to replicate services in multiple machines. This work addresses the problem os supporting such replication solution, while exploring the power of multi-core machines. To this end, we propose to support the replication of Macro-components in a cluster of machines. In this dissertation we present the design of a middleware solution for achieving such goal. Using the implemented replication middleware we have successfully deployed a replicated Macro-component of in-memory databases which are known to have scalability problems in multi-core machines. The proposed solution combines multi-master replication across nodes with primary-secondary replication within a node, where several instances of the database are running on a single machine. This approach deals with the lack of scalability of databases on multi-core systems while minimizing communication costs that ultimately results in an overall improvement of the services. Results show that the proposed solution is able to scale as the number of nodes and clients increases. It also shows that the solution is able to take advantage of multi-core architectures.RepComp project (PTDC/EIAEIA/108963/2008

Mediation of Lazy Update Propagation in a Replicated Database over a Decentralized P2P Architecture

While replicating data over a decentralized Peer-to- Peer (P2P) network, transactions broadcasting updates arising from different peers run simultaneously so that a destination peer replica can be updated concurrently, that always causes transaction and data conflicts. Moreover, during data migration, connectivity interruption and network overload corrupt running transactions so that destination peers can experience duplicated data or improper data or missing data, hence replicas remain inconsistent. Different methodological approaches have been combined to solve these problems: the audit log technique to capture the changes made to data; the algorithmic method to design and analyse algorithms and the statistical method to analyse the performance of new algorithms and to design prediction models of the execution time based on other parameters. A Graphical User Interface software as prototype, have been designed with C #, to implement these new algorithms to obtain a database synchronizer-mediator. A stream of experiments, showed that the new algorithms were effective. So, the hypothesis according to which 201C;The execution time of replication and reconciliation transactions totally depends on independent factors.201D; has been confirmed

Partial replication with strong consistency

In response to the increasing expectations of their clients, cloud services exploit geo-replication to provide fault-tolerance, availability and low latency when executing requests. However, cloud platforms tend to adopt weak consistency semantics, in which replicas may diverge in state independently. These systems offer good response times but at the disadvantage of allowing potential data inconsistencies that may affect user experience. Some systems propose to adopt solutions with strong consistency, which are not as efficient but simplify the development of correct applications by guaranteeing that all replicas in the system maintain the same database state. Therefore, it is interesting to explore a system that can offer strong consistency while minimizing its main disadvantage: the impact in performance that results from coordinating every replica in the system. A possible solution to reduce the cost of replica coordination is to support partial replication. Partially replicating a database allows for each server to only be responsible for a subset of the data - a partition - which means that when updating the database only some of replicas have to be synchronized, improving response times. In this dissertation, we propose an algorithm that implements a distributed replicated database that offers strong consistency with support for partial replication. To achieve strong consistency in a partially replicated scenario, our algorithm is in part based on the Clock-SI[10] research, which presents an algorithm that implements a multi-versioned database for strong consistency (snapshot-isolation) and performs the Two-Phase Commit protocol when coordinating replicas during updates. The algorithm is supported by an architecture that simplifies distributing partitions among datacenters and efficiently propagating operations across nodes in the same partition, thanks to the ChainPaxos[27] algorithm.Como forma de responder às expectativas cada vez maiores dos seus clientes, as operadoras cloud tiram partido da geo-replicação para oferecer tolerância a falhas, disponibilidade e baixa latência dos seus sistemas na resposta aos pedidos. No entanto, as plataformas cloud tendem a adotar uma semântica de consistência fraca, na qual as réplicas podem variar em estado de forma independente. Estes sistemas oferecem bons tempos de resposta mas com a desvantagem de que têm de lidar com potenciais inconsistências nos dados que podem ter impacto na experiência dos utilizadores. Alguns sistemas propõem adotar soluções com consistência forte, as quais não são tão eficientes mas simplificam o desenvolvimento de aplicações ao garantir que todas as réplicas do sistema mantêm o mesmo estado da base de dados. É então interessante explorar um sistema que garanta replicação forte mas que minimize a sua principal desvantagem: o impacto de performance no momento de coordenar o estado das réplicas nos sistema. Uma possível solução para reduzir o custo de coordenação das réplicas durante transações é o suporte à replicação parcial. Replicar parcialmente uma base de dados permite que cada servidor seja apenas responsável por uma parte dos dados - uma partição - o que significa que quando são realizadas escritas apenas algumas das réplicas têm de ser sincronizadas, melhorando os tempos de resposta. Neste trabalho propomos um algoritmo que implementa um sistema de armazenamento distríbuido replicado que oferece consistência forte com suporte a replicação parcial. A fim de garantir consistência forte num cenário de replicação parcial, o nosso algoritmo é em parte baseado no algoritmo Clock-SI[10], que implementa uma base de dados parcial com multi-versões para garantir consistência forte (snapshot-isolation) e que realiza o protocolo Two-Phase Commit para coordenar as réplicas no momento de aplicar escritas. O algoritmo é suportado por uma arquitectura que torna simples distribuir partições por vários centros de dados e propagar de forma eficiente operações entre todos os nós numa mesma partição, através do algoritmo ChainPaxos[27]

Evaluating data freshness in large scale replicated databases

There is nowadays an increasing need for database replication, as the construction of high performance, highly available, and large-scale applications depends on it to maintain data synchronized across multiple servers. A particularly popular approach, used for instance byFacebook, is the MySQL open source database management system and its built-in asynchronous replication mechanism. The limitations imposed by MySQL on replication topologies mean that data has to go through a number of hops or each server has to handle a large number of slaves. This is particularly worrisome when updates are accepted by multiple replicas and in large systems. It is however difficult to accurately evaluate the impact of replication in data freshness, since one has to compare observations at multiple servers while running a realistic workload and without disturbing the system under test. In this paper we address this problem by introducing a tool that can accurately measure replication delays for any workload and then apply it to the industry standard TPC-C benchmark. This allows us to draw interesting conclusions about the scalability properties of MySQL replication