Wren: Nonblocking Reads in a Partitioned Transactional Causally Consistent Data Store
Wren: Nonblocking Reads in a Partitioned Transactional Causally Consistent Data Store
复制标题
Wren：分区事务因果一致性数据存储中的非阻塞读取
DOI：
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复制
发表时间：
2018
期刊：
影响因子：
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通讯作者：
W. Zwaenepoel
中科院分区：
文献类型：
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作者：
Kristina Spirovska;Diego Didona;W. Zwaenepoel
transmittance Causal Consistency（TCC）扩展了因果一致性，这是与可用性兼容的最强一致性模型，具有交互式读写事务，因此特别适合地理复制平台。本文介绍了Wren，这是第一个TCC系统，同时i）实现非阻塞读取操作，从而实现低延迟，ii）允许应用程序通过分片在复制站点内有效地扩展。Wren为事务执行、依赖跟踪和稳定化引入了新的协议。事务协议通过向事务提供快照来支持非阻塞读取，该快照是由本地数据中心中的每个分区安装的新的因果快照S和用于尚未包括在S中的写入的客户端侧高速缓存的联合。依赖跟踪和稳定协议只需要两个标量时间戳，从而有效地利用资源，并提供复制站点方面的可扩展性。作为这些好处的回报，Wren稍微增加了更新的可见性延迟。我们在AWS部署上评估了Wren，每个站点最多使用5个复制站点和16个分区。我们表明，与最先进的设计相比，Wren提供高达1.4倍的吞吐量和高达3.6倍的延迟。选择较旧的快照会使本地更新可见性延迟增加几毫秒。仅使用两个时间戳来跟踪因果关系将远程更新可见性延迟增加不到15%。
Transactional Causal Consistency (TCC) extends causal consistency, the strongest consistency model compatible with availability, with interactive read-write transactions, and is therefore particularly appealing for geo-replicated platforms. This paper presents Wren, the first TCC system that at the same time i) implements nonblocking read operations, thereby achieving low latency, and ii) allows an application to efficiently scale out within a replication site by sharding. Wren introduces new protocols for transaction execution, dependency tracking and stabilization. The transaction protocol supports nonblocking reads by providing a transaction with a snapshot that is the union of a fresh causal snapshot S installed by every partition in the local data center and a client-side cache for writes that are not yet included in S. The dependency tracking and stabilization protocols require only two scalar timestamps, resulting in efficient resource utilization and providing scalability in terms of replication sites. In return for these benefits, Wren slightly increases the visibility latency of updates. We evaluate Wren on an AWS deployment using up to 5 replication sites and 16 partitions per site. We show that Wren delivers up to 1.4x higher throughput and up to 3.6x lower latency when compared to the state-of-the-art design. The choice of an older snapshot increases local update visibility latency by a few milliseconds. The use of only two timestamps to track causality increases remote update visibility latency by less than 15%.
DOI：
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发表时间：
2017-03
期刊：
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影响因子：
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作者：
Syed Akbar Mehdi;Cody Littley;Natacha Crooks;L. Alvisi;N. Bronson;Wyatt Lloyd
通讯作者：
Syed Akbar Mehdi;Cody Littley;Natacha Crooks;L. Alvisi;N. Bronson;Wyatt Lloyd