Serializable Isolation for Snapshot Databases

Many popular database management systems implement a multiversion concurrency control algorithm called snapshot isolation rather than providing full serializability based on locking. There are well-known anomalies permitted by snapshot isolation that can lead to violations of data consistency by interleaving transactions that would maintain consistency if run serially. Until now, the only way to prevent these anomalies was to modify the applications by introducing explicit locking or artificial update conflicts, following careful analysis of conflicts between all pairs of transactions. This thesis describes a modification to the concurrency control algorithm of a database management system that automatically detects and prevents snapshot isolation anomalies at runtime for arbitrary applications, thus providing serializable isolation. The new algorithm preserves the properties that make snapshot isolation attractive, including that readers do not block writers and vice versa. An implementation of the algorithm in a relational database management system is described, along with a benchmark and performance study, showing that the throughput approaches that of snapshot isolation in most cases

Transaction management across data stores

Companies have evolved from a world where they only had SQL databases to a world where they use different kinds of data stores, such as key­value data stores, document­oriented data stores and graph databases. The reason why they have started to introduce this diversity of persistency models is because different NoSQL technologies bring different data models with associated query languages and/or APIs. However, they are confronted now with a problem in which they have the data scattered across different data stores. This problem lies in that when a business action requires to update the data, the data reside in different data stores, and they are subject to inconsistencies in the event of failure and/or concurrent access. These inconsistencies appear due to the lack of transactional consistency that was guaranteed in traditional SQL databases but is not guaranteed either within the NoSQL data stores or across data stores and databases. CoherentPaaS comes to remedy this need. CoherentPaaS provides an ultra­scalable transactional management layer that can be integrated with any data store with multi­ versioning capabilities. The layer has been integrated with six different data stores, three NoSQL data stores and three SQL­like databases. In this paper, we describe this generic ultra­scalable transactional management layer and focus on its API and how it can be integrated in different ways with different data stores and databases

Rethinking serializable multiversion concurrency control

Multi-versioned database systems have the potential to significantly increase the amount of concurrency in transaction processing because they can avoid read-write conflicts. Unfortunately, the increase in concurrency usually comes at the cost of transaction serializability. If a database user requests full serializability, modern multi-versioned systems significantly constrain read-write concurrency among conflicting transactions and employ expensive synchronization patterns in their design. In main-memory multi-core settings, these additional constraints are so burdensome that multi-versioned systems are often significantly outperformed by single-version systems. We propose Bohm, a new concurrency control protocol for main-memory multi-versioned database systems. Bohm guarantees serializable execution while ensuring that reads never block writes. In addition, Bohm does not require reads to perform any book-keeping whatsoever, thereby avoiding the overhead of tracking reads via contended writes to shared memory. This leads to excellent scalability and performance in multi-core settings. Bohm has all the above characteristics without performing validation based concurrency control. Instead, it is pessimistic, and is therefore not prone to excessive aborts in the presence of contention. An experimental evaluation shows that Bohm performs well in both high contention and low contention settings, and is able to dramatically outperform state-of-the-art multi-versioned systems despite maintaining the full set of serializability guarantees

LogBase: A Scalable Log-structured Database System in the Cloud

Numerous applications such as financial transactions (e.g., stock trading) are write-heavy in nature. The shift from reads to writes in web applications has also been accelerating in recent years. Write-ahead-logging is a common approach for providing recovery capability while improving performance in most storage systems. However, the separation of log and application data incurs write overheads observed in write-heavy environments and hence adversely affects the write throughput and recovery time in the system. In this paper, we introduce LogBase - a scalable log-structured database system that adopts log-only storage for removing the write bottleneck and supporting fast system recovery. LogBase is designed to be dynamically deployed on commodity clusters to take advantage of elastic scaling property of cloud environments. LogBase provides in-memory multiversion indexes for supporting efficient access to data maintained in the log. LogBase also supports transactions that bundle read and write operations spanning across multiple records. We implemented the proposed system and compared it with HBase and a disk-based log-structured record-oriented system modeled after RAMCloud. The experimental results show that LogBase is able to provide sustained write throughput, efficient data access out of the cache, and effective system recovery.Comment: VLDB201