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.
propose a data-oriented architecture (DORA) in order to eliminate the impact of contended accesses to shared memory by transaction execution threads @cite_35 . DORA partitions a database among several physical cores of a multi-core system and executes a disjoint subset of each transaction's logic on multiple threads, a form of intra-transaction parallelism. uses intra-transaction parallelism to decide the in which transactions must execute. However, the execution of a transaction's logic occurs on a single thread.
@cite_35
2101995353 1995339490 2010149990 2061197210
While hardware technology has undergone major advancements over the past decade, transaction processing systems have remained largely unchanged. The number of cores on a chip grows exponentially, following Moore's Law, allowing for an ever-increasing number of transactions to execute in parallel. As the number of concurrently-executing transactions increases, contended critical sections become scalability burdens. In typical transaction processing systems the centralized lock manager is often the first contended component and scalability bottleneck. In this paper, we identify the conventional thread-to-transaction assignment policy as the primary cause of contention. Then, we design DORA, a system that decomposes each transaction to smaller actions and assigns actions to threads based on which data each action is about to access. DORA's design allows each thread to mostly access thread-local data structures, minimizing interaction with the contention-prone centralized lock manager. Built on top of a conventional storage engine, DORA maintains all the ACID properties. Evaluation of a prototype implementation of DORA on a multicore system demonstrates that DORA attains up to 4.8x higher throughput than a state-of-the-art storage engine when running a variety of synthetic and real-world OLTP workloads. Databases can provide scalability by partitioning data across several servers. However, multi-partition, multi-operation transactional access is often expensive, employing coordination-intensive locking, validation, or scheduling mechanisms. Accordingly, many real-world systems avoid mechanisms that provide useful semantics for multi-partition operations. This leads to incorrect behavior for a large class of applications including secondary indexing, foreign key enforcement, and materialized view maintenance. In this work, we identify a new isolation model---Read Atomic (RA) isolation---that matches the requirements of these use cases by ensuring atomic visibility: either all or none of each transaction's updates are observed by other transactions. We present algorithms for Read Atomic Multi-Partition (RAMP) transactions that enforce atomic visibility while offering excellent scalability, guaranteed commit despite partial failures (via synchronization independence), and minimized communication between servers (via partition independence). These RAMP transactions correctly mediate atomic visibility of updates and provide readers with snapshot access to database state by using limited multi-versioning and by allowing clients to independently resolve non-atomic reads. We demonstrate that, in contrast with existing algorithms, RAMP transactions incur limited overhead---even under high contention---and scale linearly to 100 servers. Current trends in data management systems, such as cloud and multi-tenant databases, are leading to data processing environments that concurrently execute heterogeneous query workloads. At the same time, these systems need to satisfy diverse performance expectations. In these newly-emerging settings, avoiding potential Quality-of-Service (QoS) violations heavily relies on performance predictability, i.e., the ability to estimate the impact of concurrent query execution on the performance of individual queries in a continuously evolving workload. This paper presents a modeling approach to estimate the impact of concurrency on query performance for analytical workloads. Our solution relies on the analysis of query behavior in isolation, pairwise query interactions and sampling techniques to predict resource contention under various query mixes and concurrency levels. We introduce a simple yet powerful metric that accurately captures the joint effects of disk and memory contention on query performance in a single value. We also discuss predicting the execution behavior of a time-varying query workload through query-interaction timelines, i.e., a fine-grained estimation of the time segments during which discrete mixes will be executed concurrently. Our experimental evaluation on top of PostgreSQL TPC-H demonstrates that our models can provide query latency predictions within approximately 20 of the actual values in the average case. Given the sequential implementation of any data structure, we show how to obtain an efficient, wait-free implementation of that data structure shared by any fixed number of processes using only shared registers and CAS objects. Our universal construction is transaction friendly, allowing a process to gracefully exit from an operation that it wanted to perform, and it is cache-efficient in a multicore setting where the processes run on cores that share a single cache. We also present an optimized shared queue based on this method.