The DataSafe Failure Recovery Mechanism in the Flask Architecture

A major design goal of the Flask architecture is to separate the mechanisms of concurrency control and recovery management in database programming systems. This paper describes the DataSafe component of Flask, which is the second recovery mechanism to be implemented within the architecture and therefore provides a proof of concept. The DataSafe is closely based on the DB Cache mechanism, modified to fit into the Flask architecture. The major modification comprises the use of a separate safe map which allows pages of recovery data to be block aligned and affords opportunities for efficiency gains during recovery. The page-level locking implicit in the DB Cache is lifted from the DataSafe, permitting concurrency control and recovery to be independent. Keywords concurrency, recovery, persistent stores 1 Introduction Flask [11] is a layered architecture which has the flexibility to support different models of concurrency and different recovery mechanisms over the same data. The architec..

The MaStA I/O Cost Model and its Validation Strategy

Crash recovery in database systems aims to provide an acceptable level of protection from failure at a given engineering cost. A large number of recovery mechanisms are known, and have been compared both analytically and empirically. However, recent trends in computer hardware present different engineering tradeoffs in the design of recovery mechanisms. In particular, the comparative improvement in the speed of processors over disks suggests that disk I/O activity is the dominant expense. Furthermore, the improvement of disk transfer time relative to seek time has made patterns of disk access more significant. The contribution of the MaStA (Ma ssachusetts St Andrews) cost model is that it is structured independently of machine architectures and application workloads. It determines costs in terms of I/O categories, access patterns and application workload parameters. The main features of the model are: . Cost is based upon a probabilistic estimation of disk activity, broken down into s..

Flask : an architecture supporting concurrent distributed persistent applications

Submitted to BNCOD96 The work was supported by ESPRIT III BRA 6309 — FIDE2 and EPSRC Grant GR/J67611Distributed application systems have become a popular and provenly viable computing paradigm. There are a number of reasons for this such as: the geographical dispersal of information; the improved reliability of multiple computer systems; and the possibility of concurrent execution of applications. As yet no single model of distribution has been pervasive and since the impact of failure semantics varies with the software architecture of applications, it is unlikely that one model will ever dominate. It is difficult to assess or even to compare the attributes of different models especially when run over the same data. This is often made more difficult in that most implementations of distributed models are closed systems with built-in protocols, failure reporting and concurrency control. The Flask architecture, presented here, takes the approach of providing a layered architecture which has the flexibility to support different models of distribution that can run over the same data. To demonstrate the feasibility of Flask an example distributed application is described using the architecture.Postprin

The DataSafe Failure Recovery Mechanism in the Flask Architecture

A major design goal of the Flask architecture is to separate the mechanisms of concurrency control and recovery management in database programming systems. This paper describes the DataSafe component of Flask, which is the second recovery mechanism to be implemented within the architecture and therefore provides a proof of concept. The DataSafe is closely based on the DB Cache mechanism, modified to fit into the Flask architecture. The major modification comprises the use of a separate safe map which allows pages of recovery data to be block aligned and affords opportunities for efficiency gains during recovery. The page-level locking implicit in the DB Cache is lifted from the DataSafe, permitting concurrency control and recovery to be independent. Keywords concurrency, recovery, persistent stores 1 Introduction Flask [11] is a layered architecture which has the flexibility to support different models of concurrency and different recovery mechanisms over the same data. The architec..