Instant restore after a media failure

Media failures usually leave database systems unavailable for several hours until recovery is complete, especially in applications with large devices and high transaction volume. Previous work introduced a technique called single-pass restore, which increases restore bandwidth and thus substantially decreases time to repair. Instant restore goes further as it permits read/write access to any data on a device undergoing restore--even data not yet restored--by restoring individual data segments on demand. Thus, the restore process is guided primarily by the needs of applications, and the observed mean time to repair is effectively reduced from several hours to a few seconds. This paper presents an implementation and evaluation of instant restore. The technique is incrementally implemented on a system starting with the traditional ARIES design for logging and recovery. Experiments show that the transaction latency perceived after a media failure can be cut down to less than a second and that the overhead imposed by the technique on normal processing is minimal. The net effect is that a few "nines" of availability are added to the system using simple and low-overhead software techniques

A Multi-key Transactions Model for NoSQL Cloud Database Systems

NoSQL cloud database systems are new types of databases that are built across thousands of cloud nodes and are capable of storing and processing Big Data. NoSQL systems have increasingly been used in large scale applications that need high availability and efficiency but with weaker consistency. Consequently, such systems lack support for standard transactions which provide stronger consistency. This paper proposes a new multi-key transactional model which provides NoSQL systems with standard transaction support and stronger level of data consistency. The strategy is to supplement current NoSQL architecture with an extra layer that manages transactions. The proposed model is configurable where consistency, availability and efficiency can be adjusted based on application requirements. The proposed model is validated through a prototype system using MongoDB. Preliminary experiments show that it ensures stronger consistency and maintains good performance

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

Okapi: Causally Consistent Geo-Replication Made Faster, Cheaper and More Available

Okapi is a new causally consistent geo-replicated key- value store. Okapi leverages two key design choices to achieve high performance. First, it relies on hybrid logical/physical clocks to achieve low latency even in the presence of clock skew. Second, Okapi achieves higher resource efficiency and better availability, at the expense of a slight increase in update visibility latency. To this end, Okapi implements a new stabilization protocol that uses a combination of vector and scalar clocks and makes a remote update visible when its delivery has been acknowledged by every data center. We evaluate Okapi with different workloads on Amazon AWS, using three geographically distributed regions and 96 nodes. We compare Okapi with two recent approaches to causal consistency, Cure and GentleRain. We show that Okapi delivers up to two orders of magnitude better performance than GentleRain and that Okapi achieves up to 3.5x lower latency and a 60% reduction of the meta-data overhead with respect to Cure